Distribution Of Nitrate Concentrations Research Articles

Mapping specific groundwater nitrate concentrations from spatial data using machine learning: A case study of chongqing, China

Groundwater resources is not only important essential water resources but also imperative connectors within the intricate framework of the ecological environment. High nitrate concentrations in groundwater can exerting adverse impacts on human health. It is imperative to accurately delineate the distribution characteristics of groundwater nitrate concentrations. Four different machine learning models (Gradient Boosting Regression (GB), Random Forest Regression (RF), Extreme Gradient Boosting Regression (XG) and Adaptive Boosting Regression (AD)) which combine spatial environmental data and different radius contributing area was developed to predict the distribution of nitrate concentration in groundwater. The models use 595 groundwater samples and included topography, remote sensing, hydrogeological and hydrological, climate, nitrate input, and socio-economic predictor. Gradient Boosting Regression model outperforms the other models (R2 = 0.627, MAE = 0.529, RMSE = 0.705, PICP = 0.924 for test dataset) under 500 m radius contributing area. A high-resolution (1 km) groundwater nitrate concentration distribution map reveal in the majority of the study area, groundwater nitrate concentrations are below 1 mg/L and high nitrate concentration (>10 mg/L) proportion in southeast, northeast and central main urban area karst valley regions is 1.89%, 0.91%, and 0.38% respectively. In study area, hydrogeological conditions, soil parameters, nitrogen input factors, and percentage of arable land are among the most influential explanatory factors. This work, serving as the inaugural application of utilizing effective spatial methods for predicting groundwater nitrate concentrations in Chongqing city, furnish decision-making support for the prevention and control of groundwater pollution, particularly in areas primarily dependent on groundwater for water supply and holds profound significance as a milestone achievement.

The Variability in the Distribution of Nitrate Concentration at the Surface of the Southeast Indian Ocean

The Variability in the Distribution of Nitrate Concentration at the Surface of the Southeast Indian Ocean

Estimating Surface Nitrate Concentrations in the Coastal Areas of the Around Savu Sea and Southern Sumba Island Based on Remote Sensing Data

Nitrate is an essential nutrient in phytoplankton's photosynthesis process. In addition, phytoplankton uses nitrate for their growth and reproduction. Nitrate abundance on the coast will affect primary productivity and biogeochemical cycles. The availability of nitrate observation data, especially around the Savu Sea coast, is minimal. In this study, the estimation of nitrate in the coastal area of the southern part of Sumba Island and the eastern part of Savu Island by using the generalized additive model (GAM). Seventy-one nitrate observation data were used to build the GAM model, and remote sensing data were used as input data for nitrate estimation. Sea Surface Temperature (SST) and chlorophyll-a data were obtained from Aqua-MODIS. Sea Surface Salinity (SSS) and Sea Surface Windspeed (SSW) data were obtained from a Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) Soil Moisture-Ocean Salinity (SMOS), and Advanced Scatterometer (ASCAT), respectively. This study uses the Generalized Additive Model (GAM) approach to predict the distribution of nitrate concentrations and determine the main driving factors associated with nitrate. Based on the result, temperature is the dominant factor in nitrate estimation, while chlorophyll-a has a relatively small influence. The best model to predict nitrate distribution uses four parameters, namely SST, SSS, SSW, and chlorophyll-a. The validation results of the expected nitrate value obtained from the model with the observed nitrate value obtained results with the same value range of 0 - 0.35; the difference is the value of the distribution. From the comparison results, the R2 value is 0.357.

Health Risk Assessment of Nitrate in Drinking Water with Potential Source Identification: A Case Study in Almaty, Kazakhstan.

