Risk-based prioritization methodology for the classification of groundwater pollution sources

A Bayesian network-GIS probabilistic model for addressing human disturbance risk to ecological conservation redline areas

Population growth and associated ecological space occupation are posing great risks to regional ecological security and social stability. In China, "Ecological Conservation Redline" (ECR) that prohibited urbanization and industrial construction has been proposed as a national policy to resolve spatial mismatches and management contradictions. However, unfriendly human disturbance activities (e.g., cultivation, mining, and infrastructure construction) still exist within the ECR, posing a great threat to ecological stability and safety. In this article, a Bayesian network (BN)-GIS probabilistic model is proposed to spatially and quantitatively address the human disturbance risk to the ECR at the regional scale. The Bayesian models integrate multiple human activities, ecological receptors of the ECR, and their exposure relationships for calculating the human disturbance risk. The case learning method geographic information systems (GIS) is then introduced to train BN models based on the spatial attribute of variables to evaluate the spatial distribution and correlation of risks. This approach was applied to the human disturbance risk assessment for the ECR that was delineated in 2018 in Jiangsu Province, China. The results indicated that most of the ECRs were at a low or medium human disturbance risk level, while some drinking water sources and forest parks in Lianyungang City possessed the highest risk. The sensitivity analysis result showed the ECR vulnerability, especially for cropland, that contributed most to the human disturbance risk. This spatially probabilistic method can not only enhance model's prediction precision, but also help decision-makers to determine how to establish priorities for policy design and conservation interventions. Overall, it presents a foundation for later ECR adjustments as well as for human disturbance risk supervision and management at the regional scale.

Preliminary risk assessment of regional industrial enterprise sites based on big data

An accurate and inexpensive preliminary risk assessment of industrial enterprise sites at a regional scale is critical for environmental management. In this study, we propose a novel framework for the preliminary risk assessment of industrial enterprise sites in the Yangtze River Delta, which is one of the fastest economic development and most prominent contaminated regions in China. Based on source-pathway-receptors, this framework integrated text and spatial analyses and machine learning, and its feasibility was validated with 8848 positive and negative samples with a calibration and validation set ratio of 8:2. The results indicated that the random forest performed well for risk assessment; and its accuracy, precision, recall, and F1 scores in the calibration set were all 1.0, and the four indicators for the validation set ranged from 0.97 to 0.98, which was better than that for the other models (e.g., logistic regression, support vector machine, and convolutional neural network). The preliminary risk ranking of industrial enterprise sites by the random forest showed that high risks (probabilities) were mainly distributed in Shanghai, southern Jiangsu, and northeastern Zhejiang from 2000 to 2015. The relative importance of the site industrial, production, and geographical features in the random forest was 69%, 22%, and 9%, respectively. Our study highlights that we could quickly and effectively establish a priority (or ranking) list of industrial enterprise sites that require further investigations, using the proposed framework, and identify potentially contaminated sites.

An integrated assessment methodology for management of potentially contaminated sites based on public data

Ranking assessment of potentially contaminated sites (PCS) provides a great quantity of information (namely the risk screening list) that is usually examined by environmental managers, and therefore reduces the cost of risk management in terms of site investigation. Here we propose an integrated assessment methodology to establish a risk screening list of PCS in China using the Choquet integral correlation coefficient (ICC), which takes the uncertainty and interaction of PCS attributes into explicit account. The proposed method globally considers the importance and ordered positions of PCS attributes while reflecting their overall ranking. The model evaluation and actual validation results demonstrate the success in PCS ranking by the proposed method, which is superior to other methods such as the intuitionistic fuzzy multiple attribute decision-making, the technique for order preference by similarity to an ideal solution, and the weighted average. The resulting spatial distribution of Choquet ICC indicates that high-attention PCS in China are mainly located in Guangdong, Jiangsu, Zhejiang, and Shandong Provinces. This study is the first attempt to conduct a ranking assessment of PCS across China. The proposed assessment method based on Choquet ICC offers a step towards establishing a risk screening list of PCS globally.

