A risk-based methodology for ranking environmental chemical stressors at the regional scale

Characteristics and risk assessment of heavy metals in groundwater at a typical smelter-contaminated site in Southwest China

To explore the characteristics and evaluate the risk of heavy metals in groundwater at a typical smelter-contaminated site, this study focuses on a representative a historical arsenic smelting plant in Southwest China, where the primary historical products were metallic arsenic (∼1000 tons/year) and arsenic trioxide (∼2000 ton/year). The results demonstrated As and Pb as the main pollutants in soil, and As and Cd as main pollutants in groundwater through soil profiling and quarterly groundwater analysis. The maximum As and Pb in the surface soil were 76800 and 2290 mg/kg, respectively, with As vertically infiltrating the deep gravel-sand layer (18-20 m). The groundwater pollution distribution progressively increased along flow direction, influenced by seasonal surface runoff and infiltration fluctuations. The groundwater pollutant concentrations during the dry season notably surpassed those during the wet season, with maximum As and Cd concentrations of 111.64 mg/L and 19.85 μg/L during the dry season, respectively. Furthermore, the analytic hierarchy process (AHP) was applied to evaluate the comprehensive risk of contaminated-site across pollution source load, regional groundwater intrinsic vulnerability, and evaluation of nearby sensitive receptors. The results revealed that the carcinogenic risk of lead in surface soil was moderate to high, while arsenic posed a high carcinogenic risk, contributing to an overall carcinogenic risk proportion of 89.6% in surface soil. Exposure through groundwater intake was identified as the primary pathway, with carcinogenic and noncarcinogenic risks exceeding those through skin contact. The final weights result demonstrated that the principal risk factors are the intrinsic arsenic load and protective target characteristics of regional groundwater at this site. This study provides a reference for comprehensive assessments of similarly contaminated industrial and smelting sites.

Risk Field of Rock Instability using Microseismic Monitoringdata in Deep Mining

With the gradual depletion of surface resources, rock instability caused by deep high stressand mining disturbance seriously affects safe mining. To create effective risk management, a rockinstability risk field model using microseismic monitoring data is proposed in this study. Rockinstability risk was presented visually in 3D visualization. The in-situ microseismic monitoringdata was collected and analyzed to make calculation of peak ground velocity (PGV), peak groundacceleration (PGA), energy flux, energy and seismic moment. Indicator weights of PGV, PGA, energyflux are confirmed by using the analytic hierarchy process (AHP) to calculate risk severity. The Copulafunction is then used to solve the joint probability distribution function of energy and seismic moment.Then the spatial distribution characteristics of risk can be obtained by data fitting. Subsequently, thethree-dimensional (3D) risk field model was established. Meanwhile, the established risk field isverified by comparing monitoring data without disturbance and the blasting data with disturbance.It is suggested that the proposed risk field method could evaluate the regional risk of rock instabilityreasonably and accurately, which lays a theoretical foundation for the risk prediction and managementof rock instability in deep mining.

A PETAR method for risk assessment of human health and environment on the regional scale

Risk assessments are necessary to effectively reveal the state of the degradation of living environments on a regional scale. However, risk assessments are often limited by time, cost, and technology, which make conducting effective evaluations difficult. Thus, in this study, the procedure for ecological tiered assessment of risk (PETAR) method was used to analyze the human health and environmental risks in Daye, China. This method first used the United States Environmental Protection Agency's risk assessment approach to qualitatively determine the risk sources, pressures, receptors, and effect endpoints and constructed a conceptual model of threats to the human living environment. Each risk-prone subregion was then evaluated using the fuzzy logic method. Next, a quantitative assessment was conducted for the subregions with the most serious environmental degradation. Finally, quantitative analyses were performed to verify the original hypotheses. The results showed that the high-risk areas were distributed in the industrial regions of Daye, wherein mining and processing clusters and mining settlements are widespread and confirmed the locations of the particular subregions with the most serious human health and environmental risks. This study also validated the practicality of the PETAR method for human health risk assessments in mining areas with large-scale, multifactor, and multihazard paths. Integr Environ Assess Manag 2023;19:239-253. © 2022 SETAC.

Causal Analysis of Ecological Impairment in Land Ecosystem on a Regional Scale: Applied to a Mining City Daye, China

We adopted a weight of evidence approach to establish a causal analysis of an impaired land ecosystem on a regional scale; namely, Daye, a traditional mining city in China. Working processes, including problem statements, a list of candidate causes, and a conceptual model were developed to represent a causal hypothesis for describing land degradation. Causal criteria were applied to integrate multiple lines of evidence. Then, various pieces of evidence were scored to either strengthen or weaken our causal assumptions. Results showed that habitat alteration, heavy metal accumulation, organic pollutants, water eutrophication, and nutrient runoff were the probable causes of land ecosystem impairment in Daye. Meanwhile, noxious gas, toxicants, altered underground runoff, atmospheric deposition, and acid rain were identified as possible causes. The most unlikely causes were altered hydrology, altered earth surface runoff, and soil erosion. Soil salinization, soluble inorganic salts, biological species invasion, and pathogens were deferred as delayed causes due to lack of adequate information. The causal analysis approach was applied to identify the primary causes of land degradation and implement accurate protective measures in an impaired land ecosystem.