Infant mortality in Kazakhstan is six times higher compared with the EU. There are several reasons for this, but a partial reason might be that less than 30% of Kazakhstan's population has access to safe water and sanitation and more than 57% uses polluted groundwater from wells that do not comply with international standards. For example, nitrate pollution in surface and groundwater continues to increase due to intensified agriculture and the discharge of untreated wastewater, causing concerns regarding environmental and human health. For this reason, drinking water samples were collected from the water supply distribution network in eight districts of Almaty, Kazakhstan, and water quality constituents, including nitrate, were analyzed. In several districts, the nitrate concentration was above the WHO and Kazakhstan's maximum permissible limits for drinking water. The spatial distribution of high nitrate concentration in drinking water was shown to be strongly correlated with areas that are supplied with groundwater, whereas areas with lower nitrate levels are supplied with surface water sources. Based on source identification, it was shown that groundwater is likely polluted by mainly domestic wastewater. The health risk for infants, children, teenagers, and adults was assessed based on chronic daily intake, and the hazard quotient (HQ) of nitrate intake from drinking water was determined. The non-carcinogenic risks increased in the following manner: adult < teenager < child < infant. For infants and children, the HQ was greater than the acceptable level and higher than that of other age groups, thus pointing to infants and children as the most vulnerable age group due to drinking water intake in the study area. Different water management options are suggested to improve the health situation of the population now drinking nitrate-polluted groundwater.

Analysis of the Distance between the Measured and Assumed Location of a Point Source of Pollution in Groundwater as a Function of the Variance of the Estimation Error

The localization of pollution sources is one of the main tasks in environmental engineering. For this paper, models of spatial distribution of nitrate concentration in groundwater were created, and the point of highest concentration was determined. This point represents the assumed location of the pollution source and differs from the actual location, so there is a certain distance between the measured and assumed location. This paper puts forward a new hypothesis that the distance between the measured and the assumed location is a function of the variance of the estimation error. The scientific contribution of this paper is based on the fact that the interaction of statistical and geostatistical methods can locate the dominant point source of pollution or narrow down the search area. The above hypothesis is confirmed by the example of the Varaždin wellfield, which was closed due to an excessively high groundwater nitrate concentration. Seven different interpolation methods were used to create spatial distribution models. Each method provides a different model, a different variance of the estimation error, and estimates of the location of the pollution source. The smallest value of variance of the estimation error of 1.65 was obtained for the minimum curvature interpolation method and the largest value of variance (24.49) was obtained for the kriging with logarithmic variogram. Our results show a nonlinear and monotonic relationship between the distance and the variance of the estimation error, so logarithmic and rational quadratic models were fitted to the scatter point data. The models were linearized, a t-test was performed, and the results show that the models can be considered reliable, which is confirmed by the values of the coefficients of determination of the linearized models, which are around 0.50. The obtained results can be used in planning additional research work to determine the measured location of the pollution source. The research methodology we used is universal and can be applied to other locations where high concentrations of certain contaminants have been detected in groundwater in alluvial aquifers.

Predictive modeling and analysis of key drivers of groundwater nitrate pollution based on machine learning

Nitrate comtamination of shallow groundwater in agricultural intensification regions is a prevalent and global environment issue affecting food security, human health, and the water ecology. Developing a prediction model for groundwater nitrate contamination is crucial for protection of groundwater resources. Machine learning modeling offer potentials to predict contamination, but often fails to adequately screen input features in complex non-linear environments before modeling. Additionally, there is a tendency to overlook the interpretable description of the relationship between major water chemistry parameters and nitrate concentration after modeling, which hinders scientific decision-making by water resource managers. In this study, a dataset consisting of hydrochemical test results from 316 groundwater samples collected between 2011 and 2015 in intensive agricultural areas of Northeast China was collected. Prior to modeling, the dataset was grouped based on land use type, vadose zones types, and thickness. Based on the grouping of data sets, self-organizing map (SOM) and Spearman's coefficients were employed to identity the correlations between water chemical parameters and nitrate concentration (NO3–-N), which provided a logical basis for selecting the key input variables. A radial basis function artificial neural network (RBF ANN) prediction model and principal components regression (PCR) models was constructed using the dataset, and particle swarm optimization algorithm was applied to determine optimal parameter combinations of the RBF ANN. After conducting modeling, the advantages and disadvantages of RBF ANN and principal components regression (PCR) models were thoroughly examined and discussed, and the primary factors that impact nitrate concentrations in groundwater were analyzed using a PCR model. The results revealed that the RBF ANN model showed greater accuracy, while the PCR model offered better interpretability. Therefore, the integration of the two models is advantageous for nitrate prediction research. Human activities, rainfall, land-use types, vadose zones types and thickness are external factors that affecting nitrate concentration, and the difference between nitrification. Denitrification, and dissimilatory nitrate reduction to ammonium (DNRA) is internal driving factors for the change of nitrate concentration in the aquifer, and partially determine differences in the spatial distribution of aquifer nitrate concentrations under various land use types. This study provides a comprehensive approach for predicting nitrate pollution in groundwater under non-linear environmental conditions, supporting decision making in sustainable groundwater development, management, and conservation.

