Groundwater Pollution Sources Research Articles

Joint inverse estimation of groundwater pollution source characteristics and model parameters based on an intelligent particle filter

Accurate groundwater pollution source estimation (GPSE) is the prerequisite for efficiently designing groundwater pollution remediation strategies and pollution risk assessment. However, deterministic point estimation of pollution source characteristics may not completely solve the problem for practical situations. Therefore, an interval estimation, capable of providing more comprehensive and abundant information for decision-makers, should also be considered. In GPSE, the particle filter (PF) method is a good means for the interval estimation. However, some inherent problems in the general PF method may compromise the estimation accuracy. Thus, this study made two improvements to the general PF method as a solution. First, to solve the problem of its weak local search ability when tackling complex problems, this study embedded the optimization method with strong local search ability into the importance sampling process, to strengthen the local search ability, better optimize the particle position, and guide the overall distribution of particles to move towards the true distribution. Secondly, in the resampling process, all particles need to be resampled in each iteration, which may lead to plunging into the local optima. Thus, this study introduced an intelligent resampling process that intelligently adjusted the number of resampling particles in each iteration to avoid plunging into the local optima. Through two improvements on the PF method, an intelligent PF method was innovatively constructed for GPSE, improving the estimation accuracy. Additionally, to alleviate the computational cost caused by the frequent simulation model invocation, we adopted a deep learning method (deep belief neural network method) to build the surrogate model of the simulation model, ensuring high approximation accuracy to the simulation model. Also, we applied the theories and methods mentioned above to two respective scenarios (inorganic and organic groundwater pollution). We simultaneously estimated the pollution source characteristics and simulation model parameters to verify the effectiveness. Finally, we obtained the point estimation, posterior probability distribution, and interval estimation results, capable of providing a more reliable and comprehensive reference for decision-makers.

Groundwater pollution source identification and health risk assessment in the North Anhui Plain, eastern China: Insights from positive matrix factorization and Monte Carlo simulation

Groundwater contaminants from natural and anthropogenic sources pose a serious threat to the ecological environment and public health. In this study, 30 groundwater samples were collected from shallow wells at a large central water source in the North Anhui Plain, eastern China. Hydrogeochemical methods, positive matrix factorization (PMF) model, and Monte Carlo simulation were used to determine the characteristics, sources, and human health risks of inorganic and organic analytes in groundwater. The groundwater was weakly alkaline with high total hardness and was dominated by HCO3-Mg·Ca, HCO3-Ca·Mg, and HCO3-Ca·Mg·Na hydrochemical facies. The concentration of naphthalene was at a safe level, while the concentrations of F−, NO3− and Mn in 16.7 %, 26.7 % and 40 % of the samples, respectively, exceeded threshold risk-based values based on Chinese groundwater quality standards. Hydrogeochemical methods revealed that water-rock interactions (including weathering of silicate minerals, dissolution of carbonates, and cation exchange), acidity, and runoff conditions control the migration and enrichment of these analytes in groundwater. The PMF model indicated that local geogenic processes, hydrogeochemical evolution, agricultural activities, and petroleum-related industrial sources were the main factors affecting groundwater quality, with contributions of 38.2 %, 33.7 %, 17.8 %, and 10.3 %, respectively. A health risk evaluation model based on Monte Carlo simulation indicated that 77.9 % of children were exposed to a total noncarcinogenic risk above safe thresholds, about 3.4 times higher than the risk to adults. The main contributor to human health risk was F− originating from geogenic processes; thus, F− was identified as a priority for control. This study demonstrates the feasibility and reliability of combining source apportionment techniques and health risk assessment to evaluate groundwater quality.

