Nitrate Concentrations In Groundwater Research Articles

Numerical groundwater flow and nitrate transport assessment in alluvial aquifer of Varaždin region, NW Croatia

Study regionThe Varaždin alluvial aquifer located in the Drava River valley. Study focusThe study area is characterized by agricultural activity, which raised concerns due to the high nitrate concentration in groundwater. The present study aims to evaluate future nitrate concentrations in groundwater using the numerical groundwater flow and transport modeling. The regional model was generated in GMS software, using the MODFLOW code for steady-state groundwater flow model, and MT3DMS code for nitrate transport model. Advective-dispersive transport was simulated, without a chemical retardation process. The calibrated model was used to investigate the evolution of groundwater nitrate concentrations for the next 20 years under four scenarios: a) current nitrate input; b) zero input from wastewater; c) agricultural input reduced by 50%; d) input from natural vegetation and surface water New hydrological insights for the regionThe scenario analysis demonstrated that reducing the nitrate input from agricultural areas yields a considerable reduction of nitrate in groundwater, while the impact of wastewater is negligible. Neither of the scenarios reached concentrations below threshold value of 50 mg/L for the entire aquifer in the next 20 years. The nitrate concentration in the northern part of the aquifer will remain low, mainly due to the dilution from river. The central part of the aquifer is highly dependent on changing the on-ground nitrate concentration, showing inertia regarding the nitrate attenuation in groundwater.

Restoring groundwater quality at the drinking water catchment scale: A multidisciplinary and participatory approach

Preserving or restoring the quality of groundwater resources with regard to nitrate is a major challenge. To date, useful and easily applicable tools to identify the best measures to implement at local scale are lacking. An innovative methodology is proposed to identify cost-effective restoration measures at the drinking water catchment scale. The methodology is based on the articulation of two tools: a model simulating nitrate groundwater contamination time series and an economic evaluation, within a participatory approach. It was applied to a representative drinking water catchment in Northwest France that has been affected by nitrate contamination for decades. Five scenarios of measures (changes in fertilization and intercropping practices, or in cropping patterns) were co-constructed with stakeholders, evaluated in terms of cost and impact on groundwater nitrate concentration, and discussed with stakeholders. Overall, two scenarios stand out. Introducing hemp in crop rotations is the only scenario generating an economic benefit, but for a very low impact on nitrate concentration (decrease of 4 mg/l by 2050 for 10% of hemp in crop rotations). Introducing alfalfa in crop rotations is the most effective measure to decrease nitrate concentration (decrease of 23 mg/l by 2050 for 20% of alfalfa in crop rotations), and for a moderate cost compared to the other scenarios (25–51 €/ha/year). Results show that substantial changes in cropping patterns – more important than those imagined initially by stakeholders (e.g. converting 40% of cropland to alfalfa) are needed to restore groundwater quality targets in the medium-long term. Measures deemed promising and achievable by the stakeholders proved to be insufficiently effective. The approach developed has been shown to provide valued and trusted information to stakeholders and to objectify debates. Stakeholders have shown interest in the evaluation of costs and further socio-economic information, in addition to the evaluation of the effectiveness of measures on groundwater quality, validating the multidisciplinary dimension of the approach. The approach has the advantage of being easy to implement, and is therefore applicable to other study sites where needed.

Temporal and Spatial Variation Characteristics and Driving Factors of Nitrogen of Shallow Groundwater in Hetao Irrigation District

