Risk Of Groundwater Pollution Research Articles

Early Warning of Regional Groundwater Pollution： A Case Study for Plain Area of Barkol-Yiwu Basin

Groundwater pollution early warning is an effective means for regional groundwater pollution prevention. The groundwater pollution early warning model coupled with the current situation of groundwater quality, groundwater quality variation trend, and groundwater pollution risk were applied to the plain area of Barkol-Yiwu Basin, and the regional scale groundwater pollution early warning was realized by combining the early warning of groundwater quality status and trend. The TOPSIS method based on comprehensive weight was used to evaluate the current situation of groundwater quality. The variation trend of groundwater quality was analyzed by calculating the trend interpolation results of 18 in-situ groundwater quality monitoring wells. The groundwater vulnerability map, groundwater pollution load map, and groundwater function value map were superimposed using the superposition index method to evaluate groundwater pollution risk. The results showed that the groundwater quality was good and relatively good, and the poor groundwater quality in some areas was mainly affected by the shallow groundwater depth and the large porosity of the vadose zone. Groundwater quality was stable from 2011 to 2022, mainly due to the leakage of wastewater generated by industries and agriculture into groundwater, resulting in the deterioration of groundwater quality in some areas. Groundwater pollution risk was generally low, and the dual effects of high vulnerability and high pollution load of groundwater led to local areas with high pollution risk. The early warning level of groundwater pollution was generally low, and the heavy and highly heavy warning areas accounted for 16.4% and 17.5% of the study area, respectively, mainly distributed in Xiamaya Township of Yiwu County and northern Santanghu Town, Dahongliuxia Township, and Dahe Town of Barkol County. The quaternary sediments exposed were mainly sandy pebbles, with developed pores and strong water permeability. The interception and adsorption capacity of pollutants were weak. Pollutants produced by industries, agriculture, and life easily leaked into groundwater aquifers, resulting in poor groundwater quality and high risk of groundwater pollution, which ultimately led to a high early warning level of groundwater pollution in some areas. The research on early warning of groundwater pollution provided an important theoretical basis for the development of groundwater pollution remediation.

Quantitative Assessment and Validation of Groundwater Pollution Risk in Southwest Karst Area

AbstractGroundwater pollution risk assessment is a useful tool for groundwater pollution prevention and control. However, it is difficult to accurately quantify groundwater flow and contaminant fluxes in karst areas and different types of karst areas have different hydrogeological characteristics. Therefore, the assessment of groundwater pollution risk in karst areas must use different assessment indicator systems. This study developed a new methodology that modified the vulnerability assessment model PLEIK, determined pollutant fluxes considering hydrogeological conditions, and revised parameter weights using the random forest method. The resulting PLEIKD-RF model was used to assess the risk of groundwater contamination in the southwestern karst region and its validity was verified. The results showed that the groundwater pollution risk in the region was low, with 65.64% of the low and relatively low risk areas located in the middle and high mountainous regions. 11.81% of the high and relatively high risk areas were sporadically located in the western and central regions, which were mainly controlled by the distribution of the pollution sources and the karst development. The accuracy of the results of groundwater pollution risk assessment in the study area was 71.87% as verified by the horizontal difference method. The results of the sensitivity analysis indicated that accurate, detailed, and representative data on the protective layer, surface water-groundwater interactions, and pollution source loads would improve the accuracy of groundwater pollution risk zoning. This assessment method provided a reference for similar assessments and the results provide a basis for the protection and management of groundwater resources in the region.

Mapping and assessment of groundwater pollution risks in the main aquifer of the Mostaganem plateau (Northwest Algeria): utilizing the novel vulnerability index and decision tree model.

