Prediction of PFAS bioaccumulation in different plant tissues with machine learning models based on molecular fingerprints.

Due to the wastewater irrigation or biosolid application, per- and polyfluoroalkyl substances (PFASs) have been widely detected in agriculture soil and hence crops or vegetables. Consumption of contaminated crops and vegetables is considered as an important route of human exposure to PFASs. Machine learning (ML) models have been developed to predict PFAS uptake by plants with majority focus on roots. However, ML models for predicting accumulation of PFASs in above ground edible tissues have yet to be investigated. In this study, 811 data points covering 22 PFASs represented by molecular fingerprints and 5 plant categories (namely the root class, leaf class, cereals, legumes, and fruits) were used for model development. The Extreme Gradient Boosting (XGB) model demonstrated the most favorable performance to predict the bioaccumulation factors (BAFs) in all the 4 plant tissues (namely root, leaf, stem, and fruit) achieving coefficients of determination R as 0.82-0.93. Feature importance analysis showed that the top influential factors for BAFs varied among different plant tissues, indicating that model developed for root concentration prediction may not be feasible for above ground parts. The XGB model's performance was further demonstrated by comparing with data from pot experiments measuring BAFs of 12 PFASs in lettuce. The correlation between predicted and measured results was favorable for BAFs in both lettuce roots and leaves with R values of 0.76 and 0.81. This study developed a robust approach to comprehensively understand the uptake of PFASs in both plant roots and above ground parts, offering key insights into PFAS risk assessment and food safety.