Exposure assessment of emerging contaminants: Rapid screening and modeling of plant uptake

With the advent of new chemicals and their increasing uses in every aspect of our life, considerable number of emerging contaminants are introduced to environment yearly. Emerging contaminants in forms of pharmaceuticals, detergents, biosolids, and reclaimed wastewater can cross plant roots and translocate to various parts of the plants. Long-term human exposure to emerging contaminants through food consumption is assumed to be a pathway of interest. Thus, uptake and translocation of emerging contaminants in plants are important for the assessment of health risks associated with human exposure to emerging contaminants. To have a better understanding over fate of emerging contaminants in plants, both experimental and modeling studies are necessary. In our studies, various machine learning techniques were used to develop models with improved accuracies as compared to traditional models. Our results showed that neural network models can considerably improve the accuracy of predictions for transpiration stream concentration factor (TSCF) and root concentration factor (RCF). With fuzzy logic as another capable machine learning technique, it was possible to develop new relationships between chemical properties and uptake efficiency. In our experimental studies, the uptake of various organic compounds including estriol, bisphenol A, carbamazepine, DEET, lincomycin, acetaminophen, and 2,4-dinitrotoluene was in a good agreement with predictive models. The moderately hydrophobic compounds had the highest TSCF values. However, our findings indicated that the role of factors other than physicochemical properties should be taken into account. Degradation of chemical compounds and partitioning to plant root constituents other than lipids (lignin, cellulose, and protein) are some examples --Abstract, page iv