GSA Annual Meeting in Indianapolis, Indiana, USA - 2018

Paper No. 281-10
Presentation Time: 4:10 PM


SHARMA, Sanjeev1, BHATTACHARYA, Prosun2, KUMAR, Debraj1, PERUGUPALLI, Prashanth3 and VON BRÖMSSEN, Mattias4, (1)Excel Dots AB, Svartviksslingan 90, Bromma, SE-167 39, Sweden, (2)KTH-International Groundwater Arsenic Research Group, Dept of Sustainable Development, Environmental Science and Engineering, KTH Royal Institute of Technology, Teknikringen 10B, Stockholm, SE-10044, Sweden, (3)Spectral Insights India Pvt. Ltd, 5th Floor, Discoverer Block, ITPL, Whitefield Road, Bangalore, 560066, India, (4)Ramböll Water, Ramböll Sweden AB, Stockholm, SE-104 62, Sweden

Since the past four decades, abstraction of groundwater for drinking water and irrigation has increased manifold especially in developing countries like India, Pakistan, and Bangladesh due to depletion of surface water bodies or higher level of pollution. In Bangladesh alone there are more than 10 million tubewells, which include the shallow tubewells drilled by private drillers Many of these tubewells have high level of arsenic (As) presence in aquifers. It is utmost important to identify the safe aquifers and strictly ensures that new tubewells are safe from both drinking and agriculture point of view. SASMIT project team at KTH Sweden developed a novel handheld sediment colour tool, which facilitates the local drillers to target safe aquifers for safe tubewell installation. SASMIT studies was based on the local drillers know how of the colour of the sediments and its relation to presence of Arsenic in the aquifers. This study was made to advance this tool to be developed as a fully artificial intelligence (AI) based digital ASMITAS (Arsenic Mitigation at Source) tool based on spectral scanning of the sediment color. ASMITAS uses a snapshot HSI camera and an advanced algorithm to predict the Arsenic level by scanning the sediments that can be extracted during the drilling process. With more number of samples and various drilling location, tool can be scalable to cover large geographical area and hence can be very useful tool for local drillers and other stakeholders for smart decision making for installation of safe tubewells for mitigating As at source level.

The outcomes of the present study indicates that ASMITAS has the potential to overcome the challenges by means of digitalizing the sediments colours and comparing with a base reference library with very advanced algorithm based on Artificial Intelligence to estimate the color of the sediments followed by determination of As concentration on real time basis. The ASMITAS tool can be used by the local drillers to identify the color of the sediments in a more accurate manner for targeting the safe aquifers for installation of safe drinking water wells. The tool can also be upgraded with the input of the concentration of extractable As in the sediments for characterization of the toxicity in different sediments.