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Ann Ojeda: Great thanks being close so i'll kind of give you a layout if you have a second screen or you're able to.

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Ann Ojeda: pull double duty, it might be nice to have the program with you on the right hand side or as your with your screen as well, and we have three sections for our session today, the first two will involve.

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Ann Ojeda: Live talks and then the third section, we have a series of posters that'll be five minutes of a recorded presentation and five minutes of Q amp a so we hope that diversity can keep your attention and we've kind of grouped the talk so that we have.

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Ann Ojeda: talks that are similar together, and then we have a 10 minute break and another group of talks a 10 minute break and then posters so during those breaks, I do hope that you are able to stand up, take a couple of deep breaths roll your shoulders back move your ankles and risks.

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Ann Ojeda: Because sitting in zoom for an hour, and this is the second day of our third day if you took the vaccinating the earth course or.

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Ann Ojeda: field trips that we've been in front of the computer, so I do hope we can all take a step away take a deep breath and come back a little recharged and ready to learn more about environmental contaminants.

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Ann Ojeda: So with that we'll jump right in the first talk is are invited talk, so this is Rachel quite from.

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Ann Ojeda: Duke university with multiple contaminants and multiple standards geochemistry in health, in North Carolina groundwater quality so Rachel, thank you for being here and thank you for agreeing to kick us off this morning and we're really excited to hear about your work.

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Ann Ojeda: Rachel I can hear you I think you're muted.

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Ann Ojeda: Okay, no problem.

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Rachel Coyte: Sorry, it looks like I have to stop sharing my screen, where I can and.

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Rachel Coyte: sorry about that little delay, and thank you so much for inviting me and i'm happy to see all of you here this morning today i'm going to talk about half of my dissertation work.

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Rachel Coyte: Which is dealing with the issue of multiple contaminants and multiple standards, this is going to cover look at you chemistry and health in the case of North Carolina groundwater.

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Rachel Coyte: i'm going to start by making a few acknowledgments i'd like to acknowledge everyone who really made this work possible either through help with field work and help with some of the writing involved help with the lab work, it was truly a collaborative effort.

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Rachel Coyte: And i'm going to start off this talk i'm digging into the subject matter that first brought this problem multiple standards to my attention which is hex minute chromium in North Carolina.

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Rachel Coyte: hexagon the chromium is one of the oxidation states of chromium usually, when we're looking at chromium and water we're seeing either trying like chromium or hexvix chromium so chromium three or chromium six.

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Rachel Coyte: The former chromium three is a essential nutrients for humans that trace amounts, whereas hexagon chromium is a known carcinogen.

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Rachel Coyte: So that extra three electrons really makes a difference um chromium six is going to be the more soluble of the two typically we don't see chromium three in water, except that very low pH is.

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Rachel Coyte: The exception is it can complex with organic matter but, for the most part, most of what we're seeing.

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Rachel Coyte: soluble in water at natural conditions is going to be chromium six and then finally we're usually going to see chromium and water that's more oxidizing and that has a bit of a higher pH.

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Rachel Coyte: The health effects of ingesting hexagon chromium are a bit controversial we've known for years that X men and chromium is an issue for inhalation.

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Rachel Coyte: But there's a lot of Gray area when it comes to the ingestion effects, the effects of having it in your drinking water and our regulations are pretty consistent, as a result.

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Rachel Coyte: Take, for example, the US EPA guideline which is 100 micrograms per liter and it is regulated as total chromium not specifically as hex of chromium that more toxic oxidation state.

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Rachel Coyte: This is a regulation that was put forth in 1994 and it was meant to protect against dermal exposures only in 2008 the EPA announced that it was going to be looking at this standard again.

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Rachel Coyte: But there hasn't really been any movement since then dmz administrative code since we're dealing in North Carolina also has its separate standard for.

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Rachel Coyte: chromium in drinking water, but it actually only applies to groundwater and that's 10 micrograms per liter also regulated as total premium, not as excellent chromium.

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Rachel Coyte: And then, finally, we have a non enforceable standard that the north Carolina Department of Health and human services came up with in 2014.

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Rachel Coyte: Which is meant to represent a one in 1 million lifetime risk of cancer, which is 0.07 micrograms per liter regulated as our put forth as chromium six and again, this is not an enforceable regulatory standards just a health advisory level.

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Rachel Coyte: And this is much more similar to some of the standards put forth by the state of California by, for example, which i've also met with some controversy and.

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Rachel Coyte: It is important to note that, even though these first two standards are enforceable, they are only enforceable for public water systems, so the quality of water from private wells is generally only measured at the Homeowners expense, and so we have.

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Rachel Coyte: A lot of wells, for which these standards even the ones that are enforceable simply don't apply.

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Rachel Coyte: In.

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Rachel Coyte: The.

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Rachel Coyte: The North Carolina department of environmental quality started to look at.

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Rachel Coyte: wells that we're located around coal ash ponds as a part of it, environmental monitoring effort looking for a possible contamination for the from these coal ash ponds.

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Rachel Coyte: And what they found were levels of excellent chromium above that health advisory level, they also issued to help advisory level for the magnesium and they were high levels of the nadeem as well, but i'm going to focus, at least initially here on the chromium story.

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Rachel Coyte: They sent hundreds of these letters telling people not to use their well water for drinking and cooking which was understandably quite alarming.

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Rachel Coyte: Because this was done in the context of monitoring around Polish ponds people immediately assumed that it was from coal ash, but actually further research by.

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Rachel Coyte: Our group at Duke found that in fact it is naturally occurring, and it is mostly associated with may 5 geology, which is actually in some ways a bigger problem.

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Rachel Coyte: In North Carolina context than if it were coming from just Kalash because in North Carolina we have nearly 4 million people that rely on groundwater as their primary source of drinking water and 2.5 million of those are on private wells.

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Rachel Coyte: Again this 2.5 million which are on private wells had likely never had their water measured for any kind of environmental contamination.

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Rachel Coyte: And so the risks of this hexagon that chromium exposure, now that we know that it's naturally occurring were really unknown because there had been very little monitoring of.

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Rachel Coyte: This oxidation state of chromium throughout the state, so that brings me to kind of my first motivating question here, which is what is the magnitude of chromium six contamination in North Carolina groundwater.

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Rachel Coyte: To look at this question I compiled a data set of 1362 grand water samples, this is comprised of samples from the United States geological survey.

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Rachel Coyte: samples that are lab collected samples from the north Carolina department of environmental quality and samples from the United States EPA, as you see, Mr three Program.

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Rachel Coyte: In total, we had 1356 observations for total chromium and 865 observations for chromium six.

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Rachel Coyte: and

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Rachel Coyte: When we compare the data set to the published guidelines we see very few violations of either of those two regulatory standards either the EPA federal regulatory standard or the ncaa administrator codes groundwater standard.

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Rachel Coyte: In the data step is a hold only one violation of the EPA standard and only 20 violations throughout the entire state of that state standard, but when we look at the health advisory level, we see a completely different story.

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Rachel Coyte: Overall you're getting over half of wells, in violation that we looked at in the state in violation of that regulatory standard.

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Rachel Coyte: And we also see that these violations are not necessarily especially coherent, so we have a much greater percentage of samples that are violating in the Piedmont, which is sort of this central area of North Carolina that along the coast in the coastal plain.

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Rachel Coyte: So the takeaways here are that violations of chromium total chromium regulatory standards are rare, but violations of health guidelines for chromium six are pretty common.

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Rachel Coyte: And chromium total chromium and chromium six are both higher in the Piedmont, then in the coastal plain, which is actually consistent with our findings that they tend to the chromium tends to be associated with more basic challenges.

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Rachel Coyte: So, because I was unable to measure all 4 million wells in North Carolina The next question is that i'm interested in is what approaches, we could possibly take to predicting where we have six occurrence in North Carolina groundwater.

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Rachel Coyte: The first I took was a spatial approach based off geology because we found that there was a geology link and what I did was pretty simple and that's to calculate exceeds probabilities of that North Carolina health advisory level, based on geology.

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Rachel Coyte: I looked at a few different levels of detail here.

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Rachel Coyte: The formation level, which is a much more detailed level of geology but because I decided to set a requirement that a unit have at least 10 observations before calculating and exceeds probability.

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Rachel Coyte: there's not a lot of the state that really isn't covered by this map, however, when we do dig a little bit deeper into this data, we can see that most of the formations that have high probabilities of exceeding that chromium six hdl.

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Rachel Coyte: Our Meta igneous formations, some of them are may fit again, we found that higher concentrations tend to be in may fit in our first study.

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Rachel Coyte: But we also had some that were fell sick and so, even though that total concentrations of chromium and those formations the groundwater from those formations tends to be lower they still bought can violate the health advisory level.

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Rachel Coyte: I also looked at the belt level of geology which is.

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Rachel Coyte: A less detailed unit, it provides greater coverage of the entire state, but it is a greater generalization it's a little bit less precise, how are the advantage is that using this belt level of geology we can look at the risks that may be so in.

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Rachel Coyte: That may be associated with this and a little bit better detail so i'm.

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Rachel Coyte: Looking at this map, we can see that some of the fastest growing parts of the state.

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Rachel Coyte: And some of the parts of the states that have the highest level of groundwater, the highest number of groundwater light individuals tend to be in these areas of red have high.

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Rachel Coyte: probability of seeing that North Carolina hdl and I want to point out, in particular, there is around wake county, this is the.

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Rachel Coyte: The capital raleigh and in North Carroll of the state and also Mecklenburg county, which is where Charlotte North Carolina is that's our largest city these areas have a.

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Rachel Coyte: very fast growing sprawling suburban you know satellite area and when those new communities are being built, often the communities are choosing to go on grab water, as opposed to getting connected to some kind of municipal surface water.

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Rachel Coyte: So there is a reason for the high portion of groundwater users in those areas and surrounding those areas.

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Rachel Coyte: I also looked at an individual approach, which aims to provide a simple and cost effective way of predicting if a particular well as above that ncaa to.

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Rachel Coyte: North Carolina health advisory level to target wells for testing, this is useful because Christian rock aquifers, have a lot of spatial heterogeneity so you and your neighbors wells aren't necessarily going to have the same chemistry.

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Rachel Coyte: And well testing can be really expensive, so we want to get Homeowners as much information as possible.

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Rachel Coyte: So that we can better target what wells we're looking at.

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Rachel Coyte: What approach, it could be look at all the reports a total chromium and wealth total chromium is a more routine measurement then from six so there's a chance if you've had trace metals measured in your well before you've had total chromium measured.

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Rachel Coyte: In our data shows that most of the chromium and North Carolina well water is from your sex.

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Rachel Coyte: However, there are still many wells in the state which has never had for him six measure before and often detection limits for chromium our quote total chromium are actually higher than 0.07 micrograms let up pull her leader level we are interested in for X Vedic chromium.

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Rachel Coyte: So I also looked at a multiple linear regression approach which uses parameters that are easy to measure, so all of these can be measured, either by looking at a map or by using a fairly inexpensive leader at a house.

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Rachel Coyte: They are physically related to chromium in some way, and they have sufficient observations in our data set to perform a cross validation I used to bit regression to deal with some of my missing data problems.

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Rachel Coyte: And some left sensor data and 84% of samples that I classified using this method work correctly classified cross validation I use the one out cross validation.

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Rachel Coyte: And, and only 6% of the wells that exceeded the health advisory guideline were incorrectly classified, so this word decently well.

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Rachel Coyte: The takeaways here or that spatial probability mass of chromium six can point us to areas that may be at higher risk, particularly because of that combination of fast growing population.

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Rachel Coyte: High portion of well users and high probability of seeding and see how and then also we can use an easy to obtain field data to make a pretty simple prediction that works quite well.

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Rachel Coyte: As well as greater than that North Carolina health advisory level for chromium six.

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Rachel Coyte: Really on to part two, which is to look at co occurrences, and there are a variety of reasons that we can be interested in.

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Rachel Coyte: And contaminant co occurrences, but what I want to focus on today is the idea of the mixture effect, which is that the combined effects of two contaminants could pose additional risks because have synergistic.

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Rachel Coyte: synergistic effects and pose additional risks to human health, beyond that of the individual contaminants.

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Rachel Coyte: This hasn't actually been widely studied and trace elements, most of the literature, I think, focuses on independence raptors, but it still is there's value of studying the existence of these co occurrences, I think.

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Rachel Coyte: So my question is what groundwater contaminants tend to co occur and by.

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Rachel Coyte: But specifically for the North Carolina case i'm going to be looking at the redux sensitive elements uranium chromium arsenic and banana them because they're the most common gog and it contaminants that are found in the state at health relevant concentrations.

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Rachel Coyte: We first need to define co occurrence and there's a few different ways that we can go about this, but i'm going to go with a fairly simple definition of.

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Rachel Coyte: two or more species exceeding guideline values and the same well measurement this has a few drawbacks one it doesn't get at the magnitude of those co occurrences so.

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Rachel Coyte: This approach is going to take a uranium concentration of 31 micrograms per liter and treated the same way as a uranium concentration of 300 micrograms per liter.

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Rachel Coyte: The reason that i'm doing this, you know I could take a more water quality index approach, but we don't really have the health data to support that building that kind of an index, in this case so i'm going with the simpler case.

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Rachel Coyte: The second problem is the idea of conflicting standards.

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Rachel Coyte: I briefly talked about this earlier with chromium.

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Rachel Coyte: But if we look here we have a set of health standards on the left for each of those reactions developments I talked about, and we have the regulatory standards on the right.

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Rachel Coyte: there's a big gap here.

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Rachel Coyte: If i'm using the N ch AE l for chromium banana diem and the.

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Rachel Coyte: US EPA maximum contaminant level goal for uranium and arsenic and then on the right i'm using the EPA is in cells.

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Rachel Coyte: And there's a massive difference between these two, so the choice that you make in terms of am I going to compare these based on health standards or am I going to compare these based on regulatory standards is going to have a large effect on the degree of co occurrence that you calculate.

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Rachel Coyte: To show this visually i've actually plotted the violations with health experiences of health standards on the left and experiences of regulatory standards on the right and again, these are actually exceeded probabilities and.

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Rachel Coyte: When we look at the one contaminant level, so the probability of exceeding health advisory levels on the left.

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Rachel Coyte: of any one of those four contaminants that I listed or regulatory standards in the right, we can see already a very large gap between the two, you have a pretty high.

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Rachel Coyte: probability of exceeding one contaminant health standards i'm pretty much throughout the entire of the state, but especially in the Piedmont region, whereas your probability of exceeding regulatory standards is fairly low.

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Rachel Coyte: When we get down to the two contaminant level, we can see an even bigger gap and that there is not a single case in our entire data set of.

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Rachel Coyte: Two contaminants exceeding regulatory levels, at the same time, but we have numerous and indeed we have greater than a 50% exceeds probability, and many of those Piedmont belts belts of exceeding to contaminants.

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Rachel Coyte: When we look at what to those contaminants are co occurring at that health advisory level.

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Rachel Coyte: Or at the health standard level, we see that mostly it's going to be fanatic chromium and what's causing this is a both a shared geology and a shared chemistry so both financing and chromium are mostly coming from neath formations with some sort of a mythic component to it.

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Rachel Coyte: And they also both tend to be more mobile under oxidizing conditions, whereas if you take the case of chromium and uranium, for example.

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Rachel Coyte: they're both going to be more mobile under oxidized conditions, however.

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Rachel Coyte: chromium is going to be more associated with those most basic formations, whereas uranium is going to be more associated with those toxic formations and so they could occur in a much less degree.

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Rachel Coyte: arsenic tended to coworker, least of all with the other contaminants mostly because it's in North Carolina at least associated with more suboxone groundwater.

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Rachel Coyte: But it does have a moderate degree of co occurrence with uranium, because they are both affected by pH a great deal, so higher pH probably leads to more these orbs and pressures on both of those which is a big effect on the solubility of both elements in groundwater systems.

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Rachel Coyte: So the takeaways here are that elemental co occurrence is influenced by source and geochemistry.

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Rachel Coyte: The choice of your standards for calculating or co occurrence is really important it's going to have a very large effect on how you end up calculating that.

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Rachel Coyte: And then, finally, more research is going to be needed to really understand the risks associated with these co occurrence us and with that i'll put up my email address and I don't know if we have time for questions or not.

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Ann Ojeda: Yes, we do Thank you so much Rachel.

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Ann Ojeda: again feel free everyone to use your emojis and use the chat to ask Rachel questions i'll moderate those.

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Ann Ojeda: But i'll kick you off with maybe an easy one, while people are digesting this, and that was a fantastic presentation Thank you so much, and one of my questions is.

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Ann Ojeda: Is there a domestic well water program in North Carolina to translate your research or your understanding of these co occurrences and exceeded probabilities to the hands of the Homeowners.

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Rachel Coyte: know we got a lot of pushback from the state when we first started talking about this and, and I think there has been a lot of tensions within the state program about how to communicate.

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Rachel Coyte: They were putting a bit of an awkward position with the 0.05 micrograms per liter health advisory level that is such a you know gap from regulatory standards and.

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Rachel Coyte: There hasn't been a lot of clarity from the state and communicating with Homeowners on this issue.

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Ann Ojeda: yeah we see that an Alabama to we are starting a domestic well water program here now Alabama because of the same as similar issues so it's interesting to see health each State handles these really complicated and.

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Ann Ojeda: Where worrisome issues for Homeowners and then giving them resources to try to manage that.

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Rachel Coyte: There have been some great programs in North Carolina and looking at legacy contamination, so a lot of new development is built on reclaimed farmland, for example, and.

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Rachel Coyte: These tend to have high concentration of pesticides or some other organic contaminants and if you do find this is the case in your well there are state grant programs, you can apply to.

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Rachel Coyte: But they are only four cases of legacy contamination, if you have do genetic contamination in your groundwater you don't really have much recourse through the state to help get some kind of filtration system again.

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Ann Ojeda: Thank you, any other questions from the audience, we have time for one more.

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Ann Ojeda: mean club.

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Ming-kuo Lee: Rachel enjoy your presentation.

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Ming-kuo Lee: i'm wondering what you show a lot of trust in him and information is your database, also including major ions there you can make.

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Ming-kuo Lee: Interpretation or petrochemical phases, maybe you know you mentioned there's a lot of heterogeneity in mind in a bedrock.

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Ming-kuo Lee: As well i'm wondering where you also looking at manager, while you're fishing to trust him looking at potential mixing it may be that different mythologies meteorology aquifers.

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Rachel Coyte: Yes, absolutely and so and actually the second paper quit and then gosh which was published in 2020 this we do look at some of the major Ion chemistry.

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Rachel Coyte: And, in particular, what we're finding is that redux and pH are pretty big drivers of the solubility of all four of these elements, not necessarily in the way that you would expect, so, for example, for banana and chromium.

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Rachel Coyte: Very would predict that you're going to have high very oxidizing water and high pH water is going to more promote the solubility for us to However, what we're finding in North Carolina is that it's actually lower so more neutral.

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Rachel Coyte: To slightly acidic pH that we're finding these water it's in and what we think that is is a effective residents time so it's actually the more important thing is the oxidation state and getting that fresh.

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Rachel Coyte: oxidation or oxidized oxygen high oxygen concentrations groundwater that's quite young, which is entering the aquifer.

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Rachel Coyte: oxidizing these contaminants and is going to then have a lower pH because of just the lower residence time and that's the more important factor for these two as opposed to the pH effect, so we do have a bit of analysis so Sir the hydro chemistry.

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Ming-kuo Lee: Thank you.

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Ann Ojeda: Thank you Rachel.

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Ann Ojeda: What that will give you one last round of applause and.

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Rachel Coyte: Thank you.

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Ann Ojeda: Next, we have Natalia Molina from auburn university presenting on the effects of dissolved organic matter fluorescence on iron three complex ation Thank you Natalia Oh, and as a reminder, if you have your people up i'll give you a two minute warning.

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Ann Ojeda: When you're two minutes before the Q amp a session.

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Natalia Malina: Okay, I hope you can hear me.

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Natalia Malina: Okay, good morning i'm really happy to be part of this GSA sectional meeting, and today I want to present our ongoing project on compensation of Ireland three islands with dissolved organic matter and influence on face come on compensation on fluorescence also do em.

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Natalia Malina: To start with, I want to bring the term this organic matter of for those who are not familiar with it.

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Natalia Malina: So this organic matter is the fraction of organic carbon which can pass for the filters with or size point 45 micrometres.

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Natalia Malina: There are general two types of software granting metro region in the aquifers, it can be a lot of donors origin and out of kronos origin, I looked on us.

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Natalia Malina: source means originated from the land, for instance leeches from soil or decomposition of plants and after under our donors, a region we understand the surface, like algae microbial breakdowns of aquatic carbon.

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Natalia Malina: So on the original defines the chemical composition of DRM and usually.

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Natalia Malina: Usually they're.

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Natalia Malina: Under do em and we understand such a complex polymer looking like something like these beekman I color on the slide and.

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Natalia Malina: it's structured depends so on the soil type, which are in contact with water, all the blonde surrounding their aquifer.

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Natalia Malina: But.

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Natalia Malina: Do I am stays very complex Boehner and a scientific community Community Community try to understand this composition, which is difficult in case of analytics and.

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Natalia Malina: And also do I am can different the structure of deal i'm can be different in different regions of the world, so there is no.

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Natalia Malina: Unified structure of DRM and also i'm.

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Natalia Malina: Here, what is important in new am it's it's functional groups in case of methyl compensation, and we would assume that nowick group and cookbooks in the group place the.

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Natalia Malina: future role and can influence them both the complication of metals and also the flora sense properties of DRM so we hypothesize that 30 cents properties of DRM can predict iron free compensation capacity of the single deal and.

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Natalia Malina: So in our search we used three types four types of DRM.

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Natalia Malina: And we chose swanee river human substances as and we purchase it from natural organic methods.

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Natalia Malina: These human substances be used as the reference of such a classical on deal answers because it's well started and lots of research present in the literature, using the student type.

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Natalia Malina: Also, we sampled the local opponent water which represent that out of donors or region because of huge amount of our job error and also we sampled.

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Natalia Malina: cold, so we extracted do from to cause and.

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Natalia Malina: I will return to this later on to the method of extraction of this call do am from these calls, so this to call samples were collected from the Gulf coast and one sample was collected from an active clay mine in hot springs.

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Natalia Malina: county and in our process and another are wrong that velocity country also in our Kansas.

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Natalia Malina: And we extracted DRM from calls are using go to pure water, so that we put some on eight grams of course in the flesh, can we get water pure water, and then they we extract it.

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Natalia Malina: Each at 80 degrees systems and for hot spring first we defined the duration of this extraction and first we use five days extraction.

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Natalia Malina: And we sampled each day and analyze juicy and calculate server to understand if there's enough what duration, should it be and often realizing of QC and server.

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Natalia Malina: We determine that today's is enough and that's why for blahs kiko we keep these today's and for articles, we also keep on today's with swanee river humid as it we just assaulted in water pure water and for the pond water we just proceeded as we sampled it.

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Natalia Malina: So do I am it's usually we can divide you infraction by such a complex polymer which comprise from the hydrophobic s it's human cases and father cases and also, we can analyze simple components which is present, which presenting.

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Natalia Malina: In the course and first we developed analytical methods of analyzing DRM structure so for simple components, we applied to cms gas chromatography mass spectrometry.

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Natalia Malina: And for the America part of the compounds so for human and for the case it we use techniques, like a high.

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Natalia Malina: Pressure weekly tomato graffiti with says exclusion column, and we use different detectors we used up detector and fluorescence detector to.

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Natalia Malina: to analyze the two to see the home first and flora for so which is present in DRM and also we use like General technique like movies spectrometry and do you see for measuring the general characteristics of the DRM.

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Natalia Malina: hbc with signs exclusion allowed us to divide each deal am i'm.

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Natalia Malina: into them on two different fractions and to define the size of these frictions.

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Natalia Malina: I hear, there is the general across jurisdictions of our do I am i'm on services, so we determined that blocky DRM.

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Natalia Malina: is highest in to see.

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Natalia Malina: And what suwanee i'm on the sukkah value insulin is the highest super determine there are multi-city of the deal i'm and we also defined determined our in our.

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Natalia Malina: In our samples, and in this case hot spring was the highest with our own three concentration.

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Natalia Malina: Here are the results of html CSS exclusion analysis with UV detector so we used to be detector in this case, because our calibration substances, only visible and.

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Natalia Malina: So we use the detector for the self determination and we found out that.

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Natalia Malina: pretty much they're all all our cause of the same size, but we can.

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Natalia Malina: We can see the different so off our coast truck show with the point water because water it's them threatened with 532 downtown's is predominant and also we.

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Natalia Malina: can see difference of swannery vacuuming cases compared with heartbreak and philosophy and we can clearly see different fractions and we can collect these fractions from htc says excluded.

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Natalia Malina: chromatography um do you see a Meta analysis of what extracts and call it stress shows us then different compounds groups so which.

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Natalia Malina: We can find in our calls, and here the structures and then compound composition in all the DRM types are almost the same so the predominant come on components groups for alcohol carb oxalic acid police arrived and females.

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Natalia Malina: But alcohol.

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Natalia Malina: predominated in savannah and on water but curb oxalic acid.

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Natalia Malina: components of a predominant in blocky and hot springs call, so this is the difference we can distinguish between call source or do em and on border and so ronnie.

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Natalia Malina: can make edits we also i'm determined that there are essence the typical for essence of these DRM different deal types.

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Natalia Malina: And by oxidation emission metrics so these additional metrics shows the efflorescence of our goals at to page to page two and page seven, and by these technique, we found out that their maximum fluorescence range of our calls and we found out that philosophy at beach 6.8.

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Natalia Malina: Have like the highest fluorescence beaks compared to our DM types, but I would say that the oxidation and mission ranges of fluorescence for all the goals of British of all our analyze that it goes up pretty much the same.

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Natalia Malina: We on our HP RC technique with says exclusion chromatography we can be only one i'm oxidation emission wavelengths during one injections and.

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Natalia Malina: But peace allow this technique allowed us to divide different fractions so inside the huge molecular of DRM and we and.

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Natalia Malina: We beat from the literature their expectation emission wavelengths which corresponds to fumigated Folic acids and post the horizon and we injected different coasts in our agency and we define that determine that philosophy call our.

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Natalia Malina: shows highest fluorescence so fuming folic acid and post all right, so all rights compared with our direct sources and.

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Natalia Malina: followed by code spraying qpr talking about human cases and father cases and in both copyrights has frank and savannah pretty much the same.

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Natalia Malina: So after that we did the experiment with the addition of our into our solutions or form, so we picked velocity for the first experiment first and we edit I run on solution in our do I am.

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Natalia Malina: Do i'm solution and we found out that after additional foreign the concentration or foreign was 20 bpm.

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Natalia Malina: We observed the formation of new Beak on UV detector, so this is a new home for formed and we define the size of these complex we speculated, this is the new complex with the size of 16,000 items.

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Natalia Malina: And these.

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Natalia Malina: On the size of a pea correspond to their okay correspond to them.

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Natalia Malina: Because inspirational fire and we also do the same, we injected Iran.

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Natalia Malina: We analyze the flora sense of classical and we also observe the formation of that and other peek.

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Natalia Malina: At the name of Jesus Christ, and the size of the speech was 5000 pounds and we also have observed the decrease of larger sense of humor cases fluorescents with the concentration of Iran.

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Natalia Malina: and different fractional for velocity DM rigorous responded differently to this additional parent and the maximum degrees, we observed on their highest fraction crisis oppression so.

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Natalia Malina: This is blue line which decreased significantly, with the addition of iron so to megan good conclusion of all of these stages of our search we define that do em correspond the floor sense of deal and correspond to the addition of iron we see.

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Natalia Malina: beaks new formation of weeks on UV detectors ELENA also an fluorescence detectors so it's like on your chrome a form and flora for formed and just and also.

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Natalia Malina: As usual, our research brings more questions than it answers and.

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Natalia Malina: We need to determine and to prove that this is the compensation of iron with doin this new peaks and we need to define the REACH, what is the home of foreign forms and to.

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Natalia Malina: determine their floor form which was fun and also our future research food to go to do understanding efficiency of DRM.

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Natalia Malina: Publication with iron and also the comparison of the different structures of dealing with these efficiencies and we have in mind the method of software so it's really.

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Natalia Malina: Complex ation of free Iran, which is nice and Nice colorful flasks on there right, so thank you for your attention and i'm happy to answer your questions, if you have some of them.

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Ann Ojeda: Thank you Natalia.

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Ann Ojeda: i'll open the floor up to any questions.

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Ann Ojeda: so well, we have just a couple of minutes, and while people are thinking, I have a quick question and what do you think is happening to draw to create that new chrome for for for.