As an important food production area in the north of China, the Hetao plain is extremely vulnerable to nitrate pollution caused by agricultural production activities and additional factors. Thus, it is of great significance for the environmental protection and rational use of groundwater to detect the current situation of groundwater nitrate pollution, temporal and spatial evolution characteristics, and main influencing factors in the Hetao irrigation district. We selected the Wualte irrigation area as the study area, and the characteristics of temporal and spatial changes in groundwater nitrate concentration and the main influencing factors in this area were explored. We used statistical analysis to analyze the nitrogen content of groundwater in the study area, and the Piper three-line diagram was used to explore the characteristics of chemical composition and evolution; furthermore, we used ion ratio and correlation analysis methods to explore the source of NO3--N in groundwater. The results showed that NO3--N was the main existing form of nitrogen in the Wulate irrigation area, and its concentration varied from 0.01 to 60.00 mg·L-1, with an exceeding standard rate of 10.50%. In terms of time, the characteristic of time change was that the NO3--N concentration in August of groundwater was the highest (average 6.61 mg·L-1), followed by that in October (6.22 mg·L-1) and November (6.25 mg·L-1), and that in March (average value of 1.77 mg·L-1) was the lowest. With the influence of rainfall and irrigation, the NO3--N in the soil was infiltrated into the groundwater, showing the characteristic that wet season and concentrated irrigation periods were higher than those in other periods. Spatially, it appeared as southwest (8.87 mg·L-1)>northwest (4.25 mg·L-1)>east (0.89 mg·L-1), mainly due to the original geological conditions, land use, and domestic waste stacking. In addition, the concentration of NO3--N of groundwater in the study area was closely related to the depth of groundwater and redox conditions but was relatively less affected by the concentration of water chemical ions. Therefore, identifying the temporal and spatial distribution characteristics and main sources of groundwater nitrogen pollution can provide a scientific basis for scientific fertilization, groundwater nitrate pollution control, and water safety.

Effect of agri-environment schemes (2007–2014) on groundwater quality; spatial analysis in Bavaria, Germany

Degradation of groundwater quality and contamination of drinking resources is one of the most widespread and harmful impacts of over-fertilisation in agriculture. As part of the Common Agricultural Policy (CAP), agri-environment schemes (AES) have as main objective the protection and management of the farm environment, including groundwater quality. In this article a spatial econometric model is applied to evaluate the impact of AES on groundwater nitrate concentrations. Bavaria, a federal state of Germany, is used as a case study, due to the findings of high nitrate concentrations in the groundwater. A significantly negative effect is found between AES expenditures focusing on grassland management and nitrogen concentrations, while AES focusing on crop management, organic farming and preservation of cultural landscape did not show a significant effect. The assessment of other factors such as cereals and forage showed a statistically positive effect on nitrate concentrations. However, loam soil texture, rainfall, and residential area were found to negatively affect nitrate concentrations.

Application of novel data-mining technique based nitrate concentration susceptibility prediction approach for coastal aquifers in India

In water resource management and pollution control research, prediction of nitrate concentration in groundwater gets utmost priority in the last few years. Thus, our current research work aims to identify the nitrate susceptibility areas of coastal districts of eastern India using three data mining techniques of random forest (RF), boosting and bagging approach. To make groundwater nitrate concentration susceptibility map, fifteen nitrate conditioning factors were identified using multi-collinearity analysis and identify relative importance of nitrate variability using MDA method. The resampling method of four K-Fold cross validation (CV) technique was used to preparing inventory dataset and respective modelling purpose. Seven statistics methods including receiver operating characteristics-area under curve (ROC-AUC) and Taylor diagram have been applied for evaluating the performance of all applied models. The outcomes ensure that boosting model is more efficient followed by bagging and RF. Taylor diagram also revealed Boosting (r = 0.93) is most optimal model followed by Bagging (r = 0.89) and RF (r = 0.88). From aforementioned results, our study revealed that boosting is the well performed model to delineate groundwater nitrate concentrate susceptibility map (GNCSM) in regional level which also will be helpful to worldwide researcher to find out nitrate susceptibility zone in coastal environment and it may be fruitful to the different policy makers to take accurate decision for water management in the current study area.

Comprehensive Assessment of the Effect of Various Anthropogenic Activities on the Groundwater Quality

Water pollution had become a major problem due to its’ negative impact on the human health. Effects of humaninduced actions on groundwater quality were examined in this study. The physicochemical, heavy metals and microbial parameters of groundwater, sampled during the two major climatic periods in Nigeria, were measured according to APHA approved procedures. Results obtained from laboratory tests revealed that anthropogenic activities had substantial effect on the groundwater quality. The groundwater TDS, nitrate, BOD, chloride and phosphate concentrations varied from 23.93 to 42.32 mg/L, 0.54 to 2.16 mg/L, 2.23 to 4.72 mg/L, 10.78 to 19.15 mg/L, and 0.22 to 0.36 mg/L respectively. Likewise, Cd concentration fluctuated between 0 and 0.001 mg/L, Cu varied between 0 and 0.149 mg/L, Fe varied between 0 and 0.293 mg/L, Pb varied between 0 to 0.105 mg/mL, Zn varied between from 0 and 0.768 mg/L, while Ni fluctuated between 0 and 0.001 mg/L. The findings revealed that areas with poor sanitary situations had poor groundwater quality, compared to the areas with improved sanitary situations. Regarding the microbial population, the highest Total Bacteria and Fungi Counts recorded in the groundwater were 1.11×102 cfu/mL and 1.23×102 cfu/mL respectively. Similarly, the highest recorded Enterobacterial spp., Staphylococus arurius, E. coli, Proteus spp. and Shegeela spp. populations were 26.22×102 cfu/mL, 1.23×102 cfu/mL, 0.41 MPN/100 mL, 0.12 cfu/ml and 0.30×102 cfu/mL respectively. Although, the groundwater physicochemical parameters and heavy metals concentrations were within safe drinking water limits; the groundwater was largely contaminated with pathogenic microorganisms, mostly during the rainy season.