Estimation of nitrate concentration and its distribution in the northwestern Pacific Ocean by a deep neural network model

As a fundamental nutrient for marine biogeochemical processes, the magnitude and spatial distribution of nitrate concentrations are insufficiently measured in the interior ocean. In the present study, a deep neural network (DNN) model was developed to estimate nitrate concentrations in the upper northwestern Pacific Ocean (NPO). This model takes the temperature and salinity profiles as the primary input variables. Since the subtropical and tropical regions are featured by different spatial patterns of nitrate concentrations, we separately trained the model to improve the prediction skill. The predictive results indicate that the DNN model performs well in depicting both spatial and seasonal variability of nitrate concentrations. The sensitivity experiments show that temperature is the dominant factor for the nitrate estimation, while salinity has a relatively small effect, but it cannot be ignored in improving the prediction accuracy. Furthermore, using the temperature and salinity data from World Ocean atlas (2018), we found our DNN model has a good generalization ability on nitrate estimation in NPO. This model can be applied to further studies on nitrate's spatiotemporal variability and mechanism around the global ocean.

Assessment of groundwater vulnerability to nitrates using the GIS-based DRASTIC and SI methods: a case study in Zacharo area, Greece.

A vulnerability assessment of the aquifers in the agricultural area of Zacharo in SW, Peloponnese, Greece, was conducted using the DRASTIC index and the susceptibility index (SI). Sensitivity analysis was conducted and thematic maps for each parameter were generated to analyse the impact of individual parameter on the collective groundwater vulnerability. Results derived from the DRASTIC and SI maps revealed that the extremely highly vulnerable zones are concentrated at three coastal sites in the western part of the study area. Data from these maps also indicate low vulnerability areas throughout the eastern part of the region. The distribution of nitrate concentrations in groundwater is better correlated with the DRASTIC (79.2%) compared to SI (60.2%). Neither method takes into consideration the impact of dilution and nitrate to ammonium reduction, on the nitrate content of groundwater, thus overestimating the vulnerability index. Moreover, the SI method overestimates the impact of olive groves' land use type on the susceptibility index, thus resulting to a lower correlation with the observed nitrate concentrations.

SPATIAL AND TEMPORAL DISTRIBUTION OF NITRATES AND NITRITES IN THE FARMS’ WATER OF TIMIS COUNTY, ROMANIA: A CASE-STUDY

The present study aimed to investigate the spatial and temporal distribution of nitrates and nitrites in the farms’ water of Timis County. The sample for the study consisted of 4028 analysis bulletins of the nitrate and nitrite concentrations, measured in 41 farms and local water sources over two years (2016- 2242 and 2017- 1904 water samples) by the Surveillance Service, Public Health Direction, Timis. The data has been analysed with an observational case study of the spatial and temporal distribution of nitrate and nitrite concentrations in the farm water in Timis County. It has been found that the mean concentrations of nitrite and nitrate were lower than the standards, with a statistically significant difference (t=8.11, Sig.&lt; 0.001) between the 2 years of study and with higher values recorded in 2016 than in 2017. A specific spatial distribution of the mean concentrations of these two substances was slightly high in old, polluted localities and pig farms in Timis County.

