A synthetic health risk assessment based on geochemical equilibrium simulation and grid spatial interpolation for zinc (II) species.

Soil heavy metal pollution has become a global issue involving environmental safety and human health risks. This paper quantified the sources of heavy metals by positive matrix factorization (PMF) model and explored the spatial distribution of heavy metals by means of grid scales, with an industrial site as the study area in Suzhou. The PMF identified four pollution sources of heavy metal in soil, and the quantitative results revealed that industrial activities (33.5%) contributed the most to heavy metals, followed by soil parent materials (30.8%) and agricultural activities (19.7%). Zinc (Zn) was screened out as the targeted metal (TM) through the potential ecological risk assessment, the metal species of which was simulated by the geochemical software PHREEQC. This research aimed to determine the dominant metal species of TM with high-risk levels to realize the transformation of toxic metal species. Herein, according to the morphological evolution of metal species, the activity and concentration of the Zn ion species were obtained for both carcinogenic and non-carcinogenic risk assessment. The evaluation of the optimized human health risk demonstrated that the associated health risk of Zn (II) ions depended predominantly on its metal speciation. Overall, the optimized carcinogenic and non-carcinogenic risk value of Zn2S32- for adults was 2.01E-04 and for children was 1.31, resulting in corresponding hazardous risk to humans, which accounted for high-risk levels of 61.5% and 58.5% for adults and children, respectively. The OHRA method can provide a reference for the decision-making of soil heavy metal pollution and remediation for specific heavy metals in polluted areas.

Projections of meteorological drought based on CMIP6 multi-model ensemble: A case study of Henan Province, China.

Future changes in drought events are critical for risk assessment and associated policymaking. In this study, the future changes in meteorological droughts in Henan Province, China are explored. Random forests downscaling model is first constructed based on ERA5 reanalysis data and meteorological observations. The model is validated using evaluation indices such as R2 and RMSE, and is shown to be able to capture the relationship between large-scale predictors and monthly precipitation. The validated random forests downscaling model is driven by multiple global climate models (GCMs) from the Phase 6 of the Coupled Model Intercomparison Project (CMIP6) under three emission scenarios for projecting three future drought characteristics (duration, frequency, and intensity). Results show that drought frequency decreases in most areas of Henan while drought duration and intensity increase in various degrees. Some differences are also observed among different emission scenarios, especially under SSP2-4.5, where the magnitudes of changes in drought duration and intensity are lower relative to other scenarios. The decrease in drought frequency in most areas is found to be caused by increases in monthly mean precipitation in this study. Changes in drought duration and intensity are related to a combination of increases in precipitation mean and variability.

Exploring human health risk assessment based on the screening of primary targeted metal and chemical balance simulation of ionic speciation in an industrial area, China.

A comprehensive human health risk assessment methodology based on major hazard element screening and morphological evolution simulation is proposed. The primary targeted metal (PTM) screened by classical health risk assessment was introduced into chemical balance simulation to obtain speciation distribution and corresponding risk. According to the results of risk assessment of morphological evolution of primary targeted metal (PTM), a potential methodology for the remediation is proposed, which could reduce the risk level efficiently and quickly by changing the pH of soil environment with additional acid and alkali substances. A case study was performed in a dye factory in Suzhou city, Jiangsu Province, China. The results of classical health risk assessment showed that the regional health risk index for children exceeded tolerance value of 1, in which Cr accounted for high risk level of 61%. Chemical balance simulation results showed that CrO42- and CaCrO4 had the highest risk index, and the change of pH value would affect the proportion of CrO42- and CaCrO4 in Cr6+ ionic speciation, which may indirectly change the risk level. It is recommended to adjust the pH of the soil environment to quickly reduce regional human health risk. This study will provide a theoretical basis for public health protection and site restoration management.

Planning of municipal solid waste management under dual uncertainties.

Municipal solid waste management is a complex and multidisciplinary problem, involving a number of impact factors associated with various uncertainties. In this study, a hybrid interval-parameter possibilistic programming (IPP) approach was developed and applied for planning municipal solid waste management under dual uncertainties. The IPP improves upon the existing management approaches by allowing possibility distributions of the lower and upper bounds of some interval parameters in the objective function and interval information in the modelling coefficients to be effectively incorporated within its optimization. By introducing the concept of possibilistic interval numbers, the dual uncertainties can be communicated into the optimization process and the resulting solutions, such that the generated decision schemes can effectively reflect the highly complex system features under uncertainty. The results of the case study indicate that useful information can be obtained for providing feasible decision schemes for waste flow allocation. Different decision schemes can be generated by adjusting waste flow allocation patterns within the solution intervals. Lower decision variable values should be used to obtain lower system cost of waste treatment and disposal under advantageous conditions, and higher decision variable values should be used under demanding conditions (worst case conditions). A strong desire to acquire the lower system cost will lead to the decreased probability of meeting the treatment and disposal requirements (i.e. the increased risk of unforeseen conditions); willingness to accept the upper limit of the system cost will guarantee that waste treatment and disposal requirements are met.