Legacy nitrate and trace metal (Mn, Ni, As, Cd, U) pollution in anaerobic groundwater: Quantifying potential health risk from “the other nitrate problem”

Denitrification in soils and aquifers sustains low nitrate concentrations in many anaerobic groundwaters despite massive inputs of N from agriculture. However, this ecosystem service sometimes comes at the cost of trace metal mobilization and concerns have been raised that denitrification in anaerobic groundwater may lead to trace metal contamination. But it remains unclear, if denitrification must necessarily result in trace metal concentrations that are potentially harmful for humans. For example, formation of iron(oxy)hydroxides after the reaction of nitrate with pyrite may reduce rather than increase the mobility of certain trace metals in aquifers. We quantified the potential health risk resulting from denitrification-associated trace metal pollution (Mn, Ni, As, Cd, U) in anaerobic groundwater with different degrees of nitrate pollution in >800 wells located in Northern Germany. Overall, observed rates of violations of legal quality standards for U, As and Cd are moderate in the study area (<10% of all wells) but elevated for Mn (>50%), which is a common contaminant under the often anaerobic conditions in the study area. However, in groundwater where denitrification had partially proceeded, the risk for drinking water standard violations was higher for Ni, Cd and U (up to a factor of 4 for Ni) as compared to anaerobic groundwater without denitrification, but lower for Mn and As. Especially poorly buffered groundwaters with pH < 5.5 are at risk of Ni and Cd contamination resulting from denitrification, while the opposite is true for Mn and U. Thus, we establish a clear linkage between nitrogen biogeochemistry and trace metal mobility and show how perturbations of groundwater redox and pH conditions by nitrate can further deteriorate groundwater quality. In addition, we find that the combination of oxygen concentrations of lower than 1 mg L−1, and nitrate concentrations above 1 mg L−1 allows for identification of denitrification (as evidenced by excess N2 in groundwater) with 90% accuracy. The methodology developed herein can be used to inform water treatment planning about potential trace metal contamination when drinking water should be produced from anaerobic aquifers beneath agricultural landscapes.

Random forest and nature-inspired algorithms for mapping groundwater nitrate concentration in a coastal multi-layer aquifer system

Accurate prediction of groundwater nitrate concentrations is critical for pollution control and sustainable groundwater resource management. However, Ant optimization (Ant), Firefly, Multiple Objective Evolutionary (MOE), and Grey Wolf Optimization (GWO), are novel data-driven hydrid algorithms that are barely used in groundwater pollution studies. Yet, areas susceptible to groundwater nitrate concentration have not been evaluated in the Mekong Delta. In this paper, we develop five novel hybrid algorithms: Ant, Firefly, MOE, GWO and Particle Swarm Optimization (PSO) which are integrated with the random forest (RF) algorithm for mapping groundwater nitrate concentrations in the coastal multi-aquifers of the Mekong Delta. The geographic information system (GIS) environment was used to construct 24 conditional factors believed to affect groundwater nitrate contamination. In 216 wells, nitrate concentrations were collected and quantified, and the results were utilized as a dependent parameter in modeling. To assess the effectiveness of these proposed hybrid models, evaluation measures such as statistical error indices, confusion matrices, Taylor diagram, the probability density function of error, and scatter plots were used. The RF-Firefly model (RMSE = 6.25, AUC = 0.961) performed the best based on testing datasets, followed by the RF-MOE (RMSE = 8.67, AUC = 0.878), the RF-Ant (RMSE = 7.27, AUC = 0.874), the RF-GWO ( RMSE = 7.35, AUC = 0.87), RF-PSO (RMSE = 10.24, AUC = 0.864) and RF ( RMSE = 15.85, AUC = 0.816) models. The results of mapping groundwater nitrate concentrations also showed that the south-central region had a very high nitrate concentration (> 45 mg/L) compared to other places. It was also found that extensive agricultural activities, wells near farming areas, and overexploitation of groundwater were the main causes of nitrate pollution in the study. The proposed models show their capability and application in predicting groundwater nitrate pollution in various deltaic locations.