Priority control factors for heavy metal groundwater contamination in peninsula regions based on source-oriented health risk assessment

Peninsula regions in China face serious environmental issues, such as heavy metal (HM) groundwater contamination. However, attempts to investigate the pollution sources and health risks of HM contamination in such regions require considerable resources and costs. Moreover, the priority control factors for groundwater HMs remain unclear. In this study, absolute principal component score/multiple linear regression (APCS/MLR) was used to quantify the groundwater pollution sources of typical peninsular areas in China, and a health risk assessment (HRA) was performed to assess the health risks caused by different sources. The results showed that the concentrations of Mn and Fe were higher than those of other HMs, and HM pollution was high in shallow groundwater. The dominant source of HMs in groundwater was agricultural activities (31.12 %), followed by natural sources (26.33 %), industrial activities (22.47 %), and atmospheric deposition (20.09 %). The non-carcinogenic risks to residents were acceptable, whereas the carcinogenic risks were high. Agricultural sources, atmospheric deposition sources, and Cr and As were identified as the priority control factors for HM groundwater contamination. This study has implications for improving the control of groundwater HM contamination in peninsula regions and ensuring sustainable groundwater development.

Simultaneous identification of groundwater contaminant source and hydraulic parameters based on multilayer perceptron and flying foxes optimization.

Groundwater contaminant source identification (GCSI) has practical significance for groundwater remediation and liability. However, when applying the simulation-optimization method to precisely solve GCSI, the optimization model inevitably encounters the problems of high-dimensional unknown variables to identify, which might increase the nonlinearity. In particular, to solve such optimization models, the well-known heuristic optimization algorithms might fall into a local optimum, resulting in low accuracy of inverse results. For this reason, this paper proposes a novel optimization algorithm, namely, the flying foxes optimization (FFO) to solve the optimization model. We perform simultaneous identification of the release history of groundwater pollution sources and hydraulic conductivity and compare the results with those of the traditional genetic algorithm. In addition, to alleviate the massive computational load caused by the frequent invocation of the simulation model when solving the optimization model, we utilized the multilayer perception (MLP) to establish a surrogate model of the simulation model and compared it with the method of backpropagation algorithm (BP). The results show that the average relative error of the results of FFO is 2.12%, significantly outperforming the genetic algorithm (GA); the surrogate model of MLP can replace the simulation model for calculation with fitting accuracy of more than 0.999, which is better than the commonly used surrogate model of BP.

Strategic Monitoring of Groundwater Quality Around Olusosun Landfill in Lagos State for Pollution Reduction and Environmental Sustainability

As urbanization and population increase in the megacity, there is a need for engineering intervention and strategic monitoring of groundwater around landfills for environmental sustainability, pollution reduction and public health. This study evaluated water’s physical and chemical parameters in wells and boreholes near the Olusosun landfill in Lagos State to determine how they impact groundwater quality. An Atomic Absorption Spectrometer (AAS) was used to evaluate groundwater samples obtained from five locations within the dump site. Some water parameters, such as dissolved oxygen (DO), iron (Fe), lead (Pb), manganese (Mn), and magnesium (Mg), had concentrations that were higher than the WHO, NESREA, and Nigerian Standard for Drinking Water Quality (NSDWQ) standard limits in some sampling sites, with mean concentrations of 0.33 mg.L-1, 0.04 mg.L-1, 0.74 mg.L-1, and 0.74 mg.L-1, respectively. A small amount of lead was identified in the groundwater of the study area. A major source of air and groundwater pollution, the Olusosun landfill has a detrimental impact on the health of those who live there. Solid waste, groundwater interactions, and contaminated migration into the nearby neighbourhood were studied. It was observed that the degradation of waste products in dump sites releases harmful leachate into the groundwater. Even though some heavy metal concentrations in the study area are still within WHO, NESREA, and NSDWQ standard limits, investigations and further monitoring should be conducted regularly to assess the concentrations of heavy metals in groundwater.