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Ann Ojeda: that's a much higher molecular weight, with the addition of iron so that's interesting that there's a new peak that shows up when you add iron, what do you think is driving that.

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Natalia Malina: I think that iron can make the breach with the fractions of DRM some of the fractions which we see, and we see the the huge decrease or the signal of the like the hartley them the highest fraction that you're.

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Natalia Malina: In terms of the size, so if you.

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Natalia Malina: Think of like combining five or six fractions of 1000 doubt owns it would bring you these huge complex.

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Natalia Malina: And so I think this is bridging of different fractions between Iran and do an.

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Yuehan Lu: attorney.

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Yuehan Lu: I hope, questions there's the time i'm good talk I enjoy it so just falling apart and question i'm wondering if you have any plans in terms, clarifying the structure of this complex.

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Yuehan Lu: What are the techniques that available to try to understand what Hypo breach of complex we're we're being established within the structure.

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Natalia Malina: So for now it's nice question I was thinking about these because it's an ongoing project we just.

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Natalia Malina: trying to find out the technique, right now, and we are he he sees as exclusion, we can separate, so we can collect these fractions of the Beak and somehow to analyze it on.

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Natalia Malina: The first is just see a mess, but just a mess I don't think that it will give us the structure will give us the simple compounds and.

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Natalia Malina: These like maybe some spectral technique first are what I need it's interesting to find if there are an inside of the speaking out to collect the speak and to find this iron inside and.

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Natalia Malina: This is this was my thoughts and maybe fd I are will help to understand that.

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Natalia Malina: brings like to bring some light on these complex.

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Yuehan Lu: Well, I agree, I think FDR I will definitely tell you, if I are is combined you know, in terms of to tell you the molecular weight right in terms of like where it is combine and everything, so we probably need another technique.

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Ann Ojeda: Okay, thank you Natalia.

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Ann Ojeda: we'll move on to the next speaker this is Nicholas hammond coming to us from Virginia tech.

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Ann Ojeda: and his talk is assessing short term variability of iron manganese cycling and a drinking water reservoir using high frequency water quality sensor so Nicholas, thank you for being here and the floor is yours.

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Nick: awesome Thank you.

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Nick: let's see, let me just share my presentation real quick okay.

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let's go here.

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Nick: Alright, great.

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Nick: So thank you all for being here today um.

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Ann Ojeda: Nick i'm going to pause you for a second because I guess I should switch your displays we're seeing.

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Nick: Are you seeing presentation let's see.

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Nick: Is that better now.

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Ann Ojeda: display setting here i'm you should be.

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Caleb Eldridge: worried yeah.

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Rachel Coyte: yeah that's better.

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Madeline Schreiber (she/her): that's a neck.

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Madeline Schreiber (she/her): that's good.

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Nick: Okay awesome cool, thank you for catching that i'm still learning the ins and outs of zoom even after a whole year of you know, having that being my predominant mode of conversation but.

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Nick: Without further ado, yes hi everyone, my name is Nick i'm from Virginia tech and.

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Nick: Today i'm going to be discussing a little bit about my research on iron and manganese cycling and drinking water reservoirs.

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Nick: And some of the ways that we can collect high frequency measurements of these metals in the environment.

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Nick: So what you're seeing here on the screen is a picture of falling creek reservoir that's a drinking water reservoir near roanoke Virginia, and this is where I.

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Nick: conduct all of my fieldwork so i'll be telling you a lot about falling progress for today, but before I dive into that.

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Nick: I just want to give you a little bit of background knowledge about why we care to study iron manganese in drinking water systems and a little bit about what we know about how they behave in the environment.

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Nick: So this may be stating the obvious to many of you, but I just wanted to emphasize that we depend on freshwater lakes and reservoirs for clean water.

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Nick: In the United States, you know, over three quarters of our population relies on public supply water for your household use.

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Nick: And over 60% of that public supply water comes from surface water sources such as lakes and reservoirs so in the US and around the world, we depend on clean water, coming from these types of water bodies.

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Nick: So when considering water quality in lakes and reservoirs it's really important to consider iron manganese in particular because they both have some interesting.

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Nick: negative effects on water quality.

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Nick: Both of these metals can negatively affect the teas that order and the color of the water, such as what you see here with someone had you know bright orange iron rich water coming out of their tap.

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Nick: Which is not a situation anybody wants to be in they can both also cause damage to our water infrastructure by corroding and forming deposits in plumbing systems.

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Nick: And both iron manganese can actually have been linked to human health risks when consumed in drinking water at access quantities so for.

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Nick: All of these reasons, the EPA and the World Health Organization has kind of set these standard threshold values of point three milligrams per liter of iron and Point five milligrams per liter of manganese in drinking water.

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Nick: So, now that we've established why it's important to care about iron manganese in drinking water, I just want to give you a little bit of background on what we understand about how they behave in freshwater systems so.

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Nick: The most important thing to remember is that both iron and manganese are highly redux dependent, they are also influenced by pH but in you know circle neutral pH values that are found in a lot of surface water systems redux conditions are the predominant control over there.

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Nick: Over there faith and distribution.

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Nick: So in like well mixed oxygenated waters are going to manganese are typically in their insoluble forms found in the sentiments of a laker reservoir.

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Nick: However, this all is subject to change.

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Nick: I can advance my slide here let's see.

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Nick: There we go.

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Nick: Due to a process that commonly occurs in temperate regions of the world, known as thermal stratification and this happens in the summer months when a lake or reservoir.

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Nick: heats up on even the and the, the water column becomes stratified and so that happens, and the bottom waters become physically disconnected from the top waters of the laker reservoir.

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Nick: You see, these two distinct layers known as the hyper elimination and the EPA linnean which are divided by a drastic layer where.

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Nick: temperature decreases drastically, known as the thermal climate.

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Nick: So the effect this has on iron and manganese is that once these the stratification sets in.

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Nick: The Hypo linnean fairly quickly becomes depleted of oxygen and this shifts the redux conditions in favor of the reduced more soluble form of iron manganese and at this point, they tend to diffuse upwards out of the sediments and into the water column.

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Nick: And this continues until they diffuse upward and reach this oxalic boundary layer with apple Indian and at that point they become oxidized both iron manganese oxidized fairly rapidly in the President presence of oxygen.

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Nick: And so, this kind of completes an entire V Doc cycle.

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Nick: Where they are reduced release from the sediments become oxidized returned to the sediments and this kind of ensues throughout the summer months.

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Nick: However, we do see hi accumulations of these reduce metals throughout the summer and as the summer progresses.

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Nick: Until the final phase, when in the fall time in the winter cooler weather sort of forces the reservoir to actually D stratify and remix.

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Nick: Through a process that we call turnover and when this happens, the typically the whole water column becomes well oxygenated and we see Aaron and manganese become oxidized and returned to the sediments again and very low concentrations in the water column.

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Nick: So with that background, on sort of these broader seasonal cycles American manganese.

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Nick: That kind of sets us up for the driving questions and hypotheses for this current project.

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Nick: Because we do know a good bit about these seasonal cycles.

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Nick: However, we don't really know as much about their short term dynamics, so if we're looking at how these cycles ensue over daily or even hourly time horizons there hasn't been a whole lot of research on that subject.

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Nick: However, a lot of biogeochemical processes in the environment they do actually exhibit these ephemeral periods of high reactivity which, in the ecological literature has been.

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Nick: turned pot moments of biogeochemical processing there's kind of a classic example of that being denied vacation in stream right period zones where.

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Nick: we've observed very high rates of the unification sort of in conjunction with sort of rapidly fluctuating hydrological or environmental conditions.

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Nick: So that leads us to pose the question are there hot moments of iron manganese cycling and lakes and reservoirs.

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Nick: That would be missed if we are only collecting coarser resolution, of course, certain poor resolution data, as is typical of most water quality monitoring programs.

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Nick: And my hypothesis for this project is that both iron and manganese cycling for both metals, is highly variable over the short time horizons, because a lot of the environmental.

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Nick: conditions that drive their cycling are also highly variable over short time spans especially dissolved oxygen as I discussed earlier it's one of the key drivers of metal cycling.

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Nick: So if we want to answer this question, we first have to answer the question how are we going to actually observe their dynamics in the environment, and you know short term variability.

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Nick: And that's an interesting question because traditionally the the approach to measuring iron manganese and any.

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Nick: type of water body would be to collect manual samples by hand and then take them back to a laboratory to analyze on some type of some type of mass mass spec or something like that.

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Nick: However, an alternative approach that i'm proposing here is to actually measure metals concentrations in the field at a heightened portal frequency using an optical sensor.

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Nick: which you know, the use of optical sensors and for water quality monitoring and environmental sensing is a rapidly growing field it's been used to measure other.

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Nick: environmental parameters such as dissolved oxygen and dissolved organic matter and a lot of other things like that so we're proposing to try that out with iron manganese.

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Nick: So for this study we actually have.

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Nick: High Frequency water quality monitoring sensor called the multiplex or sensor or mugs for short.

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Nick: which was developed by a collaborator buyers down at nc state his name is francoise Zhang and what this multiplex or sensor is is, essentially, as you can see in the photo here it's essentially just a big black pelican case filled with.

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Nick: A microcontroller and a pair of Celtic pump and some valves that are connected to tubing and the tubing extends down into the water body that you're studying.

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Nick: And, depending on the link to the tubing the pumpkin retrieve samples from different depths or different locations in the water body.

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Nick: And then it pumps these samples through a field spectrum photometer called the scan and the spectrum photometer actually measures, the UV this absorbent spectra of in the water sample for 212 wavelengths.

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Nick: And then we take these data and we put them into partial the squares regression models which we calibrate with our manually collected laboratory analyzed samples and using these statistical models, we can then predict iron manganese concentrations based off those absorbent values.

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Nick: So do you test out the efficiency and efficacy of using this sensor during a three week period last fall.

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Nick: We collected data using both the High Frequency sensor as well as collecting manual samples and roughly a weekly frequency and kind of compared and contrasted the data from weekly sampling versus the hourly data that we're able to obtain from the High Frequency sensor.

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Nick: We conducted our experiment at falling creek reservoir, which is a small drinking water reservoir located near vinton Virginia and falling creek does experience this thermal stratification every year throughout the entire summer.

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Nick: And a few interesting things to point out is that this reservoir has been sampled fairly intensively for a number of years now for a few other research projects that are going on there.

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Nick: And there's also a number of other high frequency sensors that are collecting continuous data on other water quality parameters such as dissolved oxygen temperature pH and things like that.

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Nick: And finally, another interesting thing about falling creek is that it has a hyperlink medic oxygenation system installed there, which is essentially just.

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Nick: An engineered system that injects oxygen directly into the bottom, the hypothalamic waters of the reservoir, and this is, this has been installed in an attempt to sort of combat some of those issues created by access levels of iron and manganese and other metals in the drinking water.

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Nick: So you can see that depicted by the black line on the map there on the right and also next to that is a star which denotes our primary sampling location where all the data for this project were collected.

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Nick: And that's also the deepest side of the reservoir.

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Nick: cool alright so.

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Nick: Now we'll take a look at some of the data from that three week period that I described earlier.

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Nick: What you're seeing here is a time series of total iron concentration for the entire three weeks the y axis is total iron and the X axis is time.

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Nick: And the different colors represent a different depth in the reservoir where we collect data, so in this case we have six discrete deaths and, as you can see there's a pretty distinct contrast between the epidemic and the hyperloop medic waters.

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Nick: As we would expect to see the hyper linnean has much higher higher concentrations, while the reservoir is stratified.

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Nick: But an interesting thing to point out, with this time series is we were actually able to capture that process of reservoir turnover occurring, which actually occurred in early November last year.

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Nick: And at that point, you can see, actually leading up to that point the the concentration gradient in higher and actually kind of decreases until they both.

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Nick: till all the water all the concentrations converge around that point on, on November 2, at which point we can tell that the water has mixed and it's got fairly homogenous and fairly low concentrations of iron so that kind of.

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Nick: gives evidence for that cycling, that I was discussing earlier.

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Nick: So these are interesting trends, to look at, but now let's kind of compare this to what we see if we are looking at hourly observations, as opposed to weekly observations.

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Nick: So this time series shows the exact same thing of total higher concentrations, however, instead of the weekly manual samples, these are hourly predictions based on our high frequency sensor.

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Nick: And as you can see there's a lot of variability and I learned that.

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Nick: Is missed if you just collect a weekly sample and then interpolate a straight line between those points we saw big shifts and concentration and up to.

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Nick: Three or four milligrams per liter that transpired over sub daily time horizons and a lot of kind of crazy dynamics that at this point I can't explain what's causing a lot of these but.

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Nick: it's very interesting to show that, like you know we've essentially confirmed our hypothesis that you know, we really do miss out a lot of this short term fluctuation that is going on.

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Nick: Whenever we don't monitor it on these high frequency timescales.

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Nick: And the final thing I would like to point out on this plot is that you know these are actually still predictions, based on a statistical regression so there's always going to be some error associated with these.

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Nick: Which is why i've reported a few skill metrics at the bottom of the slide here being cross validated mean squared error and also the R squared coefficient.

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Nick: Or are predicted versus observed values and both of these do indicate that we were able to obtain a fairly robust model fit, however, you know, there should be some kind of confidence interval associated with all of these predictions.

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Nick: So this is a you know it's it's certainly very interesting to look at all these plots and look at how things evolve over time, however.

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Nick: It doesn't really tell us a whole lot about the underlying processes, unless we can kind of connect the dots between these trends we're seeing and iron and also some.

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Nick: Environmental processes that could be driving these trends.

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Nick: So that's going to be the next steps of this project, I just want to kind of look at a little bit of some of the potential for that, today, by focusing in on.

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Nick: strictly the 1.6 meter depth and the data we have for that depth, because we also have a lot of other high frequency sensor data collected at the same depth as that.

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Nick: So if we kind of look at some other time series plots from the same depth but looking at other environmental parameters such as dissolved oxygen and temperature and fluorescent dissolved organic matter, we can definitely see that there's a lot of trends.

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Nick: And a lot of correlation between these different environmental variables and just visually you can see a lot of interesting things going on here.

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Nick: However, really to be able to kind of draw an inference from this, we need to be able to quantify these this type of correlation so.

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Nick: The next steps on this project will be to take these high frequency sensor predictions of iron and manganese and.

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Nick: start building some time series models auto regressive time series models that we can be able to parse out, what are the dominant environmental drivers of these trends and then also do some sexual decomposition of the time series, such as wavell and analyses.

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Nick: That will enable us to quantify the temporal trends in these times series, because the.

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Nick: there's definitely evidence for dial cycling going on which you can see, pretty clearly in like the right hand side of the time series but also there's a lot of random kind of chaotic behavior going on, that would be interesting to be able to quantify it's period period so.

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Nick: that's going to be the next steps for this project, I just want to kind of wrap it all up by reiterating that.

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Nick: You know, through this work we've been able to demonstrate that metal cycling is highly dynamic over sub weekly time spans, and this will be super useful knowledge for drinking water management and also for our fundamental knowledge of aquatic bio geochemistry.

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Nick: So with that, I would like to thank all the many people whose.

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Nick: whose work was instrumental in making this project happen.

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Nick: And yes, it's been a very much a team, a team project, and so I couldn't have been alone, and with that, if I have time, I would be happy to answer any questions.

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Ann Ojeda: Thank you, Nick that was excellent, very, very interesting data and we probably have time for one question if anybody has a burning question in their soul and, but we do have a break coming up after this so feel free to hang around during the break.

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Ann Ojeda: And type questions in the chat we're happy to do that as well.

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Ann Ojeda: So i'll ask the question that was put in the chat I can it's a cool system, and do you leave this in the field over the whole time or so over the year of your study, do you leave this in the field.

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Nick: that's a great question, yes, so we were deploying it for kind of a three week period as a sort of a trial run, this time, however, the goal is to be able to say deploy it for an entire summer or you know multiple months at once.

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Nick: With kind of some minimal maintenance, you might need to look for instance falling is can be a serious issue with the sensor system when you take reduced iron and pump it up into anoxic environment it likes to oxidize and you know, make a lot of issues with that.

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Yuehan Lu: So.

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Nick: You would have to maintain it to a certain degree, however, the idea is to be able to have this deployed continuously for longer time periods and.

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Ann Ojeda: Great Thank you um, so we will move on to the next speaker so thank you, Nick.

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Ann Ojeda: The next speaker is Dr mean Kohli on behalf of Connor came here at auburn university.

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Ann Ojeda: i'm speaking about implementation of geographic information systems to understand spatial distribution and correlations of cancer cases and groundwater contamination and the rural community of fruit Hurst Alabama.

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Ann Ojeda: Thank you, Dr Lee.

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Ming-kuo Lee: Everybody see the slides.

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Ann Ojeda: Yes, we can see your slides.

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Ming-kuo Lee: It Thank you and.

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Ming-kuo Lee: So today i'm going to present research on potential connection.

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Ming-kuo Lee: Between groundwater contaminants and cancer cases.

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Ming-kuo Lee: In a rural community of course in Alabama, as I mentioned that comma Kim is busy with his field work, so I present on his behavior.

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Ming-kuo Lee: and want to say that this project is really taking a for involve many of my colleagues and all burn and university Alabama so i'm just lucky.

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Ming-kuo Lee: While i'm there to share the data with you today, so the resident of Rojas approach all been back in 2016 for their environmental and social child, especially they aware water in cancer cases so far 2013 to 2017.

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Ming-kuo Lee: They are for children leukemia cases for this small community with population around 600 so this leukemia incident response to be higher than the national.

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Ming-kuo Lee: Average user them rates, so the tag on the lower right shows the national trend in leukemia incident rates in for both female and male.

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Ming-kuo Lee: So you probably be able to see that this rate kind of increase in the past two decades, so the question is this slight increase was caused by genetic mutation or maybe because of the increasing.

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Ming-kuo Lee: Demand for exposures and so many of the rest of them in this Community still still use probably well for as their primary one episode, so that the Community is he got want to know what's in there well.

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Ming-kuo Lee: So my colleague Dr loci shoots can.

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Ming-kuo Lee: conduct a wholesale account for meeting and also conduct Community survey try to find clients history or cancers and also collect some demographic data.

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Ming-kuo Lee: And so we use a Community based research, that means that the science, the data collection is driven by the concern and needs of the Community.

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Ming-kuo Lee: So we track several Community Community residents in guam other simply for geochemical analysis try to identify potential contaminants in there were water.

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Ming-kuo Lee: And they also involved in the installation of water filters and try to get rid of this reduce the potential exposure to contaminating well.

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Ming-kuo Lee: So let me take you to the study area on the right hand side, you can see a different color represent five.

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Ming-kuo Lee: Major geological provinces in Alabama so from a hearse study areas located in talladega built and a lot of whale was true withdrawing from.

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Ming-kuo Lee: geological Union known as a happening fields, which is a metamorphic rock and so the previous study as far as enrichment of uranium.

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Ming-kuo Lee: In a movie rock may release.

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Ming-kuo Lee: Residents so.

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Ming-kuo Lee: So my colleague Natasha the mobile she was going to give a presentation later today so her preliminary data show the sound of well has elevated level radon reach several thousands of people curious for the trees.

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Ming-kuo Lee: So the tag on the lower left shows the EPA mad upgrade on zoom so we focus on the red color zone one So those are a rose geology could potentially produce higher level Ray.

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Ming-kuo Lee: And so you notice the study here in northeast Alabama.

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Ming-kuo Lee: zone wine, including our study area in carbon county.

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Ming-kuo Lee: So even several previous study has been established potential correlation between radon and looky Chilean leukemia but house risk of.

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Ming-kuo Lee: Especially radon.

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Ming-kuo Lee: In.

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Ming-kuo Lee: Drinking water still not well understood.

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Ming-kuo Lee: So we have three.

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Ming-kuo Lee: objectives, the first objectives, I understand the.

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Ming-kuo Lee: The water chemistry or both municipal water.

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Ming-kuo Lee: And we're watering in terms of.

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Ming-kuo Lee: Major stress and radon and.

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Ming-kuo Lee: Again, any manager it's.

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Ming-kuo Lee: The second object is, since we have some survey data apple cancers.

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Ming-kuo Lee: So we try to understand the spatial correlation between.

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Ming-kuo Lee: While other contaminants in the cancer occurrence for objective, where we use in different type of water filter we try to better understand their capability to.

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Ming-kuo Lee: A 10 year renewal rate down from where water.

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Ming-kuo Lee: So the water was sample from 25 private whale also from.

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Ming-kuo Lee: Co why the spring, so the current Musa water is coming from colada spring, which is connected to not carbonate group, but most of the whale water is with encoding old for her city well, is it withdrawing from happening field.

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Ming-kuo Lee: And so, this map on ryan's his shoulder the locations all of those whales.

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Ming-kuo Lee: So just show you some preliminary survey results now again because the Community is equal to now personally aware water, so we see already higher response rates morning 60% was a total morning 790 hormones so morning 50% response use where water is their primary source of drinking.

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Ming-kuo Lee: And, according to the survey.

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Ming-kuo Lee: About 15% of responding with the polar case or four different types of cancer leukemia lymphoma stomach in lung cancer and also since 1987.

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Ming-kuo Lee: They are about 36 cases of leukemia and inform our case record there's more than one cases per year, a higher than the national national incident rates.

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Ming-kuo Lee: And we see a high percentage over this for different type of cancer cases they all use where water is the primary source of water, especially for example for the stomach cancer hundred percent of stomach cancer cases the use the whale.

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Ming-kuo Lee: So we we collect happening fi lights from today will have four mineral and both geochemistry analysis, so we try to identify potential.

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Ming-kuo Lee: geological source of revenue was potentially may release rate.

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Ming-kuo Lee: and

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Ming-kuo Lee: So the xr the analysis, you see upper right, and so we identify co-writes home brands l bites and pipelines, you can also see on the left hand sides.

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Ming-kuo Lee: In some of the minerals are required visit we also see a, so this is a deep core sample, we can also see the fractures me whos the migration ravens.

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Ming-kuo Lee: I want to mention that the the pirates shown over here with thing is a significance because pyro I may be the potential host okay real where you rain you.

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Ming-kuo Lee: may reduce redux so we also use ICP Ms to quantify the trust element content in happening fi lights, and so the background lower whites compare the trust element concentration of.

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Ming-kuo Lee: happening fi live from within well one with the average upper continental crust so the blue bar here shows those in.

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Ming-kuo Lee: happening field in orange bar here representing loads of airbridge continental class So you can see that so results show about 50% of enrichment of your radiance but for us Allah trust them and such an awesome week zinc in chrome you also show song enrichment.

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Ming-kuo Lee: So the question is what in the world.

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Ming-kuo Lee: So this table is kind of busy, and show show show the founding of the trust and concentration in where water.

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Ming-kuo Lee: Okay, I don't expect you to read a major funding is most of the trust and the concentrations of below the EPA MC you, so this is good news for the communities.

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Ming-kuo Lee: And so, this red box here shows the radar in where water, you can see that a wider range from one to more than 1000 people charisma lead so several whales like saving the radon level recommended by the EPA.

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Ming-kuo Lee: And also, according to the gc Ms analyses, there are several whale shows organic compounds.

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Ming-kuo Lee: That beast to Fo X or stalin's sorry I had to and not organic chemist, and this is known as the HP, so this is very common organic compound associated with household prestige toys shoes.

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Ming-kuo Lee: and detergents API does has a.

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Ming-kuo Lee: MC or is ready low point 006 milligrams per liter and however again a health risk of the HP still not well understood for the emerging contaminants P P bass.

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Ming-kuo Lee: We have seven whales analyzed for pfs they all show up or detected is what it difficult to quantify the pfs and because of a show lobby more the seven whales analyze so this indicates a widespread occurrence and pfs miss Wales and.

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Ming-kuo Lee: So I think more study I needed to study the source in the migration or like those organic compounds in emerging contaminants.

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Ming-kuo Lee: Such as key.

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Ming-kuo Lee: And we also compare the.

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Ming-kuo Lee: squad at your chemistry or municipal water and.

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Ming-kuo Lee: The private will walk.

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Ming-kuo Lee: And so, again we do anticipate what are the different water chemistry because municipal water is coming from a command called me awkward.

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Ming-kuo Lee: And so you can see that.

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Ming-kuo Lee: is dominated by kissing magnesium is turning points for the primary where I was mostly the waters withdrawn from happening fi lights and because of heterogeneity is in those way or water.

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Ming-kuo Lee: As well screen I think range from 14 to morning 300 feet different range of where screen and so it's probably because of different mineralogy different dissolve dissolve do we see a wide range of.

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Ming-kuo Lee: chemical composition.

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Ming-kuo Lee: And i'm arriving size of Piper diagram we add the negative charge and ions show similar story again for the private where you can see.

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Ming-kuo Lee: The way the scan through a wider range of a while the chemistry indicate.

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Ming-kuo Lee: heterogeneity and maybe indicate.

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Ming-kuo Lee: A.

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Ming-kuo Lee: lot of mixing happen in in opera.

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Ming-kuo Lee: So in this study.

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Ming-kuo Lee: We also.

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Ming-kuo Lee: install a water filter reverse osmosis and active carbon in.

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Ming-kuo Lee: Western House just see the capability to remove radon in well.

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Ming-kuo Lee: So this time i'm proud the radar measurements are both control sample in future, simple, so the control sample we made your radar directly from where before.

454
01:19:55.070 --> 01:20:08.900
Ming-kuo Lee: Like they pass through the fields roots and so this is a time series pod upper time series shoulder control sample So you can see there for the first 20 minutes.

455
01:20:09.950 --> 01:20:27.170
Ming-kuo Lee: The radon level kind of continue increase, and so we interpret this is because we look at a regular manual array down from the old or age water standing reciting inside real case.

456
01:20:28.490 --> 01:20:43.700
Ming-kuo Lee: Of a once the where is courage, we see a more stable really is there still some frustration, but for this control sample the radar in the way of water rich about 2500 people can respond beat.

457
01:20:44.990 --> 01:20:52.250
Ming-kuo Lee: The two times since then on the bottom shows the filter sample that pass through both.

458
01:20:53.420 --> 01:21:17.570
Ming-kuo Lee: Reverse as masses and also the active carbon So you can see a wedding know radon concentration actions below two people curious police routes, so this indicates those two fields as well effectively remote almost 98% operate on our farm well.

459
01:21:18.590 --> 01:21:20.930
Ming-kuo Lee: So this is good news for the communities.

460
01:21:22.910 --> 01:21:26.960
Ming-kuo Lee: So Connor also collect the 2010.

461
01:21:28.370 --> 01:21:38.930
Ming-kuo Lee: census data just looking at the radar in the census Barclay so on the left hand side, see the population density per square kilometer.

462
01:21:39.320 --> 01:21:47.990
Ming-kuo Lee: So there's no other communities, you see a weighted low population density show is this broadcast so the higher population see that.

463
01:21:48.800 --> 01:21:59.720
Ming-kuo Lee: near the downtown for hers also along the highway 78 so on the right hand side show the main radon concentration census Barclays.

464
01:22:00.560 --> 01:22:19.700
Ming-kuo Lee: And so different college shows the average rate on concentration calculated from interpretation or 25 radar measurements and so you can see, there are several things as well to the west of the for her show the highs Raven.

465
01:22:22.400 --> 01:22:32.060
Ming-kuo Lee: So Connor also you, created by various man to show the special correlation between raised on a different type of canes.

466
01:22:32.870 --> 01:22:52.100
Ming-kuo Lee: So on the left hand side show the special correlation between radon and leukemia so this white collar shows the most significant special correlation the College shows the area with significant correlation you can see, there are six.

467
01:22:53.180 --> 01:23:06.170
Ming-kuo Lee: cents a spot for the west of hers shows the high high correlation so this means that area was a high rate down also high occurrence of cancer per capita.

468
01:23:09.110 --> 01:23:20.870
Ming-kuo Lee: In those a rehearsal they are pink collar show high low correlation simply means hi Ray down low occurrence of kings.

469
01:23:22.370 --> 01:23:24.680
Ming-kuo Lee: bow longer point y'all they lost most of the.

470
01:23:26.390 --> 01:23:27.830
Ming-kuo Lee: Community they have a low.

471
01:23:29.030 --> 01:23:30.380
Ming-kuo Lee: populations.

472
01:23:32.240 --> 01:23:49.790
Ming-kuo Lee: And this library color purple color shows low high concentration indicate low rate i'm concentration or high recurrence of radon and so on the right hand by hand side you see a similar.

473
01:23:51.140 --> 01:23:54.290
Ming-kuo Lee: correlation for random form.

474
01:23:56.660 --> 01:24:13.250
Ming-kuo Lee: And so, in conclusions we see a range of radon level range from one to more than 1000 people correspond eaters and most trust them and up below the US EPA MC.