Characterizing Dominant Field-Scale Cropping Sequences for a Potato and Vegetable Growing Region in Central Wisconsin

Crop rotations are known to improve soil health by replenishing lost nutrients, increasing organic matter, improving microbial activity, and reducing disease risk and weed pressure. We characterized the spatial distribution of crops and dominant field-scale cropping sequences from 2008 to 2019 for the Wisconsin Central Sands (WCS) region, a major producer of potato and vegetables in the U.S. The dominant two- and three-year rotations were determined, with an additional focus on assessing regional potato rotation management. Our results suggest corn and soybean are the two most widely planted crops, occurring on 67% and 36% of all agricultural land at least once during the study period. The most frequent two- and three-year crop rotations include corn, soybean, alfalfa, sweet corn, potato, and beans, with continuous corn being the most dominant two- and three-year rotations (13.2% and 8.5% of agricultural land, respectively). While four- and five-year rotations for potato are recommended to combat pest and disease pressure, 23.2% and 65.9% of potato fields returned to that crop in rotation after two and three years, respectively. Furthermore, 5.6% of potato fields were planted continuously with that crop. Given potato’s high nitrogen (N) fertilizer requirements, the prevalence of sandy soils, and ongoing water quality issues, adopting more widespread use of four- or five-year rotations of potato with crops that require zero or less N fertilizer could reduce groundwater nitrate concentrations and improve water quality.

Geospatial Distribution of Age-Adjusted Incidence of the Three Major Types of Pediatric Cancers and Waterborne Agrichemicals in Nebraska.

This study was conducted to examine, at the county level, the relationship between pediatric cancer incidence rate and atrazine and nitrate mean concentrations in surface and groundwater. A negative binomial regression analysis was performed to investigate the association between central nervous system (CNS) tumors, leukemia, lymphoma, and atrazine and nitrate mean concentrations in groundwater. The age‐adjusted brain and other CNS cancer incidence was higher than the national average in 63% of the Nebraska counties. After controlling for the counties socio‐economic status and nitrate concentrations in groundwater, counties with groundwater atrazine concentrations above 0.0002 µg/L had a higher incidence rate for pediatric cancers (brain and other CNS, leukemia, and lymphoma) compared to counties with groundwater atrazine concentrations in the reference group (0.0000–0.0002 µg/L). Additionally, compared to counties with groundwater nitrate concentrations between 0 and 2 mg/L (reference group), counties with groundwater nitrate concentrations between 2.1 and 5 mg/L (group 2) had a higher incidence rate for pediatric brain and other CNS cancers (IRR = 8.39; 95% CI: 8.24–8.54), leukemia (IRR = 7.35; 95% CI: 7.22–7.48), and lymphoma (IRR = 5.59; CI: 5.48–5.69) after adjusting for atrazine groundwater concentration and the county socio‐economic status. While these findings do not indicate a causal relationship, because other contaminants or cancer risk factors have not been accounted for, they suggest that atrazine and nitrate may pose a risk relative to the genesis of pediatric brain and CNS cancers, leukemia, and lymphoma.

Groundwater‐surface water exchange as key control for instream and groundwater nitrate concentrations along a first‐order agricultural stream