Analisis Kandungan Nitrat dan Nitrit pada Air Tanah di Sekitar Perkebunan Kelapa Sawit

The research was conducted on oil palm plantations in Sumber Harapan Village with an area of ​​1,585 hectares of oil palm plantations or 92.66% of the total area of ​​1,710.51 hectares of village. This study was conducted with the aim of knowing the relationship and effect of distance, groundwater level, fertilization dose and time and condition of dug wells on nitrate and nitrite concentrations in the study area and the distribution of nitrate and nitrite concentrations in ground water around oil palm plantations Sumber Harapan Village. Determination of the location of the sample points was carried out using the purposive sampling method. Nitrate and nitrite compound tests were carried out on 15 groundwater sample points using the Palintest 7500 photometer. In the results, 14 units of dug wells (93.33%) are unstandard and 1 unit (6.67%) is following standar based on the Guidelines for Housing and Rural Settlement Development Criteria for PUPR RI number 5 of 2016. The results showed that the highest nitrate concentration was at location S14 at 4.140 mg/l and the lowest nitrate was at location S13 at 0.063 mg/l. The highest nitrite concentration value was at location S4 of 0.028 mg/l and the lowest nitrite was at S14 of 0.000 mg/l. Concentrations of nitrate and nitrite are evenly distributed throughout the study area with a radius of 100 meters from the groundwater sample point. The results showed that the concentration of nitrate and nitrite was still below the quality standard based on the Regulation of the Minister of Health Number 32 of 2017

Health risk assessment of nitrate and fluoride toxicity in groundwater contamination in the semi-arid area of Medchal, South India

Hydrogeochemical controlling variables for the high rate of groundwater contamination in a shallow hard rock aquifer of the semi-arid region of Medchal District, Telangana State, South India, and its associated health risk to children and adults were studied in detail. A total of 56 groundwater samples were analysed for major ion chemistry in pre- and post-monsoon seasons in 2019 year. Spatial distribution, hydrochemical facies, water–rock interaction, health risk assessment, carcinogenic risk, and principal component analyses were carried out to assess the water quality. Spatial distribution of nitrate and fluoride concentrations, high values were observed in the northern, southern, central, and south-western parts of the region. In terms of NO3−, about 88% and 63% and for F− 45% and 32% of the groundwater samples are non-acceptable limits of nitrate 45 mg/l and fluoride 1.5 mg/l during pre- and post-monsoon seasons, respectively. Modified Gibb’s plot showed the majority of the area is dominated by rock dominance and evaporation mechanisms. Statistical analysis reveals that water chemistry is governed by weathering of feldspar minerals and the cation exchange reaction mechanism. The petro-graphical studies revealed the dominance of plagioclase, orthoclase, quartz, biotite, apatite, and hornblende minerals in the host rock. The factor analysis results reveal that the geogenic and anthropogenic activities contribute to groundwater chemistry. Health risk assessment was carried out by calculating the hazard quotient (HQ) on the basis of intake exposure of groundwater, as per the USEPA. Results were obtained for total hazard index value greater than 1 for adults and children, which causes non-cancerous health effects.

Multi-objective optimal model for sustainable management of groundwater resources in an arid and semiarid area using a coupled optimization-simulation modeling.

Excessive exploitation of groundwater resources can increase the concentration of pollutants in addition to the progressive drawdown of groundwater table. In this research, to achieve aquifer quantitative and qualitative (QQ) sustainable development, an optimal scenario for withdrawing from operation wells is proposed. At the first step, the aquifer QQ simulation was carried out with the GMS model. The developed code in MATLAB2018b in the second step provides the link between the simulation and the NSGA-II optimization tools. In the third step, a multi-objective coupled optimization-simulation model based on GMS and NSGA-II developed. Finally, optimal scenario was chosen based on applying the multiple criteria decision-making (MCDM) and Berda Aggregation Method (BAM). The results show that reducing the current withdrawal rate to 51.55% can establish the QQ stability of the aquifer. This decrease in groundwater abstraction has led to a 4.6m increase in groundwater level (GWL) over 3years (average 19cm per month). The spatial and temporal distribution of nitrate concentration after applying the optimal discharge of wells shows the nitrate concentration in central and eastern parts of the aquifer has greatly reduced. Developed sustainable management model can be used to provide a real operation planning of wells to improvement of the QQ status of groundwater in each unconfined aquifer.