475
01:24:14.840 --> 01:24:26.630
Ming-kuo Lee: But for the php and pfs are presenting some of the whale and more study, we need to identify the souls and their migration.

476
01:24:29.480 --> 01:24:29.990
Ming-kuo Lee: and

477
01:24:31.100 --> 01:24:42.830
Ming-kuo Lee: Again high percent is a release for different type of cancer may or us aware Walker is the primary source of drinking waters, and we do see a.

478
01:24:44.000 --> 01:25:08.840
Ming-kuo Lee: Special correlation between radon in four different types of cancer, mostly to the west of four hertz and the last one is good news, can help the Community reduce exposure to raid on using way effective reverse as masses and active carbon filters.

479
01:25:09.980 --> 01:25:15.170
Ming-kuo Lee: And so, this is all I have happy happy to answer any question.

480
01:25:17.720 --> 01:25:20.450
Ann Ojeda: Thank you mean quote, that was an excellent presentation.

481
01:25:21.620 --> 01:25:39.440
Ann Ojeda: I do want to acknowledge that it is 927 So if you want to take a step away and the next presentation will not start until 935, but we can use the next few minutes to answer some questions about Dr lee's presentation and there was one.

482
01:25:40.940 --> 01:25:47.900
Ann Ojeda: question put in the chat has raised on in basement air been measured in conjunction with radon in the groundwater.

483
01:25:51.980 --> 01:25:52.400
Ming-kuo Lee: Oh.

484
01:25:53.630 --> 01:26:00.770
Ming-kuo Lee: So we we do not have the data directly major from.

485
01:26:02.330 --> 01:26:09.350
Ming-kuo Lee: The people, Wales, so all the rate on measurement essentially is coming from directly from the web.

486
01:26:11.780 --> 01:26:14.000
Ming-kuo Lee: And so, again we.

487
01:26:15.710 --> 01:26:16.250
Ming-kuo Lee: The.

488
01:26:20.270 --> 01:26:21.650
Ming-kuo Lee: So we try to get.

489
01:26:23.120 --> 01:26:24.260
Ming-kuo Lee: The information.

490
01:26:26.180 --> 01:26:26.900
Ming-kuo Lee: From.

491
01:26:29.960 --> 01:26:36.110
Ming-kuo Lee: We also have radium measurements so there's different phone radon in a way of water.

492
01:26:37.280 --> 01:26:45.020
Ming-kuo Lee: So the quick answer to these questions that we do not have direct measurement in the basement.

493
01:26:47.270 --> 01:26:56.360
Ming-kuo Lee: Again it's dependent upon because that were the worst queen wrench on 32 more than 300 feet.

494
01:26:57.890 --> 01:27:01.760
Ming-kuo Lee: So we don't know the change in terms of read on concentration.

495
01:27:03.470 --> 01:27:06.560
Ming-kuo Lee: By the time that the groundwater reach the surface.

496
01:27:09.800 --> 01:27:11.390
Ann Ojeda: A lot of basements in the area.

497
01:27:12.590 --> 01:27:13.580
Ming-kuo Lee: A lot of a basement.

498
01:27:16.880 --> 01:27:36.530
Ming-kuo Lee: So, most of the ravens coming from probably produce from the fracture so as senior show a few a slide show you the fractions and So yes, oh you mean the basement the how the basement of the House yeah no i'm sorry I thought I thought you talked about.

499
01:27:37.550 --> 01:27:38.030
Ming-kuo Lee: The bait.

500
01:27:39.260 --> 01:27:55.280
Ming-kuo Lee: The aquifers, so they are some NGO measurements of the houses sound of a major miss kind of hanging around about four to five Pico periods per liter in a.

501
01:27:56.390 --> 01:28:08.090
Ming-kuo Lee: In source, those are very different from radon in the water, which can range up to several thousands people corresponding eaters so up here does have.

502
01:28:09.260 --> 01:28:23.720
Ming-kuo Lee: An seo for raid on you know, am I believe is a full peak appears on the turn off all the drinking water, they only have recommending them, which is about 4000 people curious.

503
01:28:25.820 --> 01:28:29.630
Ming-kuo Lee: Sorry, I interpreter I misinterpreted the questions.

504
01:28:31.820 --> 01:28:33.980
Ann Ojeda: Thanks I think Italia also has a question.

505
01:28:35.750 --> 01:28:46.550
Natalia Malina: yeah thanks for the Nice presentation i'm just curious about these graph of efficiency of reverse osmosis compared to the activated carbon.

506
01:28:47.810 --> 01:29:01.370
Natalia Malina: And then on the Center right that they efficiency almost the same it's surprising for me, because they activated carbon it's I think it's cheaper than a rush osmosis right so it's like it's the same efficiency.

507
01:29:02.240 --> 01:29:10.010
Ming-kuo Lee: Right, and so this is actually a very good question so we're trying to find a cost effective future for the Community.

508
01:29:10.880 --> 01:29:29.720
Ming-kuo Lee: This is aware, low income community, so the cost of different fields that will make a big difference so for antonia's question, the reason I think we see a similar resolve for reverse us masses, because for the reverse osmosis filter to actually have three different types of filters.

509
01:29:30.890 --> 01:29:43.400
Ming-kuo Lee: That they actually do have active carbon included I have they have a tour additional filters and sentiment filter and also the reverse as masters.

510
01:29:44.180 --> 01:29:56.870
Ming-kuo Lee: Reverse osmosis used to taking all dissolved by seven Center fielders essentially fuel filter all the segments and that's why you see a similar result because.

511
01:29:57.440 --> 01:30:09.860
Ming-kuo Lee: Both filter companion accurate carbon filters and and reverse osmosis as much as more expensive, I want to see how.

512
01:30:10.640 --> 01:30:34.880
Ming-kuo Lee: These two different type of filter want to compare the performance for thing, the conclusion is that the act of carbon work, as well as diverse as masters so next phase we try to see again how long the duration of those filter, and so we show a few results is show you again.

513
01:30:36.440 --> 01:30:46.760
Ming-kuo Lee: What is short time periods, the question is can how long this field, or can last before they have to be replace.

514
01:30:49.910 --> 01:30:51.740
Ann Ojeda: Can we have another question from Natasha.

515
01:30:52.280 --> 01:31:03.080
Natasha Dimova: And this was actually my question because I actually have used activated carbon to purge my instruments, when we measure very high.

516
01:31:03.410 --> 01:31:15.470
Natasha Dimova: De Dum concentrations in error, sometimes you have to switch to an area that you have low radon and you have to get rid of the radio, that is accumulated in the Chamber so dirty he recommends using a.

517
01:31:15.950 --> 01:31:25.010
Natasha Dimova: activated carbon to get rid of it, the problem is that this gas straightens taste trapped in the pores of the activated carbon.

518
01:31:25.310 --> 01:31:34.490
Natasha Dimova: And then, when you flash next time you can actually contaminate if it's in line in line going into the instrument.

519
01:31:35.000 --> 01:31:51.200
Natasha Dimova: Your instrument because it's not chemically bound it's a physical absorption on on the end it's basically trapped in the poor space so i'm very curious to see a time series of this activated carbon.

520
01:31:52.580 --> 01:32:05.600
Natasha Dimova: treatment, at least for like a month, it can be done because it's very temporal solution for very short time it's my opinion from my experience.

521
01:32:06.260 --> 01:32:22.940
Ming-kuo Lee: Now pay song was actually a way you could comment and based on e commerce results Okay, and so our so in commerce cases work he only took the measurements and.

522
01:32:24.410 --> 01:32:26.120
Ming-kuo Lee: on a monthly basis.

523
01:32:27.260 --> 01:32:28.730
Ming-kuo Lee: And so, perhaps.

524
01:32:29.810 --> 01:32:40.640
Ming-kuo Lee: As Natasha suggest we need a longer periods of testing, especially in the field, so one of.

525
01:32:41.780 --> 01:32:45.260
Ming-kuo Lee: The PhD student currently essentially looking at.

526
01:32:46.370 --> 01:32:57.590
Ming-kuo Lee: Essentially, again address the first questions duration of this also type of see how if we extend a period of measurements and.

527
01:32:59.030 --> 01:33:09.530
Ming-kuo Lee: Especially for active carbon and how we can see the radar approximate over a longer period of time and.

528
01:33:09.740 --> 01:33:13.490
Natasha Dimova: Just to add something very quickly, I know that i'm taking out of my time, but.

529
01:33:15.650 --> 01:33:29.660
Natasha Dimova: The good thing with radon and the filter is that you can take the filter and leave it alone for 20 days at a time will decay, because the half life is about 3.4 days on two days.

530
01:33:29.930 --> 01:33:34.460
Natasha Dimova: And then you can use it again, you don't have to do any treatment to recover the filter.

531
01:33:34.760 --> 01:33:47.120
Natasha Dimova: Because of the short half lives so, which is, I think the huge advantage of using although it's very temporal, but they can be recovered very quickly you just have to lead them back before one month and then put them back.

532
01:33:48.320 --> 01:33:52.430
Ming-kuo Lee: I think there's a one more concern is that dissolve rating.

533
01:33:53.840 --> 01:33:59.780
Ming-kuo Lee: I understand Natasha collect me wrong, essentially, they can be absorbed by.

534
01:34:00.890 --> 01:34:01.940
Ming-kuo Lee: oxide, so all of a.

535
01:34:01.940 --> 01:34:02.450
Sudden.

536
01:34:05.090 --> 01:34:07.790
Ming-kuo Lee: They accumulate to substantial quantity.

537
01:34:08.600 --> 01:34:11.150
Ming-kuo Lee: And can we continue flush the wheel water.

538
01:34:11.360 --> 01:34:13.010
Natasha Dimova: They become a source.

539
01:34:13.160 --> 01:34:14.330
Ming-kuo Lee: Like become the source.

540
01:34:14.360 --> 01:34:14.690
Yes.

541
01:34:15.740 --> 01:34:18.470
Ming-kuo Lee: Sir, you have pretty limited data showing what is low.

542
01:34:18.860 --> 01:34:19.880
Ming-kuo Lee: dissolve radian.

543
01:34:19.880 --> 01:34:23.180
Ming-kuo Lee: Concentration will water yeah.

544
01:34:23.240 --> 01:34:23.630
Ming-kuo Lee: So let's.

545
01:34:23.780 --> 01:34:25.040
Natasha Dimova: Talk about that yeah.

546
01:34:25.400 --> 01:34:29.900
Ming-kuo Lee: I think that's probably low relative low potential for lack some.

547
01:34:32.960 --> 01:34:40.040
Ann Ojeda: Okay, thank you and i'll hand over the next set of introductions they'll go from 935.

548
01:34:41.210 --> 01:34:42.980
Ann Ojeda: Until 1115.

549
01:34:43.850 --> 01:34:44.480
Ann Ojeda: So you want.

550
01:34:44.660 --> 01:34:46.130
Ann Ojeda: To these Thank you yeah.

551
01:34:46.520 --> 01:34:49.010
Yuehan Lu: All right, well very interesting discussions.

552
01:34:50.060 --> 01:35:03.650
Yuehan Lu: We had to move on, for today and so okay so before we start just a reminder that you can ask the question either after the talk oh.

553
01:35:04.820 --> 01:35:08.360
Yuehan Lu: Sending your questions in the chat box throughout the talk.

554
01:35:09.650 --> 01:35:25.070
Yuehan Lu: I will, when you have two minutes left, I will just be my hand to remind you that, so our first speaker is Dr Natasha the mo BA from the University of Alabama so go ahead to share a screen attention.

555
01:35:27.290 --> 01:35:34.280
Yuehan Lu: and her talk is on the feet oh anthropogenic nitrate in organic rich coastal sediment.

556
01:35:36.350 --> 01:35:39.530
Natasha Dimova: And thank you everybody for coming on.

557
01:35:40.070 --> 01:35:44.660
Natasha Dimova: The nitrogen story that i'm going to present today is a compilation of.

558
01:35:44.990 --> 01:35:55.940
Natasha Dimova: work that has been done for the last few years in my research group mostly by Daniel montiel who graduated to a couple of years ago, but the work has been published, you can look it up.

559
01:35:56.300 --> 01:36:16.010
Natasha Dimova: i'm also presenting some new laboratory experiments that my master's student Alex one more did, and the last year, which brings more insights into the groundwater, the role of groundwater for the nutrients changes in coastal Obama.

560
01:36:17.600 --> 01:36:35.210
Natasha Dimova: And so the motivation for this work is um this annually occurring anoxic advancing mobile video i'm showing and the video which are cold, do you believe, and this is basically um fish that.

561
01:36:36.470 --> 01:36:46.910
Natasha Dimova: swims to a shallow waters, because it's forced by the low oxygen in deeper waters and that's why you see all this fish in the shoreline.

562
01:36:47.930 --> 01:37:10.220
Natasha Dimova: The local people, as I said, call them julie's and one of the earlier hypothesis by researchers in our department Jeff teague and his then graduate student or goulet was that perhaps high nitrate and English ground water wells which day detected, is the reason for.

563
01:37:12.650 --> 01:37:35.570
Natasha Dimova: For blooms in the area which will consumed oxygen which will drive the fish too low to hire oxygen water is in shirt in more shallow waters and whoever we were just fizzle about this because evenly events are annoying to area for very, very long time, you see a new sort of.

564
01:37:37.130 --> 01:37:38.030
Natasha Dimova: This kind of.

565
01:37:39.050 --> 01:37:44.450
Natasha Dimova: You know event that happens in the 1867, and so we think that.

566
01:37:45.080 --> 01:37:57.350
Natasha Dimova: If nitrate, which could be result of agricultural activities in land and the cost and cost of balance out Obama is the reason for this event, this kind of doesn't line up.

567
01:37:57.950 --> 01:38:09.410
Natasha Dimova: So we wanted to look further and the quality of groundwater, but also where groundwater occurs, so this is published data from Daniels work on.

568
01:38:10.040 --> 01:38:26.360
Natasha Dimova: We use radio and this time as a tracer to trace groundwater, as you already know, red one is very high, and groundwater compared to surface water, so if you are able to see some hydrate and concentrations and surface water, that would be an indication that a.

569
01:38:26.870 --> 01:38:37.880
Natasha Dimova: entrance of groundwater and the series, or we are hotspots how we call them, and so the map on the left side shows this data and analysis, we found that from this.

570
01:38:38.510 --> 01:38:56.240
Natasha Dimova: Surveys both surveys that we have preferential pathways of groundwater under each sort of mobile Bay so when we measure dissolved oxygen in the same areas, we find that those preferential SIP urges are also associated with.

571
01:38:57.320 --> 01:39:11.660
Natasha Dimova: Low dissolved oxygen so we suggested that is the connection between the oxygen in surface waters with higher ground water discharge and we tried to understand.

572
01:39:13.010 --> 01:39:33.500
Natasha Dimova: What is the quality and what drives the quality of this low oxygen groundwater so to do this we installed series of visitors, which were and are perpendicular to the shoreline as this diagram shows total of five visitor and it's both.

573
01:39:34.850 --> 01:39:56.420
Natasha Dimova: is short and wish for, I remember the shore is the one with the preferential pathways of groundwater and we sampled those words for these visitors for about 30 years during dry and what season to see variations of water quality, through time and what do we see and ensure is that.

574
01:39:58.070 --> 01:40:06.110
Natasha Dimova: We see very high concentration in England, Wales, which have been simple previously by other researchers.

575
01:40:07.130 --> 01:40:19.220
Natasha Dimova: And, but as groundwater percolates through the aquifer we see a decrease in nitrate concentrations and when we do to mass balance we find that the water dot.

576
01:40:20.150 --> 01:40:32.690
Natasha Dimova: Immediately sips and the coastline is actually very depleted in nitrate days, but actually negative miles balance for nitrate However, what we see.

577
01:40:33.530 --> 01:40:45.770
Natasha Dimova: Is increase of ammonium and do N, and you can see, on this disorder so you're measuring huge levels, these are actually flexes but based on concentrations.

578
01:40:46.670 --> 01:41:03.050
Natasha Dimova: Huge levels of ammonium and do one, so we have a surplus of nitrogen, but this nitrogen is now and reduced form so on the West shore, we saw relatively high nitrate which.

579
01:41:03.710 --> 01:41:17.120
Natasha Dimova: Also, was attenuated as groundwater percolates through the cost toll sediments but we didn't see this dramatic increase of the reduced form which is ammonium and do when.

580
01:41:17.480 --> 01:41:28.070
Natasha Dimova: So when we did the total balance for an H nitrogen in the two sides and we found that you to this high flexes of ammonium and do when we have.

581
01:41:28.400 --> 01:41:35.180
Natasha Dimova: large surplus of total nitrogen on the shore which potential these broad there by groundwater.

582
01:41:35.660 --> 01:41:43.820
Natasha Dimova: And we have the efficiency of nitrogen on the West shore because nitrate was attenuated as it travels through the subsurface.

583
01:41:44.660 --> 01:41:53.450
Natasha Dimova: And so we suggested that is God is definitely a source for this ammonium and do one on the ensure, but it also plays.

584
01:41:54.200 --> 01:42:03.080
Natasha Dimova: The sediments play a role of a scene for nitrate is groundwater travel and this Atlantic Ocean interface.

585
01:42:03.590 --> 01:42:13.130
Natasha Dimova: So this prompted us to look at the sentiments and the role of the sediments an interaction between the sediment and the sediments and groundwater.

586
01:42:13.400 --> 01:42:22.940
Natasha Dimova: on how this impacts that groundwater quality, so we collected sediment course and hypoxia impacted side you see a photo of this.

587
01:42:23.270 --> 01:42:37.700
Natasha Dimova: One of this course, and then immediately what you will not is that we have this layers of sense and very dark black sediments which potentially we identify as.

588
01:42:38.510 --> 01:42:54.260
Natasha Dimova: Organic Claire different organic Clara so within the sediment structure in this area, so the new research questions were, what are the nitrogen biogeochemical transformations.

589
01:42:55.100 --> 01:43:03.050
Natasha Dimova: Which result and this change and alteration of groundwater quality and what is the role of this organic matter in the whole picture.

590
01:43:04.310 --> 01:43:20.360
Natasha Dimova: And so we hypothesize based on the nitrogen cycle just very simple, you know simplified presentation on this diagram that nitrate and growing water is removed through the processes of dentistry vacation or DNA array.

591
01:43:21.530 --> 01:43:33.800
Natasha Dimova: And the organic reach layer is a source for reduce nitrogen of as doin and ammonium, and this is a result of mineralization of organic matter.

592
01:43:34.580 --> 01:43:51.410
Natasha Dimova: So we focus from now on, on this organic reach sediments so we collected went back and collect it more course, and these are photos of slice course and you can see those alternating sediment have.

593
01:43:52.040 --> 01:44:08.210
Natasha Dimova: sent core side sediments with organic reach sediments we slice this course in five centimeter sections and we work with this different sections to characterize.

594
01:44:09.200 --> 01:44:22.880
Natasha Dimova: A physical parameters, as well as how the sediments would interact with different levels of nitrate, so the results from the physical characterizations.

595
01:44:24.080 --> 01:44:39.020
Natasha Dimova: This is a core, that is, about two meter deep I was slice and, as I said in five centimeters sections so each of these sections are analyzed for water content prostitute organic content and hydraulic conductivity was calculated.

596
01:44:39.530 --> 01:44:54.830
Natasha Dimova: And so what we found um are two distinctive organic reach lyrics one is between 42 and 65 centimeters and the other is between 98 and 122 centimeters.

597
01:44:55.190 --> 01:45:20.270
Natasha Dimova: And so, this areas that I highlighted are also associated with higher perversity which eventually is connected with preferential pathways of groundwater in the field, so our groundwater is definitely traveling through these organic reach layers and potentially changing composition.

598
01:45:21.830 --> 01:45:27.380
Natasha Dimova: So after we did this i'm the different sections of this course were.

599
01:45:28.430 --> 01:45:38.120
Natasha Dimova: Put in glass jars and we did multiple incubation experiments, and so we did four different treatments.

600
01:45:38.630 --> 01:45:49.250
Natasha Dimova: Different treatment first treatment was we will try pure carbon free water, the other one was the second one was we've grown water natural grown, what did I was collected in.

601
01:45:49.730 --> 01:46:07.430
Natasha Dimova: Well, that we knew is contaminated with nitrate levels and this groundwater of nitrate where they were about 250 my promoters and we also incubated this different sections with 408 hundred micro Molar of nitrate.

602
01:46:08.090 --> 01:46:24.470
Natasha Dimova: We kept the incubation for about five days, five days are based on our previous measurements in the field of groundwater slippage in the area and roughly calculated residence time and the subsurface.

603
01:46:25.460 --> 01:46:38.240
Natasha Dimova: And so, every time when we incubated this sediments we after the five days, we will take out pour water from these jars and analyze for nitrogen.

604
01:46:39.650 --> 01:46:51.860
Natasha Dimova: And those are the first results for nitrate and therefore denied trade treatments So what do you see here are the results from oh four incubation.

605
01:46:52.520 --> 01:47:05.750
Natasha Dimova: Oh, with the ultrapure carbon free water treatment imposed on the 400 800 and ground water treatment, starting from the left to the right.

606
01:47:06.530 --> 01:47:21.170
Natasha Dimova: The dashed line in each of those graphs indicates the level of nitrate that wasn't a system by Edit to the system and, as I also highlighted here the areas where we have.

607
01:47:21.830 --> 01:47:32.870
Natasha Dimova: This organic reach layers and what you can see, if you look, for example at 800 micro Molar treatment um we see that nitrate.

608
01:47:33.260 --> 01:47:42.800
Natasha Dimova: decreases with target with relatively high nitrate and then in the organic reach layer potentially decreases and then the alternating.

609
01:47:43.190 --> 01:47:53.450
Natasha Dimova: sediment courtside sediment layer it goes up again because it doesn't undergo any changes it goes back to 800 micro Molar, which is the treatment.

610
01:47:53.900 --> 01:48:11.150
Natasha Dimova: And then again it goes down, and the second organic reach layer and then goes up back when it doesn't interact with the sudden, so this is a very, very clear indication that the organic reach layer Kappa.

611
01:48:12.260 --> 01:48:16.640
Natasha Dimova: transforms this nitrate and eliminated from the system.

612
01:48:18.350 --> 01:48:33.260
Natasha Dimova: And so what we did then, with this result is to calculate nitrate removal rates by by subtracting two consecutive concentrations and for a time of incubation.

613
01:48:33.800 --> 01:48:41.750
Natasha Dimova: And so again Those are the three treatments that had some nitrate into them groundwater 408 hundred.

614
01:48:42.140 --> 01:48:51.920
Natasha Dimova: My promoters and you see higher it a mover rates is expected from the previous graph and highlighted areas where we had.

615
01:48:52.460 --> 01:49:15.530
Natasha Dimova: Higher organic content, and so we also plotted this removal raise versus the average organic matter of concentration is percentage and we found a good um linear correlation between the data which indicates that this is first order reaction, most likely have done a terrific vacation.

616
01:49:16.700 --> 01:49:30.260
Natasha Dimova: So this was the fate of the nitrate in the system, but remember that the filter results also show incredibly high fox's of ammonium and do one, so this.

617
01:49:30.920 --> 01:49:39.410
Natasha Dimova: This slide i'm showing the total nitrogen nitride and nitrate and nitride we were able, yes to measure nitride.

618
01:49:39.980 --> 01:49:51.140
Natasha Dimova: Because of the anoxic conditions, as well as on the bottom graphs are doing ammonium and calculated excess of nitrogen and the system.

619
01:49:51.530 --> 01:50:02.120
Natasha Dimova: And so, if you look justin on the bottom series of graphs you see that in the organic reach layer we see increase of ammonium and the system.

620
01:50:02.780 --> 01:50:06.740
Natasha Dimova: So we have relatively low and the top layer is then increases.

621
01:50:07.310 --> 01:50:20.210
Natasha Dimova: In the first organic reach layer then decreases again in the San Diego area and then increases again you're in the organic REACH and then decreases again, so this is a gun very firm.

622
01:50:21.140 --> 01:50:32.240
Natasha Dimova: indication that the organic reach layer is the scores for this ammonium and the one high fluxus is the reason for this plexus.

623
01:50:33.740 --> 01:50:34.220
Okay.

624
01:50:35.270 --> 01:50:59.150
Natasha Dimova: And so we wanted to identify the excess further indexes of nitrogen, so we plotted from these data nitrogen axis versus dissolve organic nitrogen for the four different treatments and you can see that we have again good correlations showing indicating towards the source, which is.

625
01:51:01.040 --> 01:51:03.530
Natasha Dimova: Most likely this organic reach layer

626
01:51:05.720 --> 01:51:11.780
Natasha Dimova: This was done, mostly also to prove the fact that i'm doing could come from.

627
01:51:12.980 --> 01:51:19.190
Natasha Dimova: For organic fertilizers, but, in our case, that was not the case.

628
01:51:21.230 --> 01:51:24.620
Natasha Dimova: And to identify the nitrogen sources, we also use.

629
01:51:25.670 --> 01:51:33.800
Natasha Dimova: Stable isotopes so from all the field data we collected, including data from the rivers that and her mobile bay.

630
01:51:34.910 --> 01:51:48.020
Natasha Dimova: So we plotted and 15 and 18 from nitrate and in this scatter plot you see groundwater with the closer calls and.

631
01:51:49.400 --> 01:52:00.680
Natasha Dimova: The squares are immobile a water and triangles are you were waters and what do you can see is that most of our data most more than 50% of the data.

632
01:52:01.310 --> 01:52:19.880
Natasha Dimova: is coming from organic so nitrogen, so what happens in the system is that groundwater is enriched and is a monument do one which potentially oxidizes in the sea in the presence of oxygen and becomes nitrate for the system.

633
01:52:21.410 --> 01:52:35.210
Natasha Dimova: So in conclusion um what we found is that the distribution of groundwater discharge and mobile based very heterogeneous and that's the result of the mythological heterogeneity in the subsurface.

634
01:52:35.720 --> 01:52:43.400
Natasha Dimova: We found a the sediments are seeing for nitrate most likely through the processes of done a terrific occasion.

635
01:52:44.270 --> 01:53:05.690
Natasha Dimova: We found also that the organic reach layers are source for exorcism nitrogen and hypoxia impacted areas of G beliefs, and we believe that the julie's i'm probably occur as a result of developing of bottom up hypoxia which is happening during the summer.

636
01:53:06.860 --> 01:53:19.130
Natasha Dimova: When the water is stratified we have higher fluxus of anoxic groundwater which consumes the oxygen and the bottom layers and develop this hypoxia.

637
01:53:20.180 --> 01:53:32.630
Natasha Dimova: So what we recommend is that future modeling work on large scale cuz theaters these geochemical changes because.

638
01:53:33.320 --> 01:53:49.820
Natasha Dimova: If somebody would have gone and sample just the groundwater well, which is a high nitrate would have have model and calculating much higher nitrate flax is going to the bay, which is, as you still not the case.

639
01:53:50.930 --> 01:54:03.920
Natasha Dimova: That I think is very good also i'm example to prompt coastal budgets to re examine groundwater discharge or submarine groundwater these charges, we call it.

640
01:54:04.430 --> 01:54:24.920
Natasha Dimova: Because it is a factor and a total nitrogen budget, and this systems and, finally, I wanted to bring your attention to something that just came this week it's a paper which we publish on the global scale of groundwater discharge and nutrient flexes delivered.

641
01:54:26.150 --> 01:54:38.420
Natasha Dimova: along by as God around the world, and if you can look at this results are you see that of the more than 200 Todd is that we, as we looked at.

642
01:54:39.440 --> 01:54:50.240
Natasha Dimova: groundwater in all this kit in most of these cases is delivered as anoxic SIS, such as do when and if you look at.

643
01:54:51.380 --> 01:55:06.110
Natasha Dimova: The Di n versus the ap ratio it's always much higher than redfield ratio, so this anoxic water with high nitrogen content must be consider and.

644
01:55:07.130 --> 01:55:22.280
Natasha Dimova: This end of budgets, and this is especially when I want to bring attention to looking at hypoxia and Gulf of Mexico, the current statement is that hypoxia is the.

645
01:55:23.540 --> 01:55:24.290
Natasha Dimova: result.

646
01:55:24.380 --> 01:55:26.720
Natasha Dimova: Of the River discharge but.

647
01:55:26.780 --> 01:55:29.840
Yuehan Lu: groundwater should be consistent or any no question.

648
01:55:31.520 --> 01:55:36.020
Natasha Dimova: So this is all I have and i'm glad to take some questions.