AbstractLower‐order streams (first‐ and second‐order) define the initial, landscape‐related, chemical signature of stream water in catchments. To date, first‐order streams have been perceived as predominantly draining systems, which collect water and solutes from the surrounding groundwater and surface runoff and simply mirror the chemical composition of the inputs. In this study, the impact of stream‐groundwater exchange fluxes on water chemistry of a first order agricultural stream (Schönbrunnen) and its connected groundwater in south‐western Germany was assessed combining222Rn, dissolved ions (chloride, sulphate, nitrate), and salt tracer tests with investigations of stream discharge and groundwater hydraulic gradients. The findings suggest that stream‐water chemistry in lower‐order streams is governed by an intricate interplay between dynamic, bidirectional water and solute exchange between groundwater and the stream leading to a pronounced hydrologic turnover along the studied reaches. High nitrate concentrations (up to 79 mg/L as NO3−) in stream water were attenuated in downstream direction (a mean value of 39 mg/L) without an increase in discharge, suggesting that redox processes occurring during sediment passage in sequential infiltration and exfiltration zones affect stream water chemistry. Nitrate in stream water infiltrating into the aquifer at distinct losing spots was subject to denitrification within the first few decimetres of the streambed, while concurrent exfiltration of low‐nitrate groundwater into the stream at gaining spots compensated for flow losses and in turn diluted instream nitrate concentrations. In summary the findings imply that (1) instream mixing resulting from the bidirectional exchange of water between groundwater and the stream (hydrologic turnover) affects instream nitrate concentrations, (2) denitrification in the streambed of losing reaches and the near‐stream aquifer significantly contributes to reactive nitrate turnover and elimination, and (3) oxidation of ammonium could be a secondary source of nitrate inputs into the stream.

Contaminated Groundwater by Nitrate in Sidoarum Village, Godean District, Sleman Regency, Special Region of Yogyakarta

Sidoarum Village, Godean District, Sleman Regency, Special Region of Yogyakarta become one of the developing areas for getting influenced by Yogyakarta City center which is only 8 kilometers away. Population growth has occurred from 2010 to 2019 which has led to land use changes from agricultural areas to residential areas. Land use change, population growth, incorrect space using, excessive use of fertilizers, agricultural activities, and industrial waste disposal affects the groundwater quality resources, especially nitrate (NO3) compound. This research was aimed to determine the distribution of groundwater quality based on nitrate parameters and to analyze the relationship between land use changing and nitrate concentrations in groundwater. This mixed-method research used to survey and mapping methods to collect the data. The analysis was conducted by laboratory tests and spatial analysis method using Arc GIS software. The sampling of groundwater was carried out by purposive sampling method based on differences in land use. The results indicated that most of the groundwater in Sidoarum Village has been contaminated by nitrate due to activity in agricultural areas like intense fertilizer application and farming in household-scale in densely populated areas, especially with the bad sanitation system. The distribution of nitrate contaminated groundwater was correlated to the types of land use. The monitoring of land use change is needed especially from the agricultural areas to residential areas due to excessive population growth.

Effect of high nitrate contamination of groundwater on human health and water quality index in semi-arid region, South India

The specific objective of the present is to evaluate the human health issue due to the continuous consumption of nitrate-contaminated groundwater among the various age groups of people. In the study, 40 groundwater samples were collected during the post-monsoon season, and the major ions were analysed in a laboratory. Chadha plot revealed that weathering of parent rocks, ion exchange process and leaching of salts from the rocks are primary sources of groundwater contamination. Nitrate concentration varied from 24 to 78 mg/L with a mean of 46.45 mg/L. Nitrogen pollution index (NPI) value divulged that 40% and 17.5% of sample locations are moderately and significantly polluted due to elevated nitrate concentration in groundwater. The human health risk assessment model revealed that health issues are among the various age groups which are infants > kids > children > aged peoples > adults. The nitrate’s identified sources are leaching of salts from the rocks, using synthetic fertilizers, uncovered septic tanks and improper disposal of household waste from the residential area. Therefore, periodic inspection of water supply, health check-up and inspection of underground pipelines are the remedial measures that should be taken to reduce the severe effects of nitrate-contaminated drinking water in the study area.

Hydrogeochemical insights into the signatures, genesis and sustainable perspective of nitrate enriched groundwater in the piedmont of Hutuo watershed, China

