Meta-Analysis Enables Prediction of the Maximum Permissible Arsenic Concentration in Asian Paddy Soil
It is now well-established that not just drinking water, but irrigation water contaminated with arsenic (As) is an important source of human As exposure through water-soil-rice transfer. While drinking water As has a permissible, or guideline value, quantification of guideline values for soil and ir...
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Frontiers Media S.A.
2021-12-01
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| Series: | Frontiers in Environmental Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenvs.2021.760125/full |
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| author | Jajati Mandal Sudip Sengupta Soumyajit Sarkar Abhijit Mukherjee Abhijit Mukherjee Michael D. Wood Simon M. Hutchinson Debapriya Mondal |
| author_facet | Jajati Mandal Sudip Sengupta Soumyajit Sarkar Abhijit Mukherjee Abhijit Mukherjee Michael D. Wood Simon M. Hutchinson Debapriya Mondal |
| author_sort | Jajati Mandal |
| collection | DOAJ |
| description | It is now well-established that not just drinking water, but irrigation water contaminated with arsenic (As) is an important source of human As exposure through water-soil-rice transfer. While drinking water As has a permissible, or guideline value, quantification of guideline values for soil and irrigation water is limited. Using published data from 26 field studies (not pot-based experiments) from Asia, each of which reported irrigation water, soil and rice grain As concentrations from the same site, this meta-analysis quantitatively evaluated the relationship between soil and irrigation water As concentrations and the As concentration in the rice grain. A generalized linear regression model revealed As in soil to be a stronger predictor of As in rice than As in irrigation water (beta of 16.72 and 0.6, respectively, p < 0.01). Based on the better performing decision tree model, using soil and irrigation water As as independent variables we determined that Asian paddy soil As concentrations greater than 14 mg kg−1 may result in rice grains exceeding the Codex recommended maximum allowable inorganic As (i-As) concentrations of 0.2 mg kg−1 for polished rice and 0.35 mg kg−1 for husked rice. Both logistic regression and decision tree models, identified soil As as the main determining factor and irrigation water to be a non-significant factor, preventing determination of any guideline value for irrigation water. The seemingly non-significant contribution of irrigation water in predicting grain i-As concentrations below or above the Codex recommendation may be due to the complexity in the relationship between irrigation water As and rice grains. Despite modeling limitations and heterogeneity in meta-data, our findings can inform the maximum permissible As concentrations in Asian paddy soil. |
| first_indexed | 2024-12-19T02:51:31Z |
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| language | English |
| last_indexed | 2024-12-19T02:51:31Z |
| publishDate | 2021-12-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Environmental Science |
| spelling | doaj.art-b1766fc4c387482aadd064ba1bed983a2022-12-21T20:38:37ZengFrontiers Media S.A.Frontiers in Environmental Science2296-665X2021-12-01910.3389/fenvs.2021.760125760125Meta-Analysis Enables Prediction of the Maximum Permissible Arsenic Concentration in Asian Paddy SoilJajati Mandal0Sudip Sengupta1Soumyajit Sarkar2Abhijit Mukherjee3Abhijit Mukherjee4Michael D. Wood5Simon M. Hutchinson6Debapriya Mondal7School of Science, Engineering and Environment, University of Salford, Salford, United KingdomDepartment of Agricultural Chemistry and Soil Science, Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, IndiaSchool of Environmental Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, IndiaSchool of Environmental Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, IndiaDepartment of Geology and Geophysics, Indian Institute of Technology Kharagpur, Kharagpur, IndiaSchool of Science, Engineering and Environment, University of Salford, Salford, United KingdomSchool of Science, Engineering and Environment, University of Salford, Salford, United KingdomInstitute of Medical and Biomedical Education, St. George’s College, University of London, London, United KingdomIt is now well-established that not just drinking water, but irrigation water contaminated with arsenic (As) is an important source of human As exposure through water-soil-rice transfer. While drinking water As has a permissible, or guideline value, quantification of guideline values for soil and irrigation water is limited. Using published data from 26 field studies (not pot-based experiments) from Asia, each of which reported irrigation water, soil and rice grain As concentrations from the same site, this meta-analysis quantitatively evaluated the relationship between soil and irrigation water As concentrations and the As concentration in the rice grain. A generalized linear regression model revealed As in soil to be a stronger predictor of As in rice than As in irrigation water (beta of 16.72 and 0.6, respectively, p < 0.01). Based on the better performing decision tree model, using soil and irrigation water As as independent variables we determined that Asian paddy soil As concentrations greater than 14 mg kg−1 may result in rice grains exceeding the Codex recommended maximum allowable inorganic As (i-As) concentrations of 0.2 mg kg−1 for polished rice and 0.35 mg kg−1 for husked rice. Both logistic regression and decision tree models, identified soil As as the main determining factor and irrigation water to be a non-significant factor, preventing determination of any guideline value for irrigation water. The seemingly non-significant contribution of irrigation water in predicting grain i-As concentrations below or above the Codex recommendation may be due to the complexity in the relationship between irrigation water As and rice grains. Despite modeling limitations and heterogeneity in meta-data, our findings can inform the maximum permissible As concentrations in Asian paddy soil.https://www.frontiersin.org/articles/10.3389/fenvs.2021.760125/fullarsenicricepaddy soilirrigation watermeta-analysisdecision tree |
| spellingShingle | Jajati Mandal Sudip Sengupta Soumyajit Sarkar Abhijit Mukherjee Abhijit Mukherjee Michael D. Wood Simon M. Hutchinson Debapriya Mondal Meta-Analysis Enables Prediction of the Maximum Permissible Arsenic Concentration in Asian Paddy Soil Frontiers in Environmental Science arsenic rice paddy soil irrigation water meta-analysis decision tree |
| title | Meta-Analysis Enables Prediction of the Maximum Permissible Arsenic Concentration in Asian Paddy Soil |
| title_full | Meta-Analysis Enables Prediction of the Maximum Permissible Arsenic Concentration in Asian Paddy Soil |
| title_fullStr | Meta-Analysis Enables Prediction of the Maximum Permissible Arsenic Concentration in Asian Paddy Soil |
| title_full_unstemmed | Meta-Analysis Enables Prediction of the Maximum Permissible Arsenic Concentration in Asian Paddy Soil |
| title_short | Meta-Analysis Enables Prediction of the Maximum Permissible Arsenic Concentration in Asian Paddy Soil |
| title_sort | meta analysis enables prediction of the maximum permissible arsenic concentration in asian paddy soil |
| topic | arsenic rice paddy soil irrigation water meta-analysis decision tree |
| url | https://www.frontiersin.org/articles/10.3389/fenvs.2021.760125/full |
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