649
01:55:37.610 --> 01:55:51.740
Yuehan Lu: Thank you Natasha very interesting Hawk Unfortunately we do not have time for questions, but please go ahead and leave your questions in the chat box, if you have any questions for Natasha very interesting research.

650
01:55:52.190 --> 01:56:01.400
Yuehan Lu: Alright, so we'll move on to the next talk by lorena laurita can you let me know how you pronounce your last name place.

651
01:56:07.010 --> 01:56:08.450
Yuehan Lu: I think you're muted that.

652
01:56:09.440 --> 01:56:10.790
Yuehan Lu: We could cannot hear you.

653
01:56:16.850 --> 01:56:19.370
Nivanthi Mihindukulasooriya: Alright sorry about that yeah.

654
01:56:19.790 --> 01:56:22.550
Nivanthi Mihindukulasooriya: And yes, I must.

655
01:56:22.850 --> 01:56:29.390
Nivanthi Mihindukulasooriya: move on to me the could assume that my last name, are you seeing my presentation.

656
01:56:29.810 --> 01:56:33.980
Yuehan Lu: yeah, we will see a split screen right now, can you.

657
01:56:34.790 --> 01:56:41.540
Yuehan Lu: Oh swap yo present her will with yeah okay perfect.

658
01:56:41.570 --> 01:56:41.810
Yuehan Lu: Okay.

659
01:56:41.840 --> 01:56:43.820
Nivanthi Mihindukulasooriya: Go ahead okay.

660
01:56:44.840 --> 01:57:08.120
Nivanthi Mihindukulasooriya: So good morning everyone I am 20 i'm an assistant professor of geology admin and Kentucky university so bear with my long title What it means is, this is a comparison of three different alcohol monitoring methods at a great lip estuary So why is it important to monitor phytoplankton.

661
01:57:11.210 --> 01:57:26.930
Nivanthi Mihindukulasooriya: We all are aware of harmful algal blooms it a B or habs for short, are a growing issue in almost every us state it has increased by 30 times compared to the 1960s and according to.

662
01:57:28.010 --> 01:57:40.400
Nivanthi Mihindukulasooriya: A survey in 2006 economic impact of habits in Medina orders average to be $82 million per year, but definitely the numbers might have gone up by now.

663
01:57:41.210 --> 01:57:53.060
Nivanthi Mihindukulasooriya: So Hamza and Sylvia oxygen depletion associated with habits are some of the most scientifically complex coastal issues challenging the health of the US coastal systems.

664
01:57:54.140 --> 01:58:03.830
Nivanthi Mihindukulasooriya: Though the scientific complexity of the habs are mainly due to the lack of understanding of the factors affecting the blues.

665
01:58:05.450 --> 01:58:12.770
Nivanthi Mihindukulasooriya: There are several different methods of monitoring these algal bloom so direct cell counts HP llc.

666
01:58:13.700 --> 01:58:29.810
Nivanthi Mihindukulasooriya: or third fan of the quantitative polymerase chain reactions can be car for short, is a DNA extraction, but the challenges with all most of these methods, they are expensive time consuming and require.

667
01:58:31.160 --> 01:58:41.090
Nivanthi Mihindukulasooriya: Specifically trained personnel, so this study what i'm going to talk about here is a reflect on spec spectroscopy.

668
01:58:41.840 --> 01:58:52.130
Nivanthi Mihindukulasooriya: It is a rapid and cost effective approach to identify color producing agents in water, including phytoplankton as well as suspected minerals.

669
01:58:53.030 --> 01:58:58.610
Nivanthi Mihindukulasooriya: So basically the theory of the method is measuring the inverse of the absorption absorption spectra.

670
01:58:59.300 --> 01:59:07.430
Nivanthi Mihindukulasooriya: And then the data can be compared with reflect reflectance spectra have known minerals, similar to what we do in acts are the methods.

671
01:59:08.300 --> 01:59:17.720
Nivanthi Mihindukulasooriya: So the method has been successfully applied to estimate chlorophyll concentrations in suspended sediments in from tell your legs elements, as well as.

672
01:59:18.050 --> 01:59:25.880
Nivanthi Mihindukulasooriya: A marine sediment and also to estimate lt and cyanobacteria from multi spectral and hyper spectral in majors.

673
01:59:26.150 --> 01:59:37.160
Nivanthi Mihindukulasooriya: So in this talk, I will be comparing my results from possible reflectance with alcohol so cell counts and felt Florida Florida medically measured chlorophyll concentrations.

674
01:59:37.910 --> 01:59:48.350
Nivanthi Mihindukulasooriya: A little bit about the study area this study was done in the old woman creek, which is a small creek that drains into the Western basing of Lake erie.

675
01:59:50.150 --> 02:00:01.850
Nivanthi Mihindukulasooriya: And the Western basing of Lake erie is the one that is mainly get a lot of algal blooms, and this is a really interesting story because.

676
02:00:02.960 --> 02:00:09.020
Nivanthi Mihindukulasooriya: The flow from their story into the lake seasonally closed by forming a barrier bar.

677
02:00:10.640 --> 02:00:18.740
Nivanthi Mihindukulasooriya: And this estuary drains through through an agricultural area, so it is perfect for algal bloom development.

678
02:00:20.390 --> 02:00:21.350
Nivanthi Mihindukulasooriya: So here again.

679
02:00:22.760 --> 02:00:40.880
Nivanthi Mihindukulasooriya: This is the estuary and we sample the story in three consecutive years and 2016 when we did the sampling the barrier bar was closed and 2017 it was open, while in 2018 I don't have a picture, but the berry about was again closed.

680
02:00:42.470 --> 02:00:54.920
Nivanthi Mihindukulasooriya: So we assembled in 2016 we sample for different locations within the estuary or l w m we see and Mr i'm going to be using these acronyms when I explained the results.

681
02:00:56.480 --> 02:01:10.040
Nivanthi Mihindukulasooriya: In 2017 and 2018 we sample the two additional locations one, including out on the lake show and then extra side and which has a lot of algal bloom to allotment.

682
02:01:11.960 --> 02:01:13.940
Nivanthi Mihindukulasooriya: little bit about the weather.

683
02:01:15.020 --> 02:01:35.150
Nivanthi Mihindukulasooriya: In 2016 you can see, this little red circle that's where our sampling side is in 2016 at the average precipitation was much below the long term average and 2016 was about sort of 2017 was above average and 2018.

684
02:01:36.050 --> 02:01:45.920
Nivanthi Mihindukulasooriya: was below average so they were very contrasting hydrology and weather conditions, when we did the work which is good.

685
02:01:47.060 --> 02:02:00.080
Nivanthi Mihindukulasooriya: So what we did was we collected water samples at these sites and then we brought samples into the lab and in this lab we filtered 250 milliliters of the samples through.

686
02:02:01.490 --> 02:02:16.910
Nivanthi Mihindukulasooriya: Point four micrometer je FF filter papers glass fiber filter papers and the color reflectance was measured on the oven right filled the papers you're saying the spectral photometer.

687
02:02:19.190 --> 02:02:26.660
Nivanthi Mihindukulasooriya: In addition to that, we also measured, as I said earlier, chlorophyll concentrations you'll sing a white side.

688
02:02:27.980 --> 02:02:39.980
Nivanthi Mihindukulasooriya: sand and the filter papers will wait before and after trying to calculate the suspended sediments so once the reflectance was measured we get a huge.

689
02:02:40.010 --> 02:02:40.730
Nivanthi Mihindukulasooriya: Data set.

690
02:02:41.000 --> 02:02:57.890
Nivanthi Mihindukulasooriya: So, in order to decompose a signal we did principal component analysis, because the conditions are very different between the tree sampling is I did three separate principal component analysis on the three days from the data from the three years.

691
02:02:59.750 --> 02:03:15.530
Nivanthi Mihindukulasooriya: And then the extracted components were compared to us saying stepwise multiple linear regression against the published database of mineral and peach pit MED reflectance spectra that was compiled by the usgs.

692
02:03:16.190 --> 02:03:20.570
Nivanthi Mihindukulasooriya: And some of them were also measured in the Kent state sentimentality lab.

693
02:03:21.410 --> 02:03:35.300
Nivanthi Mihindukulasooriya: i'm not going to show you all the principal components what i'm going to be focusing on is the first component and the second component so 2016 the first component was diatom and in light and.

694
02:03:36.080 --> 02:03:44.900
Nivanthi Mihindukulasooriya: first component correlated with data and the light and explain the 61.5% of the variants of reflectance data.

695
02:03:46.130 --> 02:03:57.110
Nivanthi Mihindukulasooriya: And 2017 the first component explained it's 49.6% of the variants and was a mixture of green LTE cyanobacteria anti towns.

696
02:03:57.410 --> 02:04:10.670
Nivanthi Mihindukulasooriya: And finally, in 2018 46% of the variants was explained by sign of bacteria a mixture of cyanobacteria and fight for every three so if I go directly is an accessory pigment.

697
02:04:11.690 --> 02:04:21.260
Nivanthi Mihindukulasooriya: Common in red lt and crypto fighter, so I would not expect any red lc and there's fresh water environments of this is mainly.

698
02:04:22.460 --> 02:04:25.520
Nivanthi Mihindukulasooriya: crypto fight, which is a group of lt.

699
02:04:27.110 --> 02:04:41.030
Nivanthi Mihindukulasooriya: So, moving on to the second component in 2016 the second component was cyanobacteria green algae and 2017 it was clay meditate and khloe right, while in.

700
02:04:42.080 --> 02:04:47.600
Nivanthi Mihindukulasooriya: it's a mixture of again chloride, and people, the place and the final bacteria.

701
02:04:52.070 --> 02:05:04.640
Nivanthi Mihindukulasooriya: Okay, so this is the spectrum for Tom, this is the chlorophyll that was measured from flora maitri 2017 verses 2018 we did not have access to to.

702
02:05:05.540 --> 02:05:23.270
Nivanthi Mihindukulasooriya: fluorescent spectral photometer for chlorophyll in 2016 and so, if you look at the numbers in 26 2017 the chlorophyll levels are showing the fully your traffic to lower your traffic, the overall trend is an increasing chlorophyll friend.

703
02:05:25.640 --> 02:05:37.670
Nivanthi Mihindukulasooriya: Is the same it's an increasing overall trend, but the chlorophyll concentration to a much higher meaning from very low traffic to upper hyper you traffic conditions.

704
02:05:40.160 --> 02:05:52.550
Nivanthi Mihindukulasooriya: So comparison of the principal components, with the chlorophyll so, and this is 2017 data, the vpc one call related well the chlorophyll concentration.

705
02:05:54.260 --> 02:06:08.390
Nivanthi Mihindukulasooriya: So this club COM correlation confirms our ability to use this as a proxy for alcohol abundance and then the second component correlates with the clay.

706
02:06:09.020 --> 02:06:29.240
Nivanthi Mihindukulasooriya: The total suspended sediment and so this correlation confirms our ability to use this as a proxy for suspended sediments so here is the same comparison from 2018 chlorophyll which was this vpc one and suspended sentiment was as i'm the second component.

707
02:06:32.870 --> 02:06:45.320
Nivanthi Mihindukulasooriya: So this is the comparison between the different methods to assess the cell towers, so what I did was I converted the cell counts into a percentage.

708
02:06:46.370 --> 02:07:00.920
Nivanthi Mihindukulasooriya: If you look at here and the sign of bacteria cell comes negatively correlated with chlorophyll concentration, as well as five co signing So if I could send me some major pigment and.

709
02:07:01.310 --> 02:07:11.540
Nivanthi Mihindukulasooriya: These two are basically other major pigment come on come pigment common to sign a bacteria, ideally, this should be a positive correlation however there.

710
02:07:12.650 --> 02:07:13.970
Nivanthi Mihindukulasooriya: It is negative.

711
02:07:16.370 --> 02:07:35.690
Nivanthi Mihindukulasooriya: So i'm going to move on to the tempura variations so in 2017 when we multiply was our pan, one thing that we noticed the bar graph is the precipitation as with the precipitation the principal component one decreased but eventually.

712
02:07:37.100 --> 02:07:39.620
Nivanthi Mihindukulasooriya: After two to three days from the rainfall.

713
02:07:41.000 --> 02:07:43.550
Nivanthi Mihindukulasooriya: The algos abandoned started to increase.

714
02:07:46.760 --> 02:08:03.920
Nivanthi Mihindukulasooriya: In 2018 if you look at that and the rainfall is much, much more over compared to 2017 and we do see some decreases, but they do not basically coincide with the rainfall events so it's kinda like a very mishap correlation.

715
02:08:07.040 --> 02:08:16.400
Nivanthi Mihindukulasooriya: But if you look at the principal components that are a green line where he says the VIP speed it really it matches very well.

716
02:08:18.320 --> 02:08:25.790
Nivanthi Mihindukulasooriya: And so does the second component, which is the suspended sediment that matches perfectly with the temporal trend of the wind.

717
02:08:27.950 --> 02:08:28.640
Nivanthi Mihindukulasooriya: and

718
02:08:29.660 --> 02:08:44.930
Nivanthi Mihindukulasooriya: Just for the curiosity I, I made a GIs maps to show the variation of the principal components, and this is the first day of 220 17 after a lot of rainfall, you can see that chlorophyll.

719
02:08:46.580 --> 02:09:08.900
Nivanthi Mihindukulasooriya: dark colors are high high phytoplankton the chlorophyll was much more abandoned in the upper part of the estuary while by the third day it is moving through the open mouth bought into the lake and, finally, by the end of our sampling period it's evenly distributed throughout the story.

720
02:09:11.990 --> 02:09:27.080
Nivanthi Mihindukulasooriya: And this is the segment concentration so after a lot of rainfall there's a lot of sediment in the estuary by the third day of sampling, we could see that the sediment.

721
02:09:27.920 --> 02:09:38.780
Nivanthi Mihindukulasooriya: are mainly concentrated in the upper part of the machinery, there was less sediment moving into the lake unit, but we could still see that the settlement transportation and.

722
02:09:40.760 --> 02:09:52.310
Nivanthi Mihindukulasooriya: The third day of sorry the last day of sampling, or we could see as the segments are mainly in only in the estuary but not a lot into lake erie because.

723
02:09:52.640 --> 02:10:04.610
Nivanthi Mihindukulasooriya: As we concluded the sampling see Santa Monica was progressively closing so what one thing that these maps are showing us is the significance of the estuary as a sentiment sync.

724
02:10:06.890 --> 02:10:10.850
Nivanthi Mihindukulasooriya: move on to conclusions I know a lot of results and methods.

725
02:10:11.990 --> 02:10:22.460
Nivanthi Mihindukulasooriya: So basically what what this cranes are telling us is the storm water causes a significant impact on the assurance sediment and phytoplankton abundance.

726
02:10:23.840 --> 02:10:31.790
Nivanthi Mihindukulasooriya: Wind patterns predominantly control the dispersal of phytoplankton during and the sentiments during dry conditions.

727
02:10:32.630 --> 02:10:49.100
Nivanthi Mihindukulasooriya: And, and when it was raining the immediate declining alcohol abandoned was due to the flushing of the flight of plankton into the lake through the open multiple conditions so that's why we did not see an immediate decline when the multiple was closed.

728
02:10:54.560 --> 02:11:11.360
Nivanthi Mihindukulasooriya: As the mouth, as the rainwater retreats like we saw an increase in all types of arm pipe phytoplankton, so this is mainly associated with the availability of nutrients as the nutrients are available, the phytoplankton like to be lulu.

729
02:11:14.300 --> 02:11:28.580
Nivanthi Mihindukulasooriya: So i'm also comparison of the different method, the reflectance signals decomposed using the visible derivative spectroscopy does provide a rapid of all of you have the color producing agents.

730
02:11:29.030 --> 02:11:39.860
Nivanthi Mihindukulasooriya: In water, including pigments as the less suspended settlements and the results from the spectroscopy was about spectroscopy matches well with the flora maitri.

731
02:11:41.180 --> 02:11:44.390
Nivanthi Mihindukulasooriya: But not so much with the cell towns.

732
02:11:45.710 --> 02:11:58.010
Nivanthi Mihindukulasooriya: We can they would negatively or poorly correlated so another one of the things that we can do is counting additional cells or convert the cell counts into the cell volume.

733
02:11:59.150 --> 02:12:06.620
Nivanthi Mihindukulasooriya: But this will also increase the analysis time we cannot get rapid data we have to spend hours and hours in the lab.

734
02:12:08.420 --> 02:12:15.920
Nivanthi Mihindukulasooriya: But yeah so that will also limit our applicability to use the cell counting to larger water bodies.

735
02:12:16.970 --> 02:12:26.630
Nivanthi Mihindukulasooriya: With that, I would like to end my presentation and I want to thank the Northwest Missouri State University for providing funding and.

736
02:12:27.380 --> 02:12:45.710
Nivanthi Mihindukulasooriya: awesome some awesome undergraduate students that helped with the sampling and the data analysis and the old woman creek estuary staff and my mentor Professor artists from Kent State University, I would take any questions if I have time Thank you so much.

737
02:12:47.330 --> 02:12:53.450
Yuehan Lu: Thank you loretta we have time for a couple questions.

738
02:13:03.380 --> 02:13:04.100
Nivanthi Mihindukulasooriya: yeah and.

739
02:13:05.750 --> 02:13:07.430
Ann Ojeda: Yes, great presentation, thank you.

740
02:13:08.330 --> 02:13:10.010
Ann Ojeda: One of my questions is how you.

741
02:13:10.040 --> 02:13:13.580
Ann Ojeda: converted your I assume use the F bomb sensor.

742
02:13:14.690 --> 02:13:15.050
Ann Ojeda: On your.

743
02:13:17.540 --> 02:13:17.780
Nivanthi Mihindukulasooriya: phone.

744
02:13:19.130 --> 02:13:20.780
Nivanthi Mihindukulasooriya: For the fluorescent microscopy.

745
02:13:21.980 --> 02:13:23.960
Nivanthi Mihindukulasooriya: it's a wise, I saw and.

746
02:13:24.890 --> 02:13:29.330
Nivanthi Mihindukulasooriya: yeah so the detectable exhaust ideas to the Exxon, yes, yes that's right yes.

747
02:13:29.630 --> 02:13:34.550
Ann Ojeda: yeah so the detector is, is it the FDA approved that you used.

748
02:13:35.900 --> 02:13:38.330
Nivanthi Mihindukulasooriya: seat I think it's a see Dom right.

749
02:13:38.600 --> 02:13:39.470
Ann Ojeda: Okay, see john.

750
02:13:39.710 --> 02:13:40.040
Nivanthi Mihindukulasooriya: yeah.

751
02:13:40.130 --> 02:13:44.510
Ann Ojeda: yeah what's the uncertainty on your back calculation to chlorophyll than.

752
02:13:45.950 --> 02:14:04.070
Nivanthi Mihindukulasooriya: um that's a really good question, I do not know, I know that the the florist of the sea, Dom and they have terms they do have their areas right they do I do feel, with the with the vendors, a lot of segments they doing to feel weak signal yeah that's right.

753
02:14:05.570 --> 02:14:09.020
Ann Ojeda: Right yeah we're we're looking into a similar project and that's one of our hurdles.

754
02:14:09.350 --> 02:14:11.660
Ann Ojeda: is speaking oh yeah that uncertainty.

755
02:14:12.380 --> 02:14:21.290
Nivanthi Mihindukulasooriya: Right that's true I was also wondering like I don't know if that the girl chlorophyll concentrations that we get from that one is accurate.

756
02:14:22.460 --> 02:14:27.950
Nivanthi Mihindukulasooriya: Yes, I think, like the best approaches like extracting and like doing the spectroscopy.

757
02:14:29.120 --> 02:14:29.360
yeah.

758
02:14:30.530 --> 02:14:31.760
Nivanthi Mihindukulasooriya: I agree, I think there's a.

759
02:14:32.210 --> 02:14:39.140
Ann Ojeda: matrix of X, especially if you're seeing high CDs or high some of your other maybe high dissolved organic matter.

760
02:14:39.710 --> 02:14:40.460
Nivanthi Mihindukulasooriya: I can let.

761
02:14:40.520 --> 02:14:42.080
Ann Ojeda: Your with that back calculation.

762
02:14:42.680 --> 02:14:44.420
Nivanthi Mihindukulasooriya: Right yes yeah.

763
02:14:46.970 --> 02:14:53.090
Nivanthi Mihindukulasooriya: yeah that's something that I would appreciate feedback from if there's anyone in the audience that have experienced with.

764
02:14:56.480 --> 02:14:57.890
Yuehan Lu: Now, their questions.

765
02:15:01.760 --> 02:15:02.810
Yuehan Lu: I have a quick question.

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02:15:02.810 --> 02:15:03.230
Nivanthi Mihindukulasooriya: I was.

767
02:15:03.740 --> 02:15:04.730
Yuehan Lu: Not so.

768
02:15:04.910 --> 02:15:06.980
Yuehan Lu: i'm really interested in the.

769
02:15:08.570 --> 02:15:15.080
Yuehan Lu: The reflectance message you're introducing that's can be a really quick rapid message.

770
02:15:16.190 --> 02:15:17.510
Yuehan Lu: To produce those.

771
02:15:18.680 --> 02:15:26.030
Yuehan Lu: So what what type parameters, can you get from the reflect those measurements.

772
02:15:27.380 --> 02:15:30.410
Nivanthi Mihindukulasooriya: So the instrument that I was using.

773
02:15:31.430 --> 02:15:42.440
Nivanthi Mihindukulasooriya: The county come in all aspects of photometer that when that's a handheld unit, it can measure the reflections in the range of 400 to 700 the visible range.

774
02:15:43.580 --> 02:15:58.520
Nivanthi Mihindukulasooriya: And it measures at 10 nanometers scale, so you can, if you like, you can get take the reflections and you can measure it and however big your pigment and mineral database that you will get good results.

775
02:15:59.690 --> 02:16:20.210
Nivanthi Mihindukulasooriya: But you can also get the lab spec spec of a Tommy does that can go up to the the near infrared range as well, so that that will be ideal for if you have a lot of sediment in your in your samples but yes, I would be happy to talk to you after like in a break awesome.

776
02:16:21.140 --> 02:16:23.780
Yuehan Lu: Okay sounds good yeah all right.

777
02:16:24.890 --> 02:16:32.510
Yuehan Lu: Thank you very much Florida so let's move on to the next talk um.

778
02:16:33.740 --> 02:16:34.880
Nivanthi Mihindukulasooriya: Let me stop sharing.

779
02:16:40.100 --> 02:16:41.720
Yuehan Lu: um so our next.

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02:16:44.450 --> 02:16:48.260
Yuehan Lu: To her is Claire Claire I am here.

781
02:16:48.710 --> 02:16:54.230
Claire O'Loughlin: hi yes my camera has been acting funny so i'm just gonna keep it off, just to be safe.

782
02:16:55.130 --> 02:16:59.810
Yuehan Lu: Okay, do you want okay so tell me know how you.

783
02:17:01.160 --> 02:17:02.690
Yuehan Lu: pronounce your last name, please.

784
02:17:02.930 --> 02:17:22.370
Yuehan Lu: Oh laughlin laughlin okay awesome Okay, so our next speaker is Claire laughlin and she will talk about the elimination of complex nitrogen dynamics a nurturer and a man made pond systems, so can I replace your hair to share your story.

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02:17:27.800 --> 02:17:29.270
Claire O'Loughlin: Is everyone able to see that.

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02:17:30.110 --> 02:17:33.290
Yuehan Lu: Yes, perfect okay awesome.

787
02:17:35.450 --> 02:17:51.110
Claire O'Loughlin: So, my name is Claire laughlin and I am a graduate student at the College of charleston in the environmental and sustainability studies program and today i'll be talking to you about my research on the delineation of complex nitrogen dynamics and natural and man made pond systems.

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02:17:52.850 --> 02:18:02.930
Claire O'Loughlin: So over the past few decades, the southeastern United States hasn't experienced a great increase in population and land development when compared to the rest of the United States.

789
02:18:03.710 --> 02:18:14.360
Claire O'Loughlin: This increase in development has led to an increase in the amount of impervious surfaces that are now in the environments, and that is very clear to see when looking at these two images.

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Claire O'Loughlin: This is an aerial image to the left of downtown charleston and the surrounding areas from 1984, as you can see downtown is pretty developed, but there is a lot of green space up.

791
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Claire O'Loughlin: north of the peninsula and off to either side there isn't as much development in certain areas.

792
02:18:34.160 --> 02:18:50.150
Claire O'Loughlin: Comparing that to this aerial footage this aerial photo of charleston from 2016 you can see just how much development has taken place both downtown on the peninsula and in the surrounding areas and just how much more impervious surfaces are now here than there once were.

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Claire O'Loughlin: So these images show some of the significant flooding that downtown charleston experiences after major storm events which happened quite frequently.

794
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Claire O'Loughlin: The increase in impervious surfaces.

795
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Claire O'Loughlin: Including roads roofs parking lots and more along with the destruction of natural habitat from the rapid development that has occurred increases the amount of polluted rich runoff that occurs during storm events.

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02:19:16.790 --> 02:19:31.430
Claire O'Loughlin: This runoff is a major source of pollution and impairment of aquatic systems as when it travels through the urban environment, it will capture these pollutants and bring them bring and deposit them into receiving bodies of water, such as lakes ponds and other coastal waterways.

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Claire O'Loughlin: So there are many different stormwater mitigation measures that are in place that can help alleviate the stress of this pollutant rich run off.

798
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Claire O'Loughlin: On a receiving aquatic ecosystem and the method i'm or the measure i'm going to talk about today are stormwater ponds.

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Claire O'Loughlin: stormwater retention ponds temporarily store store run off in the pond where it undergoes hydrological and biogeochemical processes that transform and transport pollutants.

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Claire O'Loughlin: Water is then discharged from ponds to receiving bodies of water via overflow structures infiltration groundwater flow and ideally this discharge from the pond will contain less pollutants, then the water going into the pond and then in the pond itself.

801
02:20:17.600 --> 02:20:24.020
Claire O'Loughlin: So there are many different types of pollutants that are present in urban runoff and then stormwater ponds as well.

802
02:20:24.290 --> 02:20:36.860
Claire O'Loughlin: These can include trace metals organic chemical contaminants pathogens and organic matter that can be rich in nutrients and I will be discussing nitrogen specifically as the nutrient pollution in stormwater ponds.

803
02:20:37.760 --> 02:20:47.180
Claire O'Loughlin: These two images show two different stormwater ponds that are experiencing excess nutrients levels and then nitrogen and ponds can be.

804
02:20:47.510 --> 02:21:06.290
Claire O'Loughlin: In multiple forms, including nitrate nitrates ammonia ammonium and organic n so nitrogen is essential to ecosystem and organism health and growth, but an excess it can degrade water quality cause algal blooms and eutrophication in the environment, and that is what we are seeing.

805
02:21:07.430 --> 02:21:14.750
Claire O'Loughlin: In these two images here stormwater ponds are ideal environments for these consequences of nutrient pollution.

806
02:21:15.080 --> 02:21:27.740
Claire O'Loughlin: Due to their stratify water columns warm temperatures typically high nutrient loads and pour water circulation, all of these factors combined make them a perfect place to experience algal blooms and eutrophication.

807
02:21:32.540 --> 02:21:42.770
Claire O'Loughlin: So this image shows the various hydrological processes that occur within a pond and that runoff will undergo while in a storm water pond so.

808
02:21:44.030 --> 02:21:51.080
Claire O'Loughlin: pond or runoff will enter the stormwater ponds via drain inflow infiltration overland flow or exchange.

809
02:21:51.710 --> 02:22:01.280
Claire O'Loughlin: Once in the ponds the runoff and nutrients will undergo mixing and shot a vacation in the water column, which will move them through the water column, and the pond itself.

810
02:22:01.670 --> 02:22:08.210
Claire O'Loughlin: They will also undergo sedimentation which can bury nutrients best removing them from the water column, and then also.

811
02:22:09.410 --> 02:22:26.600
Claire O'Loughlin: mineralization which can release them back into the water column, so this removal is only temporary, in this context water than will exit the pond via drain flow groundwater flow infiltration and exchange and from there, it will move on to the next environments in receiving body of water.

812
02:22:29.480 --> 02:22:34.940
Claire O'Loughlin: nutrients and runoff will also undergo biogeochemical processes in stormwater ponds as well.

813
02:22:35.510 --> 02:22:41.330
Claire O'Loughlin: Some of these include the microbiome processing of nitrogen, such as the next vacation and electrification.

814
02:22:41.750 --> 02:22:54.110
Claire O'Loughlin: uptake and released by plants algae and other organisms living in the pond and then scorpion onto sentiments and suspended solids so while these processes occur in stormwater ponds.