Evaluation of groundwater vulnerability to pollution using DRASTIC, composite DRASTIC, and nitrate vulnerability models

Background: Groundwater protection against pollution is a very important issue. Groundwater vulnerability maps are useful tools for protecting aquifers and assessing the potential for contamination. Therefore, the purpose of this study was to prepare vulnerability maps and perform sensitivity analysis to identify the most influential factors in the vulnerability of the studied aquifer. Methods: In this study, groundwater vulnerability to pollution was evaluated using DRASTIC, composite DRASTIC, and nitrate vulnerability (NV) models. Drastic is an index for the systematic assessment of potential groundwater pollution. In this method, the drastic index was calculated from the total weight and rank of the factors. For this purpose, first the factors affecting the pollution transfer, were weighted, ranked, and merged using GIS software. Then, using the overlapping techniques and after applying the necessary weight coefficients on each layer, a map of the vulnerability area of the study aquifer was prepared. Results: It was revealed that there is a significant linear relationship between all three models with the distribution of nitrate concentration. Accordingly, it was the most efficient NV model, followed by the composite DRASTIC (CD) and DRASTIC models, respectively. Also, in the studied aquifer, the DRASTIC index was between 147 and 136, the combined DRASTIC index was between 70 and 190, and the nitrate index was between 13 and 132. Conclusion: Vulnerability assessment of the studied aquifer using DRASTIC, and combined DRASTIC, and NV indices shows that, according to the DRASTIC index, 69.7% of the studied aquifer was in the medium vulnerability class. Also, according to the combined DRASTIC index, the largest area (53.62%) has low vulnerability and 31.56% has moderate vulnerability. But according to the nitrate index, 77.16% of the aquifers had very low vulnerability.

Distribution of Nitrate, Phosphate and N/P Ratio in Maninjau Lake, West Sumatra, Indonesia

This research aim to study of spatial distribution of Nitrate and Phosphate concentration, and also N/P ratio in waters of Maninjau Lake (0°19’ S; 100°12’E)), West Sumatera Province, Indonesia, collected from 11 sampling points of water samples which were performed during 3 (three periods) May, June and July 2017. Other major sources of environmental nitrogen and phosphate are fertilizers and wastewater discharged from farms and livestock facilities, and sewage from residential areas which leak into lakes. The Nitrate and phosphate concentrations in Maninjau Lake waters, the results of the analysis found that Nitrate values ranged from 0.02-0.40 mg/L mg/L (quality standard 10 mg/L), while phosphate ranged from 0.12-0.54 mg/L mg/L (quality standard 0.2 mg / L). Nitrogen (N) and phosphorus (P) generally limit the main productivity in lakes. The N/P Ratio calculation results are known that 0.29-0.58, the value shows that there has been an excessive addition of phosphate elements into the waters, so that it appears that the N element is a limiting element that affects the biological conditions of ecosystems such as phytoplankton biomass, species composition which is likely to occur dominance of certain types. This situation is quite reasonable, considering that around the study site is a residential area that discharges household wastewater that uses detergents into the waters. Anthropogenic nitrogen and phosphorus supply to natural ecosystems has increased them in many lakes.