Water plays a pivotal role in socio-economic development in Algeria. However, the overexploitations of groundwater resources, water scarcity, and the proliferation of pollution sources (including industrial and urban effluents, untreated landfills, and chemical fertilizers, etc.) have resulted in substantial groundwater contamination. Preserving water irrigation quality has thus become a primary priority, capturing the attention of both scientists and local authorities. The current study introduces an innovative method to mapping contamination risks, integrating vulnerability assessments, land use patterns (as a sources of pollution), and groundwater overexploitation (represented by the waterhole density) through the implementation of a decision tree model. The resulting risk map illustrates the probability of contamination occurrence in the substantial aquifer on the plateau of Mostaganem. An agricultural region characterized by the intensive nutrients and pesticides use, the significant presence of septic tanks, widespread illegal dumping, and a technical landfill not compliant with environmental standards. The critical situation in the region is exacerbated by excessive groundwater pumping surpassing the aquifer's natural replenishment capacity (with 115 boreholes and 6345 operational wells), especially in a semi-arid climate featuring limited water resources and frequent drought. Vulnerability was evaluated using the DRFTID method, a derivative of the DRASTIC model, considering parameters such as depth to groundwater, recharge, fracture density, slope, nature of the unsaturated zone, and the drainage density. All these parameters are combined with analyses of inter-parameter relationship effects. The results show a spatial distribution into three risk levels (low, medium, and high), with 31.5% designated as high risk, and 56% as medium risk. The validation of this mapping relies on the assessment of physicochemical analyses in samples collected between 2010 and 2020. The results indicate elevated groundwater contamination levels in samples. Chloride exceeded acceptable levels by 100%, nitrate by 71%, calcium by 50%, and sodium by 42%. These elevated concentrations impact electrical conductivity, resulting in highly mineralized water attributed to anthropogenic agricultural pollution and septic tank discharges. High-risk zones align with areas exhibiting elevated nitrate and chloride concentrations. This model, deemed satisfactory, significantly enhances the sustainable management of water resources and irrigated land across various areas. In the long term, it would be beneficial to refine "vulnerability and risk" models by integrating detailed data on land use, groundwater exploitation, and hydrogeological and hydrochemical characteristics. This approach could improve vulnerability accuracy and pollution risk maps, particularly through detailed local data availability. It is also crucial that public authorities support these initiatives by adapting them to local geographical and climatic specificities on a regional and national scale. Finally, these studies have the potential to foster sustainable development at different geographical levels.

Integrating HYDRUS-2D and Bayesian Networks for simulating long-term sludge land application: Uncovering heavy metal mobility and pollution risk in the soil-groundwater environment

The release of sludge-derived heavy metals (HMs) to soil and their subsequent migration into groundwater poses a significant challenge for safe and low-carbon sludge land application. This study developed a predictive framework to simulate 60-year sludge land application, evaluating the risk of HMs pollution in the soil-groundwater environment and assessing the influence of soil and water properties. HYDRUS-2D simulations revealed that highly mobile Cu, Ni, and Zn penetrated a 10 m soil layer over a 60-year period, contributing to groundwater pollution. In contrast, Cr was easily sequestered within the topsoil layer after 5-years continuous operation. The non-equilibrium parameter α could serve as an indicator for assessing their potential risk. Furthermore, the limited soil adsorption sites for Pb (f = 0.02772) led to short-term (1-year) groundwater pollution at a 0.5 m-depth. Bayesian Networks model outcomes indicated that humic-like organics crucially influenced HMs transformation, enhancing the desorption of Cd, Cu, Ni, Pb, and Zn, while inhibiting the desorption for Cr. Additionally, electrical conductivity promoted the release of most HMs, in contrast to the Mn mineralogy in soil. This study bridges the gap between the macro-level HMs migration trends and the micro-level adsorption-desorption characteristics, providing guidance for the safe land application of sewage sludge. Environmental ImplicationThis study introduces a framework integrating HYDRUS-2D simulations with Bayesian Networks to assess the risks of groundwater pollution by heavy metals (HMs) over a 60-year sludge application. Sludge-derived Cu, Ni, and Zn are found to penetrate soil up to 10 m and exceed safety limits, with the non-equilibrium parameter α serving as an indicator for pollution risk. The importance of nutrients from sludge-amended soil for the transformation of HMs in the subsurface environment highlights the need for enhanced sludge management, specifically through more detailed regulation of nutrient composition. These findings contribute to developing precise strategies for the long-term sludge land application.