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02:22:54.860 --> 02:23:11.510
Claire O'Loughlin: Typically occur in all stormwater ponds the characteristics of the ponds and the nutrients itself are really what drives how these processes occur, and to what extent so water quality parameters biological community and structure nutrient type and concentration all play a role in the.

816
02:23:12.710 --> 02:23:15.800
Claire O'Loughlin: extent that these processes occur within ponds.

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02:23:17.390 --> 02:23:26.540
Claire O'Loughlin: So the objectives of my research are to qualitatively and quantitatively delineate hydrological and biogeochemical cycling of nitrogen within pond systems.

818
02:23:27.380 --> 02:23:34.850
Claire O'Loughlin: With that information, we hope to create a systems model that incorporates biogeochemical cycling of nitrogen in pond environments.

819
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Claire O'Loughlin: This model can then hopefully be applied to urban stormwater ponds environments to better understand their specific role in processing nutrients in coastal environments in an attempt to preserve the downgrade and water quality of receiving bodies of water.

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02:23:53.840 --> 02:24:05.960
Claire O'Loughlin: So here we have a almost like preliminary model of what we kind of hope to make this shows the expected nitrogen inputs outputs and movements within a typical pond system.

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Claire O'Loughlin: This information comes from the existing literature on the subject.

822
02:24:10.010 --> 02:24:24.500
Claire O'Loughlin: So you can see, groundwater will enter the pond an upward gradients and will exit out a downward gradient and as this groundwater enters and exits upon it will bring whichever nutrients it contains in and out of the pond with its.

823
02:24:25.430 --> 02:24:36.770
Claire O'Loughlin: Nitrogen also undergoes transformations in the pond you can see here notification the transformation of organic nitrogen to nitrate and the nitrate reduction nitrate back to organic nitrogen.

824
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Claire O'Loughlin: Again this is dependent on many factors, particularly characteristics of the pond including do temperature is there a appropriate carbon source presence of biological organisms sunlight, etc.

825
02:24:52.190 --> 02:25:02.180
Claire O'Loughlin: So in addition to run off entering the pond via overflow and groundwater structures atmospheric nitrogen also enters and.

826
02:25:02.780 --> 02:25:12.530
Claire O'Loughlin: Nitrogen leaves the pond and enters the atmosphere as well, so it's important to understand how the role atmospheric nitrogen plays in the nitrogen concentration of the pond.

827
02:25:14.630 --> 02:25:22.790
Claire O'Loughlin: It is believed, as per the literature that the notification is believed to be the primary mechanism of nitrogen removal from pond environments.

828
02:25:23.210 --> 02:25:32.420
Claire O'Loughlin: And while there's plenty of evidence to support this claim there have been few studies on the internal nitrogen processing that occurs within stormwater ponds specifically.

829
02:25:32.660 --> 02:25:39.560
Claire O'Loughlin: So the goal of this research is to help fill that gap and get a better understanding of what is happening under the surface of the pond.

830
02:25:41.000 --> 02:25:50.000
Claire O'Loughlin: And how nitrogen is transforming and cycling to paint a clear picture of exactly what is occurring regarding nitrogen as a whole in pond systems.

831
02:25:52.070 --> 02:26:10.820
Claire O'Loughlin: So this is the study site where we will be doing our research, this is the stoner preserve located in maggot South Carolina this property is owned and maintained by the College of charleston the previous owner had a freshwater pond dug out, you can see it outlined here in blue.

832
02:26:12.650 --> 02:26:18.260
Claire O'Loughlin: This groundwater or i'm sorry this fresh water pond is fed by groundwater, surface runoff and precipitation.

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02:26:18.830 --> 02:26:32.510
Claire O'Loughlin: most often and you can see here this arrow is indicating the direction of groundwater flow from high gradient to low gradient the groundwater is traveling south and south eastern direction towards the stoner river, which you can see.

834
02:26:33.800 --> 02:26:35.930
Claire O'Loughlin: towards the east southeast direction of.

835
02:26:37.010 --> 02:26:37.820
Claire O'Loughlin: Your screen.

836
02:26:39.350 --> 02:26:56.480
Claire O'Loughlin: The dots surrounding the pond refer to various monitoring wells surrounding the pond monitoring, while P being our most upstream well monitoring well as being downstream, but further away from the pond and monitoring wells you envy being very close to the pond itself.

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Claire O'Loughlin: So here we have some more images of the study site itself to the left is the actual pond itself and land management activities within the watershed of this pond have led to an increase in nutrients entering.

838
02:27:12.050 --> 02:27:17.210
Claire O'Loughlin: The pond over the past 10 and 15 or 10 to 15 years and this has resulted.

839
02:27:17.930 --> 02:27:28.910
Claire O'Loughlin: In a thick layer of duckweed that is on top of the pond year round, so this Green right here is not algae it is actually duckweed a small disc shaped plant that lives on top of.

840
02:27:29.510 --> 02:27:40.910
Claire O'Loughlin: freshwater ecosystems, and this is a direct result of the influx of nutrients into this environment to the right here, we have one of the monitoring wells, this is monitoring well V.

841
02:27:41.840 --> 02:27:54.590
Claire O'Loughlin: All of the monitoring walls resemble this one right here they're all made of PVC piping and installed, so they reach the groundwater at a depth of between three and six meters below the ground surface.

842
02:27:55.220 --> 02:28:08.240
Claire O'Loughlin: So this site wallington not a traditional urban stormwater pond environment, it was chosen, because it is accessible due to the College owning it and it has various research and monitoring infrastructure previously installed.

843
02:28:08.870 --> 02:28:23.300
Claire O'Loughlin: But we can use for this research and the goal is to understand how nitrogen is being cycled through this environment to create our model and have that model be hopefully applicable to a more urban stormwater ponds setting.

844
02:28:25.820 --> 02:28:33.860
Claire O'Loughlin: So, because we are interested in how nitrogen cycles in within and out of the pond data is collected at both.

845
02:28:34.190 --> 02:28:47.840
Claire O'Loughlin: All of the groundwater are monitoring wells, and the pond itself, so in this picture here I am measuring the depth of the groundwater below the surface of the water and the casing using a water level tape.

846
02:28:48.200 --> 02:28:58.310
Claire O'Loughlin: The depth below the casing for all for monitoring wells are measured every time we go out to sample and then a pair Celtic pump is used to obtain groundwater samples.

847
02:28:59.000 --> 02:29:09.380
Claire O'Loughlin: from each of the monitoring wells to the right here, we also measure the depth or i'm sorry the height of the pond every time we go out using the tool pictured here.

848
02:29:09.860 --> 02:29:20.720
Claire O'Loughlin: You can also get a clearer picture of the duckweed that is here in the pond by looking at this picture as well, in addition to looking at the height of the pond we also take.

849
02:29:22.070 --> 02:29:25.370
Claire O'Loughlin: Water samples from the water column directly from the pond.

850
02:29:26.210 --> 02:29:36.440
Claire O'Loughlin: While we are in the field samples are filtered and water quality parameters, including dissolved oxygen temperature pH and conductivity are measured using a portable orion meter.

851
02:29:36.800 --> 02:29:47.510
Claire O'Loughlin: And then, when we return to the lab samples are diluted and an island chromatograph is used to determine the forms of nitrogen present in the samples, and what concentrations these forms exists.

852
02:29:48.470 --> 02:29:54.680
Claire O'Loughlin: It is this information that will be used to create our quantitative model, which will be done using adobe illustrator and that lab.

853
02:29:57.470 --> 02:30:03.710
Claire O'Loughlin: So here we have an example path of what we have so far regarding nitrogen concentrations and forms.

854
02:30:04.820 --> 02:30:17.180
Claire O'Loughlin: This both of these images show nitrate nitrogen and nitrate Nigeria nitrate nitrogen concentrations from September 25 2020 and then a month later, about a month later October 23 2020.

855
02:30:19.100 --> 02:30:30.440
Claire O'Loughlin: These arrows are indicating the direction of groundwater flow towards the stoner river so in both of these instances well P, has the smallest concentration of.

856
02:30:31.160 --> 02:30:38.900
Claire O'Loughlin: Nitrogen out of all the groundwater wells, and the pond which was expected due to it being the most upstream well out of the form.

857
02:30:39.560 --> 02:30:46.130
Claire O'Loughlin: Monitoring well as also has a smaller comparatively amount of nitrogen present in this.

858
02:30:46.760 --> 02:30:55.820
Claire O'Loughlin: Groundwater as well, this was also expected modern well s is our deepest well and we only started getting samples.

859
02:30:56.480 --> 02:31:12.710
Claire O'Loughlin: From this monitoring well when it was starting to get a little cooler so September, October it started giving us consistent samples monitoring will s is also furthest away from the pond out of the four so we believe that this monitor or this groundwater is more reflective of.

860
02:31:14.630 --> 02:31:20.630
Claire O'Loughlin: Groundwater upstream of the pond and it's not being directly impacted by the activities going on in the pond.

861
02:31:21.320 --> 02:31:35.120
Claire O'Loughlin: Monitoring wells V and you both consistently give us higher concentrations of nitrogen every time we go out to sample, however, which one has the greatest concentration changes, as you can see, in September.

862
02:31:35.870 --> 02:31:44.630
Claire O'Loughlin: Monitoring well V has the greatest concentration and then in October monitoring well you has the greatest concentration, so this changes almost every time we go sampling.

863
02:31:45.740 --> 02:32:01.370
Claire O'Loughlin: The pond itself is acting as a massive nitrogen reservoir with a great concentration seen almost every time we go out sampling and it's important to note that this these concentrations are nitrate nitrogen and nitrate nitrogen.

864
02:32:02.510 --> 02:32:16.640
Claire O'Loughlin: put together and that between two and 3% of each of these samples is nitrate nitrogen and between 97 and 98% of the sample is nitrate nitrogen so in this ecosystem, we are seeing.

865
02:32:17.270 --> 02:32:24.950
Claire O'Loughlin: vastly more nitrates than nitrates in the system, which is a result we didn't initially expect so that was kind of a surprise to learn.

866
02:32:26.840 --> 02:32:40.640
Claire O'Loughlin: So our goal is to create a model that shows all aspects of nitrogen movement and cycling transformations throughout the pond so we definitely need a lot more information in order to create a successful model to do that.

867
02:32:41.390 --> 02:32:50.840
Claire O'Loughlin: We need to accurately quantify notification and Dean nitric vacation in our system, and this can help determine the role atmospheric nitrogen plays.

868
02:32:52.250 --> 02:32:54.950
Claire O'Loughlin: In the nitrogen concentration and cycling of this pond.

869
02:32:55.640 --> 02:33:09.440
Claire O'Loughlin: Do unification also requires a carbon source to occur, so we need to analyze sediments and water samples for organic carbon to determine if identification is occurring, and if it is possible that or if it could even occur in this environments.

870
02:33:10.880 --> 02:33:15.410
Claire O'Loughlin: We also need to determine specific groundwater flow in and out of the pond system.

871
02:33:17.690 --> 02:33:31.040
Claire O'Loughlin: Groundwater you know brings water and the nutrients present in that water into and out of the pond system so understanding the specific flow can help us determine exactly how much nitrogen is entering and entering and exiting the system in this manner.

872
02:33:31.490 --> 02:33:41.270
Claire O'Loughlin: And we should be able to do that, using darcy's law and our measurements of the depth of the ground watering mel's wells, and the pond itself.

873
02:33:43.460 --> 02:33:52.310
Claire O'Loughlin: We need to analyze sentiment to learn more about the sediment water interface and role of mineralization and sedimentation when it comes to nitrogen additions and removals.

874
02:33:52.760 --> 02:34:05.360
Claire O'Loughlin: The literature has some literature, has stated that mineralization and night vacation could potentially be occurring at greater rates than Dean, education, and if that is so that would make the ponds and ground water sources.

875
02:34:06.230 --> 02:34:17.960
Claire O'Loughlin: And sources, rather than n sinks and to determine if that is what is occurring in this environment, we need to learn more about the settlement water interface, and the concentration and forms of nitrogen in this environment.

876
02:34:19.700 --> 02:34:25.250
Claire O'Loughlin: We also need to take a look at the specific biological uptake and release of nitrogen regarding by auto.

877
02:34:25.760 --> 02:34:36.680
Claire O'Loughlin: And this needs to be quantified to determine its impacts microbe and duckweed analyses to see how they are transporting and transforming nitrogen throughout the pond are essential in completing this.

878
02:34:38.960 --> 02:34:51.770
Claire O'Loughlin: So, as I mentioned more analysis needs to be done to better understand the nitrogen dynamics this and other stormwater ponds and to confirm the possibilities mentioned previously, and some lab limitations have limited our ability to do this so far.

879
02:34:53.180 --> 02:34:58.160
Claire O'Loughlin: We need to conduct measurement of different forms of nitrogen using multiple techniques.

880
02:34:58.610 --> 02:35:08.660
Claire O'Loughlin: We want to use a ultraviolet visible light spectrum photo meter to determine nitrate and nitrate samples in the pond as well, to ensure that what we are seeing is correct.

881
02:35:09.200 --> 02:35:16.250
Claire O'Loughlin: That there is significantly more nitrate in the system, then nitrate and that the concentrations are correct.

882
02:35:16.550 --> 02:35:30.170
Claire O'Loughlin: We also need to measure to see the cat Ion forms of nitrogen that are present in both the stormwater ponds or i'm sorry the pond and groundwater as well, this will give us insight on the additional forms of nitrogen in this ecosystem.

883
02:35:32.270 --> 02:35:40.760
Claire O'Loughlin: Organic carbon needs to be measured using a tlc analyzer, and this can help determine the extent of the carbon source in the pond for Dean education purposes.

884
02:35:42.380 --> 02:35:57.350
Claire O'Loughlin: Total nitrogen analysis of sediment also using a UV it is is necessary to better determine or better understand nitrogen transport over sediment water interface, and the role of remodeler ization and nature, education, possibly occurring at the sentiment water interface.

885
02:35:58.310 --> 02:36:06.140
Claire O'Loughlin: Biological assessments of microbes and duck leads to determine their role also must be looked into the analyses of nature, fires and teenager fires.

886
02:36:06.710 --> 02:36:10.430
Claire O'Loughlin: We can do this, possibly using a PCR are stable isotope methods.

887
02:36:10.940 --> 02:36:19.070
Claire O'Loughlin: So these analyses can help give insight as to why the system has a great concentrate or appears to have a great concentration of nitrate compared to nitrates.

888
02:36:19.490 --> 02:36:31.910
Claire O'Loughlin: and help fill in what we are missing and is necessary in order to complete the model we want to that can be applied to stormwater ponds and help protect the water quality of receiving bodies of water in an urban ecosystem.

889
02:36:33.290 --> 02:36:36.800
Claire O'Loughlin: Thank you, and if I still have time for questions i'd be happy to answer any that you have.

890
02:36:39.050 --> 02:36:43.970
Yuehan Lu: In your career yeah we still have two minutes for questions.

891
02:36:48.830 --> 02:36:49.700
Yuehan Lu: Any questions.

892
02:36:56.690 --> 02:37:19.700
Yuehan Lu: So far, it's been clear um so you are mentioning the complex between the duckweed and micro and their potential low in the nitrogen dynamics and now that which is highly prevalent in many you traffic streams in Alabama as well, can you elaborate on.

893
02:37:20.750 --> 02:37:27.980
Yuehan Lu: What the potential rose dockery has all these microbes like a nitrate fire and denied to fire.

894
02:37:29.300 --> 02:37:46.640
Claire O'Loughlin: So, from my literature reviews, I have found that duckweed does a really good job of absorbing various forms of nitrogen, so I would imagine that it does act as a mechanism to remove nitrogen from the ecosystem.

895
02:37:47.660 --> 02:38:01.730
Claire O'Loughlin: i'm not quite sure I can't quantify that for you in this pond system, because we have not begun that process, yet, but that is what I expect to see that I do think it will act as a mechanism to remove nitrogen from this ecosystem.

896
02:38:05.780 --> 02:38:06.410
Thank you.

897
02:38:08.450 --> 02:38:10.370
Yuehan Lu: Any other questions for Claire.

898
02:38:14.000 --> 02:38:22.280
Ann Ojeda: hi Claire yes, I have a question thanks for your presentation um my question is really related to ground water flow and residence time within that.

899
02:38:23.330 --> 02:38:27.200
Ann Ojeda: Little within the pond um can you speak to that a little bit.

900
02:38:29.600 --> 02:38:40.550
Claire O'Loughlin: So we are not entirely sure exactly what the residents, time is that is something I will definitely be needing to look into in order to determine the inflows and outflows.

901
02:38:41.360 --> 02:38:56.450
Claire O'Loughlin: So I and there's not been much work done on this specific pond in general, so I don't really have any numbers to work off to give you um but I definitely will be looking into that in the immediate future and can definitely add that, to this presentation and get back to you on that.

902
02:38:58.010 --> 02:38:58.820
Ann Ojeda: Thanks no problem.

903
02:39:00.440 --> 02:39:03.590
Yuehan Lu: Alright, thank you Claire um, so we are going to.

904
02:39:03.620 --> 02:39:04.490
Yuehan Lu: continue.

905
02:39:04.520 --> 02:39:24.950
Yuehan Lu: With the next presentation our next speaker as Mary paquet from the University Alabama her talk will be limitations implications and solutions for wastewater disposal, you know alabama's black belt Mary Please go ahead and share your screen.

906
02:39:42.230 --> 02:39:44.570
Yuehan Lu: You Mary singing a muted.

907
02:39:46.130 --> 02:39:47.750
Mary Hastings Puckett: All right, there we go.

908
02:39:49.700 --> 02:39:54.980
Mary Hastings Puckett: Please excuse me, I have like the pollen call So if I mute for a minute that might be coughing.

909
02:39:56.150 --> 02:40:00.110
Mary Hastings Puckett: So Good morning, my name is Mary Hastings pocket, and I am a graduate students at.

910
02:40:01.160 --> 02:40:12.530
Mary Hastings Puckett: University of Alabama department of geological sciences i'm presenting today on behalf of research group with Jillian Max Brown and Dr mark Elliott from department of civil construction environmental engineering Alabama.

911
02:40:12.920 --> 02:40:25.460
Mary Hastings Puckett: And Greg guthrie and Dr Bennett bearded at the geological survey of Alabama and today i'm presenting on the limitations implications and solutions for wastewater disposal and alabama's black belt.

912
02:40:28.370 --> 02:40:36.650
Mary Hastings Puckett: So the Alabama black Belt is a region of low population density high poverty and high incidence of pathogen related illnesses.

913
02:40:37.190 --> 02:40:41.600
Mary Hastings Puckett: And centralized sewer systems serve a low percentage of the population in the region.

914
02:40:42.230 --> 02:40:53.240
Mary Hastings Puckett: and rural communities you most often see on site septic system to utilize but because of the underlying geology in the black belt use of septic systems is limited.

915
02:40:54.080 --> 02:41:06.080
Mary Hastings Puckett: Also, most sewage systems are extremely expensive and the black Belt is an area of high poverty so most households rely on the use of a straight pipe disposal system, so I will talk about in a moment.

916
02:41:07.190 --> 02:41:15.860
Mary Hastings Puckett: So, if you look at a map of Alabama like a belt it the black belt goes through the Center of the state in the area highlighted in red.

917
02:41:18.350 --> 02:41:27.290
Mary Hastings Puckett: So the black belt has been in the media on nationally and internationally, because of these water and sanitation problems.

918
02:41:27.680 --> 02:41:43.160
Mary Hastings Puckett: And 2017 you want officials to wear the black belt area came out with a report that said Alabama has the worst poverty in the developed world and they were shocked that a first world country could have such problems with water and sanitation.

919
02:41:44.300 --> 02:41:59.990
Mary Hastings Puckett: and November 30 2020 a new yorker article came out about the album in a black belt and I pulled a quote out from a lexus okay Oh, you said, an Alabama and trends, poverty and unusual geology have created a public health disaster.

920
02:42:01.640 --> 02:42:04.550
Mary Hastings Puckett: So that leads me into the black belt geology.

921
02:42:05.300 --> 02:42:18.410
Mary Hastings Puckett: So in the black belt, if you grew up in Alabama like I did, and took Alabama history you grew up learning that the black Belt is the pretty like landscaping Alabama with this rich black topsoil, which is why it got its name.

922
02:42:19.310 --> 02:42:30.500
Mary Hastings Puckett: So that is true, but it's what's underlying the black belt that's the problem for the sewage systems so underneath that we have chocolate layers most commonly the marvel chalk.

923
02:42:31.730 --> 02:42:36.050
Mary Hastings Puckett: So, as this chalk, is whether you get these.

924
02:42:37.640 --> 02:42:46.400
Mary Hastings Puckett: soils that can be shrinks well clays clay loam and silty clay loam, also known as vertical soils.

925
02:42:47.570 --> 02:43:00.020
Mary Hastings Puckett: So the problem with these virtus so soils is that they shrink and swell with changes and moisture content and the top image, you see, in a dry condition, where the soil.

926
02:43:01.100 --> 02:43:04.100
Mary Hastings Puckett: build volume shrinks and he's deep why cracks or forms.

927
02:43:04.490 --> 02:43:17.150
Mary Hastings Puckett: And these open up these pathways where anything from the surface can flow down the so subsurface so you can get these contaminants that end up flowing into the subsurface, but when there's moisture and it ran.

928
02:43:17.660 --> 02:43:25.910
Mary Hastings Puckett: The soil volume expands and you've seen the bottom that you end up with this klay layer which is basically impermeable.

929
02:43:26.390 --> 02:43:40.550
Mary Hastings Puckett: So the problem with that is that septic systems rely on infiltration of the wastewater into the subsurface so you can remove pathogens from the water, so if you have impermeable soils then you're limited by the.

930
02:43:41.660 --> 02:44:00.140
Mary Hastings Puckett: Your use of disposal methods such as it is in the black belt so looking at another map of the area of the saturated hydraulic conductivity we see a lot of red some orange and yellow so overall very, very low hydraulic conductivity on average spot point.

931
02:44:01.250 --> 02:44:11.210
Mary Hastings Puckett: You want feet per day with the water table, ranging from six inches or greater than 80 feet so very inconsistent water table that's very low permeability.

932
02:44:13.430 --> 02:44:19.430
Mary Hastings Puckett: So you have two common disposal methods and the United States you're probably familiar one with.

933
02:44:19.850 --> 02:44:32.930
Mary Hastings Puckett: If you grew up in an urban or suburban area you're probably used to what's known as either a centralized municipal or off site disposal method, so what happens is your sewage goes in to.

934
02:44:33.440 --> 02:44:43.970
Mary Hastings Puckett: piping and goes into sewage systems where they move towards municipal treatment facilities, where they are treated off site that's mostly in a fluent areas.

935
02:44:44.720 --> 02:44:51.350
Mary Hastings Puckett: it's expensive but it's extremely effective if you grew up in a more rural community like I did in southern Alabama.

936
02:44:51.680 --> 02:45:03.560
Mary Hastings Puckett: You most likely had a centralized or onsite disposal system which we might also call septic tanks, so how this works is the sewage moves into a septic tank.

937
02:45:03.890 --> 02:45:10.160
Mary Hastings Puckett: Where the solid settle in the tank and then the liquid moves into what's called a drain field.

938
02:45:10.640 --> 02:45:28.070
Mary Hastings Puckett: So the drain field will discharge the water into the subsurface where it relies on the soil matrix to treat the water, so if you have impermeable players, like the black belt often does this makes that dream field system not possible.

939
02:45:30.320 --> 02:45:38.660
Mary Hastings Puckett: So that leads me to the common method that we see in the black belt region, known as street pipes So what are straight pipes.

940
02:45:39.020 --> 02:45:55.040
Mary Hastings Puckett: Straight pipes discharge untreated wastewater from a home to the surface, typically it's going to go move to an adjacent property, the trench or stream occasionally you have what's known as a community line that connects multiple homes to one large state straight pipe.

941
02:45:56.270 --> 02:46:00.230
Mary Hastings Puckett: These are actually unregulated and on permitted sewage system.

942
02:46:02.180 --> 02:46:12.230
Mary Hastings Puckett: So on the left, we see a few images that are a lot actual images from black but reason, if you can kind of see here on the left, you can see the pipe right next to the home.

943
02:46:12.650 --> 02:46:22.910
Mary Hastings Puckett: And it moves over here on the right, where it's dumped out just in the open and pipes are on the surface and then they don't want the service, and these are areas where children and animals and.

944
02:46:24.140 --> 02:46:33.830
Mary Hastings Puckett: Live you see in the bottom left image, you have a ball for probably a child right, you can see a dog sitting right next to dump sewage which there's major health risks.

945
02:46:34.490 --> 02:46:43.850
Mary Hastings Puckett: And the image on the right, the top right corner, you can see what a straight pipe would look like, and so you have your House, the pipe that just jumps trying to open.

946
02:46:44.420 --> 02:47:00.080
Mary Hastings Puckett: The Left is that sewer connection we looked at earlier, the municipal or centralize where it is from your home to sewer system, the bottom is your septic system where you go into the septic tank and then into the drain view and then the bottom right is an option of straight pipe.

947
02:47:01.220 --> 02:47:08.060
Mary Hastings Puckett: That could help slightly where you would like a septic tank have a tank where the solid would settle before moving on into your straight pipe.

948
02:47:09.110 --> 02:47:16.520
Mary Hastings Puckett: So what are these problems with straight pipes, I mean the main one that's probably jumping out to everyone right now is you're discharging raw sewage.

949
02:47:16.880 --> 02:47:26.630
Mary Hastings Puckett: Which is incredibly dangerous for human health and the health of wildlife, you have diminished water quality as you're dumping these dangerous bacteria and pathogens, to the water.

950
02:47:27.020 --> 02:47:36.560
Mary Hastings Puckett: Which leads to a core quality of life for residents in the area, not to mention we don't know how many are actually in use, not only in Alabama but nationwide.

951
02:47:38.150 --> 02:47:42.890
Mary Hastings Puckett: There are some legality issues as well you're violating the clean water act you're dumping.

952
02:47:43.520 --> 02:48:00.080
Mary Hastings Puckett: pathogens and contaminants straight into water which could result in fines arrest or condemnation of home, which in these areas of high poverty residents can afford these fines, you can have informal social consequences as well, you open yourself up to lawsuits from your.

953
02:48:01.310 --> 02:48:08.150
Mary Hastings Puckett: neighbors and then sometimes, as we saw earlier Community activists publicize the issue we've seen or heard.

954
02:48:08.540 --> 02:48:19.760
Mary Hastings Puckett: Politicians bring it up with, I think, Alexander our costume protesters talk about it or a book or Al Gore mentioned alabama's poverty and dangerous water conditions in the black belt.

955
02:48:21.350 --> 02:48:33.230
Mary Hastings Puckett: So in Alabama we have three known counties that actually we have data from four straight pipes, the first big county, which is the upper one right, the right southern right of.

956
02:48:33.740 --> 02:48:44.750
Mary Hastings Puckett: tuscaloosa and your Shelby and Jefferson counties and 2005 4000 homes were sampled and of those 15% had straight pipe systems and also.

957
02:48:45.350 --> 02:48:52.940
Mary Hastings Puckett: 35% or observe to have some type of septic tank failure, which was most often raw sewage sitting on the ground.

958
02:48:53.570 --> 02:49:15.140
Mary Hastings Puckett: By 11 years later and wilcox county much smaller sample size of 289 was looked at about 7% were permitted 60% were straight pipe systems, whereas 33% were unpermitted but there were no visible straight pipes, so no known data on that and then inhale county.

959
02:49:16.160 --> 02:49:37.040
Mary Hastings Puckett: Which is right South the tuscaloosa where mantels located about 411 were surveyed 35% were permitted 6% or straight pipe systems and 59% on permitted, with no visible straight path so an explanation for the variability between these nearby counties they're not far off when each other.

960
02:49:38.720 --> 02:49:48.920
Mary Hastings Puckett: there's better soils in northern hale county like I said we're mountain phil is also in mountable they're strict have stricter enforcement of these unpermitted sewer systems.

961
02:49:49.700 --> 02:50:00.110
Mary Hastings Puckett: In the city limits and then wilcox county has the higher rate of poverty between these three counties which is most likely why there's the high incidence of straight pipes.

962
02:50:02.060 --> 02:50:08.420
Mary Hastings Puckett: So why didn't we oh in the US, we also have some straight pipe data nationwide but also once again.

963
02:50:09.680 --> 02:50:21.410
Mary Hastings Puckett: Not that much so, if you look at this map 15 states have any State straight pipe that all the ones in the white or where we have none that's not saying there isn't that just means there's no data.

964
02:50:22.580 --> 02:50:38.780
Mary Hastings Puckett: So Alabama has is one of the ones with three more counties you notice only one state has data for all counties, and that is Minnesota, and that is because in 2006 they actually pass them straight Lol are straight pipe legislation.

