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| Predicting uptake risk of PFAS in plant roots using transfer learning from hydroponic to soil systems |
| Received:October 08, 2024 |
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| KeyWord:per- and polyfluoroalkyl substances（PFAS）;plant root absorption and accumulation;machine learning;transfer learning |
| Author Name | Affiliation | E-mail | | QIAN Yifan | State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 211135, China
University of Chinese Academy of Sciences, Beijing 100049, China | | | PEI Chenhao | State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 211135, China
University of Chinese Academy of Sciences, Beijing 100049, China | | | Lü Chen | State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 211135, China | | | WU Tongliang | State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 211135, China | | | LIU Cun | State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 211135, China | liucun@issas.ac.cn | | WANG Yujun | State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 211135, China
University of Chinese Academy of Sciences, Beijing 100049, China | yjwang@issas.ac.cn |
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| Abstract: |
| In order to precisely predict the absorption and accumulation of per- and polyfluoroalkyl substances（PFAS）in plant roots, a total of 668 data points from PFAS plant uptake studies in hydroponic and soil systems covering 19 PFAS species were collected. The features such as molecular descriptors, experimental conditions, and crop properties were used to construct four machine learning models, which were used to predict the root concentration factor（RCF）. The Extreme Gradient Boosting Tree（XGB）model performed the best, with R2 values of 0.69 and 0.83 and RMSE values of 0.51 and 0.28 for the test sets, respectively. Due to the easier study of PFAS absorption and accumulation in hydroponic systems, a transfer learning model was established from hydroponic to soil systems to improve the prediction accuracy of RCF in complex soil systems through knowledge transfer. The optimal transfer model achieved R2 of 0.86 and RMSE of 0.25 for test set, showing a significant improvement in accuracy. Analysis of SHAP feature importance revealed that exposure time, soil pH, and PFAS concentration are the top three factors affecting RCF in soil. This study predicts the PFAS absorption and accumulation of plant root in soil through the construction of machine learning and transfer learning models, achieving the transfer from simple to complex systems. It provides new insights for evaluating the environmental risks of PFAS contamination in soils. |
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