Groundwater nitrate contamination has been a global issue threatening water supply, especially in arid and semiarid piedmonts. An extensive hydrogeochemical investigation was performed in an intensively human-impacted semiarid piedmont in North China. Groundwater is nearly neutral to alkaline in nature with large variations of salinity. The concentration of nitrate in groundwater ranges from 4.89 mg/L to 402.40 mg/L with 64.29% of values beyond the permissible limit of 50 mg/L recommended by World Health Organization. Hydrogeochemistry changes gradually from fresh HCO3-Ca type waters to moderately saline Cl-Ca type with the increase of nitrate content in groundwater. Anthropogenic inputs from residential land usage are responsible for the nitrate enrichment and salinity increase. The Entropy-weighted water quality index value ranges from 29 to 327 with a mean of 82, and 21.43% of sampled groundwaters under the categories from medium to extremely poor quality for drinking purpose. Non-carcinogenic risks posed by nitrate, nitrite and ammonia via dermal and oral pathways were assessed for infants, children, adult females and males. The overall non-carcinogenic risk of investigated groundwaters ranges from 2.91 × 10−1 to 2.38 × 101 (mean of 4.00 × 100) for infants, 1.29 × 10−1 to 1.06 × 101 (mean of 1.78 × 100) for children, 5.74 × 10−2 to 4.70 × 100 (mean of 7.90 × 10−1) for adult females, and 4.24 × 10−2 to 3.47 × 100 (mean of 5.83 × 100) for adult males. Health risks vary but all population groups are at risk. These health threats are mainly posed by nitrate through the oral ingestion pathway, with minors being more susceptible. Therefore, measures including awareness improvement of local residents, environmental protection, differential water supplies based on water quality, and targeted water treatment are recommended to achieve sustainable groundwater resource development in arid and semiarid piedmonts.

Assessment of Risk and Social Impact on Groundwater Pollution by Nitrates. Implementation in the Gallocanta Groundwater Body (NE Spain)

Groundwater is an essential resource for humans concerning freshwater supply; therefore, preserving and protecting its quality is necessary. Risk assessment, based on hazard, intrinsic vulnerability information and mapping, may be considered as a key aspect of sustainable groundwater management. An approach has been made by combining the Nitrogen Input Hazard Index and the hydrogeological parameters considered in a modified DRASTIC method. A three-level classification has been used to determine the degree of risk, and the thresholds have been established following measurable criteria related to the potential nitrate concentration in groundwater. The second part of the study focused on estimating the socioeconomic impact of groundwater pollution by relating the degree of risk and social vulnerability to groundwater pollution. The method has been tested in the Gallocanta Groundwater Body (Spain). As a result, a risk map and an impact map are provided. The risk map shows that 67% of the study area can be classified as moderate and high-risk areas, corresponding to high hazard sources located in moderate and high vulnerability zones, whereas the impact of groundwater pollution is classified as moderate in the whole groundwater body. The proposed analysis allows comparison between aquifers in different areas and the results required by water authorities to implement control and mitigation measures.

Groundwater Recharge Assessment Using Multi Component Analysis: Case Study at the NW Edge of the Varaždin Alluvial Aquifer, Croatia

Exploring the interaction between precipitation, surface water, and groundwater has been a key subject of many studies dealing with water quality management. The Varaždin aquifer is an example of an area where high nitrate content in groundwater raised public concern, so it is important to understand the aquifer recharge for proper management and preservation of groundwater quality. The NW part of the Varaždin aquifer has been selected for study area, as precipitation, Drava River, accumulation lake, and groundwater interact in this area. In this study, groundwater and surface water levels, water temperature, water isotopes (2H and 18O), and chloride (Cl−) were monitored in precipitation, surface water, and groundwater during the four-year period to estimate groundwater recharge. Head contour maps were constructed based on the groundwater and surface water levels. The results show that aquifer is recharged from both Drava River and accumulation lake for all hydrological conditions–low, mean, and high groundwater levels. The monitoring results of water temperature, chloride content, and stable water isotopes were used as tracers, i.e. as an input to the mixing model for estimation of the contribution ratio from each recharge source. The calculation of mixing proportions showed that surface water is a key mechanism of groundwater recharge in the study area, with a contribution ratio ranging from 55% to 100% depending on the proximity of the observation well to surface water.

Predictive modeling of groundwater nitrate pollution and evaluating its main impact factors using random forest