Quantifying and predicting ecological and human health risks for binary heavy metal pollution accidents at the watershed scale using Bayesian Networks

The accidental leakage of industrial wastewater containing heavy metals from enterprises poses great risks to resident health, social instability, and ecological safety. During 2005-2018, heavy metal mixed pollution accidents comprised approximately 33% of the major environmental ones in China. A Bayesian Networks-based probabilistic approach is developed to quantitatively predict ecological and human health risks for heavy metal mixed pollution accidents at the watershed scale. To estimate the probability distributions of joint ecological exposure once a heavy metal mixed pollution accident occurs, a Copula-based joint exposure calculation method, comprised of a hydro-dynamic model, emergent heavy metal pollution transport model, and the Copula functions, is embedded. This approach was applied to the risk assessment of acute Cr⁶⁺-Hg²⁺ mixed pollution accidents at 76 electroplating enterprises in 24 risk sub-watersheds of the Dongjiang River downstream watershed. The results indicated that nine sub-watersheds created high ecological risks, while only five created high human health risks. In addition, the ecological and human health risk levels were highest in the tributary (the Xizhijiang River), while the ecological risk was more critical in the river network, and the human health risk was more serious in the mainstream of the Dongjiang River. The quantitative risk assessment provides a substantial support to incident prevention and control, risk management, as well as regulatory decision making for electroplating enterprises.

Emergency material location-allocation planning using a risk-based integration methodology for river chemical spills

It is critical for emergency material preparedness in the pre-accident phase to provide location-allocation planning and improve rescue capacity in an effective emergency response time due to increasing frequency of river chemical spills. In this study, an effective two-stage evaluation and selection framework is developed integrating fuzzy multi-criteria decision-making (MCDM) method and multi-objective optimization model to obtain the optimal emergency material location-allocation (EMLA) scheme for coping with river chemical spills. In the evaluation stage, the emergency material warehouse alternatives are evaluated by a fuzzy TOPSIS method based on environmental risk assessment. In the selection stage, the EMLA optimization scheme is identified by a multi-objective optimization model to allocate emergency materials for all the risk sources in a time-effective manner. The two-stage evaluation and selection framework is then applied in Jiangsu province, China. The EMLA optimization scheme finally selects the best five emergency material warehouses (WZ1, WZ 4, WZ 5, WZ 18, and WZ 25) for Jiangsu province with the relative closeness 0.6014, 0.4676, 0.5179, 0.3360, and 0.2935, respectively. The EMLA results demonstrate that the developed framework could obtain EMLA optimization scheme with the objective of minimum emergency rescue points and maximum integrative rescue abilities and provide all the risk resources emergency materials in a quick response for river chemical spills in the pre-accident phase.

Methodology of spatial risk assessment for arsenic species associated with sampling and analysis results optimization

The conventional risk assessment methods may be defective for more effective health risk assessment due to ignoring heavy metal species and the accuracy and integrity of sampling and analysis results. Using the accurate and integral data to quantify the human health effects of metal species can provide great support for more effective health risk assessment. This study presents a new methodology to optimize sampling and analysis results for implementing the spatial human health risk of heavy metal species in contaminated sites. The method integrated Entropy method and Inverse Distance to a Power (IDW) for obtaining the effective risk, and mapping the visual risk distribution of metal species. The results of its application with ingesting arsenic via oral route on adults showed that carcinogenic and non-carcinogenic risks of As were influenced by its species. The risk of HAsO₄^-2, H₃AsO₃, H₂AsO₄^-, H₂AsO₃^- and AsS(OH)HS^- exceeded threshold that was significantly harmful to human health in study area. This method broadened the scope of human health risk assessment and provided a basis for government policy-making and site remediation.