Assessing the Gray Water Footprint Impact of Pesticide Use in Tommy Atkins Mango Cultivation: A case study in the Semi-Arid Region of São Francisco Valley, Brazil

Mango production, particularly of the Tommy Atkins variety, is prominent in the São Francisco Valley region of Northeast Brazil due to favorable conditions and year-round cultivation facilitated by irrigation and growth regulators. However, concerns arise over its significant water footprint and heavy pesticide usage, prompting a study on the gray water footprint of pesticides applied to Tommy Atkins mangoes in Sub-middle São Francisco Valley-Pernambuco, Brazil. Utilizing models by Hoekstra et al. (2011) and Paraiba et al. (2014), the study estimated the Gray Water Volume (GWV) and assessed potential water contamination using the GUS Index and GOSS method. Results suggest minimal groundwater pollution risk but moderate surface water contamination risk. The GWV ranged from 106 to 107 m3 ha-1, with the model of Paraiba et al. (2014) showing higher environmental sustainability due to considering toxicity to aquatic non-target organisms. Despite this, both models indicate a high gray water footprint. The pesticide ranking derived from these findings can aid in selecting environmentally safer pesticide mixtures for mango cultivation, aiming to balance water protection and agricultural productivity.

Relationship between nitrate, heavy metal, and sterols contents in Japanese agricultural soils with risk of groundwater pollution

In Japanese agricultural lands, nitrate-nitrogen contamination of soil and groundwater often occurs due to the application of livestock excrements and compost. Therefore, rural soils in Japan were sampled and analyzed for nitrate-nitrogen leaching, heavy metal content, and sterols associated with livestock excrement and compost to calculate contamination risk indicators. The results were analyzed using self-organizing maps and cluster analysis. Nitrate-nitrogen content using water extraction was detected in most of the sampled soils. In addition, many samples from areas that were already severely contaminated with nitrate-nitrogen showed particularly high concentrations. Coprostanol, an indicator of fecal contamination, was detected in more than half of the samples. The main source of nitrate-nitrogen contamination in these areas is livestock excrement and compost. Self-organization maps showed that areas with high nitrate-nitrogen contamination also corresponded to areas with high copper and zinc soil contents. The self-organization maps and cluster analysis resulted in five clusters: a nitrate-contaminated group mainly originating from livestock excrement and compost, a heavy metal-contaminated group, a general group, a nitrate-contaminated group mainly originating from chemical fertilizers, and a contaminated group with potentially hazardous substances requiring attention. Authorities and decision-makers can use the results to prioritize areas requiring remediation.

Activated carbon amendment of sand in the base of a permeable pavement reduces total nitrogen and nitrate leaching

Urban runoff from impermeable surfaces contains various pollutants. Stormwater samples were collected for one year from car parks on the campus of Newcastle University, located in northeast England, to monitor seasonal variation in stormwater properties and leachate quality following stormwater percolation through pilot-scale, outdoor permeable pavements. The pilot study compared an innovative ‘pollution munching’ permeable pavement with 2 % activated carbon (AC) amendment in the sand base with a conventional, un-amended sand base permeable pavement. Faecal coliforms were detected in stormwater at an average value of 3.75 ± 0.79 log10 CFUs per 100 mL. The permeable pavements without and with AC had mean log removal values of 0.81 ± 0.35 and 0.70 ± 0.35 for these faecal bacteria. The absence of genetic markers for human host associated Bacteroides (HF183) in eleven out of twelve stormwater samples showed that the faecal bacteria were mainly from animal sources. 16S rRNA gene sequencing results confirmed the presence of nitrifying bacteria from the genera Nitrosomonas, Nitrobacter, Nitrosococcus, Nitrospira, and Nitrosospira in stormwater. Nitrification and nitrate leaching was more notable for the conventional permeable pavement and may pose a groundwater pollution risk. Two percent AC amendment of the sand base reduced nitrate and total nitrogen leaching significantly compared with the conventional permeable pavement, by 57 ± 15 % and 40 ± 20 %, respectively. The AC amendment also resulted in significantly reduced Cu and DOC leaching, and lesser accumulation of PAHs by passive samplers embedded in the permeable pavement base. Hydraulic tests showed that the AC amended base layer still met the design specifications for permeable pavements, making it a promising proposition for pollution reduction in Sustainable Drainage Systems (SuDS).

