Artificial Intelligence Modelling to Support the Groundwater Chemistry-Dependent Selection of Groundwater Arsenic Remediation Approaches in Bangladesh
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 <inline-formula&g...
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MDPI AG
2023-10-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/15/20/3539 |
| _version_ | 1797572008706310144 |
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| author | Ruohan Wu Laura A. Richards Ajmal Roshan David A. Polya |
| author_facet | Ruohan Wu Laura A. Richards Ajmal Roshan David A. Polya |
| author_sort | Ruohan Wu |
| collection | DOAJ |
| description | 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mfrac><mrow><mrow><mo>[</mo><mrow><mi>Fe</mi></mrow><mo>]</mo></mrow><mo>−</mo><mn>1.8</mn><mrow><mo>[</mo><mi mathvariant="normal">P</mi><mo>]</mo></mrow></mrow><mrow><mrow><mo>[</mo><mrow><mi>As</mi></mrow><mo>]</mo></mrow></mrow></mfrac></mrow></semantics></math></inline-formula>, 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mfrac><mrow><mrow><mo>[</mo><mrow><mi>Fe</mi></mrow><mo>]</mo></mrow><mo>−</mo><mn>1.8</mn><mrow><mo>[</mo><mi mathvariant="normal">P</mi><mo>]</mo></mrow></mrow><mrow><mrow><mo>[</mo><mrow><mi>As</mi></mrow><mo>]</mo></mrow></mrow></mfrac></mrow></semantics></math></inline-formula> 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mfrac><mrow><mrow><mo>[</mo><mrow><mi>Fe</mi></mrow><mo>]</mo></mrow><mo>−</mo><mn>1.8</mn><mrow><mo>[</mo><mi mathvariant="normal">P</mi><mo>]</mo></mrow></mrow><mrow><mrow><mo>[</mo><mrow><mi>As</mi></mrow><mo>]</mo></mrow></mrow></mfrac></mrow></semantics></math></inline-formula> 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<sup>2</sup> 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. |
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| language | English |
| last_indexed | 2024-03-10T20:48:34Z |
| publishDate | 2023-10-01 |
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| series | Water |
| spelling | doaj.art-21277601d4744eccb01a4d20977a08ca2023-11-19T18:29:05ZengMDPI AGWater2073-44412023-10-011520353910.3390/w15203539Artificial Intelligence Modelling to Support the Groundwater Chemistry-Dependent Selection of Groundwater Arsenic Remediation Approaches in BangladeshRuohan Wu0Laura A. Richards1Ajmal Roshan2David A. Polya3Department of Earth and Environmental Sciences, School of Natural Sciences and Williamson Research Centre for Molecular Environmental Sciences, The University of Manchester, Manchester M13 9PL, UKDepartment of Earth and Environmental Sciences, School of Natural Sciences and Williamson Research Centre for Molecular Environmental Sciences, The University of Manchester, Manchester M13 9PL, UKDepartment of Earth and Environmental Sciences, School of Natural Sciences and Williamson Research Centre for Molecular Environmental Sciences, The University of Manchester, Manchester M13 9PL, UKDepartment of Earth and Environmental Sciences, School of Natural Sciences and Williamson Research Centre for Molecular Environmental Sciences, The University of Manchester, Manchester M13 9PL, UKGroundwater 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mfrac><mrow><mrow><mo>[</mo><mrow><mi>Fe</mi></mrow><mo>]</mo></mrow><mo>−</mo><mn>1.8</mn><mrow><mo>[</mo><mi mathvariant="normal">P</mi><mo>]</mo></mrow></mrow><mrow><mrow><mo>[</mo><mrow><mi>As</mi></mrow><mo>]</mo></mrow></mrow></mfrac></mrow></semantics></math></inline-formula>, 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mfrac><mrow><mrow><mo>[</mo><mrow><mi>Fe</mi></mrow><mo>]</mo></mrow><mo>−</mo><mn>1.8</mn><mrow><mo>[</mo><mi mathvariant="normal">P</mi><mo>]</mo></mrow></mrow><mrow><mrow><mo>[</mo><mrow><mi>As</mi></mrow><mo>]</mo></mrow></mrow></mfrac></mrow></semantics></math></inline-formula> 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mfrac><mrow><mrow><mo>[</mo><mrow><mi>Fe</mi></mrow><mo>]</mo></mrow><mo>−</mo><mn>1.8</mn><mrow><mo>[</mo><mi mathvariant="normal">P</mi><mo>]</mo></mrow></mrow><mrow><mrow><mo>[</mo><mrow><mi>As</mi></mrow><mo>]</mo></mrow></mrow></mfrac></mrow></semantics></math></inline-formula> 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<sup>2</sup> 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.https://www.mdpi.com/2073-4441/15/20/3539groundwaterarsenicremediationmachine learning |
| spellingShingle | Ruohan Wu Laura A. Richards Ajmal Roshan David A. Polya Artificial Intelligence Modelling to Support the Groundwater Chemistry-Dependent Selection of Groundwater Arsenic Remediation Approaches in Bangladesh Water groundwater arsenic remediation machine learning |
| title | Artificial Intelligence Modelling to Support the Groundwater Chemistry-Dependent Selection of Groundwater Arsenic Remediation Approaches in Bangladesh |
| title_full | Artificial Intelligence Modelling to Support the Groundwater Chemistry-Dependent Selection of Groundwater Arsenic Remediation Approaches in Bangladesh |
| title_fullStr | Artificial Intelligence Modelling to Support the Groundwater Chemistry-Dependent Selection of Groundwater Arsenic Remediation Approaches in Bangladesh |
| title_full_unstemmed | Artificial Intelligence Modelling to Support the Groundwater Chemistry-Dependent Selection of Groundwater Arsenic Remediation Approaches in Bangladesh |
| title_short | Artificial Intelligence Modelling to Support the Groundwater Chemistry-Dependent Selection of Groundwater Arsenic Remediation Approaches in Bangladesh |
| title_sort | artificial intelligence modelling to support the groundwater chemistry dependent selection of groundwater arsenic remediation approaches in bangladesh |
| topic | groundwater arsenic remediation machine learning |
| url | https://www.mdpi.com/2073-4441/15/20/3539 |
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