Stochastic modelling of solute mass discharge to identify potential source zones of groundwater diffuse pollution

In heavily urbanised areas, groundwater diffuse pollution is recognised as one of the most insidious threats to groundwater quality. Diffuse pollution originates from multiple small sources releasing a low contaminant mass over a relatively large area; the lack of a defined plume in groundwater, the limited leaked mass, and the fact that leakage may have occurred in the past and be now ceased, make these sources difficult to locate and characterise. In addressing this environmental issue, an inverse approach based on the Null space Monte Carlo stochastic method has been applied in the framework of an innovative methodology with the aim to locate potential source areas distributed in a large (120 km²) urban area. To simplify the problem and better understand the limitations and effectiveness of the proposed methodology, the analysis has been performed using a groundwater model with fixed (i.e., determined by a previous calibration) hydraulic conductivity and flow boundary conditions. The only source of uncertainty considered in the study is the PCE mass discharge from all model cells of the topmost layer. After implementing and calibrating a deterministic solute transport model, multiple random realisations of mass discharge fields were generated, all of which are history-match constrained and hydrogeologically plausible. The obtained stochastic parameter sets were used to investigate the statistical distribution of the solute mass discharge and map the areas that are more likely to host unknown sources of PCE. Although the application of the NSMC stochastic method on the synthetic case study has provided promising results, it has also highlighted that multiple sources of uncertainty (e.g., continuity and duration of each source, attenuation processes) could adversely affect the reliability of the results in a real-world context, in which the effect of other uncertain parameters (hydraulic conductivity amongst all) would need to be considered in addition. This study offers new insights to the problem of aquifer diffuse pollution by providing key information on the potential source zones and on the areas that urgently need to be prioritised for further investigations.

An intelligent clustering method for devising the geochemical fingerprint of underground aquifers

Geochemical fingerprinting is a rapidly expanding discipline in the earth and environmental sciences, anchored in the recognition that geological processes leave behind physical, chemical and sometimes also isotopic patterns in the samples. Furthermore, the geochemical fingerprinting of natural cycles (water, carbon, soil and biota fingerprinting) are influenced by the anthropogenic impact and by the climate change. So, their monitoring is a tool of resilience and adaptation. In recent years, computational statistics and artificial intelligence methods have started to be used to help the process of geochemical fingerprinting. In this paper we consider data from 57 wells located in the province of Ferrara (Italy), all belonging to the same geological group and separated into 4 different aquifers. The aquifer from which each well extracts its water is known only in 18 of the 57 cases, while in other 39 cases it can be only hypothesized based on geological considerations. We devise a novel technique for geochemical fingerprinting of groundwater by means of which we are able to identify the exact aquifer from which a sample is extracted with a sufficiently high accuracy. Then, we experimentally prove that out method is sensibly more accurate than typical statistical approaches, such as principal component analysis, for this particular problem.

Prioritization of potentially contaminated sites: A comparison between the application of a solute transport model and a risk-screening method in China

Numerous potential contaminated sites in China pose a substantial risk to human health and the local ecology. Thus, there is an urgent need to prioritize and further investigate potential contaminated sites and determine those that pose a threat in this regard. Newly developed by the Ministry of Ecology and Environment, the Risk Screening Method (RSM) scoring system is employed to assess soil and groundwater risk across China. In this study, the RSM is tested at a screening level and compared with the EPACMTP model, a solute transport model developed for the risk assessment of land disposal sites. First, a regional sensitivity analysis is conducted for EPACMTP model parameters, and those with significant sensitivity are compared with the risk indicators in the RSM. Second, 28 sites are evaluated by both prioritization methods in order to compare RSM risk scores and EPACMTP model simulations. Our results show that the RSM have similar risk assessing factors as EPACMTP model and its promising capability of prioritizing high-risk sites with very little available data. However, it does provide a conservative assessment, as risks at some sites are over-estimated, so further investigation is recommended for sites with high RSM risk scores. In addition, the initial screening should be documented by additional investigations at sites in order to prove the potential risk. The length of the period considered in the assessment has a great influence on prioritization results for heavy metals. As longer time scale will result in higher risk, its selection reflects the balance of current cost and future risk. The EPACMTP model provides a range of possible risks and can assess them within different timeframes. It is suggested to conduct further comparisons between the RSM and the solute transport models for sites from other areas, types of industries and more mobile compounds.