Groundwater quality in plains and basins of arid and semi-arid regions with increased agriculture and urbanization development faces severe nitrate pollution, which is affected by both climate and anthropogenic activities. Here, shallow groundwater nitrate concentrations in the Yinchuan Region in central Yinchuan Plain were modeled during 2000, 2005, 2010, and 2015 using random forest. Multiple spatial environment factors were taken as predictor variables. The relative importance of these factors was also calculated using the constructed model. Remote sensing and GIS methods were used to compile various environmental factors to generate training and test sets for training and validation of the random forest model. Mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) between the observed and predicted groundwater nitrate concentrations were used to measure the model performance. As indicated by these metrics, the random forest model for groundwater nitrate prediction was performed well. The relative importance of the predictor variables computed by the model indicated groundwater nitrate was mainly affected by the distance to the Yellow River, meteorological elements (precipitation, evaporation, and mean air temperature), and water level elevation. Additionally, urban and arable land were the two land use/land cover types that mainly influenced groundwater nitrate concentration in the Yinchuan Region, of which urban land was more influential than arable land as a result of intense expansion of urban land from 2000 to 2015. Overall, the current study provides an approach to integrate multiple environmental factors for groundwater quality study and is also significant for sustainable groundwater management in the Yinchuan Region.

A meta-modeling approach for hydrological forecasting under uncertainty: Application to groundwater nitrate response to climate change

We suggest a quantitative predictive approach based on meta-modeling techniques for long-term forecasting in hydrology. We apply our scalable methodological framework to a representative groundwater body in Emilia-Romagna region of Italy to evaluate the potential impact of climate change on groundwater nitrate concentration. The approach allows to (i) handle the uncertainty associated with the projection of climate variables, (ii) preserve an accurate description of flow and transport processes, and (iii) drastically accelerate computationally intensive simulations, at a time. A long-term climate scenario (i.e. referred to the 30-years 2061–2090) is developed for the study area by considering changes in the natural recharge to groundwater, the baseflow of the main rivers, and the rate of nitrate leaching. Global sensitivity analysis is performed to assess the relative influence of these factors on the quantity of interest (QoI), i.e. the long-term prediction of groundwater nitrate concentration. Our analysis enables also us to compute the spatial statistics and variability of the moments of the QoI over the study area and to perform Monte Carlo simulations to fully characterize the probabilistic behavior of the QoI at selected sensitive locations.

The Performance of Artificial Neural Network Approaches to Estimate the Nitrate Concentration in Groundwater

The estimation of the pollution concentration in groundwater is important, since it is one of the key resources of water supply. Nitrate (NO3-N) is one of the well-known indicator parameters in groundwater pollution. Using historical data, artificial neural networks can be utilized to estimate the nitrate concentration in groundwater. In this study, a sample dataset, which is derived from a survey analysis in the literature, is used to estimate the nitrate concentration of groundwater (i.e., target parameter) with respect to six different well characteristics (i.e., input parameters). The effect of different hydrogeological parameters of the wells on the nitrate concentration is focused for the first time in this study. The performance of two different ANN approaches, namely BPNN and GRNN, is evaluated comparatively by means of their regression performances. Considering regression results of ANN models, it can be concluded that the GRNN (R=0.99) algorithm works slightly better than the BPNN (R=0.98) algorithm with this dataset. Correlation results indicate that the most important characteristics of the wells to estimate the nitrate pollution are the well depth, depth below water table, clay above screen, and depth to well screen, respectively. Moreover, all these characteristics are inversely related to nitrate concentration of the well.

Identification of Groundwater Contamination in a Rapidly Urbanized Area on a Regional Scale: A New Approach of Multi-Hydrochemical Evidences.

Efficient identification of groundwater contamination is a major issue in the context of groundwater use and protection. This study used a new approach of multi-hydrochemical indicators, including the Cl-Br mass ratio, the hydrochemical facies, and the concentrations of nitrate, phosphate, organic contaminants, and Pb in groundwater to identify groundwater contamination in the Pearl River Delta (PRD) where there is large scale urbanization. In addition, the main factors resulting in groundwater contamination in the PRD were also discussed by using socioeconomic data and principal component analysis. Approximately 60% of groundwater sites in the PRD were identified to be contaminated according to the above six indicators. Contaminated groundwaters commonly occur in porous and fissured aquifers but rarely in karst aquifers. Groundwater contamination in porous aquifers is positively correlated with the urbanization level. Similarly, in fissured aquifers, the proportions of contaminated groundwater in urbanized and peri-urban areas were approximately two times that in non-urbanized areas. Groundwater contamination in the PRD was mainly attributed to the infiltration of wastewater from township-village enterprises on a regional scale. In addition, livestock waste was also an important source of groundwater contamination in the PRD. Therefore, in the future, the supervision of the wastewater discharge of township-village enterprises and the waste discharge of livestock should be strengthened to protect against groundwater contamination in the PRD.