965
02:50:40.430 --> 02:51:00.320
Mary Hastings Puckett: So this gives residents 10 months to install correct weight water wastewater treatment systems, if not, they will be charged $500 fines, a month, so since from 2006 to 2010 Minnesota reported 184 straight pipes were eliminated there's still further work.

966
02:51:01.400 --> 02:51:06.740
Mary Hastings Puckett: To be done, but clearly Minnesota is doing the work to regulate permanent and change the systems.

967
02:51:07.850 --> 02:51:22.100
Mary Hastings Puckett: So why do we have this lack of data nationwide so there's no adequate adequate national inventory, and this is mainly because in 1990 the US census stopped collecting wastewater data.

968
02:51:22.520 --> 02:51:33.290
Mary Hastings Puckett: Not to mention the systems are illegal, so people aren't likely to voluntarily give up the information, not to mention it's a complex situation for government agencies.

969
02:51:36.860 --> 02:51:55.820
Mary Hastings Puckett: So there's some limitations and regulating and permitting so wastewater discharge to service wire requires repairing access, as well as an MP D s permit, which is the national pollutant discharge elimination system from it and that's unrealistic for most households in the black belt.

970
02:51:56.930 --> 02:52:03.410
Mary Hastings Puckett: exclusions during federal investments and sanitation for structure highly impacts, the area.

971
02:52:04.070 --> 02:52:13.760
Mary Hastings Puckett: Older properties lack inspections and then redlining prevents access to loans and redlining is illegal discriminatory practice and which mortgage lenders.

972
02:52:14.210 --> 02:52:25.640
Mary Hastings Puckett: deny loans to certain communities, because of the racial or socio economic characteristics of that applicants Community it's illegal but it's very common in the black belt.

973
02:52:26.330 --> 02:52:47.600
Mary Hastings Puckett: Not to mention contractors employ exploited, a practice and result in unquote permitted systems, not to mention like I said very impoverished area fines for enforcement cause further financial hardship, so how does finding an impoverished Community actually get results.

974
02:52:49.040 --> 02:52:59.180
Mary Hastings Puckett: That brings me to those financial limitations changing these systems over to from straight pipes to centralize or decent tries systems is expensive.

975
02:53:00.200 --> 02:53:09.080
Mary Hastings Puckett: who's gonna be responsible for those costs, I mean most of septic tanks systems costs more than the mobile homes that are prevalent in the black belt.

976
02:53:09.350 --> 02:53:18.230
Mary Hastings Puckett: So these residents can afford it so who's going to take these upfront costs the local or regional government, the state government, the Federal Government.

977
02:53:18.770 --> 02:53:30.200
Mary Hastings Puckett: there's no policy in place to do that the moment, not to mention moving forward once the systems are in place, who is responsible financially for the operation and maintenance costs.

978
02:53:32.360 --> 02:53:39.620
Mary Hastings Puckett: So we have to characterize the problems, as we move forward so pathogens and other contaminants directly affect human health in the environment.

979
02:53:40.010 --> 02:53:48.680
Mary Hastings Puckett: The exposure pathways are complex and one solution is not going to work for every community, but there are some possible solutions that we can do.

980
02:53:49.670 --> 02:54:00.140
Mary Hastings Puckett: You can expand existing conventional sewers but that gives the problem that most sewers run only to the city limits and the black belt and a lot of residents live outside city limits.

981
02:54:02.360 --> 02:54:15.590
Mary Hastings Puckett: You can advance on site treatment systems, which is also expensive connection fees are expensive, or you can use what's called a decentralized cluster system, which we believe is the most effective and has the most potential.

982
02:54:16.640 --> 02:54:29.180
Mary Hastings Puckett: So this is called a clustered system approach and each homes can have a septic tank, which is the primary treatments facility and then looks at enter a two inch line and flow to a treatment unit.

983
02:54:29.660 --> 02:54:37.370
Mary Hastings Puckett: Further off site where we could have better soil area with the drain field, which can then flow into the subsurface.

984
02:54:38.300 --> 02:54:47.330
Mary Hastings Puckett: So it's the most affordable issue, overall, which would have the best quality of life and address the health issues, for the most residents.

985
02:54:47.750 --> 02:54:57.890
Mary Hastings Puckett: But widespread application is expensive and it's limited by the economic conditions in the region, once again, would require some type of state and or federal funding.

986
02:55:01.610 --> 02:55:11.210
Mary Hastings Puckett: Widespread implications these issues and the album is black belt, so that we have a need to integrate not only the scientific elements of wastewater but the social.

987
02:55:11.630 --> 02:55:26.420
Mary Hastings Puckett: Clean water equals better Community health, we would see less cases of hookworm and we build up these poor communities, and we can inform future policies, not only for our black belt community, but for the rest of the state and maybe the rest of the country.

988
02:55:27.860 --> 02:55:36.320
Mary Hastings Puckett: So our password is to help these communities in residence, we can expand upgrade the existing municipal sewer systems.

989
02:55:37.160 --> 02:55:47.540
Mary Hastings Puckett: fines on on site systems fine clusters homes, where we can put in those decent rise clusters fine cost effective methods and financial models.

990
02:55:48.290 --> 02:56:00.170
Mary Hastings Puckett: And i'm fine who can manage the systems moving forward, how can we regulate them other than just putting in fines and then give a how to guide to these communities how how we can upgrade the sewage systems.

991
02:56:02.510 --> 02:56:09.830
Mary Hastings Puckett: So we have further work to do and our research approach, which has already begun with Dr Elliot and Jillian Max Brown.

992
02:56:10.490 --> 02:56:19.280
Mary Hastings Puckett: they've done some site by site inspections and the black belt gone through and surveyed and looked at the system and what's already in place, what systems, what could they do.

993
02:56:19.910 --> 02:56:37.340
Mary Hastings Puckett: What waterways are already there and then we could get some data from some local stakeholders so that to be septic system Installers health department staff locals who know the area jillian's already started working on flow routing through GIs just where she's.

994
02:56:38.450 --> 02:56:54.740
Mary Hastings Puckett: using it to show the flow pathways of water in the area also she's put in locations have known straight pipes and municipal systems and areas that we're going to sample or water sampling for microblog biological and chemical contaminants.

995
02:56:56.960 --> 02:57:03.230
Mary Hastings Puckett: And then to conclude straight pipe discharges are present in the world and uncertain areas.

996
02:57:03.770 --> 02:57:13.880
Mary Hastings Puckett: there's a wide range of things that caused them, I mean the geology of the black Belt is difficult and the socio demographic conditions don't help the situation.

997
02:57:14.240 --> 02:57:23.060
Mary Hastings Puckett: it's propagated by the failure or inability of state and local governments to monitor regulate and help and it's largely overlooked by both researchers and government.

998
02:57:23.750 --> 02:57:32.900
Mary Hastings Puckett: We need to document these adverse health, environmental outcomes and then it's such an great area of research for everyone.

999
02:57:33.200 --> 02:57:41.360
Mary Hastings Puckett: I mean there's a need for engineering geology biology sociology political science to all work together and build up this Community.

1000
02:57:42.170 --> 02:57:48.710
Mary Hastings Puckett: And so, with that i'd like to acknowledge all of these amazing sources of funding and all the people that have.

1001
02:57:49.190 --> 02:58:05.960
Mary Hastings Puckett: worked on this and will continue to work on this and, with that i'd like to open up for questions here you have a contact info for all people all authors on this presentation and Gray got three is also on the call so if he wants to jump in on questions that would be great.

1002
02:58:07.460 --> 02:58:09.860
Mary Hastings Puckett: Okay, get back to this.

1003
02:58:10.760 --> 02:58:15.230
Yuehan Lu: All right, thank you, Mary we have time for one quick question.

1004
02:58:19.430 --> 02:58:34.370
Ming-kuo Lee: Mary Could you elaborate on your wastewater treatment in soil, did you are you thinking about natural attenuation by the natural microbes or more engineering approach.

1005
02:58:35.000 --> 02:58:48.860
Mary Hastings Puckett: and definitely want to go with some more natural approach in the area, if you can, but like I said, most likely doing the septic approach might not be possible, which would you be using the drain field.

1006
02:58:49.880 --> 02:58:50.660
Mary Hastings Puckett: approach.

1007
02:58:52.310 --> 02:58:55.130
Greg Guthrie: very lucky I can add on to that as well.

1008
02:58:56.780 --> 02:58:59.420
Greg Guthrie: In the cluster systems typically with.

1009
02:59:00.590 --> 02:59:13.340
Greg Guthrie: The septic tanks are going to, of course, remove the solids and and then we will use a filter which will take it either secondary or even even tertiary frequently will install.

1010
02:59:14.030 --> 02:59:26.390
Greg Guthrie: UV at the end of the treatment, maybe a filter system or something like that, before it's introduced into the disposal system, whether that'll be direct discharge.

1011
02:59:27.560 --> 02:59:39.560
Greg Guthrie: Because at that time it'll it'll meet tm DL requirements or depending on the soul, whether you can actually use a drip field, or some kind of disposal method.

1012
02:59:42.320 --> 02:59:42.740
Ming-kuo Lee: Thank you.

1013
02:59:45.530 --> 03:00:04.070
Yuehan Lu: All right, thank you, Mary a great talk very important environment issue that needs immediate attention, and so, if you guys have any questions for Mary and Greg non please feel free to leave your questions, through the chat box and we are running.

1014
03:00:04.130 --> 03:00:06.530
Yuehan Lu: A little bit late on time, so we.

1015
03:00:06.620 --> 03:00:19.970
Yuehan Lu: have to move along to the last talk of this section, so the last talk will be given by RU RU Midi F lobby I apologize if I.

1016
03:00:20.240 --> 03:00:22.310
Yuehan Lu: pronounced your name incorrectly.

1017
03:00:22.940 --> 03:00:32.930
Yuehan Lu: And he is going to talk about a cluster analysis occlusion sauces based on their combination yeah.

1018
03:00:33.170 --> 03:00:33.800
Ayo Afolabi: Thank you.

1019
03:00:43.730 --> 03:00:45.110
Ayo Afolabi: Can you all see my slide.

1020
03:00:45.650 --> 03:00:46.220
Yes.

1021
03:00:48.530 --> 03:01:03.830
Ayo Afolabi: Okay, so I would like to welcome everyone to this presentation, so do his work was put together by Jenny and have some fun sense department of diversity and an assistant Professor he sends it back now.

1022
03:01:05.360 --> 03:01:11.930
Ayo Afolabi: And I am the Alpha lobby good rustling and starts the apartment and abilities to that in those tracks is imaginary.

1023
03:01:12.320 --> 03:01:18.860
Ayo Afolabi: So today i'll be presenting on cluster analysis of pollution sources, based on the same composition.

1024
03:01:19.520 --> 03:01:32.060
Ayo Afolabi: So i'll be going through the introduction, we all know what apple isn't as been a major issue over the years and scientists walk to prevent water pollution and.

1025
03:01:32.930 --> 03:01:43.100
Ayo Afolabi: One of the effect of water pollution is the creation of water quality, which makes what an unsafe for domestic that industry, I use it also after the aquatic ecosystem as well.

1026
03:01:43.490 --> 03:01:53.780
Ayo Afolabi: we've been able to identify that physical position of water, but this country, with its own code as well, which makes identification of fecal matter matters of necessary.

1027
03:01:55.220 --> 03:02:08.330
Ayo Afolabi: better indicator of missing have been used over time to test samples for physical pollution, but this method as a drawback, because it cannot distinguish the type of all sorts of ethical position.

1028
03:02:09.470 --> 03:02:20.540
Ayo Afolabi: or limit at all in 1996 cycles see the use of bomb makers, which are stairs which solve this problem, what our sales stairs are just about.

1029
03:02:20.900 --> 03:02:39.140
Ayo Afolabi: Chemical component, the a subgroup of sales with eyedrops a group, as indicated is induced diagram or ah yeah but i'll synthesize by the organism as well, the major figures says include group will stay no cholesterol cholesterol know system stare.

1030
03:02:41.840 --> 03:02:57.200
Ayo Afolabi: So distributed and consideration of details the theory and the major contributing factor, are the diet, the indigenous here and the nlp but there which i'll explain a bit better in the diagram and we'll sit in this diagram.

1031
03:03:01.850 --> 03:03:02.210
Ayo Afolabi: Okay.

1032
03:03:05.870 --> 03:03:13.520
Ayo Afolabi: Okay, oh when we can let's say diet like meat, it contains cholesterol.

1033
03:03:14.900 --> 03:03:21.470
Ayo Afolabi: So as a human being ingested about here in I got converted called Lester into drupal steno.

1034
03:03:22.250 --> 03:03:36.890
Ayo Afolabi: Also for the car like when they ingest grass, the grass contained system stare the bacteria in their gut converted to try to fall too fall it's a couple Center and also that our phones for the chicken as well.

1035
03:03:39.200 --> 03:03:45.980
Ayo Afolabi: As they need to develop a robot model which takes into consideration for system data from different sources.

1036
03:03:46.370 --> 03:03:59.030
Ayo Afolabi: All over the world, so this study Arabs, to try and difficult pollution souls by exploring arrow both struck cluster structure of vehicles still and with what influence.

1037
03:03:59.780 --> 03:04:09.590
Ayo Afolabi: Our method we're able to sort of from different channels that are be published over time and I listed some of the journals here for you to see.

1038
03:04:10.970 --> 03:04:19.430
Ayo Afolabi: So then we'll move up cleaning after you graduate outlander data we did standardization, we did an exploratory data analysis.

1039
03:04:20.060 --> 03:04:38.750
Ayo Afolabi: Before using clustering methods to actually explore whether we asked cluster in our data so we're able to obtain the optimal structure which, for now, we are not so much concerned about using the we didn't close as some of government thought all this analysis, we don't using the software.

1040
03:04:40.550 --> 03:04:41.930
Ayo Afolabi: So far is that and.

1041
03:04:43.250 --> 03:04:52.790
Ayo Afolabi: So this really shows the data distribution on the y axis, we are the count of the data that is the number of observation for a particular type.

1042
03:04:54.380 --> 03:05:02.720
Ayo Afolabi: of animal feces why on the X axis, we have the admin offices like from from this diagram It shows.

1043
03:05:04.340 --> 03:05:15.650
Ayo Afolabi: The cow the animal services for the cow we have like six observation why for the wastewater influx we have three or four the magnificence we also have three as well.

1044
03:05:18.320 --> 03:05:31.880
Ayo Afolabi: So, for some of the animals that we are not familiar weeks they're going to put some look like this one, they didn't go looks like a dog, you have the mark fine those are bits the cigar those I bet to do is allow.

1045
03:05:35.870 --> 03:05:54.230
Ayo Afolabi: The estimated data analysis, we did we notice that some offices have some unique still profile attached to them like for the month fishes we know to the for the month window to the out I constitution of group will stay now.

1046
03:05:55.850 --> 03:06:12.020
Ayo Afolabi: Looking at this diagram on the left, so this group, who said no contribution in the micro grandpa ground for the human and also looking at the right and diagram for these stigmas Tara will notice the rosella here.

1047
03:06:13.130 --> 03:06:24.890
Ayo Afolabi: Are the Ad concentration and the side compared to others, we also a loop for the cholesterol and the coolest to know as well we noticed for the cholesterol.

1048
03:06:25.430 --> 03:06:33.350
Ayo Afolabi: The dingo and dog like like I showed diagram of the dingo dingo it looks more like a dog, and I believe this is due to.

1049
03:06:33.740 --> 03:06:41.780
Ayo Afolabi: The eating, they are eating patented diet, maybe they could the consumer of fatty tissue that laid out both calista in.

1050
03:06:42.650 --> 03:07:00.680
Ayo Afolabi: India, the face, I also for the coolest no the dog, and there are times, where I our Constitution compared to order We also notice in Madrid concentration of cholesterol in College as well, although i'll come back to this diagram later.

1051
03:07:02.360 --> 03:07:23.060
Ayo Afolabi: Also for the capacity zero the rosella for this as a high concentration compared to all the analysis and also for for the capitalist and on the right hand side we noticed the air during to on the end during the war and the cat faces as a higher concentration as well.

1052
03:07:24.740 --> 03:07:39.170
Ayo Afolabi: For the fifth Kara do this as a higher concentration compared to all those white on the right hand side for the system standard the sheep feces arrington puzzle the rv our concentration as well.

1053
03:07:40.790 --> 03:07:56.900
Ayo Afolabi: From our result we did he Eric who taught string and I saw shows that the Monday with water in flint the arrow Sir or sell offices and cow, so I asked and formed a world to find cluster.

1054
03:07:58.340 --> 03:08:11.120
Ayo Afolabi: We use the three s's that does a within some square metre to come to come put up tomato stout value which indicate for this method measures ECON partners of the clustering.

1055
03:08:11.780 --> 03:08:21.740
Ayo Afolabi: This indicator for, but we are not concerned much about this at the moment because we want to take a deep look into the subs clusters in our diagram.

1056
03:08:23.240 --> 03:08:33.800
Ayo Afolabi: Also, we we we plot that a diagram which shows the relationship between the animal feces and the St Sarah composition.

1057
03:08:35.090 --> 03:08:51.950
Ayo Afolabi: On the right hand side This shows the animal surfaces and on on the eggs other issues is still composition, taken a look at this diagram when we look at this column on the left on that purpose to know.

1058
03:08:53.720 --> 03:08:59.240
Ayo Afolabi: Checking the cluster cluster will see from this, from this point.

1059
03:09:00.500 --> 03:09:12.680
Ayo Afolabi: and looking at the legend it indicates a high concentration of group boosted, though, and I think that might have indicated why the man and the woman and.

1060
03:09:13.160 --> 03:09:29.420
Ayo Afolabi: The human faces for a well defined cluster and also for the rosella well I concentration of sister estero looking at this column, and the stigma said, all those concentration I produced error, they I.

1061
03:09:30.470 --> 03:09:50.990
Ayo Afolabi: Am also when you look for the cow as well, looking at this column this third and fourth column on the group Astana as the master know it, for it, as he is consecration Christian and that's accounted for why the cow faces form a well defined structure as well.

1062
03:09:52.790 --> 03:10:05.210
Ayo Afolabi: So the financial some of the shows were defined cluster for women with what if there is a law offices and confesses, to a large extent, so in this diagram we took we took a look at the sub cluster.

1063
03:10:06.050 --> 03:10:25.040
Ayo Afolabi: In this diagram it indicates the woman from a well defined structure we also see our influence from the world defined cluster as well, and also for the cows, a bit, although we have a few outliers in their results so far for for this sample and this sample.

1064
03:10:27.590 --> 03:10:38.660
Ayo Afolabi: foot outside this cluster mother is a work in progress for as more time is collected, it makes this model become more robust outlier we cannot delete any outliers.

1065
03:10:39.080 --> 03:10:49.100
Ayo Afolabi: On to we have more data to validate the clustering method so said this cluster structure can be successfully deployed for us in tracking pickup pollutions of.

1066
03:10:50.180 --> 03:10:56.540
Ayo Afolabi: So this is a part of their Francis so thank you for listening I don't know whether you have any questions.

1067
03:11:01.640 --> 03:11:07.100
Yuehan Lu: Great talker you maddy Thank you, we do have time for questions any questions.

1068
03:11:17.510 --> 03:11:20.030
Yuehan Lu: I can open up.

1069
03:11:21.200 --> 03:11:41.990
Yuehan Lu: While other people may be thinking about this so great talking combining geochemistry data to some of the machine learning methods so my question is that also I noticed many of y'all end Members are how a fairly low sample size.

1070
03:11:43.070 --> 03:11:56.720
Yuehan Lu: You have a lot of Members, you have if I read the grammar diagram correctly have when out to so how did that influence your model and interpretation.

1071
03:11:56.750 --> 03:11:58.700
Ayo Afolabi: Good it clear repeat the question again.

1072
03:11:59.330 --> 03:12:04.160
Yuehan Lu: So you have a lot of n Members for your.

1073
03:12:04.910 --> 03:12:07.790
Yuehan Lu: People their compensation right.

1074
03:12:08.180 --> 03:12:08.570
yeah.

1075
03:12:10.040 --> 03:12:10.580
Yuehan Lu: Then.

1076
03:12:11.810 --> 03:12:32.450
Yuehan Lu: I noticed, you have a relatively small sample size for each member so i'm just wondering, because sometimes, for example, cholesterol, which is a universal arrow depending on a died or particular person or particular animal that could influence their compensation significantly.

1077
03:12:33.320 --> 03:12:43.010
Yuehan Lu: Here is a relatively low sample size or each member how that would influence your interpretation and your mother.

1078
03:12:43.580 --> 03:12:49.970
Ayo Afolabi: yeah I, I believe, is josie ratio of these two composition.

1079
03:12:51.200 --> 03:12:55.610
Ayo Afolabi: The laws, the Middleton that affected the cluster.

1080
03:13:00.170 --> 03:13:16.460
Yuehan Lu: yeah I agree a great thing the ratios would work better than absolute concentration right producing some of the ratios could also very with died and the fate different have died within when group of animal.

1081
03:13:19.310 --> 03:13:20.390
Ayo Afolabi: Good this enough again.

1082
03:13:21.770 --> 03:13:28.040
Yuehan Lu: So, depending on the died right or the organism that you could.

1083
03:13:28.340 --> 03:13:29.750
Yuehan Lu: die yeah.

1084
03:13:29.990 --> 03:13:33.440
Yuehan Lu: I could influence some of the three color combinations right.

1085
03:13:33.710 --> 03:13:36.110
Ayo Afolabi: This there yeah definitely.

1086
03:13:38.750 --> 03:13:44.960
Ann Ojeda: yeah so um you and I think that's why we're seeing we're having to dive deeper into this cluster dingy Graham.

1087
03:13:45.650 --> 03:13:58.610
Ann Ojeda: to really get good separation between like a car cows are spread out over a couple of clusters and I think that's an artifact of where the paper originally came from, maybe different continents.

1088
03:13:59.870 --> 03:14:09.620
Ann Ojeda: Where your cows have different diets and so you end up you know we're seeing that effect pull out in our like poor resolution for cows in our ginger Graham.

1089
03:14:10.340 --> 03:14:20.000
Ann Ojeda: But I think I think all three of these humans are from three different papers so it's quite nice to see that they cluster together and the cows have may have more of a spread.

1090
03:14:21.620 --> 03:14:31.280
Ann Ojeda: Based on exactly what you're describing whether it's a local cow or you're taking a global view of a cow or a really highly localized view of a cow.

1091
03:14:36.410 --> 03:14:38.210
Yuehan Lu: Any other questions.

1092
03:14:55.610 --> 03:15:25.190
Yuehan Lu: Alright, so if there are no question that would be the conclusion of the second segment of this session, so we will take a break and come back at 1125 so we have a bad, we have a break or eight minutes so feel free to stay here for discussion and.

1093
03:15:26.300 --> 03:15:29.240
Yuehan Lu: we'll continue with the poster section.

1094
03:15:29.450 --> 03:15:30.770
Yuehan Lu: with Dr Lee.

1095
03:15:31.160 --> 03:15:32.900
Yuehan Lu: At 1125.

1096
03:15:34.490 --> 03:15:35.120
Yuehan Lu: Thank you.

1097
03:15:40.070 --> 03:15:40.610
chris.pruneau: Thank you.

1098
03:16:23.600 --> 03:16:25.190
Nivanthi Mihindukulasooriya: hello, and you there.

1099
03:16:28.220 --> 03:16:30.440
Ann Ojeda: Oh i'm going to keep sharing this i'm sorry I am here.

1100
03:16:31.190 --> 03:16:36.860
Nivanthi Mihindukulasooriya: Okay i'm curious to know what what kind of research you're planning to do with them.

1101
03:16:37.940 --> 03:16:39.470
Nivanthi Mihindukulasooriya: chlorophyll algal blooms.

1102
03:16:40.280 --> 03:16:44.840
Ann Ojeda: So we are doing i'm not doing chlorophyll i'm not gonna lie.

1103
03:16:45.740 --> 03:16:46.190
Nivanthi Mihindukulasooriya: And then.

1104
03:16:46.640 --> 03:16:49.880
Ann Ojeda: There is Edna so i'm i'm in a kind of a.

1105
03:16:51.470 --> 03:16:53.420
Ann Ojeda: One, let me check that participants here.

1106
03:16:55.910 --> 03:17:09.380
Ann Ojeda: No, well, we have a couple of projects, one is using drone imagery to assess water quality, and so a lot of these spectral features, you know even some of these reflectance measurements, we can get from our.

1107
03:17:10.400 --> 03:17:24.560
Ann Ojeda: drone spectral data and then we're correlating that to in stream parameters, and so one of the sensors that we are using is an F dumb and turbid and things like that, because those are the most the the low hanging fruit for our.

1108
03:17:26.090 --> 03:17:28.190
Ann Ojeda: Our sensor or the drone data.

1109
03:17:29.390 --> 03:17:34.670
Ann Ojeda: But even just me conceptualizing what our.

1110
03:17:37.010 --> 03:17:42.920
Ann Ojeda: What our project is going back to these nc two sensors I know there's a lot of variation and a lot of.

1111
03:17:45.560 --> 03:17:57.110
Ann Ojeda: confounders for the measurements, you know, especially that chlorophyll and you know, like Natalia presented over binding of iron to do him and that's going.

1112
03:17:57.110 --> 03:17:57.350
Nivanthi Mihindukulasooriya: To.

1113
03:17:57.680 --> 03:18:02.120
Ann Ojeda: your ability to measure fluorescence of your deal with him.

1114
03:18:02.510 --> 03:18:08.270
Ann Ojeda: Because you have his quenching or you have movement of your deal when along that spectrum so.

1115
03:18:09.470 --> 03:18:19.670
Ann Ojeda: yeah I haven't, yet we don't have any field data yet we're still trying to figure out when we deploy a sensor what what actual information are we getting you.

1116
03:18:19.940 --> 03:18:29.870
Nivanthi Mihindukulasooriya: play yes that's the that's true that's true you know if you're, especially if your water is like turbid is is it a bit.

1117
03:18:30.350 --> 03:18:40.520
Ann Ojeda: Yes, we're working in a couple of impaired water bodies here in Alabama and we have a lot of sediment erosion very, very I mean they're orange waters essentially.

1118
03:18:40.760 --> 03:18:50.780
Nivanthi Mihindukulasooriya: Oh whoa yeah yeah I wonder if they have like a correction factor that we that you can use for the for the liability.

1119
03:18:51.560 --> 03:18:53.330
Ann Ojeda: Yes, we that's what we're working on.

1120
03:18:54.440 --> 03:18:59.240
Ann Ojeda: there's a correction factor for iron concentrations and there's a Ford.

1121
03:19:00.530 --> 03:19:03.230
Ann Ojeda: F dumb sensor and then.

1122
03:19:04.460 --> 03:19:06.920
Ann Ojeda: there's correction factors for to ability as well.

1123
03:19:08.060 --> 03:19:11.450
Ann Ojeda: So hopefully that's reproducible but.

1124
03:19:12.410 --> 03:19:16.760
Nivanthi Mihindukulasooriya: yeah yeah, what do you, what do you use to what is the standard you used to.

1125
03:19:18.830 --> 03:19:24.590
Nivanthi Mihindukulasooriya: Come calibrate your chlorophyll sensory said for demean day are you using.

1126
03:19:25.880 --> 03:19:34.910
Ann Ojeda: So I I don't have one right I don't have a sense right now we haven't calibrated it and it's the, so I am buying the y si XO.

1127
03:19:35.390 --> 03:19:37.850
Ann Ojeda: So, so I think it is the road, I mean.

1128
03:19:38.930 --> 03:19:52.010
Nivanthi Mihindukulasooriya: yeah yeah that's that that's what I have, yes, but I don't know I am not hundred percent happy with it, but that's that's what everyone's using yeah we were like usgs uses it so yeah.

1129
03:19:53.060 --> 03:19:53.300
Nivanthi Mihindukulasooriya: yeah.

1130
03:19:53.660 --> 03:19:55.910
Ann Ojeda: I got the I got the same.

1131
03:19:57.740 --> 03:20:01.400
Ann Ojeda: kind of pitch from wi fi oh everybody's using it, and then you start digging into.

1132
03:20:01.760 --> 03:20:05.420
Ann Ojeda: Some of the applications and you're like Okay, maybe this is it.

1133
03:20:06.650 --> 03:20:14.090
Ann Ojeda: As constrained as it could be right, so I think one of our first goals is to reproduce them have an Italian experiments with the F, Dom sensor.

1134
03:20:14.510 --> 03:20:32.630
Ann Ojeda: As we have pretty well characterized offline measurements of our organic matter and iron complex ation so if we can do that with the sensor and understand right the correlations between art in stream parameters and then what we can do in the lab I think we're.