PCE point source apportionment using a GIS-based statistical technique combined with stochastic modelling

To meet the continuous growth of urbanised areas with the ever-increasing demand for safe water supplies, the implementation of new scientifically based methodologies can represent a key support for preventing groundwater quality deterioration. In this study, a new combined approach based on the application of the Weights of Evidence and the Null-Space Monte Carlo particle back-tracking methods was set up to assess tetrachloroethylene (PCE) contamination due to Point Sources in the densely urbanised north-eastern sector of the Milano FUA (Functional Urban Area). This combined approach offers the advantage of further enhancing the power of each individual technique by integrating both the advective transport mechanism, neglected by the Weights of Evidence, and the influence of specific factors, such as the land use variation, not considered by the Null-Space Monte Carlo particle tracking. To accurately test and explore the performance of this new approach, the analysis was carried out based on the simulation of synthetic PCE plumes using a groundwater numerical model already implemented in a previous study. The Weights of Evidence method revealed that the areas characterised by a groundwater depth lower than 17 m, a groundwater velocity higher than 2.6 × 10^-6 m/s, a recharge higher than 0.26 m/y and a significant variation of the industrial activities extent are the most susceptible to groundwater pollution. The Null-Space Monte Carlo particle back-tracking has proved to be effective in delineating the potential source zones and contaminant travel path. The proposed approach can offer additional insights for the protection of groundwater resource. The end-product provides crucial information on the zones that require to be prioritised for investigations and can be easily understood by non-expert decision-makers constituting an advanced tool for enhancing groundwater protection strategies.

Copula-based exposure risk dynamic simulation of dual heavy metal mixed pollution accidents at the watershed scale

Most heavy metal exposure and pollution results from multiple industrial activities, including metal processing in refineries, and microelectronics. These issues pose a great threat to human health, ecological balance, and even societal stability. During 2012-2017, China, in particular, faced the challenge of 23 heavy metals accidents, six of which were extraordinarily serious accidents. Accidental environmental pollution is rarely caused by a single heavy metal, but rather by heavy metal mixtures. To address the need for a joint exposure risk assessment for heavy metal mixed pollution accidents at the watershed scale, a Copula-based exposure risk dynamic simulation model was proposed. A coupled hydrodynamic and accidental heavy metal exposure model is constructed for an hourly simulation of the exposure fate of heavy metals from each risk source once accidental leakage has occurred. The Copula analysis was introduced to calculate the dual heavy metal joint exposure probability in real time. This method was applied to an acute Cr⁶⁺-Hg²⁺ joint exposure risk assessment for 43 electroplating plants in nine sub-watersheds within the Dongjiang River downstream basin. The results indicated seven risk sources (i.e., S1, S4, H18, H23, H27-H28, and H34) that presented relatively high exposure risk to their surrounding sub-watersheds. Spatially, the acute exposure risk level was highest in the tributary basin (sub-watershed XW) than in the mainstream (sub-watershed DW2) and the river network (sub-watershed RW) of the lower reaches of the Dongjiang River. This research highlights an effective probabilistic approach for performing a joint exposure risk analysis of heavy metal mixed pollution accidents at the watershed scale.

Distribution characteristics and health risk assessment of volatile organic compounds in the groundwater of Lanzhou City, China

Volatile organic compounds (VOCs) typically exist in the aqueous environment due to global anthropogenic activities. The distribution and contaminated profile (or characteristics) of VOCs in the groundwater of Lanzhou, China, were investigated in this study. Groundwater samples were collected from 30 sampling points in December 2015, and a total of 17 VOCs were analyzed by purge and trap gas chromatography-mass spectrometry. Thirteen types of VOCs were detected at 29 sampling points in the study area. Of these, dichloromethane and toluene, which were found at 22 sampling points, had the highest detection frequency (73.3%), followed by benzene (66.7%), 1,2-dichloroethane (50%), and xylenes (50%). The highest average concentration among the detected VOCs was found for chloroform (5151.5 μg/L). The spatial distribution of VOC contamination in four major urban areas of Lanzhou and the variation in VOC concentration caused by land use transitions were also analyzed. The results showed that Xigu district was the most polluted area in Lanzhou, mainly due to land use for industrial proposes. On the contrary, the samples for Anning district showed lower VOC concentrations because of better groundwater quality, which is associated with the absence of manufacturing industries in this region. The health risk assessment model developed by the United States Environmental Protection Agency was employed in this study to evaluate safety for drinking water use. This study found that despite considering the volatilization of VOCs from water due to heating, six sampling points (G05 in Qilihe district; G07 and G09 in Xigu district; G16, G17, and G15 in Chengguan district) showed non-carcinogenic risks, ranging from 1.63 to 14.2, while three points (G16 in Chengguan district, and G10 and G07 in Xigu district) exhibited high carcinogenic risks for human health, ranging from 2.94 × 10^-4 to 6.85 × 10^-4. Trichloroethylene, tetrachloroethylene, and 1,2-dichloroethylene were identified as the dominant VOCs, presenting high non-carcinogenic risk. 1,2-dichloroethane and vinyl chloride were the primary factors for high carcinogenic risk. The high-risk areas were concentrated in Xigu and Chengguan districts, suggesting the need to alert the relevant local government departments.