Оценка уязвимости карстовых подземных вод к загрязнению на основных массивах Горного Крыма с использованием региональной методики

Introduction. Assessment of groundwater vulnerability to contamination is an effective tool for scientific substantiation of the sanitary protection zones for drinking water sources. Karst ground waters of the Mountain Crimea form the main resources for water supply in the Crimean Peninsula. This study has a purpose to perform the assessme nt and mapping of karst ground waters resource vulnerability to contamination on such representative massifs of the Crimean Mountains as Chatyrdag, Dolgorukovsky, Karabi and Ay-Petri . Materials and methods. Assessment of karst ground waters vulnerability is conducted by use of Mountain -Crimean method. It has been developed on the base of some wide-tested European methods (COP-method and Slovene approach) with methodological modification and adaptation to regional conditions of the Mountain Crimea. The input data for assessment is spatial information about ground waters vulnerability factors: protective layers in vadose zone, flow concentration by streams, sinkholes and caves, precipitation regime etc. The collected spatial information was converted into attributed geo data layers, which were subsequently subjected to overlay operations in Arc GIS 10 software according to assessment scheme. Results. According to the assessment results, approximately 34 and 14% of the Chatyrdag massif area gained the category of high and very high vulnerability, on the Karabi massif – 14 and 17%, on the Dolgorukovsky massif – 14 and 2%, on the Ay-Petri massif – 19 and 7% of total area respectively. The result ing vulnerability maps demonstrate significant mosaics and contrast, which is associated with discrete distribution of protective covers and high differentiation of ground water recharge conditions within the catchments of karst massifs. Discussion. Analysis of the resulting vulnerability maps have revealed that the leading factors determining areas of high and very high vulner ability of karst waters are runoff concentration factors, while the factors of protective cover mainly determine the areas with low vulnerability. The zones of high vulnerability on the Chatyrdag and Karabi massifs are mainly coincide with areas of high density of karst forms (sinkholes and caves), while high vulnerability zones of the Ay-Petri and Dolgorukovsky massifs are predominantly associated with areas near permanent and temporary watercourses. In general, the obtained maps of groundwater vulnerability are quite consistent with the known data on the hydro-geological conditions of the assessed massifs. Conclusions. The identified differences in the area ratio and spatial distribution of ground waters vulnerability categories between the assessed massifs are a consequence of local geological, geo-morphological and hydrological features. The obtained results confirm high applicability of the Mountain-Crimean regional method for groundwater vulnerability assessment, at least for the karst massifs of the Mountain Crimea. Suggestions for practical application and direction for future research. The produced groundwater vulnerability maps create a scientific basis for delineation of water protection zones, assessing the risks of groundwater pollution and developing the territorial planning schemes within studied areas. The next stage of study is an assessment of groundwater source vulnerability for individual karst springs used in water supply.

Risk of groundwater pollution and proposals for sustainable development in Binh Thuan province, Vietnam

Risk of groundwater pollution and proposals for sustainable development in Binh Thuan province, Vietnam

Study on Groundwater Environment Monitoring in Tailing Pond Area

Luanchuan is surrounded by mountains, most of the area belongs to the mountain terrain, there are more metal mining areas, retained many large and small tailings ponds. The mining area is one of the seven major sources of groundwater pollution, that is, the tailings reservoir area has the risk of groundwater pollution. At present, the groundwater environment is investigated and evaluated, mainly the design and implementation of the groundwater environment monitoring well in the tailings reservoir area. Firstly, four monitoring Wells are built in the tailings pond. Monitoring well construction includes drilling core, reaming, pipe filling gravel, water sealing and well washing; Finally, a pumping test was carried out to determine the groundwater recharge and aquifer richness in the tailings reservoir area. The unit water inflow q of the aquifer in the area is 0.67L/s·m. After the completion of the monitoring well construction, it is determined whether the groundwater environment in the study area is polluted based on the experimental analysis. Once the water around the tailings pond is polluted, it is very likely to affect the drinking water source of the downstream villages, and the residents will seriously endanger their health after drinking.

