Practical Opportunities to Improve the Impact of Health Risk Assessment on Environmental and Public Health Decisions

Ascertaining priority control pollution sources and target pollutants in toxic metal risk management of a medium-sized industrial city

Re-suspended surface dust (RSD) often poses higher environmental risks due to its specific physical characteristics. To ascertain the priority pollution sources and pollutants for the risk control of toxic metals (TMs) in RSD of medium-sized industrial cities, this study took Baotou City, a representative medium-sized industrial city in North China, as an example to systematically study TMs pollution in RSD. The levels of Cr (242.6 mg kg-1), Pb (65.7 mg kg-1), Co (54.0 mg kg-1), Ba (1032.4 mg kg-1), Cu (31.8 mg kg-1), Zn (81.7 mg kg-1), and Mn (593.8 mg kg-1) in Baotou RSD exceeded their soil background values. Co and Cr exhibited significant enrichment in 94.0 % and 49.4 % of samples, respectively. The comprehensive pollution of TMs in Baotou RSD was very high, mainly caused by Co and Cr. The main sources of TMs in the study area were industrial emissions, construction, and traffic activities, accounting for 32.5, 25.9, and 41.6 % of the total TMs respectively. The overall ecological risk in the study area was low, but 21.5 % of samples exhibited moderate or higher risk. The carcinogenic risks of TMs in the RSD to local residents and their non-carcinogenic risks to children cannot be ignored. Industrial and construction sources were priority pollution sources for eco-health risks, with Cr and Co being the target TMs. The south, north and west of the study area were the priority control areas for TMs pollution. The probabilistic risk assessment method combining of Monte Carlo simulation and source analysis can effectively identify the priority pollution sources and pollutants. These findings provide scientific basis for TMs pollution control in Baotou and constitute a reference for environmental management and protection of residents' health in other similar medium-sized industrial cities.

Refining acute human exposure assessment to pesticides in surface water: An integrated data-driven modeling approach

The substantial spatial and temporal variability of pesticides has led to large uncertainties when determining their peak aqueous concentrations. There is however a lack of large-scale studies dealing with accurate determination of annual maximum daily concentration (AMDC) across the landscape and over time based on the publicly available monitoring data. We developed a novel data-driven approach that firstly used time series modeling to generate AMDCs for qualified water monitoring sites in the conterminous U.S. With feature variables such as pesticide use and land cover compiled into the dataset, machine learning models using eXtreme Gradient Boosting (XGBoost) and Random Forest Regressor (RF) were then developed to estimate AMDCs in surface waters across the U.S. Both models exhibited significant predictability, while a hybrid model consisting of the average predictions by XGBoost and RF model had the highest prediction accuracy (mean absolute error (MAE): 1.23; R²: 0.61). The analysis of permutation variable importance indicated that pesticide use and drainage area were the two most important drivers. Partial dependence analysis revealed that pesticide use, precipitation, cultivated crop land cover and solubility exhibited concentration-promoting effects, whereas drainage area and molecular weight had concentration-demoting effects. Soil adsorption coefficient (Koc) showed nonmonotonic effects. The hybrid model was used to predict and map AMDCs of four example pesticides, including 2,4-dichlorophenoxyacetic acid (2,4-D), atrazine, glyphosate and imidacloprid during 2016-2019 at national scale. The predictive capability was validated using independent monitoring datasets. The fully evaluated approach significantly reduced the uncertainties in modeling annual peak concentrations and served as a valuable solution for conducting geographically oriented, highly refined exposure assessments for pesticides.