Risk-based prioritization method for the classification of groundwater pesticide pollution from agricultural regions

Agricultural regions are a significant source of groundwater pesticide pollution. To ensure that agricultural regions with a significantly high risk of groundwater pesticide contamination are properly managed, a risk-based ranking method related to groundwater pesticide contamination is needed. In the present paper, a risk-based prioritization method for the classification of groundwater pesticide pollution from agricultural regions was established. The method encompasses 3 phases, including indicator selection, characterization, and classification. In the risk ranking index system employed here, 17 indicators involving the physicochemical properties, environmental behavior characteristics, pesticide application methods, and inherent vulnerability of groundwater in the agricultural region were selected. The boundary of each indicator was determined using K-means cluster analysis based on a survey of a typical agricultural region and the physical and chemical properties of 300 typical pesticides. The total risk characterization was calculated by multiplying the risk value of each indicator, which could effectively avoid the subjectivity of index weight calculation and identify the main factors associated with the risk. The results indicated that the risk for groundwater pesticide contamination from agriculture in a region could be ranked into 4 classes from low to high risk. This method was applied to an agricultural region in Jiangsu Province, China, and it showed that this region had a relatively high risk for groundwater contamination from pesticides, and that the pesticide application method was the primary factor contributing to the relatively high risk. The risk ranking method was determined to be feasible, valid, and able to provide reference data related to the risk management of groundwater pesticide pollution from agricultural regions. Integr Environ Assess Manag 2017;13:1052-1059. © 2017 SETAC.

A novel approach for exposure assessment in air pollution epidemiological studies using neuro-fuzzy inference systems: Comparison of exposure estimates and exposure-health associations

Many epidemiological studies have used proximity to sources as air pollution exposure assessment method. However, proximity measures are not generally good surrogates because of their complex non-linear relationship with exposures. Neuro-fuzzy inference systems (NFIS) can be used to map complex non-linear systems, but its usefulness in exposure assessment has not been extensively explored. We present a novel approach for exposure assessment using NFIS, where the inputs of the model were easily-obtainable proximity measures, and the output was residential exposure to an air pollutant. We applied it to a case-study on NH₃ pollution, and compared health effects and exposures estimated from NFIS, with those obtained from emission-dispersion models, and linear and non-linear regression proximity models, using 10-fold cross validation. The agreement between emission-dispersion and NFIS exposures was high (Root-mean-square error (RMSE) =0.275, correlation coefficient (r)=0.91) and resulted in similar health effect estimates. Linear models showed poor performance (RMSE=0.527, r=0.59), while non-linear regression models resulted in heterocedasticity, non-normality and clustered data. NFIS could be a useful tool for estimating individual air pollution exposures in epidemiological studies on large populations, when emission-dispersion data are not available. The tradeoff between simplicity and accuracy needs to be considered.

Decision support for risk prioritisation of environmental health hazards in a UK city

BACKGROUND

There is increasing appreciation of the proportion of the health burden that is attributed to modifiable population exposure to environmental health hazards. To manage this avoidable burden in the United Kingdom (UK), government policies and interventions are implemented. In practice, this procedure is interdisciplinary in action and multi-dimensional in context. Here, we demonstrate how Multi Criteria Decision Analysis (MCDA) can be used as a decision support tool to facilitate priority setting for environmental public health interventions within local authorities. We combine modelling and expert elicitation to gather evidence on the impacts and ranking of interventions.

METHODS

To present the methodology, we consider a hypothetical scenario in a UK city. We use MCDA to evaluate and compare the impact of interventions to reduce the health burden associated with four environmental health hazards and rank them in terms of their overall performance across several criteria. For illustrative purposes, we focus on heavy goods vehicle controls to reduce outdoor air pollution, remediation to control levels of indoor radon, carbon monoxide and fitting alarms, and encouraging cycling to target the obesogenic environment. Regional data was included as model evidence to construct a ratings matrix for the city.

RESULTS

When MCDA is performed with uniform weights, the intervention of heavy goods vehicle controls to reduce outdoor air pollution is ranked the highest. Cycling and the obesogenic environment is ranked second.

CONCLUSIONS

We argue that a MCDA based approach provides a framework to guide environmental public health decision makers. This is demonstrated through an online interactive MCDA tool. We conclude that MCDA is a transparent tool that can be used to compare the impact of alternative interventions on a set of pre-defined criteria. In our illustrative example, we ranked the best intervention across the equally weighted selected criteria out of the four alternatives. Further work is needed to test the tool with decision makers and stakeholders.

Definition and applications of a versatile chemical pollution footprint methodology

Because of the great variety in behavior and modes of action of chemicals, impact assessment of multiple substances is complex, as is the communication of its results. Given calls for cumulative impact assessments, we developed a methodology that is aimed at expressing the expected cumulative impacts of mixtures of chemicals on aquatic ecosystems for a region and subsequently allows to present these results as a chemical pollution footprint, in short: a chemical footprint. Setting and using a boundary for chemical pollution is part of the methodology. Two case studies were executed to test and illustrate the methodology. The first case illustrates that the production and use of organic substances in Europe, judged with the European water volume, stays within the currently set policy boundaries for chemical pollution. The second case shows that the use of pesticides in Northwestern Europe, judged with the regional water volume, has exceeded the set boundaries, while showing a declining trend over time. The impact of mixtures of substances in the environment could be expressed as a chemical footprint, and the relative contribution of substances to that footprint could be evaluated. These features are a novel type of information to support risk management, by helping prioritization of management among chemicals and environmental compartments.