Experimental study on photodegradation and leaching of typical pesticides in greenhouse soil from Shouguang, Shandong Province, East China

BackgroundPesticide use contributes to national food security. The dissipation pathways and degradation mechanisms of pesticides have been widely studied and pesticide residues have remained a focus of public concern. However, studies on the migration and transformation behaviors of pesticide residues in real-world greenhouse soils are insufficient. Therefore, in this study, we collected greenhouse soil from Shouguang, Shandong Province, and investigated the photodegradation and leaching of 17 common pesticides, which leave residues in the soil and are the most frequently used pesticides in Shouguang. The environmental behavior of pesticides in greenhouse soils will provide new information on pesticide residues in the real environment and provide a scientific basis for the prevention and control of pesticide pollution in greenhouse soils.ResultsThe photodegradation of trifloxystrobin followed a first-order kinetic equation, whereas those of emamectin benzoate, chlorantraniliprole, buprofezin, difenoconazole, pyraclostrobin, boscalid, tebuconazole, isoprothiolane, metalaxyl, and oxadixyl followed second-order kinetics. The half-lives of 17 pesticides under light and dark conditions ranged from 2.5–104 (mean: 36.2) and 2.6–110 (mean: 31.4) days, respectively. The half-lives of emamectin benzoate, pyraclostrobin, and metalaxyl in the light were 86.6%, 68.5%, and 94.5% of their half-lives in the dark, respectively. Chlorantraniliprole, metalaxyl, nitenpyram, diethofencarb, acetamiprid, carbendazim, and oxadixyl were leached to ≥ 90% in aqueous solution. Six pesticides, avermectin B1A, emamectin benzoate, trifloxystrobin, difenoconazole, pyraclostrobin, and buprofezin, were difficult to leach from aqueous solutions.ConclusionsThe degradation rate of some pesticides was higher in the light environment than in the dark. The leaching potential of the leachable pesticides was nitenpyram ≫ metalaxyl > acetamiprid > carbendazim > diethofencarb ≈ chlorantraniliprole > isoprothiolane > oxadixyl > boscalid ≈ tebuconazole > hexaconazole. Pesticides that are easy to leach but not easily degraded, such as chlorantraniliprole and metalaxyl, have a high potential risk of groundwater pollution, and additional degradation technologies should be used to reduce their pollution risk. The study of the photodegradation and vertical migration behavior of various pesticides is conducive to providing references for the agricultural use and pollution control of pesticides.

Mechanism and ecological environmental risk assessment of peroxymonosulfate for the treatment of heavy metals in soil

Oxidation technologies based on peroxymonosulfate (PMS) have been effectively used for the remediation of soil organic pollutants due to their high efficiency. However, the effects of advanced PMS-based oxidation technologies on other soil pollutants, such as heavy metals, remain unknown. In this study, changes in the form of heavy metals in soil after using PMS and the risk of pollution to the ecological environment were investigated. Furthermore, two risk assessment methods, the mung bean germination toxicity test and groundwater leaching soil column test, were employed to evaluate the soil before and after PMS treatment. The results showed that PMS has a strong ability to degrade complex compounds, enabling the transformation of heavy metals, such as Cd, Pb, and Zn, from stable to active states in the soil. The risk assessments showed that PMS treatment activated heavy metals in the soil, which delayed the growth of plants, increased heavy metal content in plant tissues and the risk of groundwater pollution. These findings provide a new perspective for understanding the effects of PMS on soil, thus facilitating the sustained and reliable development of future research in the field of advanced oxidation applied to soil treatment.

