Mechanistic and data-driven perspectives on plant uptake of organic pollutants.

Abstract

Establishing reliable predictive models for plant uptake of organic pollutants is crucial for environmental risk assessment and guiding phytoremediation efforts. This study compiled an expanded dataset of plant cuticle-water partition coefficients (K cw ), a useful indicator for plant uptake, for 371 data points of 148 unique compounds and various plant species. Quantum/computational chemistry software and tools were utilized to compute various molecular descriptors, aiming to comprehensively characterize the properties and structures of each compound. Three types of models were developed to predict K cw : a mechanism-driven pp-LFER model, a data-driven machine learning model, and an integrated mechanism-data-driven model. The mechanism-data-driven GBRT-ppLFER model exhibited superior performance, achieving RMSE train = 0.133 and RMSE test = 0.301 while maintaining interpretability. The Shapley Additive Explanation analysis indicated that pp-LFER parameters, ESPI, FwRadicalmax, ExtFP607, and RDF70s are the key factors influencing plant uptake in the GBRT-ppLFER model. Overall, pp-LFER parameter, ESPI, and ExtFP607 show positive effects, while the remaining factors exhibit negative effects. Partial dependency analysis further indicated that plant uptake is not solely determined by individual factors but rather by the combined interactions of multiple factors. Specifically, compounds with ppLFER parameter >4, ESPI > -25.5, 0.098 cw values from the GBRT-ppLFER model were effectively employed to estimate the plant-water partition coefficients and bioconcentration factors across different plant species and growth media (water, sand, and soil), achieving an outstanding performance with an RMSE of 0.497. This study provides effective tools for assessing plant uptake of organic pollutants and deepens our understanding of plant-environment-compound interactions.

Plant phenotyping relevance

植物による有機汚染物質の取り込み量・植物水分配係数を推定する機構モデル、機械学習モデル、統合モデルを開発・評価しており、植物の取り込み状態を抽出する計算手法が研究の中心である。

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