Artificial Intelligence Modelling to Support the Groundwater Chemistry-Dependent Selection of Groundwater Arsenic Remediation Approaches in Bangladesh

Abstract

Groundwater arsenic (As) still poses a massive public health threat, especially in South Asia, including Bangladesh. The arsenic removal efficiency of various technologies may be strongly dependent on groundwater composition. Previously, others have reported that the molar ratio [Fe]−1.8[P][As], in particular, can usefully predict the potential efficiency of groundwater As removal by widespread sorption/co-precipitation-based remediation systems. Here, we innovatively extended the application of artificial intelligence (AI) machine learning models to predict the geospatial distribution of [Fe]−1.8[P][As] in Bangladesh groundwaters utilizing our analogous AI predictions for groundwater As, Fe, and P. A comparison between the predicted geospatial distribution of groundwater As and [Fe]−1.8[P][As] distinguished high groundwater As areas where (a) sorption/co-precipitation remediation technologies would have the potential to be highly effective in removing As without Fe amendment, as well as from those areas where (b) amendment with Fe (e.g., zero-valent Fe) would be required to promote efficient As removal. The 1 km2 scale of the prediction maps provided a 100-fold improvement in the granularity of previous district-scale non-AI models. AI approaches have the potential to contribute to informing the appropriate selection and amendment of appropriate groundwater contamination remediation strategies where their effectiveness depends on local groundwater chemistry.

Keywords

• groundwater
• arsenic
• remediation
• machine learning