Groundwater pollution risk, health effects and sustainable management of halocarbons in typical industrial parks

As important chemical raw materials and organic solvents, halogenated hydrocarbons not only play an important role in economic development, but are also the main source of environmental pollution. This study proposed an improved groundwater risk assessment model system, aimed at identifying and treating contaminants at leak sites. Groundwater ubiquity score (GUS) was used to evaluate the leachability of organic pollutants. The entropy-weighted water quality index (EWQI) method was used to assess the comprehensive quality of groundwater at the site. An improved groundwater health risk assessment model was constructed to analyze the health risks of groundwater. The sources of organic pollutants were identified based on the positive matrix factorization (PMF) model. Self-organizing mapping (SOM) and the K-means algorithm were integrated to classify and manage pollution source areas. The results showed that groundwater in the study area was strongly affected by human activities. The pollution source was located in a factory near S05. Different organic pollutants were highly leachable and had high potential to contaminate surrounding groundwater. 1,2-dichloropropane and 1,2,3-trichloropropane caused the largest range of contamination. The groundwater pollution index in the study area was high, and 72% of the monitoring points were non-drinkable. Both the carcinogenic and non-carcinogenic indexes of groundwater far exceeded the international standard limits and had a great impact on human health. 1,2,3-trichloropropane and 1,2-dichloropropane were major non-carcinogenic risk factors. The leakage of pollutants and pesticide solvents were the main causes of groundwater pollution. Cluster areas III and II were areas with significant pollution impacts and needed to be monitored intensively. Most areas were cluster I, with relatively low risk. This study can provide technical support for groundwater pollution risk assessment and management in similar industrial parks.

Nitrate concentrations tracking from multi-aquifer groundwater vulnerability zones: Insight from machine learning and spatial mapping

Nitrate contamination in groundwater is a significant environmental concern that poses risks to human health and ecosystems. Several goals and targets of Sustainable Development Goals (SDGs) are related to water quality, pollution, and sustainable management of water resources, which can encompass nitrate contamination. This study conducted real fieldwork of groundwater samples at several locations in Al-Hassa, Saudi Arabia. Subsequently, experimental based on chromatography (IC) and inductively coupled plasma mass spectrometry (ICP-MS) to analyze several groundwater hydro-geochemical elements. The study aimed to employ spatial mapping and advanced standalone optimization learning, including Elman Neural Network (ELNN), Gaussian Process Regression (GPR) models as nonparametric kernel-based probabilistic, and Random Forests (RFs) for tracking and modelling the nitrate (NO3) (mg/L) concentration. The outcomes were validated using several performance indicators and 2D-graphical methods. The resultant NO3 concentration in Al-Hassa was 77.9 mg/L, and the lowest was 9.8 mg/L. Despite marginal accuracy being obtained for most model combinations, GPR-C2 proved merit and reliable for modelling NO3 concentration with Wolffman Index (WI)=0.99 and PBAIS=−0.0001. The finding indicated that Al-Hassa regions are highly prone to NO3 pollution, further confirmed by spatial mapping. The outcomes provide insight into crucial information and decision-making for groundwater pollution risk at Al-Hassa, Saudi Arabia.

A cooperative construction strategy for multi-parameter spatial variant random fields and its application in groundwater pollution risk assessment.

The spatial variability of hydrogeological parameters is a significant source of uncertainty in groundwater numerical modeling and has a certain risk impact on the prediction of pollutant migration and transformation. Current research has focused on the effects of single-parameter spatial variant random fields or utilizing random sampling methods to randomly combine multiple-parameter spatial variant random fields while ignoring the correlation between parameters. This paper proposes an innovative concept of associated random variables to construct multi-parameter synergistic spatial variant random fields, ensuring both the spatial variability and inherent correlation of the parameters. A hypothetical case was constructed, and the Monte Carlo sampling experiment based on computer simulation was used to assess groundwater pollution risks with multiple associated parameters. The results show that hydraulic conductivity and porosity are the main sensitive parameters. The associated random variable allows for the representation of positive correlation, negative correlation, and no correlation between the hydraulic conductivity and porosity. The pollutant mass concentrations in each observation well conform to the generalized extreme value distribution, and the pollution risks of each water well as well as the concentration distribution intervals of pollutants with different probabilities can be obtained. The influence of associated parameters on the cumulative risk of contaminants in observation wells and pollution degree range is only related to their mathematical distribution and is independent of correlations between parameters. This study addresses the issues of spatial variability and inherent correlation of hydrogeological parameters, which are of great significance for groundwater pollution risk assessment and the promotion of sustainable water quality management of groundwater resources.

