EVALUATING ANTHROPOGENIC IMPACT ON WATER QUALITY OF OHIO RIVERS OVER TIME

Many natural and anthropogenic factors affect the geochemistry of surface waters like rivers and streams in rural and urban areas. Much impact on fresh water in the United States comes from non-point sources, with population and land use playing an important role. Major components of input into surface waters are chloride and sodium, derived mostly from urban contributions such as road salt, and nitrate, largely from agricultural sources like fertilizers, as well as the burning of fossil fuels. Fresh water quality historical data exists for many Ohio rivers, however much of it has never been further utilized to observe ion concentration trends over past decades.

I have tabulated past data from state and federal bases such as USGS and other sources from the 1950’s onward to identify long-term trends in ion concentrations in rivers at multiple locations throughout the greater Columbus area. In June, I sampled the same sites examined in the past and analyzed the samples for major ions and nutrients. After comparing the summer 2012 samples to those from same sites in the past, identifiable trends showing general increases in ion concentration over time were found in the river locations with long-term historical data sets. The time series of some localities are not complete due to many gaps in data collection over the past 40-50 years. A recognizable increase in nitrate concentration is observed from the 1970’s into the 1990’s with a possible indication of decrease in the 2000’s; however there is not enough information to confirm this. Clear increases in chloride concentration are observed over the time intervals in many rivers and streams. Increases in sodium are observed in some localities from the 1970's through the 1990's, but there is the least amount of data for this ion and fewer rivers sampled in the past. Documented increases in select Ohio rivers are similar to what have been observed elsewhere in the U.S., but others show no real trends. A lack of long-term, consistent data results in a lack of certainty in interpretation of time series.