Hydrogeochemical Study and Geospatial Analysis of Water Quality Using GIS based Water Index and Multivariate Statistics in Kombolcha City, Ethiopia

The study was carried out to evaluate hydro geochemistry and the risk of groundwater and surface water pollution in the Kombolcha area. To achieve this, hydrogeochemical analysis, water heavy metal, geospatial data analysis, ion ratio, correlation matrix, principal component analysis, Heavy metal Pollution Index (HPI), and Water Quality Index (WQI) methodologies were employed. A total of 36 samples (both water and effluent samples) had been collected and assessed for major physicochemical variables and heavy metals and samples were taken from hand dug wells, Borehole, springs, and rivers in the area. Hydrogeochemical methods showed groundwater mineralization due to (1) silicate weathering, (2) cation exchange processes, and (3) anthropogenic sources (i.e., contaminated discharge of sulphate, carbonate, and trace metal effluents). The study result revealed that major ions dominating the area are Ca2+ >Na+ > Mg2+ >K+, HCO3- >SO4 2- > Cl- > NO3-, and Fe> Mn >Pb> Cr> Cd for cations, anions and trace metals respectively with all heavy netals had mean concentrations above the WHO recommended limits. Calculated Pollution indices revealed 50.7% of the sample belongs to a low level of pollution, while 35% and 14.3% belong to a medium and high level of pollution respectively which consequently translating the area into high groundwater pollution zones. The correlation matrix revealed that no significant correlation exists between the water quality variables (Cl and NO3- with Fe, Pb, Cr, Mn, and Cd). PCA was applied on the data set to identify the spatial sources of pollution in groundwater and in the first principal component analysis, Mn, Fe, Cr, Pb, and Cd (in descending order) were found in amounts greater than 0.5, confirming that these metals were from anthropogenic sources. The combined assessments based on WQI and HPI, the study showed that water samples in the proximity of industrial sites are polluted by factories effluent and uncontrolled waste disposal due to urbanization.

MONITORING OF GROUNDWATER QUALITY IN LISTVYANKA SETTLEMENT (SOUTHWEST COAST OF LAKE BAIKAL)

A systematic monitoring of the hydrosphere is important and necessary to maintain a good ecological status of Lake Baikal. The article presents the results of a five-year monitoring of the groundwater and surface waters quality in Listvyanka settlement. The goal is to determine the contamination level of drinking water supply system. In the field studies, portable measurement devices were used for monitoring temperature, pH, Eh, electrical water conductivity and water levels in wells, and water samples were taken for a complete chemical analysis. The analysis of the macrocomponent composition of water was carried out by various methods (flame photometry, volumetric titration, weight method, colorimetry) at the Center for Geodynamics and Geochronology of the Institute of the Earth’s Crust SB RAS (Irkutsk); the trace element composition was determined by the ICP MS method on the Element-2 device (Finnigan MAT, Germany) at the Institute of Geochemistry SB RAS (Irkutsk); microbiological analysis of water samples was performed at the Irkutsk Interregional Veterinary Laboratory; the analysis of isotope characteristics (δ 18O and δD) of water samples was performed in the Laboratory of Hydrogeology of the Institute of the Earth’s Crust SB RAS (Irkutsk) on a Picarro L2140-i gas analyzer.The results obtained indicate a change in macrocomponent composition of waters and an uneven distribution of pollutants over the area. The content of 42 trace elements in groundwater and river waters generally does not exceed the regulatory requirements for drinking water; the exceptions are Fe and Mn. Ten wells do not meet the sanitary and epidemiological rules and norms SanPiN 2.1.4.1074-01. The main sources of groundwater pollution are leaking septic tanks of numerous hotel complexes located in the valleys and on the valley slopes. The processes of water pollution and restoration of natural regime of water are of different intensity. For groundwater and river waters, there were determined the relative concentrations of stable isotopes of hydrogen and oxygen (δD and δ18О), and δD=5.5307·δ18 O–31.815 relationship and the deuterium excess values were obtained. Characteristics δD and δ18 O are the bases of the isotope database for the aquatic environment of Listvyanka settlement.

An overview of the application of electrocoagulation for mine wastewater treatment.