Site-scale groundwater pollution risk assessment using surrogate models and statistical analysis

Petroleum pollution in soil and groundwater has emerged as a significant environmental concern worldwide. As a sustainable and cost-effective in-situ remediation technique, Monitored Natural Attenuation (MNA) exhibits significant promise in addressing sites contaminated by petrochemicals. This study specifically targets a typical petrochemical-contaminated site in northern China and employs GMS software to establish a comprehensive physical model. The model relies on time-series monitoring data of phenol concentrations spanning from 2018 to 2020, effectively simulating both the leakage and natural attenuation of phenol. Within this study, the adsorption coefficient and maximum adsorption capacity emerge as the foremost influential factors shaping the outcomes of the model. Given the inherent heterogeneity of the site and the variability of hydrochemical conditions, parameters such as dispersion, porosity, and adsorption coefficient exhibit significant uncertainties. Consequently, relying on traditional deterministic models to predict the feasibility of MNA technology is not reliable. Therefore, this study employs machine learning (ML) methods to construct stochastic parameter models based on physical processes. The Random Forest Regression (RFR) algorithm, after trained, demonstrates strong alignment with numerical model output, exhibiting an average Nash-Sutcliffe Efficiency (NSE) >0.96. Using a stochastic approach, RFR iteratively computes phenol concentration across 6000 sets of parameters. Applying probability statistics, the model shows a notable reduction in the likelihood of phenol concentrations exceeding a threshold, dropping from 64.0% to 15.7% before and after natural attenuation. In parameter uncertainty, the stochastic model emphasizes natural attenuation's efficacy in mitigating phenol pollution risk (porosity being the most influential factor). This case study proposed a novel method to quickly assess the pollution risks at petrochemical sites under the influence of the uncertainty of pollutant transport and reaction parameters. The results can provide a reference for the pollution risk assessment at petrochemical sites, especially in sites with high stratigraphic heterogeneity or insufficient transport parameter data.

Modelling Groundwater Vulnerability to Contamination using DRASTIC Model through Geospatial Techniques over Northern Kwazulu-Natal, South Africa

This study models groundwater vulnerability to contamination in three northern district municipalities (Amajuba, Zululand and Umkhanyakude) in KwaZulu Natal province in South Africa using GIS-based DRASTIC model. The method considers seven parameters: depth to water table (D), recharge (R), aquifer media (A), soil media (S), topography (T), impact of the vadose zone (I), and hydraulic conductivity (C). DRASTIC parameter maps are generated in ArcGIS environment and relevant weights assigned. A weighted overlay analysis is then employed to generate the groundwater vulnerability map for the study area. Finally, the groundwater vulnerability map is combined with land use/cover to obtain groundwater pollution risk map. Results indicate that 22, 45, 21 and 12% of the total area are under low, moderate, high, and very high groundwater contamination vulnerable zones, respectively. Low, moderate, high, and very high groundwater pollution risk are found in 23, 40, 27 and 10% of the total area, respectively. These results can be used by environmental managers, spatial planers and other policy makers in formulating integrated and sustainable development plans to ensure optimal groundwater exploitation and conservation in the northern KwaZulu Natal region.

Groundwater heavy metal(loid)s risk prediction based on topsoil contamination and aquifer vulnerability at a zinc smelting site