Implementation of long-term assessment of human health risk for metal contaminated groundwater: A coupled chemical mass balance and hydrodynamics model

Assessing human health risk using spatiotemporal migration and geochemical evolution concurrently in an area where the groundwater is contaminated with heavy metals can provide more instructive information to protect specific potential negative impacts on human health. In this research, we established a model of long-term assessment of human health risk for metal contaminated groundwater by coupling two models: the geochemical (based on the law of chemical mass balance) model and the hydrodynamics module. The hydrodynamics module is used to initially identify the total temporal concentration of various elements, and the chemical mass balance module is used to gain the concentration and ionic activity of various toxic elements according to the range of environmental pH. Effective concentrations calculated using activity weight (based on speciation and ionic activity) were introduced into the formula for risk analysis. The results of the study show that, with the exploitation and recharge of groundwater, the non-carcinogenic and carcinogenic health risks cannot be reduced to acceptable levels until 18 and 22 years, respectively. The calculated risk values of using the coupling model are lower than that of statistics or single hydrokinetics. The sensitivity analysis results show that this model is reliable. The recharge, pH and the permeability coefficient are defined as the most sensitive factors.

Introducing the risk aggregation problem to aquifers exposed to impacts of anthropogenic and geogenic origins on a modular basis using 'risk cells'

Proof-of-concept is presented in this paper to a methodology formulated for indexing risks to groundwater aquifers exposed to impacts of diffuse contaminations from anthropogenic and geogenic origins. The methodology is for mapping/indexing, which refers to relative values but not their absolute values. The innovations include: (i) making use of the Origins-Source-Pathways-Receptors-Consequences (OSPRC) framework; and (ii) dividing a study area into modular Risk (OSPRC) Cells to capture their idiosyncrasies with different origins. Field measurements are often sparse and comprise pollutants and water table, which are often costly; whereas supplementary data are general-purpose data, which are widely available. Risk mapping for each OSPRC cell is processed by dividing a study area into pixels and for each pixel, the risk from both anthropogenic and geogenic origins are indexed by using algorithms related to: (i) Vulnerability Indices (VI), which identify the potential for risk exposures at each pixel; and (ii) velocity gradient, which expresses the potency to risk exposures across the risk cell. The paper uses DRASTIC for anthropogenic VI but introduces a new framework for geogenic VI. The methodology has a generic architecture and is flexible to modularise risks involving any idiosyncrasies in a generic way in any site exposed to environmental pollution risks. Its application to a real study area provides evidence for the proof-of-concept for the methodology by a set of results that are fit-for-purpose and provides an insight into the study area together with the identification of its hotspots.

Screening of groundwater remedial alternatives for brownfield sites: a comprehensive method integrated MCDA with numerical simulation

Brownfield sites pollution and remediation is an urgent environmental issue worldwide. The screening and assessment of remedial alternatives is especially complex owing to its multiple criteria that involves technique, economy, and policy. To help the decision-makers selecting the remedial alternatives efficiently, the criteria framework conducted by the U.S. EPA is improved and a comprehensive method that integrates multiple criteria decision analysis (MCDA) with numerical simulation is conducted in this paper. The criteria framework is modified and classified into three categories: qualitative, semi-quantitative, and quantitative criteria, MCDA method, AHP-PROMETHEE (analytical hierarchy process-preference ranking organization method for enrichment evaluation) is used to determine the priority ranking of the remedial alternatives and the solute transport simulation is conducted to assess the remedial efficiency. A case study was present to demonstrate the screening method in a brownfield site in Cangzhou, northern China. The results show that the systematic method provides a reliable way to quantify the priority of the remedial alternatives.