One of the challenges of the twenty-first century is related to the discharge and disposal of mine effluents and wastewater resulting from mine dewatering, precipitation, and surface runoff in mines, especially acidic effluents that contain a variety of toxic and heavy metals and are the main sources of surface and groundwater pollution. Various physical, chemical, and biological methods have been developed and used to treat mine effluents. All proposed methods have their own disadvantages that make their use challenging. One of the new methods used for wastewater treatment is the electrical coagulation process, which has attracted the attention of researchers in recent years due to its advantages such as simplicity, environmental friendliness, and low cost. The present review focused on the applications of electrocoagulation for mine wastewater treatment as well as metals recovery. In addition, the main mechanisms, advantages, and weaknesses of electrocoagulation were reviewed.

Contamination Characteristics and Source Identification of Groundwater in Xishan Coal Mining Area of Taiyuan Based on Hydrochemistry and Sulfur–Oxygen Isotopes

Xishan mining area in Taiyuan is a typical coal industry cluster with a variety of coal-related industrial sites such as coal mines, power plants and coking plants, which seriously pollute the native ecological environment. Study of the hydrochemical characteristics and pollution sources of groundwater in the area can contribute to the ecological protection and remediation of regional groundwater resources. In this study, we collected surface water and groundwater samples from the Xishan mining area and measured and analyzed hydrochemical and sulfur–oxygen isotopes. Results showed that 64.7% of groundwater in the study area exceeded the sulfate standard due to the influence of the coal industry, with some karst groundwater up to 2000 mg/L. In the runoff and discharge area of karst groundwater, the proportion of anthropogenic input of SO42− increased, which led to the hydrochemical type of karst groundwater gradually changing from HCO3-Ca·Mg (recharge area) to SO4-Ca·Mg (discharge area). Results of sulfur–oxygen isotope tests indicated that the δ34SSO4 and δ18OSO4 values of samples were −10.01~24.42‰ and −4.90~12.40‰, respectively, and the sulfur–oxygen isotope values of some karst groundwater were close to the dissolved end of sulfide minerals, indicating their sulfate mainly came from the oxidation of pyrite. Sulfate sources in groundwater water were parsed using IsoSource model. Calculated results revealed that sulfate in pore groundwater mostly originated from pyrite oxidation, and karst groundwater in the recharge area was mainly influenced by atmosphere precipitation, while groundwater in the runoff and discharge areas were significantly affected by pyrite oxidation, accounting for up to 90% in some karst groundwater. Comparing the sulfur–oxygen isotope values of karst groundwater in 1989, 2016 and 2022, we found that the δ34SSO4 values in 2022 decreased significantly, which indicated the expansion of karst groundwater pollution in the Xishan mining area. This study highlights the pollution of regional groundwater by coal-related industrial agglomerations, and the groundwater pollution in the Xishan mining area requires urgent remediation and restoration.

Machine learning modeling using microbiome data reveal microbial indicator for oil-contaminated groundwater

Monitoring groundwater (GW) quality is essential for the sustainable management of water resources to preserve public health and ecosystem functioning. The present study developed a machine learning (ML) modeling framework using high-throughput sequencing microbiome data as input variables, which successfully predicted the status and source of GW pollution. No systematic spatiotemporal patterns in the environmental parameters and community diversity indices were observed for the GW samples taken from a total petroleum hydrocarbon (TPH)-contaminated site. In contrast, the ML modeling optimized via model selection and hyperparameter tuning led to high prediction accuracy (>98 %) in classifying the status and source of GW pollution. Feature importance analysis using the ML models (logistic regression and support vector machine with radial basis function) identified members of Rhodocyclaceae, Syntrophaceae, and Helicobacteraceae as strong indicators of GW polluted with TPHs. The identification of these microbial taxa as pollution indicators was consistent with their known ecophysiology associated with TPH metabolism. The usefulness of these microbial indicators was then validated using both conventional hypothesis testing and phylogenetic analysis. Overall, the ML modeling pipeline established in this study using microbiome data provides new information on the interaction between a set of microbial biomarkers and enhances the predictive understanding of GW pollution and its bioremediation potential.