Groundwater pollution is a recurrent problem in abandoned non-ferrous metal smelting sites, and its severity is influenced by topsoil contamination, hydrogeological characteristics, and hydrogeochemical conditions. In such unique areas, traditional methods for evaluating groundwater pollution risk are biased, as the long production history of these sites have led to highly polluted and heterogeneous soil and groundwater. Herein, based on a typical lead-zinc smelting site, As, Pb, Zn, Cd, Mn, and Ni were found to be the predominant heavy metal (loid)s in groundwater, with respective exceedance rates of 44.4%, 50.0%, 72.2%, 88.9%, 88.9%, and 61.1%. Combined with the groundwater pollution characteristics, the representative hydrogeochemical factors were screened out to optimize the following aquifer vulnerability evaluation using the AHP-DRASTICH method. A comprehensive evaluation model (DI-NCPI) for groundwater pollution risk was established by combining the DRASTICH index (DI) obtained after optimization and the Nemerow comprehensive contamination index (NCPI) of topsoil. The fit between DI-NCPI and groundwater heavy metal (loid) pollution index reached 0.956, which laterally confirms that the model has some reference value. In terms of distribution, the high-risk and very high-risk zones were mainly concentrated in the zinc smelting system, located in the southeastern and central-western parts of the site. These areas have relatively high levels of topsoil contamination and aquifer vulnerability and require focused attention in site remediation. This research highlights the importance of combining topsoil contamination and aquifer vulnerability to evaluate groundwater pollution risk in smelting areas. It provides a more targeted reference for groundwater remediation strategies in abandoned smelting sites, as well as severely polluted industrial areas.

Groundwater nitrate pollution risk assessment based on the potential impact of land use, nitrogen balance, and vulnerability

The predicting groundwater nitrate pollution risk, especially in terms of changes in fertilizing, has not been fully investigated so far. In particular, there is no comprehensive method to assess this risk in areas of different land use type, and not only in agricultural areas. The aim of this study was to develop a novel multicriteria methodology for groundwater nitrate pollution risk assessment, which meets these issues. A further aim was to determine how much this risk would change if the amount of organic and synthetic fertilization was reduced. An assumption was that groundwater pollution risk is a combination of the potential adverse impacts of land use, fertilization, and intrinsic groundwater vulnerability to pollution. The impact of fertilization was holistically evaluated by balancing nitrogen from spatially differentiated the size of the breeding, species of livestock, manure and synthetic fertilizers input, and spatially differentiated topsoil, with nitrogen uptake by different crops. The nitrate concentration in the leachate was used as a measure of the impact of fertilization. This concentration was compared to the natural baseline nitrate concentration in groundwater. Three fertilization scenarios for groundwater pollution risk assessment in two study areas were discussed. Under typical agricultural, climatic, soil, and geological conditions in Europe for the current total fertilization level of 95-120 kg N ha−1 groundwater nitrate pollution risk is low and moderate, but for fertilization of 150-180 kg N ha−1, a reduction in the total fertilization (synthetic and manure) by 40 to 50% may be required to achieve low risk of degradation of natural groundwater quality. Predictive simulations of groundwater nitrate pollution risk confirmed that reducing synthetic and organic fertilization has an effect, especially in areas with intensive fertilization. This method may allow for a holistic and scenario-based assessment of groundwater pollution risk and may help decision-makers introduce solutions to manage this risk under conditions of climate change, preservation of groundwater quality, and food security.

Groundwater vulnerability assessment in central Iran: Integration of GIS-based DRASTIC model and a machine learning approach

The study try to evaluate the susceptibility of groundwater. The DRASTIC model was implemented through GIS. Various input variables, such as water table depth, net recharge, aquifer and soil media, topography, vadose zone impact, and hydraulic conductivity, were evaluated within the model to generate a groundwater vulnerability map. Subsequently, machine-learning algorithms (SVM, RF, and GLM) employed using the SDM package in R software to optimize the DRASTIC method. To assess the performance of groundwater pollution risk models, training and validation datasets were evaluated using the ROC curve. The results revealed that approximately 40% of the study area fell within the high vulnerability range, while around 30% exhibited moderate pollution risk. Evaluation of the machine learning models indicated their effectiveness in model development. The RF model demonstrated the highest predictive power, achieving an AUC of 0.98. Additionally, the GLM and SVM algorithms achieved AUC values of approximately 76%. These algorithms can serve as efficient techniques for evaluating and managing groundwater resources. The findings underscored relatively poor groundwater quality in the study area, with excessive aquifer exploitation by the agricultural sector and infiltration of urban sewage and industrial waste identified as the primary causes of groundwater pollution. The implications of these findings are crucial for devising strategies and implementing preventive measures to mitigate water resource vulnerability and associated health risks in central Iran.