Predicting the uptake of emerging organic contaminants in vegetables irrigated with treated wastewater - Implications for food safety assessment.

Emerging organic contaminants (EOCs) undergoing incomplete removal during wastewater treatment may be found in treated wastewater (TWW) used for irrigation of agricultural products. Following uptake into edible plant parts, EOCs may eventually enter in the food chain, with associated human exposure. In the present study, we used a newly developed steady-state plant uptake model with added phloem transport to predict the uptake of four EOCs (carbamazepine, ibuprofen, ketoprofen and naproxen) into three varieties of lettuce. Input data were derived from an experimental study with vegetables grown in greenhouse and irrigated with TWW spiked with CBZ at 0, 30, 60, 120 and 210 µg/L in each variety of lettuce. Predicted carbamazepine concentrations in leaves were on average 82% higher than in roots, with good agreement between measured and calculated data. We subsequently predicted the uptake of anti-inflammatory compounds ibuprofen, ketoprofen and naproxen, for which the chemical analysis could not provide concentrations above detection limit. These three substances are weak acids and predicted concentrations in roots were higher than in the edible leaves, mainly due to phloem transport downwards. The daily dietary intake of all four EOCs was estimated for consumption of leafy vegetables, being far below usual therapeutic doses.

Biodegradation behaviour of pharmaceutical compounds and selected metabolites in activated sludge. A forecasting decision system approach.

Society's support upon chemicals over the last few decades has led to their increased production, application and discharge into the environment. Wastewater treatment plants (WWTPs) contain a multitude of these chemicals such us; pharmaceutical compounds (PCs). Often, their biodegradability by activated sludge microorganisms is significant for their elimination during wastewater treatment. In this paper the focus is laid on two PCs carbamazepine (CBZ) and diclofenac (DCF) and their main transformation products (TPs). Laboratory degradation tests with these two pharmaceuticals using activated sludge as inoculum under aerobic conditions were performed and microbial metabolites were analyzed by liquid chromatography-mass spectrometry (LC/MS-MS). In two different Mixed liquid Suspended Solids (MLSS) concentrations the biodegradability by activated sludge of CBZ and DCF were evaluated. Also, this article proposes a decision support system to optimize the prediction process of this type of pharmacological compounds. A study and analysis of the techniques of Support Vector Machine, Random Forest, Decision Trees and Multilayer Perceptron Network is carried out to select the most reliable and accurate predictor for the decision system. There are not significant differences in the removal of DCF with 30 mg MLSS/L and 60 mg MLSS/L. DCF was better removed than CBZ in all experiments studied. The TP detected in the samples were mainly 4-OH-DCF for DCF and 10, 11 EPOXICBZ for CBZ. The results show that the best models are obtained with Random Forest and Multilayer Perceptron Network techniques, with a model fit of more than 95% for both carbamazepine and diclofenac metabolites. Obtaining a root means square errors of 0.80 µg/L for the metabolite 4-OH-DCF for DCF with the technique Random Forest and a root means square errors of 1.13 µg/L for the metabolite 10, 11 EPOXICBZ for CBZ with the Multilayer Perceptron Network technique. Supplementary Information: The online version contains supplementary material available at 10.1007/s40201-023-00890-x.