Effects of preferential flow on soil nutrient transport in karst slopes after recultivation

In response to the global food shortage, a large amount of abandoned land in karst areas has been reclaimed as cultivated land, causing severe nonpoint source pollution. Preferential flow-driven soil nutrient transport on karst slopes remains poorly studied, though it is a major factor in nonpoint source pollution, as it responds to changes caused by reclamation. We explored the characteristics of soil preferential flow differences in recultivated land, grassland, and shrubland from returning farmland by dye tracer experiments and quantitatively examined the effect of preferential flow on nutrient transport. Under the condition of 40 mm precipitation, the preferential flow paths (PFPs) of the three types of plots were mainly distributed from 0 to 40 cm. The total porosity in the 20–40 cm soil layer was significantly reduced by reclamation, and the number of preferred flow paths in the 20–40 cm soil layer was significantly reduced from 60 to less than 10, which was significantly less than that in grassland and shrubland. But, reclamation results in the transport of more soil nutrients by preferential flow. The contribution rate of preferential flow to other nutrient indexes in the reclaimed land, in contrast to grassland and shrubland, was lower than zero, except for organic matter and total potassium. Moreover, when the PFP was connected to the rock–soil interface, the soil water can leak underground through the rock–soil interface quickly. Therefore, our findings indicated that reclamation reduces the distribution depth of the PFP. Still, the connection of soil preferential flow to the rock–soil interface increases the transport of soil nutrients to deep fissures and even underground rivers, thereby causing recultivated land to become one of the main sources of groundwater pollution in karst areas.

Industrial Water Demand and Wastewater Generation: Challenges for Bangladesh’s Water Industry

Bangladesh has experienced massive industrial growth in the past century. With the impressive industrial development, the country struggles to meet the demand for water and manage the wastewater generated. This paper critically analyzes Bangladesh’s three significant industrial wastewaters: textile, tannery, and landfill leachate. The textile and leather industries are major freshwater consumers and wastewater generators in Bangladesh. Most textile factories across Bangladesh operate without effluent treatment plants, causing severe water pollution. Unlike textile plants, most tanneries are located in Savar, Dhaka. Savar’s newly constructed central effluent treatment plant has serious design flaws and cannot meet discharge standards. Bangladesh has only three sanitary landfills for municipal solid waste disposal. With inadequate leachate collection and treatment facilities, the landfills have become point sources of surface water and groundwater pollution. The pollution of water resources is devastating as Bangladesh depends on surface water for irrigation and groundwater for drinking. Although Bangladesh has mandated laws and regulations for water pollution control, the lack of implementation is a significant problem. To get a better handle on industrial water demand and wastewater treatment, the Bangladesh government and the Department of Environment should work together to implement stricter laws and regulations and emphasize technical workforce development and stakeholder training.

Identification of Clandestine Groundwater Pollution Sources in Aquifer Polluted with BTEX

One of the biggest challenges in characterizing clandestine groundwater pollution sources is correctly estimating the number of such sources and their physical locations. Indiscreet underground disposal of toxic waste leading to groundwater pollution is widespread, and it is extremely difficult to detect the number of such sources and their locations. In this study, a real-life scenario of groundwater pollution in suburban New South Wales, Australia, is solved using a simulated annealing (SA)–based linked simulation optimization (LSO) technique with Benzene, Toluene, Ethylbenzene and Xylene (BTEX). The developed LSO considers the number of pollutant sources and their locations as explicit unknowns. This overcomes a crucial limitation in the earlier methods that limited the search to a few known potential source locations. The results show the applicability of the developed methodology to real-life groundwater pollution problems without making limiting assumptions about the number of sources or potential source locations being known, which has not been addressed to date.

