Projects per year
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 (Formula presented.), 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 (Formula presented.) 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 (Formula presented.) 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 km 2 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.
Original language | English |
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Article number | 3539 |
Journal | Water |
Volume | 15 |
Issue number | 20 |
DOIs | |
Publication status | Published - 11 Oct 2023 |
Keywords
- groundwater
- arsenic
- ; remediation
- machine learning
- remediation
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Dive into the research topics of 'Artificial Intelligence Modelling to Support the Groundwater Chemistry-Dependent Selection of Groundwater Arsenic Remediation Approaches in Bangladesh'. Together they form a unique fingerprint.Projects
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Future Secular Changes & Remediation of Groundwater Arsenic in the Ganga River Basin - Newton Water Quality
Polya, D. (PI), Joekar-Niasar, V. (CoI), Lloyd, J. (CoI) & Van Dongen, B. (CoI)
31/01/18 → 30/01/21
Project: Research