1135
03:20:34.250 --> 03:20:35.240
Ann Ojeda: will be at a better.

1136
03:20:36.650 --> 03:20:49.100
Ann Ojeda: A better a better place, but really I mean one of my big research topics is uncertainty in these measurements and always working in the lab is safer than working in the field.

1137
03:20:50.810 --> 03:20:51.320
Nivanthi Mihindukulasooriya: yeah.

1138
03:20:51.650 --> 03:20:52.430
Nivanthi Mihindukulasooriya: i'm really interested to.

1139
03:20:53.060 --> 03:20:57.290
Ann Ojeda: propagate that air through you know, a more like a PCA model.

1140
03:20:59.240 --> 03:21:00.860
Ann Ojeda: I think that's quite important.

1141
03:21:02.060 --> 03:21:04.880
Ann Ojeda: Because i'm you know i'm right there with you in the same.

1142
03:21:06.020 --> 03:21:07.490
Ann Ojeda: data analysis techniques.

1143
03:21:09.110 --> 03:21:13.820
Nivanthi Mihindukulasooriya: Oh yeah yeah that's an idea you're doing the principal component analysis.

1144
03:21:15.170 --> 03:21:23.480
Ann Ojeda: We do so actually Eo the last presentation he was working on a new type of analysis, typically we do PCA with our sterols.

1145
03:21:24.470 --> 03:21:25.520
Ann Ojeda: And we're trying to move.

1146
03:21:25.520 --> 03:21:28.190
Ann Ojeda: towards machine learning and some.

1147
03:21:29.840 --> 03:21:38.030
Ann Ojeda: Just different models for predicting concentrations of these contaminants in the environment but typically it's we use PCA.

1148
03:21:39.560 --> 03:21:53.000
Nivanthi Mihindukulasooriya: yeah yeah I love I love it, I mean like I love PCA, at the same time it's it can be like really confusing, what are you what are you looking at me like what what does that even mean like how much how big is this yeah.

1149
03:21:54.410 --> 03:21:54.860
Nivanthi Mihindukulasooriya: yeah.

1150
03:21:56.390 --> 03:21:59.330
Nivanthi Mihindukulasooriya: Nice yeah well nice to meet you and.

1151
03:21:59.900 --> 03:22:06.050
Ann Ojeda: it's great to meet you too, you know and feel free to reach out it sounds like our work is quite complimentary so.

1152
03:22:06.560 --> 03:22:08.450
Nivanthi Mihindukulasooriya: feel yes, that definitely.

1153
03:22:08.510 --> 03:22:16.070
Ann Ojeda: Yes, thank you especially if you run into some things that work or don't work with your F Thompson Sir I would love to hear.

1154
03:22:16.670 --> 03:22:23.300
Nivanthi Mihindukulasooriya: Yes, I will I will I will keep you posted I, are you considering buying one or do you take to see your lab has one.

1155
03:22:23.810 --> 03:22:31.310
Ann Ojeda: So I put the order into y si last week and they're going to ship us a DEMO of.

1156
03:22:33.050 --> 03:22:38.780
Ann Ojeda: F dumb for us to play with until it's shipped to us so i've already bit the bullet.

1157
03:22:39.680 --> 03:22:40.850
Nivanthi Mihindukulasooriya: that's nice yeah.

1158
03:22:42.140 --> 03:22:52.220
Nivanthi Mihindukulasooriya: it's nice yes yeah yeah Oh, I hope I hope we can get some fieldwork done this summer, because last last summer was was a waste waste of time.

1159
03:22:54.020 --> 03:22:54.350
Ann Ojeda: yeah.

1160
03:22:56.900 --> 03:22:59.990
Ann Ojeda: I know I agree, I have some students that really need data so.

1161
03:23:00.920 --> 03:23:02.840
Nivanthi Mihindukulasooriya: Yes, yeah definitely.

1162
03:23:06.170 --> 03:23:14.780
Ming-kuo Lee: I think is 1125 now so we're going to move on to the third part of our section.

1163
03:23:14.810 --> 03:23:30.950
Ming-kuo Lee: Today, then, focusing on why the resourcing GEO hills so we're going to have a Phi poster presentation with puppet range from arsenic equal a bacteria.

1164
03:23:31.760 --> 03:23:49.310
Ming-kuo Lee: and Michael Preston contaminants and so again the posters section with consists of five minutes recorded presentation, plus five minutes crushing and answer.

1165
03:23:51.440 --> 03:23:57.200
Ming-kuo Lee: And so the first poster presentation will be given.

1166
03:23:59.810 --> 03:24:12.470
Ming-kuo Lee: peyton governments, the title be developing a predictive model for taste in order episode integration or drinking water, raise.

1167
03:24:14.450 --> 03:24:15.230
Ming-kuo Lee: payton care.

1168
03:24:20.540 --> 03:24:22.820
Ann Ojeda: Something cool I think we're responsible for showing.

1169
03:24:22.820 --> 03:24:23.390
The.

1170
03:24:32.840 --> 03:24:35.720
Ming-kuo Lee: Everybody see the screen yes.

1171
03:24:42.980 --> 03:24:44.840
Ann Ojeda: Deposit for a second sure.

1172
03:24:47.510 --> 03:24:51.590
Ann Ojeda: So I do think um do you mind if I share my screen.

1173
03:24:52.010 --> 03:24:52.580
Ming-kuo Lee: yeah yeah.

1174
03:24:55.040 --> 03:24:55.370
Ming-kuo Lee: sure.

1175
03:24:56.840 --> 03:25:01.220
Ann Ojeda: Okay, so there's a button down when you share your screen that says share sound.

1176
03:25:02.840 --> 03:25:03.830
Ann Ojeda: That will.

1177
03:25:08.390 --> 03:25:12.890
Ann Ojeda: hi i'm painting goodling, and this is my research on does that work can everybody hear.

1178
03:25:13.220 --> 03:25:20.180
yeah okay development of a predictive model for taste in other episodes and regional drinking water reservoirs.

1179
03:25:21.110 --> 03:25:30.260
So, as I said, our episodes in reservoirs are an issue worldwide, and these episodes are caused primarily by high concentrations of JASMINE and am I be.

1180
03:25:30.830 --> 03:25:39.260
Which are musty odor compounds these compounds are not harmful though they do leave a bad taste in the customers mouths and cause distrust and complains to occur.

1181
03:25:40.100 --> 03:25:48.500
predictive modeling is the best way to determine better water quality management and the question is, when will the Jasmine and mid levels spike.

1182
03:25:50.810 --> 03:25:57.560
The knowledge gap and research is that models develop before us have typically focused on predicting algae outbreaks for taste and odor episodes.

1183
03:25:57.890 --> 03:26:01.490
When we know that only a small subset of algae actually produce these compounds.

1184
03:26:02.120 --> 03:26:12.380
Previous efforts to use parameters like chlorophyll a to predict outbreaks will only be successful and the reservoir is dominated by single tasting or producing algae.

1185
03:26:13.310 --> 03:26:21.170
We focused on finding the abundance of compounds and days enzymes, rather than the algae which might not even be the source of the compounds.

1186
03:26:21.680 --> 03:26:33.230
Because mid levels were under the threshold level during our sampling season, we did focus on JASMINE and the expected outcome was that the inclusion of sin face gene abundance will improve predictive models.

1187
03:26:34.250 --> 03:26:41.390
JASMINE is uncooperative toward traditional water treatment methods and the continually use of activated carbon is far too expensive.

1188
03:26:41.840 --> 03:26:52.490
We know that the compound can be produced by cyanobacteria I Tina bacteria bacteria and even some fun guy that we focus on the main contenders which are Sino and act, you know bacteria.

1189
03:26:53.180 --> 03:27:04.730
Humans are highly sensitive to these compounds able to detect them at 10 nanograms per liter or 0.01 parts per billion and we attribute this smell and taste, to the smell of rain.

1190
03:27:06.500 --> 03:27:17.750
For our locations in this research, we took samples from three different lakes and the Alabama and Georgia areas that are each used by city water utilities and we took samples over the spring summer and fall season.

1191
03:27:18.920 --> 03:27:28.910
So we then embarked on beginning to create the model using molecular data so promise of the tools that we use to target a specific gene like JASMINE seven days which produces the Jasmine.

1192
03:27:29.510 --> 03:27:39.140
Existing primer tools for JASMINE and Mr B are not very well developed and there are many issues with nonspecific amplification or exclusion of important organisms by being too specific.

1193
03:27:39.920 --> 03:27:48.980
There are a few primer sets from previous literature shown in Figure three that we used and there's a range of taxa already known for JASMINE producers within cyanobacteria.

1194
03:27:49.820 --> 03:27:56.540
You want a primer that is not so specific that it loses coverage of a variety of organisms, but you also don't want to nonspecific primer.

1195
03:27:57.140 --> 03:28:18.350
You can see that the giglio led to significant nonspecific amplification the to Ada said, lead to almost exclusive primary dimer complexes which diminishes its usefulness, the sts one show some promise for usefulness and the NGO shows non specificity also.

1196
03:28:19.550 --> 03:28:25.490
The Act do had good specificity, but a greater challenge amplifying at that higher kneeling temperature.

1197
03:28:26.900 --> 03:28:32.870
But our CEO primer that we developed shows good specificity, based on the gel only.

1198
03:28:33.620 --> 03:28:40.100
And this was across all reservoir sampled so we did integrate the CEO into the model because we did feel good about it.

1199
03:28:40.610 --> 03:28:52.760
We know that just because the gel shows the correct base pair length does not mean that it's the correct product, so we did sequence the PCR products from multiple samples across the water bodies with each of the primer sets.

1200
03:28:53.270 --> 03:28:59.390
qpr can predict abundance of JASMINE producers and sequencing that product can tell us who is likely, making it.

1201
03:29:00.290 --> 03:29:09.380
The giglio and to add a primer set show non specificity for cyanobacteria after sequencing and that included acting of bacteria and pretty good bacteria.

1202
03:29:10.250 --> 03:29:29.060
The act to primer set also should 30% incorrect product, but our cto primer set mapped 100% to cyanobacteria JASMINE synth bass, which you can see in this figure to the left, while having great variation for further genius levels, etc, that we could track with the sequencing.

1203
03:29:30.350 --> 03:29:40.670
JASMINE and mid levels and all three reservoirs were low to moderate throughout 2020, with one exception, which was auburn and I peeked above the threshold only 20% of the time.

1204
03:29:41.090 --> 03:29:49.640
which was still much more than open leica or Columbus locations did so, our best model was therefore auburn's with a carte model R squared.

1205
03:29:50.000 --> 03:29:57.410
Of 0.4 and separate multiple regression giving an R squared of point 4413 which is slightly higher.

1206
03:29:58.190 --> 03:30:06.230
The other models give very low are squares and even slightly improve without the use of the CG of abundance, which you can see in this figure to the right.

1207
03:30:07.010 --> 03:30:21.770
And this happens, because the levels rarely or never reach above the threshold level and, of course, the year that we study it is lower compared to the higher levels that we've seen in previous years, so we didn't need better variation and levels to build a model at all.

1208
03:30:23.210 --> 03:30:37.070
Overall, are developed CEO primer does have high specificity and the gene abundance measured with that primer in PCR shows some predictive power, especially with higher JASMINE levels like we see in the figure above with it right correlation.

1209
03:30:37.670 --> 03:30:48.710
The sequencing gives a great idea of who is making these compounds and this work is intended to lay the groundwork for broader use of predictions for taste in other episodes and water bodies regionally.

1210
03:30:59.120 --> 03:31:07.190
Ming-kuo Lee: We suppose have followed by five minutes questioning instance I don't think peyton's here today.

1211
03:31:13.400 --> 03:31:14.990
Ann Ojeda: I don't see her.

1212
03:31:21.740 --> 03:31:23.240
Ming-kuo Lee: So we we have.

1213
03:31:23.300 --> 03:31:24.320
Ming-kuo Lee: Two minutes.

1214
03:31:25.340 --> 03:31:25.850
Ming-kuo Lee: and

1215
03:31:27.470 --> 03:31:36.230
Ming-kuo Lee: So i'm going to go ahead to introduce the next speaker and and can help okay did he poster.

1216
03:31:38.780 --> 03:31:41.450
Ming-kuo Lee: Okay, so so next talk.

1217
03:31:42.560 --> 03:31:54.020
Ming-kuo Lee: will be given by my hero touch one and I here is a graduate student here the auburn and so he's going to talk about.

1218
03:31:54.920 --> 03:32:19.160
Ming-kuo Lee: Identification in special distribution of Michael press sticks in the coastal region of Cox Czar anger dash So this is the I think the first time today, we were here to a very specific contaminants on Michael plastics and.

1219
03:32:21.290 --> 03:32:25.550
Ming-kuo Lee: So, again yeah as going to show the.

1220
03:32:27.260 --> 03:32:28.520
Ming-kuo Lee: recorded presentation.

1221
03:32:32.510 --> 03:32:39.770
Hello everyone, welcome to our presentation that i'd love the presentation is identification specialization my capacity in the coastal region of courses wizard Bangladesh.

1222
03:32:42.500 --> 03:32:57.380
microprocessors small pieces of particle which are less than five millimeters in length and they are found in the sentiments of fresh water in my environment, the application might have plastic in our day to day life, despite Heidi to its durability lightweight and it being cheap.

1223
03:32:58.790 --> 03:33:14.360
The studio for research is the is in the Ocean region, of course, will reserve, which is a solid most just shoot of Bangladesh about doing T samples recollected from nine regions of the coastal region, which are covered and extent of 45 kilometres.

1224
03:33:16.520 --> 03:33:22.970
And it's just a field work was done in the month of February 2019 where the samples were collected from the nine regions.

1225
03:33:23.480 --> 03:33:33.980
These temples were used to do the laboratory analytical work which started with saving through which we discarded the verticals which were better than five millimeter in size.

1226
03:33:34.550 --> 03:33:52.130
Then we visually identify the my micro plastic from the sediment using the sterile microscopy using the scanning electron microscope there is on the elements, the nature and your surface morphology we reevaluate it tied in different micro plastic and then divided into five types.

1227
03:33:53.210 --> 03:34:04.700
Using the attenuated total reflectance are for your chance to inferred analyze this we are identified the presence of the type of the polymer which was done, based on their spectral range.

1228
03:34:05.750 --> 03:34:08.300
Relating it to the transmitters and absorbers.

1229
03:34:10.130 --> 03:34:29.120
Really, result in discussion visual edification has been done this on the analysis Trolls us still microscopy who were fiber fragment full form and beat has been identified fiber comprised of the 70% of the total amount of my plastic identified.

1230
03:34:30.680 --> 03:34:36.110
identified micro places where, then you reevaluate it is using scanning electron microscope the process.

1231
03:34:36.830 --> 03:34:42.650
And, based on the elements signature and surface morphology it has been divided into microfiber.

1232
03:34:43.100 --> 03:34:55.070
which consisted of Ryan, an island round showed twisted lines, whereas an island shorts traitor lines microphone which are consisted of narrowed inflexible strip eloise ages.

1233
03:34:55.550 --> 03:35:10.070
microphone, which consisted of micro holes micro fragment which represented broken our ages and, lastly, my obituary circular in shape and exhibited rough surface and irregular edges.

1234
03:35:11.390 --> 03:35:19.070
Next up for you transmute infrared and education, which was done, based on the spectral range relating to the transmitter and observance.

1235
03:35:19.730 --> 03:35:31.850
Of the micro plastics and relating it to the database and using the software in our infrared solution we identify the type of the polymer President in each of the micro plastics that we did.

1236
03:35:33.260 --> 03:35:51.290
land use, lack of analysis was done of where the concentration or the extent of the amount of micro plastic in each of the region has been related to that particular region to show the total amount of concentration and related to the increasing industrialization and urbanization.

1237
03:35:53.630 --> 03:36:01.130
Special distribution has been done, where the amount or the extent of the total number of my progress has been shown in each of the region.

1238
03:36:01.940 --> 03:36:13.730
or then special distribution, the type of the micro plus the five test of the classic has been shown and done, also the special distribution of the types of the polymer detected each of the region has been.

1239
03:36:14.210 --> 03:36:25.700
done using archers and also the special distribution, that is a categorical distribution has been done, which has been shown to the bar plot that has been done using statistical.

1240
03:36:26.840 --> 03:36:34.370
To conclude, about seven at micro plastics were detected from the 20 sentiments samples, which are collected from the nine regions.

1241
03:36:34.910 --> 03:36:47.300
Five type of my projects were detected, namely fiber fragment phone phone and did fibers micro plastics accounted for about 70% of the total detected micro plastics.

1242
03:36:48.380 --> 03:36:53.420
mm the digital polymers Ryan and polyethylene were the highest.

1243
03:36:54.350 --> 03:37:04.970
Micro plastic concentration is the highest in the region of the lovely point and color to the region, which are the tourist spot of the cox's buzzer.

1244
03:37:05.360 --> 03:37:14.270
And, as a result of which the industrialization is also rising that particular region, whereas the amount of Microbiology is the lowest in the region of portal.

1245
03:37:14.780 --> 03:37:20.720
Where there is no tourism or there's literally industrialization, so there is clearly.

1246
03:37:21.380 --> 03:37:34.280
visible or there's a clear relationship between the tourism industrialization urbanization to that of the concentration of the micro plastic that has been detected in each of the selected samples of each of the regions, thank you.

1247
03:37:39.950 --> 03:37:47.030
Ming-kuo Lee: Thank you, my heel pretty nice presentation and you don't so I understand this is your.

1248
03:37:48.440 --> 03:37:51.680
Ming-kuo Lee: thesis work back in Bangladesh.

1249
03:37:51.770 --> 03:37:52.370
Mahir Tajwar: Ah yes.

1250
03:37:53.480 --> 03:38:03.320
Ming-kuo Lee: Very good, and so we have a question from the floor, we think we have a lot of time for a lot of questions.

1251
03:38:12.980 --> 03:38:16.610
Ming-kuo Lee: So let me open up question, I think.

1252
03:38:17.810 --> 03:38:19.070
Ming-kuo Lee: No audience can.

1253
03:38:21.080 --> 03:38:23.300
Ming-kuo Lee: Think about your question, so my question is.

1254
03:38:26.900 --> 03:38:28.160
Ming-kuo Lee: So what's the fate.

1255
03:38:29.300 --> 03:38:36.020
Ming-kuo Lee: and transport last minute Michael Preston I know this is cloud new type of container.

1256
03:38:37.190 --> 03:38:45.740
Ming-kuo Lee: And what's the health risk or those terrible contaminant to the humans, could you comment on that.

1257
03:38:46.070 --> 03:38:54.260
Mahir Tajwar: Ah da contamination is rising, and the risk of micro plastic is huge, not only for the man or the aneurysm but also for the human.

1258
03:38:55.370 --> 03:39:08.810
Mahir Tajwar: When it comes to the man organism micro plastics, are those are there have been researchers who are my professors have been found in man animals, especially fishes and the animals, the other organisms that we even consume.

1259
03:39:09.380 --> 03:39:15.980
Mahir Tajwar: So microbiologist can be ingested but cannot be digested so it has been found that are due to.

1260
03:39:16.340 --> 03:39:29.630
Mahir Tajwar: A certification, the enemy, are the Marion organisms have been dying and it has all do from other researchers found that macrobiotic also can cause harm in the reproductive soul.

1261
03:39:30.020 --> 03:39:52.490
Mahir Tajwar: And in the industry and as as well, not only for the organisms, but for also humans and it had and the researchers has also lead to the conclusion that, and it can cause of obesity and also can result in cancer when if nano plastics get into the tissue of the humans.

1262
03:39:55.040 --> 03:40:16.490
Ming-kuo Lee: So, in terms of distance of transportation so where those Michael press deposit on a beach is when you close the soaps, is there right or they can be actually transported long distance by a rebirth long show current so what's the nature of physical.

1263
03:40:16.520 --> 03:40:22.640
Mahir Tajwar: Transportation So are we really didn't work really relating to the transportation, our main.

1264
03:40:23.210 --> 03:40:31.850
Mahir Tajwar: Emphasis was identification, because no such word plays into the identification micro plastic had been done in the country of Bangladesh, this was actually the first initial work.

1265
03:40:32.390 --> 03:40:39.080
Mahir Tajwar: Read into the micro plastics, so our main idea was to identify macro plastic and surely special distribution to give for the idea that.

1266
03:40:39.410 --> 03:40:48.800
Mahir Tajwar: There is micro restaurant ammunition and is rising in the region where tourism is rising as well, and as a result of which we also try to give the polymer.

1267
03:40:49.460 --> 03:40:58.640
Mahir Tajwar: By showing this picture range which the polymer can be related to the particular source and that can be through chemical analysis can be the polymer can be utilized to identify.

1268
03:40:58.940 --> 03:41:12.680
Mahir Tajwar: The directed my progress sick to death of the source, from which it might have originated, but we didn't really give get into the chemical analysis, we only concentrated on the identification and especially submission LM the total range of the poster region.

1269
03:41:14.660 --> 03:41:15.110
Thank you.

1270
03:41:17.240 --> 03:41:17.480
Ming-kuo Lee: you're.

1271
03:41:17.990 --> 03:41:19.550
Ming-kuo Lee: Welcome okay.

1272
03:41:20.000 --> 03:41:31.910
Yuehan Lu: Great now, so the first question, just to clarify, did you do any type of procedure, just to remove organic matter, the natural grandmother from the samples.

1273
03:41:32.840 --> 03:41:46.160
Mahir Tajwar: are no I did not apply any sort of specific mental to remove the organic matter, but after receiving the industrial microscopy I visually identified and collected each of the micro plastics, based on the.

1274
03:41:46.490 --> 03:42:02.900
Mahir Tajwar: Protocol of the CEO of the beach sediment Sam my plastic sampling method that is already present and based on that, based on the ship size, I identified each of the micro plastic visually and manually from the sediments using this term microscopy.

1275
03:42:03.890 --> 03:42:19.370
Mahir Tajwar: We at zoom of 35 times and then again reevaluated each of those PLA detector samples because there might be other so again, we have limited a bit each of those based on this an electron microscopy analysis okay cool yeah.

1276
03:42:19.460 --> 03:42:33.650
Yuehan Lu: So the reason i'm asking is because i'm wondering how much interference that could be caused by the natural gas, because some people do remove those before the micro plastics i'm not right.

1277
03:42:34.400 --> 03:42:41.090
Yuehan Lu: So if there are not too much interference from the National Bank matter then it's probably better to see what's that.

1278
03:42:41.660 --> 03:42:54.740
Yuehan Lu: So the next question I had was really quick, so you will concluding there's a correlation between my car plastics and the human activities So what are the proxies you use to matter human activities.

1279
03:42:55.580 --> 03:43:03.560
Mahir Tajwar: are basically the regions from all the regions from which I collected the samples out of that are there are three main reasons.

1280
03:43:04.100 --> 03:43:12.170
Mahir Tajwar: Firstly, love any point collectively and in any these two regions are the most known to respond in Bangladesh in the country, Bangladesh.

1281
03:43:12.410 --> 03:43:18.680
Mahir Tajwar: And these are the three regions from where the amount of my capacity has been found to be the highest so i'm giving this idea that.

1282
03:43:18.980 --> 03:43:25.640
Mahir Tajwar: In this region, the amount of Microsoft has been found to be the highest, whereas in the region of boredom, which is a beer and in the region of.

1283
03:43:25.880 --> 03:43:35.150
Mahir Tajwar: Our history, which is mostly visitation and notorious is found their dollar amount of micro policy is really low there so based on these abundance and distribution.

1284
03:43:35.390 --> 03:43:43.580
Mahir Tajwar: I am putting forward the idea that there is a relation between the tourism industrialization growing tourism industrial addition to that of the abundance of my blessing in that particular region.

1285
03:43:44.570 --> 03:43:47.030
Ming-kuo Lee: Okay, we need to thank you, thank you.

1286
03:43:47.060 --> 03:43:47.630
Mahir Tajwar: Thank you so much.

1287
03:43:48.590 --> 03:43:57.080
Ming-kuo Lee: We need to move on to the next poster will be presented by alicia Fisher and.

1288
03:43:58.070 --> 03:43:59.090
Ming-kuo Lee: So risha.

1289
03:43:59.360 --> 03:44:21.050
Ming-kuo Lee: graduate from auburn last year, and so this is largely her pieces presentation, and so the title will be for your in lab investigation Colorado asked me sequestration in biogenic pyro at the industrial side in Florida.

1290
03:44:25.670 --> 03:44:37.070
hi everyone i'm alicia Fisher and today i'll be presenting online master's thesis research regarding removing groundwater arsenic, from an industrial site and Florida using biogenic pyrite.

1291
03:44:38.090 --> 03:44:45.860
Ming-kuo Lee: and stay tuned because this work is being submitted for publishing so quick overview about arsenic tam nation groundwater.

1292
03:44:46.490 --> 03:44:56.510
Ming-kuo Lee: Over 100 million people around the world, especially in developing regions and Bangladesh, India and China suffer from our silicosis or arsenic poisoning.

1293
03:44:57.140 --> 03:45:03.020
Ming-kuo Lee: And this is due to these communities, using the contaminated ground water for irrigation or for drinking water.

1294
03:45:03.890 --> 03:45:12.260
Ming-kuo Lee: And because many of the affected communities live in developing regions they lack the resources needed to remove this groundwater arsenic.

1295
03:45:12.830 --> 03:45:22.670
Ming-kuo Lee: buster is an extreme need to develop cheap and quick remediation techniques what promising method was proposed by solders at all in 1996.

1296
03:45:23.240 --> 03:45:36.410
Ming-kuo Lee: Where they suggested that injecting iron sulfate mixture into an offer system would bio stimulate the sulfate producing bacteria create reducing conditions and precipitate pyrite.

1297
03:45:37.340 --> 03:45:46.250
Ming-kuo Lee: This is important because our snake has a high affinity for pirate and will be effectively sequestered onto pyrite and removed from the aqueous system.

1298
03:45:46.880 --> 03:45:55.250
Ming-kuo Lee: So using this idea we wanted to develop a new remediation technique and sequester arsenic on our city and pyrite.

1299
03:45:56.030 --> 03:46:01.550
Ming-kuo Lee: for a longer period of time, and by longer period i'm talking several months so over nine months.

1300
03:46:02.180 --> 03:46:12.020
Ming-kuo Lee: To do this we developed a new mixture using rss feed and molasses, and this will ask this is key in adding in organic source of carbon for the bacteria.

1301
03:46:12.980 --> 03:46:20.420
Ming-kuo Lee: So we inject this mixture into 11 upgraded injection wells on site and then monitor the flow of the injecting down gradient.

1302
03:46:21.230 --> 03:46:30.170
Ming-kuo Lee: We collected groundwater samples and analyze them and the precipitated by a minerals using X Ray diffraction efflorescence.

1303
03:46:31.100 --> 03:46:41.630
Ming-kuo Lee: We also use an electron Michael probe to quantify the arsenic week percentage, as well as a scanning electron microscope to confirm the existence of our city in Paris.

1304
03:46:42.620 --> 03:46:59.120
Ming-kuo Lee: So going on to the results, looking at figures, three and four, we saw that within one week of these down gradient monitoring was receiving the injection we saw Spikes in iron and sulfur as expected, as well as a simultaneous decrease in arsenic.

1305
03:47:00.140 --> 03:47:19.130
Importantly, this arsenic reduction was significant and most of the town gradient monitoring wells, where it was reduced to at or below the site remediation standard, which is an old EPA standard of 0.05 milligrams per liter so that was very encouraging looking at figure five.

1306
03:47:20.300 --> 03:47:26.090
We see that before the injection the arsenic plume was centered in the Northwest portion of the site.

1307
03:47:26.660 --> 03:47:41.450
And then fast forward nine months after the injection we see that this flu has significantly reduced in size and intensity, we also see a higher percentage of these green colors that represent meeting the safe arsenic standard.

1308
03:47:42.590 --> 03:48:01.730
However, we also see that arsenic levels are still high in some areas, represented by the yellow and orange colors we found this was due to an influx oxidizing groundwater on site after three months this destabilize the arsenic and pyrite and rereleased arsenic back and thought for.

1309
03:48:02.780 --> 03:48:10.400
Looking at figures, six and seven we confirmed that are Simeon parade is the dominant mineral that is forming as sequestering arsenic.

1310
03:48:11.840 --> 03:48:23.150
and looking at figure eight we have some representative pictures of pyrite as well as our city and pirate that forums as fanboys which are these new Crystal and spheres of high right.

1311
03:48:24.050 --> 03:48:32.420
We saw that, over the course of the nine months these frat boys increase the proportion in the samples, which was also very encouraging.