The problem of identifying arsenic anomalies in the basin of Sahand dam through risk-based 'soft modelling'

An investigation is undertaken to identify arsenic anomalies at the complex of Sahand dam, East Azerbaijan, northwest Iran. The complex acts as a system, in which the impounding reservoir catalyses system components related to Origin-Source-Pathways-Receptor-Consequence (OSPRC) viewed as a risk system. This 'conceptual framework' overlays a 'perceptual model' of the physical system, in which arsenic with geogenic origins diffused into the formations through extensive fractures swept through the region during the Miocene era. Impacts of arsenic anomalies were local until the provision of the impounding reservoir in the last 10years, which transformed it into active system-wide risk exposures. The paper uses existing technique of: statistical, graphical, multivariate analysis, geological survey and isotopic study, but these often seem ad hoc and without common knowledgebase. Risk analysis approaches are sought to treat existing fragmentation in practices of identifying and mitigating arsenic anomalies. The paper contributes towards next generation best practice through: (i) transferring and extending knowledge on the OSPRC framework; (ii) introducing 'OSPRC cells' to capture unique idiosyncrasies at each cell; and (iii) suggesting a 'soft modelling' procedure based on assembling knowledgebase of existing techniques with partially converging and partially diverging information levels, where knowledgebase invokes model equations with increasing resolutions. The data samples from the study area for the period of 2002-12 supports the study and indicates the following 'risk cells' for the study area: (i) local arsenic risk exposures at south of the reservoir, (ii) system-wide arsenic risks at its north; and (iii) system-wide arsenic risk exposures within the reservoir even after dilution.

A multi-attribute methodology for the prioritisation of oil contaminated sites in the Niger Delta

The Ogoniland region of the Niger Delta contains a vast number of sites contaminated with petroleum hydrocarbons that originated from Nigeria's active oil sector. The United Nations Environment Programme (UNEP) reported on this widespread contamination in 2011, however, wide-scale action to clean-up these sites has yet to be initiated. A challenge for decision makers responsible for the clean-up of these sites has been the prioritisation of sites to enable appropriate allocation of scarce resources. In this study, a risk-based multi-criteria decision analysis framework was used to prioritise high-risk sites contaminated with petroleum hydrocarbons in the Ogoniland region of Nigeria. The prioritisation method used a set of risk-based attributes that took into account chemical and ecological impacts, as well as socio-economic impacts, providing a holistic assessment of the risk. Data for the analysis was taken from the UNEP Environmental Assessment of Ogoniland, where over 110 communities were assessed for oil-contamination. Results from our prioritisation show that the highest-ranking sites were not necessarily the sites with the highest observed level of hydrocarbon contamination. This differentiation was due to our use of proximity as a surrogate measure for likelihood of exposure. Composite measures of risk provide a more robust assessment, and can enrich discussions about risk management and the allocation of resources for the clean-up of affected sites.

Vulnerability assessment of atmospheric environment driven by human impacts

Atmospheric environment quality worsening is a substantial threat to public health worldwide, and in many places, air pollution due to the intensification of the human activity is increasing dramatically. However, no studies have been investigated the integration of vulnerability assessment and atmospheric environment driven by human impacts. The objective of this study was to identify and prioritize the undesirable environmental changes as an early warning system for environment managers and decision makers in term of human, atmospheric environment, and social economic elements. We conduct a vulnerability assessment method of atmospheric environment associated with human impact, this method integrates spatial context of Geographic Information System (GIS) tool, multi-criteria decision analysis (MCDA) method, ordered weighted averaging (OWA) operators under the Exposure-Sensitivity- Adaptive Capacity (ESA) framework. Decision makers can find out relevant vulnerability assessment results with different vulnerable attitudes. In the Beijing-Tianjin-Hebei (BTH) region, China, we further applied this developed method and proved it to be reliable and consistent with the China Environmental Status Bulletin. Results indicate that the vulnerability of atmospheric environment in the BTH region is not optimistic, and environment managers should do more about air pollution. Thus, the most appropriate strategic decision and development program of city or state can be picked out assisting by the vulnerable results.

A new tool for assessing sediment quality based on the Weight of Evidence approach and grey TOPSIS

Sediment is an important part of an aquatic ecosystem, so it is essential to develop an effective sediment quality assessment tool. This study aims to develop a new sediment quality assessment tool using a Weight of Evidence approach in combination with the grey TOPSIS (Technique for Order Preference by Similarity, a mathematical calculation of multi-criteria decision analysis). This tool can analyze data from chemical analyses, laboratory toxicity tests and benthic community structure analyses to generate individual results from each line of evidence, and integrate data from these three lines of evidence to obtain an overall assessment through an Excel Visual Basic for Application program. The tool can compare the relative magnitude of risks among sites and rate each site with high, moderate, or low ecological risk, thus guiding us to take pertinent measures toward polluted sediment. A case study of the sediment of Dongjiang River basin, south China, demonstrated the successful application of this tool. It proved that this assessment tool can provide a comprehensive and accurate assessment of sediment quality and efficiently discriminate risks among different sites, suggesting it is a powerful tool for environment risk assessment.