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Lai X, Zhou P, Kong Y, Wu B, Zhang Q, Cui X. A machine learning and experimental-based model for prediction of soil sorption capacity toward phenanthrene. Environ Res 2024; 244:117898. [PMID: 38092242 DOI: 10.1016/j.envres.2023.117898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 11/29/2023] [Accepted: 12/06/2023] [Indexed: 12/22/2023]
Abstract
Sorption by soil is the fundamental basis for environment fate of hydrophobic organic contaminants (HOCs), which varies significantly depending on diverse properties of soils. Therefore, a generalized approach to predict HOC sorption by soils is required. In this study, 488 data points were extracted from references and adopted to develop models for estimating the sorption capacities of phenanthrene in soils using six different machine learning (ML) approaches. The extreme gradient boosting (XGBT) model demonstrated the most favorable performance, achieving a coefficient of determination of 0.91 and root-mean-square errors of 0.24 for the testing dataset. The XGBT model's performance was further demonstrated by comparing with experimental data from batch sorption tests conducted on 20 soil samples collected from 17 provinces of China. The differences between the predicted values and the experimental values were statistically equal to zero (p = 0.14). Leveraging the XBGT model together with soil properties from the Harmonized World Soil Database, the distribution of sorption capacities in Chinese soils was successfully depicted on a national scale. This research is expected to contribute to a deeper understanding of the migration of persistent organic pollutants in terrestrial system. Furthermore, the established model holds implications for more precise and scientific soil environmental management.
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Affiliation(s)
- Xinyi Lai
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, 210023, China
| | - Pengfei Zhou
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, 210023, China
| | - Yi Kong
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, 210023, China
| | - Bang Wu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, 210023, China
| | - Qian Zhang
- State Environmental Protection Key Laboratory of Soil Environmental Management and Pollution Control, Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment of China, Nanjing, Jiangsu, China; State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, 210023, China
| | - Xinyi Cui
- State Environmental Protection Key Laboratory of Soil Environmental Management and Pollution Control, Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment of China, Nanjing, Jiangsu, China; State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, 210023, China.
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