1312
03:48:33.470 --> 03:48:44.540
So overall this remediation technique was very effective and quickly, reducing arsenic levels on site and most of the down gradient monitoring wells.

1313
03:48:45.440 --> 03:49:02.000
However, we would recommend repeated injections of mixture to maintain reducing conditions and keep the arsenic in pirate stabilized for several months to over a year, so that is my presentation, I thank you all for listening and i'll take any questions at this time.

1314
03:49:07.910 --> 03:49:11.840
Ming-kuo Lee: So we have time for several question for alicia.

1315
03:49:29.510 --> 03:49:40.310
Ann Ojeda: hey Lisa I have a quick question, and this is a question that I will totally give credit to our environmental geology students, when you let us do some of your data in the class.

1316
03:49:41.420 --> 03:49:48.950
Ann Ojeda: yeah we came we all are analyzing this data it's fabulous and one of the questions that was asked is.

1317
03:49:50.630 --> 03:50:07.160
Ann Ojeda: How confident, are you or what experiments, would you perform to differentiate between sword arsenic onto your pyrite or actual our city and party right where our snake is included, you know in that mineral matrix.

1318
03:50:08.930 --> 03:50:18.740
Alicia Fischer: So I say we are pretty confident that our snake is absorbed to the pyrite through our X 30 an extra analyses.

1319
03:50:19.280 --> 03:50:34.250
Alicia Fischer: And with the electron micro we could analyze look at the microscopic level and see how arsenic was integrated into the pyrite heterogeneous Lee not homogeneous Lee so different pinpoints you could be.

1320
03:50:35.360 --> 03:50:40.730
Alicia Fischer: particles of arsenic in the iron and sulfur matrix.

1321
03:50:43.250 --> 03:50:46.160
Ann Ojeda: Great so you think it's both sword and incorporated.

1322
03:50:47.060 --> 03:50:48.920
Alicia Fischer: From what it looked like yes.

1323
03:50:50.480 --> 03:50:51.380
Ann Ojeda: Great Thank you.

1324
03:50:56.840 --> 03:51:18.980
Ming-kuo Lee: So this year, could you also comment on the long term stability of pirates, especially when the water table change because of the climate condition, you may have extended period of droughts and you stand the ramp for how's it going to affect stability of.

1325
03:51:20.420 --> 03:51:21.680
Ming-kuo Lee: Those any parents.

1326
03:51:22.220 --> 03:51:29.780
Alicia Fischer: yeah so we saw through there was a hurricane that came through during part of the study and it.

1327
03:51:30.860 --> 03:51:39.050
Alicia Fischer: Oh, there was a lot of precipitation and so more oxidizing water was introduced into the offer first since it's pretty shallow.

1328
03:51:39.620 --> 03:51:55.280
Alicia Fischer: And we saw that this be stabilized some of the pyrite after three six months, in addition to the influx of oxidizing groundwater from outside the site, so there was some destabilize station there.

1329
03:51:59.750 --> 03:52:04.220
Ming-kuo Lee: think we have maybe time for one more quick questions.

1330
03:52:14.420 --> 03:52:20.420
Ming-kuo Lee: If not, thank you so much alicia excellent presentation.

1331
03:52:20.750 --> 03:52:21.830
Alicia Fischer: Thank you for having me.

1332
03:52:22.460 --> 03:52:24.920
Ming-kuo Lee: I know you're way way too busy with your new job.

1333
03:52:26.390 --> 03:52:26.930
Alicia Fischer: Thank you.

1334
03:52:29.360 --> 03:52:34.280
Ming-kuo Lee: So with that i'm going to introduce the next speaker.

1335
03:52:36.530 --> 03:52:37.640
Ming-kuo Lee: You are lost and.

1336
03:52:38.870 --> 03:52:44.240
Ming-kuo Lee: And she's working with cattle hagar and she's also going to talk about.

1337
03:52:45.650 --> 03:52:53.870
Ming-kuo Lee: And so he's going to talk about the rising concentration of eco like bacteria in Chicago called creek and.

1338
03:52:55.190 --> 03:52:56.960
Ming-kuo Lee: Oh yeah we see.

1339
03:52:58.130 --> 03:52:58.760
Ming-kuo Lee: grady know.

1340
03:53:02.210 --> 03:53:08.900
afternoon, my name is on our lesson and I will be presenting some preliminary data on Nicola instability and discharge interval have a.

1341
03:53:10.070 --> 03:53:18.290
turtle creek runs through the counties of calhoun and talladega and Eastern Alabama, it is a tributary to the coosa river, which is a major river in Alabama.

1342
03:53:18.590 --> 03:53:23.150
And it has the 3D listed in pair of water body due to high levels are the whole on.

1343
03:53:23.900 --> 03:53:31.430
A volunteer organization called the coosa river keepers have the monitoring water quality, since 2015 throughout the upper middle and lower trickle of a creek.

1344
03:53:32.150 --> 03:53:45.140
Data from this organization shows that in 2013 about 27% of their samples exceeded the EPA limit for equal a concentration and then five years later, in 2020 50% of their samples exceeded the limit.

1345
03:53:45.800 --> 03:53:56.690
The high concentrations of the coin and the creek the health and safety of the Community at risk and pose a threat or not only the state of Alabama but it's a problem across the country.

1346
03:53:57.860 --> 03:54:07.790
Ming-kuo Lee: figure one is a map of the chocolate concrete watershed data was collected across the creek and the map, shows the nine sample sites that have been established by the coosa river keepers.

1347
03:54:08.480 --> 03:54:15.920
Ming-kuo Lee: The Center graph figure two shows the box and whisker plots for log transform Nikolai concentrations at each of our sites.

1348
03:54:16.370 --> 03:54:24.980
Ming-kuo Lee: site one is for this downstream here the confluence of the coosa river insight nine is for this upstream and the talladega national forest.

1349
03:54:25.550 --> 03:54:32.870
Ming-kuo Lee: We observe the highest Nikolai concentrations in the middle choke logo with maximum concentrations of around 1000 mtn.

1350
03:54:33.620 --> 03:54:40.400
Ming-kuo Lee: From the back and figure one you can observe the high density of residential septic tank systems, those are the little green dots.

1351
03:54:40.880 --> 03:54:47.960
Ming-kuo Lee: And we know that there are two wastewater treatment plants discharging effluent directly into Tricolor go around this area as well.

1352
03:54:48.860 --> 03:55:01.520
Ming-kuo Lee: So we ran some statistical spearman correlations to explore the relationships between Nikolai and several water quality parameters using data that we already got from the River keepers and from the usgs.

1353
03:55:02.390 --> 03:55:12.140
Ming-kuo Lee: table one, it shows our results of these correlations we found three statistically significant relationships of this data there's a positive correlation between interpretive Nikolai.

1354
03:55:12.560 --> 03:55:17.150
Ming-kuo Lee: And then, a negative correlation between both pH Nikolai and water temperature any Cola.

1355
03:55:17.900 --> 03:55:25.280
Ming-kuo Lee: For water temperature and we know a study done by Lawrence observed the opposite of what we found they found a positive relationship.

1356
03:55:25.640 --> 03:55:35.810
Ming-kuo Lee: Between water temperature and Nikolai but this could be due to their temperature range from 40 degrees Fahrenheit to 70 well our temperature range was 70 degrees to 80 degrees.

1357
03:55:37.190 --> 03:55:47.330
And although we see the statistically significant correlation between equal a pH the underlying relationship is difficult to unravel because of how I can tolerate a wide range of growth conditions.

1358
03:55:48.020 --> 03:55:56.840
And finally, the positive relationship we observed between 30 and Nikolai is supported by previous studies, with over 1000 data points.

1359
03:55:58.190 --> 03:56:11.510
This is paper by Lawrence and then money at all similarly establishes that higher density increases erosion of sediment and suspense equal and the water column, which ultimately increases for equality concentration.

1360
03:56:12.410 --> 03:56:19.880
So looking more closely at this particular relationship between coins divinity We found that a segmented or hinge progression.

1361
03:56:20.210 --> 03:56:23.960
is more appropriate to describe the data than a traditional linear regression.

1362
03:56:24.410 --> 03:56:32.510
Figure three illustrates to relationships that we have we've got the negative relationship with a colleague concentrations that are less than 10 to the one and can.

1363
03:56:33.020 --> 03:56:44.390
In a positive relationship that you collect concentrations that are greater than 10 to the one NPs This shows us that ability as a controlling factor when you call it concentrations are greater than 10 to the one and.

1364
03:56:46.220 --> 03:56:57.170
Only our goal really is to understand the Ecole high concentrations that exceed the standards set by the EPA, and many of these sites consistently exceed the standard.

1365
03:56:57.890 --> 03:57:05.030
You can see on both of the graphs they are represented by the dotted line that thought and red line is the EPA standard.

1366
03:57:05.780 --> 03:57:15.710
So the statistically significant correlations we found will help guide our work as we tease out the relationship between a Co lie and the many other variables at play our natural system.

1367
03:57:16.340 --> 03:57:26.570
And the system very insignificant correlations, particularly for discharge show that we more high dimensional data to truly understand what's going on in these natural systems.

1368
03:57:27.080 --> 03:57:33.170
Our measurement should give us a clear picture of how we collect concentrations are influenced by natural water conditions.

1369
03:57:33.470 --> 03:57:45.020
And these relationships will be helpful when we use human and animal proxies to ultimately trace equal I contamination back to anthropogenic or natural sources Thank you so much.

1370
03:57:54.140 --> 03:58:04.790
Ming-kuo Lee: Thank you for a wonderful presentation began i'm going invite audience for Craig Craig any question for ya.

1371
03:58:10.580 --> 03:58:11.690
Yuehan Lu: Alright, so.

1372
03:58:13.010 --> 03:58:30.920
Yuehan Lu: My question is so first of all, so do you know what are the sauces over equal, I mean the watershed and also after the Poli that are transported to the water body, you know, Sir flashing the water, how long they will receive.

1373
03:58:32.180 --> 03:58:45.590
Ella Larson: yeah so we have a couple different possible sources, the first one would just be natural sources, so there are some cow farms in the area just you know regular wildlife.

1374
03:58:46.250 --> 03:58:57.740
Ella Larson: waterfowl or things like that of that nature so natural wildlife and then we are second sort of category is anthropogenic inputs or Nikolai and.

1375
03:58:58.280 --> 03:59:05.810
Ella Larson: i'm not sure how clear, it was in that, but we have to wastewater treatment plants that are discharging their effluent directly into the creek.

1376
03:59:06.470 --> 03:59:13.250
Ella Larson: In that region, and then we also have that really high density of the residential septic systems.

1377
03:59:13.970 --> 03:59:26.780
Ella Larson: In our sort of hypothesis going into this, is that a lot of these septic tank systems are probably older and maybe not properly functioning and so that could be a large source of Nikolai that hasn't been quantified yet.

1378
03:59:28.370 --> 03:59:44.870
Ella Larson: And then your second question, so we know that Nikolai in rivers and surface waters studies have shown that lives anywhere from 15 to 30 days approximately it depends on you know the, the body of water.

1379
03:59:45.890 --> 03:59:48.170
Ella Larson: But that's the sort of approximation for that.

1380
03:59:50.900 --> 03:59:59.810
Yuehan Lu: You know what are the primary water printers that would accountable, the persistence persistence that we call I.

1381
04:00:01.010 --> 04:00:01.400
Ella Larson: yeah.

1382
04:00:02.570 --> 04:00:03.110
Ella Larson: I think.

1383
04:00:04.280 --> 04:00:19.220
Ella Larson: You know, it seems like there's there's a good relationship between water temperature any cooler we saw that both with our data and with the Lawrence data somewhere right around 70 degrees Fahrenheit seems like a sweet spot for them.

1384
04:00:20.360 --> 04:00:30.140
Ella Larson: into it again is like a big controlling factor we think it's because it causes it erodes sediment that might be.

1385
04:00:30.710 --> 04:00:43.250
Ella Larson: containing some Nikolai that has settled there's been some work done to talk about naturalized Nikolai and in our water systems so that's kind of what we think might be the two major.

1386
04:00:44.360 --> 04:00:45.650
Ella Larson: Mark quality drivers.

1387
04:00:49.580 --> 04:00:59.390
Yuehan Lu: Also versus due to the light right, so their presence, do you think it could be also related to the canopy ios.

1388
04:01:01.280 --> 04:01:13.730
Ella Larson: yeah yeah that's a good point too um we have I haven't really read a bunch of research about that, but yeah light light would be a big factor to that might need to consider.

1389
04:01:15.230 --> 04:01:16.820
Yuehan Lu: Thank you yeah thanks.

1390
04:01:20.450 --> 04:01:27.650
Ming-kuo Lee: Well, you mentioned that a source of equal I could be either natural anthropogenic.

1391
04:01:28.700 --> 04:01:36.410
Ming-kuo Lee: The, how do you distinguish the source or equal what kind of approach you can use.

1392
04:01:36.920 --> 04:01:39.470
Ming-kuo Lee: Yes, nature, over and over again.

1393
04:01:40.550 --> 04:01:56.120
Ella Larson: So the two major pieces that we're going to measure, alongside the concentration are sterols so I know, there was a talk a previous talk on using the sterile profiles to.

1394
04:01:56.810 --> 04:02:07.730
Ella Larson: determine legal source, so you know look for corporate Stan all citrus all those types of things to give us indications are source and then also.

1395
04:02:08.480 --> 04:02:21.140
Ella Larson: pharmaceuticals so we're using that as sort of our our human proxy is, is there a strong correlation between high levels of you know acetaminophen and co concentration.

1396
04:02:22.190 --> 04:02:32.090
Ella Larson: Along with sort of we can use that that third piece of of spatial data right so like the proximity to wastewater treatment plants or.

1397
04:02:32.690 --> 04:02:39.380
Ella Larson: You know how how much septic tank system density is there in this particular region in this study site.

1398
04:02:40.070 --> 04:02:57.080
Ella Larson: So those pieces will kind of help us give us clues that that very most upstream site is in the talladega national forest and we're kind of thinking that that will provide our our natural wildlife piece of the data we're not sure yet, but that that's kind of what we're thinking.

1399
04:02:59.960 --> 04:03:02.510
Thank you yeah thanks your.

1400
04:03:03.950 --> 04:03:08.360
Ming-kuo Lee: Time as well, again, thank you are such excellent presentation.

1401
04:03:09.920 --> 04:03:12.230
Ming-kuo Lee: So we're going to move on to our last.

1402
04:03:12.500 --> 04:03:14.450
Ming-kuo Lee: poster presentation today.

1403
04:03:15.200 --> 04:03:17.510
Ming-kuo Lee: By carrying herons.

1404
04:03:18.980 --> 04:03:28.970
Ming-kuo Lee: Korea auto Hager, students and so she's going to talk about your chemical control to and possibly compress.

1405
04:03:32.180 --> 04:03:40.490
Ming-kuo Lee: hello, my name is caitlin heron and I will be presenting the preliminary data for my current project geochemical controls of arsenic deal and complexes.

1406
04:03:41.150 --> 04:03:52.010
Ming-kuo Lee: arsenic contamination occurs worldwide from natural and anthropogenic sources research suggests arsenic complexes with dissolved organic matter to form arsenic do em complexes.

1407
04:03:52.430 --> 04:04:01.550
Ming-kuo Lee: Understanding the influence of environmental conditions on arsenic deal on complexes will provide insights on arsenic cycling and transportation international systems.

1408
04:04:02.000 --> 04:04:06.410
Ming-kuo Lee: The simplified diagram on the bottom left hand side it's a great representation of D one.

1409
04:04:06.980 --> 04:04:16.580
Ming-kuo Lee: Do is extremely complex, because of the diversity of sources and mechanisms responsible for its formation, these three functional groups carb oxalic group.

1410
04:04:17.270 --> 04:04:34.880
Ming-kuo Lee: But knowledge hydroxyl group an alcoholic hydroxyl group have been identified as commonly found in deal one to circle back my research question is to what extent do environmental conditions impact complex station an overview of my methods shown in figure one.

1411
04:04:36.050 --> 04:04:47.330
We will use three different types of deal when these samples will be ghost with arsenic and have their pH and salinity adjusted and let for 24 hours to promote complex Asian.

1412
04:04:49.040 --> 04:04:56.000
These samples will then be fraction ended with filters and each direction will be analyzed for arsenic content with ICP most.

1413
04:04:56.300 --> 04:05:07.100
The observance will be measured in the biz and he will see will be used to categorize three major types of deals, including human gas in bold bold acid and political rights.

1414
04:05:07.970 --> 04:05:15.770
We will use three different fluorescence expectations expectations expectation in a mission wavelengths to do so.

1415
04:05:16.970 --> 04:05:25.070
We have three main hypotheses with these data, and I was playing each of these individually one we expect complications be highest with increasing.

1416
04:05:25.580 --> 04:05:40.460
pH and low slimming research from work at all barrel little have suggest this trend to we expect that arsenic dion complex ation is dependent a molecular characteristics figure to indicates changes of super.

1417
04:05:41.390 --> 04:05:50.270
Which is a proxy for a romantic city across a pH range of two through 10 two different types of DRM was shown in triangles representing.

1418
04:05:50.750 --> 04:05:53.480
dalit hills and black circles for hot springs.

1419
04:05:53.960 --> 04:06:05.300
As a reminder, deal is comprised of multiple functional groups, so the variation and super indicates the functional groups and other organic structures within the deal or mongo respond to changes and pH.

1420
04:06:05.840 --> 04:06:14.480
The silver measurement is significantly different between the two types of coal suggesting that the social group content is different between these two types of do one.

1421
04:06:15.080 --> 04:06:35.030
bit your three indicates to the to the pit to 54 on the water y axis and all do and types, with or without arsenic condition on the X axis arsenic condition is defined for this figure and 100 ppb arsenic, which was used in this experiment.

1422
04:06:36.800 --> 04:06:49.880
Each deal is close to, together with no arsenic, the black circle and arsenic addition measurement the open circle, we can see a slight decrease in scuba across all the one types comparing.

1423
04:06:51.410 --> 04:07:01.550
Know arsenic arsenic addition this decrease is not significant and we'll need further analysis across different pages limiting and arsenic concentrations to understand this trend.

1424
04:07:02.390 --> 04:07:11.960
Three weeks back the complex ation is dependent on molecular weight figure four through six represents size exclusion column fluorescent chromatographs.

1425
04:07:12.290 --> 04:07:22.040
Of all three major types of deal and figure for illustrates hot springs, one of the goals figure five is funny reverse osmosis and figure six is.

1426
04:07:22.580 --> 04:07:33.950
represents pond water, the y axis is fluorescence and time is on the X axis because we use the size exclusion column we gain insight on the molecular weights within dissolve are.

1427
04:07:35.030 --> 04:07:44.840
Following the arrow in figure for heavier larger molecular weights exit column column first lighter smaller molecular weights exit later.

1428
04:07:45.200 --> 04:07:53.600
by comparing the chromatographs, we can see different peaks at different times suggesting different molecular weights within each D one type.

1429
04:07:54.050 --> 04:08:00.710
By adding arsenic, we will be able to quantify complex ation through quenching or amplifying the fluorescence of a given p.

1430
04:08:01.250 --> 04:08:14.000
Therefore, providing information on compensation molecular weights responsible responsible for complex ation it overview data already suggest changes in sukkah with pH and arsenic.

1431
04:08:15.500 --> 04:08:29.990
Additional analysis will be completed at different pages and slummy treatments to quantify changes and complex Asian an optical optical properties this data will be used to understand the relevance of laboratory predictions of arsenic do complex Asian.

1432
04:08:31.160 --> 04:08:31.580
Thank you.

1433
04:08:37.250 --> 04:08:54.470
Ming-kuo Lee: Thank you so much Kevin, we know that the conversation between trust and and organic matter could be a way to complicates it was very informative present presentation, thank you Kevin so again we're going to open the floor to questions from the audience.

1434
04:09:06.590 --> 04:09:07.430
Ming-kuo Lee: So Kevin let me.

1435
04:09:09.410 --> 04:09:28.730
Ming-kuo Lee: ask a question about your ICP Ms analysis, are you just going to focus on the inorganic arsenic or actually you're going to look at a speciation if you guys see PMs can be coupled with HP oC.

1436
04:09:32.990 --> 04:09:42.410
Caitlyn Herron: unmute myself yeah um I think initially we're going to look at the total arsenic i'm i'm really interested to see.

1437
04:09:43.550 --> 04:09:47.330
Caitlyn Herron: If there is some speciation between the.

1438
04:09:48.410 --> 04:10:00.380
Caitlyn Herron: arsenic three and arsenic five, and I think the biggest constraint with doing that might be monetary one so initially I think we're going to.

1439
04:10:02.000 --> 04:10:03.410
Caitlyn Herron: Do total and then.

1440
04:10:05.030 --> 04:10:20.330
Caitlyn Herron: Do a few samples across different concentration and P trains to see maybe a few to see if there's a huge variation and if it's worth spending the time and money to do further analysis of that.

1441
04:10:22.670 --> 04:10:26.390
Ming-kuo Lee: Thank you, the reason i'm asking is that actually.

1442
04:10:27.650 --> 04:10:32.450
Ming-kuo Lee: alicia fisher's feel sites, we have a lot of organic.

1443
04:10:33.800 --> 04:10:42.680
Ming-kuo Lee: acid organic compress it was natural sample I know that is difficult to study natural sample very complicated.

1444
04:10:44.150 --> 04:10:52.130
Ming-kuo Lee: I know, most of your lab is under will control condition, you know exactly a concentration of do in.

1445
04:10:52.910 --> 04:10:55.400
Ming-kuo Lee: That case you're interested in looking at.

1446
04:10:55.910 --> 04:10:58.850
Ming-kuo Lee: Natural sample we do have a site, we can.

1447
04:11:00.410 --> 04:11:00.890
Ming-kuo Lee: Also.

1448
04:11:02.180 --> 04:11:13.850
Caitlyn Herron: yeah we don't predict that much variation so I didn't include it the beginning but I actually use geochemist workbench with different pH and salami ranges to see how the oxidation.

1449
04:11:14.240 --> 04:11:23.120
Caitlyn Herron: State actually changes with using them and I don't see too much variation and I didn't stay in my poster but.

1450
04:11:24.200 --> 04:11:34.580
Caitlyn Herron: I have a the arsenic that we're using is a type of arsenic salt that arsenic and it's an arsenic three and the reason why we did this is.

1451
04:11:35.090 --> 04:11:47.090
Caitlyn Herron: Certain papers and I think it was Van Gogh or law rule and they found that in sediments predominantly the form of arsenic and.

1452
04:11:47.720 --> 04:12:03.290
Caitlyn Herron: The aquatic Center settlements is usually arsenic three so we're trying to really focus in on that because I think it will best represent when we go out on the field and see arsenic do complexes and we predict looking at their data that it's going to be arsenic three.

1453
04:12:04.730 --> 04:12:05.660
Caitlyn Herron: I don't know if that.

1454
04:12:05.990 --> 04:12:06.560
Ming-kuo Lee: yeah well.

1455
04:12:06.620 --> 04:12:08.780
Caitlyn Herron: Further answer answer your question.

1456
04:12:11.510 --> 04:12:14.090
Ann Ojeda: And you're also dosing these with arsenic three right.

1457
04:12:15.290 --> 04:12:16.280
Caitlyn Herron: yeah yeah.

1458
04:12:18.530 --> 04:12:19.250
Ming-kuo Lee: Your hand go ahead.

1459
04:12:20.630 --> 04:12:27.200
Yuehan Lu: Okay, think good presentation, so you were you were mentioning there this study, suggesting that.

1460
04:12:28.250 --> 04:12:35.180
Yuehan Lu: The complex the composition will be high you're in high pH values and and most of them right.

1461
04:12:35.450 --> 04:12:35.960
Then.

1462
04:12:37.010 --> 04:12:55.190
Yuehan Lu: So I didn't read that paper So could you please educate me i'm all yours today will add new information compared to this paper all do you have maybe you're expecting different findings from this paper.

1463
04:12:56.780 --> 04:13:16.640
Caitlyn Herron: yeah Thank you wonderful question, so the past papers that i've read addressing this issue, and most of the time they were using swanee it seems to be pretty heavily researched and but some of the other past papers, I think it was Bauer at all used predominantly.

1464
04:13:19.130 --> 04:13:30.530
Caitlyn Herron: samples from like different streams and lakes and most of the dvm sources, it seemed to be microbial based going back to Natal us.

1465
04:13:31.310 --> 04:13:43.730
Caitlyn Herron: talk earlier today there's two main types that it's usually from the land or microbial and so by in my research, study we're going to be using two different types of doin.

1466
04:13:44.900 --> 04:13:58.190
Caitlyn Herron: Similarly to Natalia and do that cold leaching experiment to extraction to get the deal i'm from that and I haven't seen that many papers and my literature shirt source.

1467
04:13:58.730 --> 04:14:14.000
Caitlyn Herron: searching sorry that we're using this kind of methodology to identify and do an arsenic complex ation and I think you'll provide a lot of interesting insights especially.

1468
04:14:15.470 --> 04:14:32.510
Caitlyn Herron: To communities that live near coal based areas because I hypotheses hypothesized that this is going to provide more reasonable information for potentially the groundwater arsenic complication that could be happening there.

1469
04:14:34.280 --> 04:14:43.430
Yuehan Lu: yeah Thank you I agree this vm is complex and highly variable so it's great to look at different types and nature, we do.

1470
04:14:46.220 --> 04:14:55.970
Ann Ojeda: Pay you on we also haven't seen very much data about molecular size like DEMO size, exclusion and this quenching and what fraction is actually quenching.

1471
04:14:57.530 --> 04:15:09.590
Ann Ojeda: or complex thing with our trace metals and I think that's more powerful than just saying that it does is being able to say Okay, if you have this type of organic matter that shows this fluorescence your higher risk.

1472
04:15:09.920 --> 04:15:17.720
Ann Ojeda: For organic matter complex ation and therefore mobility in your water systems so that's why we're doing all the size exclusion work.

1473
04:15:18.770 --> 04:15:21.110
Ann Ojeda: yeah because really swanee is on one end and.

1474
04:15:21.110 --> 04:15:25.970
Ann Ojeda: Our microbiome is, on the other end and we don't have a whole lot of standards in between.

1475
04:15:28.340 --> 04:15:29.480
Yuehan Lu: yeah cool thanks.

1476
04:15:32.960 --> 04:15:35.120
Ming-kuo Lee: So it looks like we're three minutes over.

1477
04:15:36.200 --> 04:15:47.840
Ming-kuo Lee: Just shoot another quick question so quick one even organic cosmic or awesome it organic do a combo which ones more toxic.

1478
04:15:48.950 --> 04:15:49.820
Ming-kuo Lee: In European.

1479
04:15:51.140 --> 04:16:03.740
Caitlyn Herron: um ah, this is a something that gets me really excited because we really don't know there hasn't been that much research and investigating the complex or the toxicity of arsenic.

1480
04:16:04.670 --> 04:16:15.770
Caitlyn Herron: Complex to do and there's one theory it's called the the login theory that most the time people assume that if arsenic is complex, to do them then.

1481
04:16:16.340 --> 04:16:24.440
Caitlyn Herron: We don't really have to worry about it, that it will stay complex and it potentially might be strong, but there are other theories out there that.

1482
04:16:25.070 --> 04:16:40.970
Caitlyn Herron: Are snake do em complexes are actually kind of weak, so they could be they could show different toxicity to ecosystems and it's I don't think it has that much research and really interesting.

1483
04:16:45.950 --> 04:16:48.440
Ming-kuo Lee: Thank you, I think, for me, is over, now.

1484
04:16:49.850 --> 04:16:54.920
Ming-kuo Lee: And so, before we were carrying go any question for Kevin.

1485
04:16:59.540 --> 04:17:01.010
Ming-kuo Lee: Thank you so much Karen for.

1486
04:17:01.310 --> 04:17:01.850
Caitlyn Herron: Thank you.

1487
04:17:01.970 --> 04:17:13.010
Ming-kuo Lee: very informative presentation, so I think this will conclude our section today yeah like to thank all the presenters for the wonderful presentation.

1488
04:17:13.760 --> 04:17:26.360
Ming-kuo Lee: and also a single head that taco taco to organize this sexually know that audience and do you have anything to add before we let everybody go.

1489
04:17:27.380 --> 04:17:32.030
Ann Ojeda: Now just reiterate Thank you everyone for presenting and sticking around for the poster session.

1490
04:17:33.050 --> 04:17:37.160
Ann Ojeda: enjoy things this afternoon and happy southeastern section.

1491
04:17:39.920 --> 04:17:38.000
Ann Ojeda: Great bye.