Assessment of groundwater vulnerability using the GIS approach-based GOD method in Surat district of Gujarat state, India

Abstract There are different sources of groundwater pollution among them industrial water disposal, seawater ingress, usage of pesticides and fertilizers in agricultural fields, and municipal and residential wastewater disposal. The aim of the study is to assess groundwater vulnerability in terms of quality using the GOD method using the Geographical Investigation System approach for Surat and its surroundings. Groundwater confinement, overlying strata and depth to water table are the three parameters that are used in the assessment of groundwater vulnerability. In this study, all three parameters are given by the conventional weights which are suggested by Foster 1987. Based on the study Mahuva, Mandvi, Umarpada and some parts of the Bardoli talukas lie in very high-vulnerability zones whereas another part of the study area lies in a high-vulnerability zone. Almost 35.98% of the area of the district lies in the higher-vulnerability zone. Depth to the water table and the overlying strata are very important parameters that effectively cause groundwater vulnerability. This study may help in groundwater quality management, and watershed management as well as it is very much useful for the policymakers and local authorities as well as the government.

Physicochemical coastal groundwater dynamics between Kauhakō Crater lake and Kalaupapa settlement, Moloka‘i, Hawai‘i

Land-based sources of groundwater pollution can be a critical threat to coral reefs, and a better understanding of “ridge-to-reef” water movement is required to advance management and coral survival in the Anthropocene. In this study a more complete understanding of the geological, atmospheric, and oceanic drivers behind coastal groundwater exchange on the Kalaupapa peninsula, on Moloka‘i, Hawai‘i, is obtained by analyzing high resolution geochemical and geophysical time-series data. In concert with multiyear water level analyses, a tidally and precipitation-driven groundwater connection between Kauhakō Crater lake and submarine groundwater discharge (SGD) fluxes are demonstrated. Results include an average discharge rate of 190 cm d−1 and the detection of water-flow pathways past cesspools that likely contribute to higher nutrient loading near the SGD sites. This underlines the importance of managing anthropogenic nutrients that enter the shallow freshwater lens such as through cesspools and are consequently discharged via SGD onto coral reef habitats.

Groundwater management: Effectiveness of mitigation measures in nitrate vulnerable zones – a Portuguese case study

Groundwater vulnerability depends on hydrogeological characteristics, land use dynamics and pollutant sources. In Europe, agriculture is the main source of diffuse groundwater pollution. The over-application of nitrogen (N) soil inputs promotes nitrate leaching to surface and groundwater and associated impacts on human health and biodiversity. Mitigation programs have been implemented across the Europe Union (EU) to reduce N loads to water bodies. The EU Nitrates Directive (1991/676/EEC) aims to reduce water nitrate pollution associated to agriculture. The reduction implies the implementation of Good Agricultural Practices and National Action Programs with mandatory measures for the Nitrate Vulnerable Zones (NVZ).The Esposende-Vila do Conde is one of the nine Portuguese nitrate vulnerable zones. The application of organic and chemical fertilizers, especially on intensive agricultural farms, without the implementation of good agricultural practices and effluent management, will promote an increase in groundwater nitrate concentration. A total of 1728 groundwater sampling points were collected to determine nitrate contents, from 2007 to 2020. A space-time database including farmer questionnaires, field data and groundwater nitrate contents were prepared.The groundwater points located in artificialized territories and agricultural areas show the highest nitrate concentration. This situation is mainly due to a decrease of surface water recharge, associated to the increase of pollutant sources, as well as inadequate agricultural practices. The areas with considerable agricultural productivity and located away from surface water show an increase in nitrate concentration. The occurrence of total organic N on the soil is not an influence of groundwater nitrate contents and there is a casual relation between groundwater nitrate contents and mineral N. The methodology allows the identification of higher groundwater nitrate content, as well as pressure, state, and response indicators. However, despite the global decrease in nitrate contents, the measures remain insufficient and appropriate mitigation will be required to reduce groundwater contamination.

