农业环境科学学报2024，Vol.43Issue(11)：2516-2524,9.DOI:10.11654/jaes.2024-0854
水培到土培体系植物根系PFAS吸收风险的迁移机器学习研究
Predicting uptake risk of PFAS in plant roots using transfer learning from hydroponic to soil systems
摘要
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.关键词
全氟与多氟化合物/植物根系吸收积累/机器学习/迁移学习Key words
per-and polyfluoroalkyl substances(PFAS)/plant root absorption and accumulation/machine learning/transfer learning分类
资源环境引用本文复制引用
钱一凡,裴晨浩,吕陈,吴同亮,刘存,王玉军..水培到土培体系植物根系PFAS吸收风险的迁移机器学习研究[J].农业环境科学学报,2024,43(11):2516-2524,9.基金项目
国家重点研发计划项目(2021YFC1809100) （2021YFC1809100）
国家自然科学基金项目(41977027)National Key Research and Development Program of China(2021YFC1809100) （41977027）
National Natural Science Foundation of China(41977027) （41977027）
