Selection of priority pesticides in Japanese drinking water quality regulation: Validity, limitations, and evolution of a risk prediction method

Screening priority pesticides for drinking water quality regulation and monitoring by machine learning: Analysis of factors affecting detectability

Proper selection of new contaminants to be regulated or monitored prior to implementation is an important issue for regulators and water supply utilities. Herein, we constructed and evaluated machine learning models for predicting the detectability (detection/non-detection) of pesticides in surface water as drinking water sources. Classification and regression models were constructed for Random Forest, XGBoost, and LightGBM, respectively; of these, the LightGBM classification model had the highest prediction accuracy. Furthermore, its prediction performance was superior in all aspects of Recall, Precision, and F-measure compared to the detectability index method, which is based on runoff models from previous studies. Regardless of the type of machine learning model, the number of annual measurements, sales quantity of pesticide for rice-paddy field, and water quality guideline values were the most important model features (explanatory variables). Analysis of the impact of the features suggested the presence of a threshold (or range), above which the detectability increased. In addition, if a feature (e.g., quantity of pesticide sales) acted to increase the likelihood of detection beyond a threshold value, other features also synergistically affected detectability. Proportion of false positives and negatives varied depending on the features used. The superiority of the machine learning models is their ability to represent nonlinear and complex relationships between features and pesticide detectability that cannot be represented by existing risk scoring methods.

Biosorption of metribuzin pesticide by Cucumber (Cucumis sativus) peels-zinc oxide nanoparticles composite

Herein, a biosorbent was prepared from cucumber peels modified with ZnO nanoparticles (CPZiONp-composite) for the biosorption of metribuzin. Characterization of the composite was accomplished using FTIR, SEM, EDX, surface area pore size analyzer and pH of point of zero charge (pH_pzc). Biosorption study was executed in batch concerning the impact of pH, composite dose, contact time, initial metribuzin concentration and temperature. The biosorption depends on pH and maximum biosorption was acquired at pH 3.0. Surface chemistry of the composite was studied by determining the pH_pzc and was found to be 6.1. The biosorption nature was investigated using isotherms and was assessed that Freundlich isotherm is well suited for the fitting of the biosorption data owing to the highest R². The maximum biosorption capacity of CPZiONp-composite was found to be 200 mg g^-1. The biosorption data were fitted in to different kinetic models and the outcomes suggesting that pseudo second order is a satisfactory model to interpret the biosorption data owing to the highest R². Thermodynamic parameters for instance entropy, enthalpy and Gibbs free energy were computed and revealed that biosorption of metribuzin onto CPZiONp-composite is spontaneous and exothermic process.

Screening and prioritizing substances in groundwater in the Beijing-Tianjin-Hebei region of the North China Plain based on exposure and hazard assessments

Screening and prioritizing hazardous substances in groundwater is crucial to monitor and control groundwater quality. Total of 283 substances were determined in 213 groundwater samples from the Beijing-Tianjin-Hebei region during 2019-2020. 184 substances were screened as candidates. 22 prioritizing indicators were evaluated and scored for the candidates to reflect their occurrence, mobility, persistence, bioaccumulation, acute and chronic ecotoxicities with different trophic levels, and long-term human health effects. Multi-attribute decision-making technologies were applied to prioritize these candidates, including analytic hierarchy process (AHP), TOPSIS and VIKOR. Greater weightings in AHP were assigned to attributes of occurrence and acute toxicity by experts' judgment. Hierarchical cluster analysis and principal component analysis were used to transform initial matrix with the 22 indicators into an orthogonalized matrix with 6 principal components, which represented general toxicity to aquatic organism and mammal, bioaccumulation, carcinogenicity & mutagenicity, persistence, and teratogenicity & endocrine, respectively. VIKOR and TOPSIS results were similar, but different from the AHP ranking. Two filter criteria harmonized their difference. Twenty-three substances were proposed as the priority substance with high hazard and potential exposure, and nitrate-nitrogen and ammonia-nitrogen were selected as additional priority substance frequently and extensively exceeding official groundwater quality standard on the regional scale.