Groundwater quality evaluation and pollution source apportionment using multivariate statistical analyses in Chuadanga municipality, Bangladesh

This is the first study to report the groundwater (GW) quality indices for drinking and irrigation purposes in Chuadanga Municipality, Bangladesh. GW samples were collected from 32 locations across the municipality's nine administrative wards. The physical parameters, such as total dissolved solids (TDS): 383 ppm, pH: 7.4, and electrical conductivity (EC): 583 μs/cm were reported, respectively. Among the detected chemical components, arsenic (As): 0.16 mg/L, iron (Fe): 0.77 mg/L, and manganese (Mn): 0.75 mg/L were found beyond the standard values. Geospatial analysis and multivariate analyses, such as correlation coefficient analysis (CCA), cluster analysis (CA), and principal component analysis (PCA), were performed to determine the potential sources of contaminants in the GW of the selected study areas. The water quality index (WQI) and heavy metal pollution index (HPI) suggest that most samples are unsuitable for drinking and irrigation. Following the water quality indices, CCA indicates the significant impact of As and Mn on water quality. The CA and PCA suggest geogenic, anthropogenic, and natural processes are mainly responsible for releasing those contaminants in GW.

Groundwater Quality Evaluation and Pollution Source Apportionment Using Multivariate Statistical Analyses in Chuadanga Municipality, Bangladesh

Groundwater Quality Evaluation and Pollution Source Apportionment Using Multivariate Statistical Analyses in Chuadanga Municipality, Bangladesh

Quantifying nitrate pollution sources of shallow groundwater and related health risks based on deterministic and Monte Carlo models: A study in Huaibei mining area, Huaibei coalfield, China

Nitrate pollution in groundwater is a global environmental concern. As a result, accurate identification of potential sources for such pollution is of critical significance to the effective control of groundwater quality. In this study, forty-nine shallow groundwater samples were collected from the Huaibei mining area. Hydro-chemical characterization, geospatial analysis technique, dual nitrate isotopes (δ15N-NO3- and δ18O-NO3-), Bayesian model and health risk assessment model were adopted for exploring the conditions, sources, proportion, and potential health risks of nitrate pollution for the first time in the study area. The results showed that the nitrate concentration ranged from 0.00 to 293.21 mg/L, and that 18.37% groundwater samples exceeded the standard of drinking water in China (GB 5749–2006). Based on the dual isotopic values of nitrate, it could be concluded that nitrification was dominated migration and transformation process of nitrogen. The results of Bayesian model showed that the proportional contributions of the potential nitrate pollution sources in shallow groundwater were manure and sewage (M&S) (39.54 %), NH4+ in fertilizer and precipitation (NHF&P) (34.93 %), soil nitrogen (SN) (14.89 %), and NO3- in atmospheric deposition (NAD) (10.64 %). The health risk assessment indicated that non-carcinogenic risks posed by NO3--N was higher for children than adults. The primary exposure pathway was oral ingestion. Monte Carlo simulation were applied to evaluate model uncertainty. The probabilities of non-carcinogenic risks were up to 12.54 % for children and 5.22 % for adults. In order to protect water quality and drinking water safety, it was suggested that effective nitrate reduction strategies and better management practices can be implemented.

Polluted aquifer inverse problem solution using artificial neural networks

The problem of identifying an unknown pollution source in polluted aquifers, based on known contaminant concentrations measurement in the studied areas, is part of the broader group of issues, called inverse problems. This paper investigates the feasibility of using Artificial Neural Networks (ANNs) for solving the inverse problem of locating in time and space the source of a contamination event in a homogeneous and isotropic two dimensional domain. ANNs are trained in order to implement an input-output relationship which associates the position. Once the output of the system is known, the input is reconstructed by inverting the trained ANNs. The approach is applied for studying a theoretical test case where the inverse problem is solved on the basis of measurements of contaminant concentrations in monitoring wells located in the studied area. Groundwater pollution sources are characterized by varying spatial location and duration of activity. To identify these unknown pollution sources, concentration measurements data of monitoring wells are used. If concentration observations are missing over a length of time after an unknown source has become active, it is more difficult to correctly identify the unknown pollution source. In this work, a missing data scenario has been taken into consideration. In particular, a case where only one measurement has been made after the pollutant source interrupted its activity has been considered.