Temporal distribution and zoning of nitrate and fluoride concentrations in Behbahan drinking water distribution network and health risk assessment by using sensitivity analysis and Monte Carlo simulation

ABSTRACT The present study aimed to investigate the spatial distribution and zoning of nitrate and fluoride concentrations in the drinking water distribution network of Behbahan city and the health risk caused by their concentration using sensitivity analysis and the Monte Carlo simulation during 2018 to 2019.In this cross-sectional study, 90 samples were collected from 45 points of the city’s drinking water distribution network in the two high-rainy and low-rainy seasons.Nitrate and fluoride concentration was measured byDR 5000 spectrophotometer and its non-carcinogenic impacts were calculated using EDI and HQ.The results showed that the average nitrate concentrations in high and low rainy seasons in the study area were 15.05 and 13.35 mg l−1, respectively.Also, the average concentrations of fluoride in high and low-rainy seasons were 0.67 and 0.76 mg L−1, respectively.The fluoride concentration in the high-rainy season showed a decreasing trend from north to south.The nitrate concentration decreased from north to south and southwest.The amount of nitrate HQ in the age groups corresponding to children, adolescents, adults and infants were 0.5, 0.4, 0.34, and 0.07, respectively.The fluoride HQ levels in the age groups corresponding to children, adolescents, adults and infants were 0.7, 0.5, 0.05, and 0.1, respectively.These results showed that children were the significant at-risk group due to these ions.According to the Monte Carlo simulator, 95th percentile caused by exposure to nitrate and fluoride ions in all age groups was more than one.Based on the sensitivity analysis, the most efficient factor in the health risk caused by nitrate and fluoride ions was the concentration of these ions in the drinking water distribution network.

Spatio-temporal variation of nitrate and nitrite levels in groundwater samples from Romania

This study investigated the spatial and temporal distribution of nitrate and nitrite concentration in groundwater from different geographical regions, in Romania. A special emphasis was put on a descriptive statistical analysis of data, namely on the determination of seasonal indices during dry and wet seasons. Nitrate and nitrite concentrations were monitored in 24 groundwater sources situated in different areas, between January 2016 and February 2020. The obtained data showed that the values of nitrate concentrations were situated between 8.03 mg/L in the North-Eastern part of Romania, 6.37 mg/L in the South-Eastern part of the country, and 3.55 mg/L in the Western part towards the center of the country. Nitrite concentration values were situated under the national maximum admitted limit, 0.5 mg/L, in all the investigated areas. The obtained data shows small changes in water quality during the monitoring period, which leads to the conclusion that, in this long interval of time, there were no significant groundwater contaminations with nitrate and nitrite. For adequate control of water pollution and rigorous management of groundwater sources, seasonal indices were calculated.

Nation-wide estimation of groundwater redox conditions and nitrate concentrations through machine learning

The protection of water resources and development of mitigation strategies require large-scale information on water pollution such as nitrate. Machine learning techniques like random forest (RF) have proven their worth for estimating groundwater quality based on spatial environmental predictors. We investigate the potential of RF and quantile random forest (QRF) to estimate redox conditions and nitrate concentration in groundwater (1 km × 1 km resolution) using the European Water Framework Directive groundwater monitoring network as well as spatial environmental information available throughout Germany. The RF model for nitrate achieves a good predictive performance with an R2 of 0.52. Dominant predictors are the redox conditions in the groundwater body, hydrogeological units and the percentage of arable land. An uncertainty assessment using QRF shows rather large uncertainties with a mean prediction interval (MPI) of 53.0 mg l−1. This study represents the first nation-wide data-driven assessment of the spatial distribution of groundwater nitrate concentrations for Germany.

Nitrate inversion based on remote sensing in the Pearl River Estuary, China

Against a background of ongoing economic and industrial development, the impact of human activities on the offshore environment has become increasingly significant in recent years. In many regions, contamination of the marine environment necessitates the need for dynamic, real-time and wide-coverage water quality monitoring and satellite remote sensing, such that multi-temporal and spatial scale observation of various oceanic parameters are realized. In this paper, using salinity, in situ spectral data, nitrate and other water quality data from five sampling campaigns in the Pearl River Estuary and adjacent waters, studies on the inversion of nitrate concentrations using an artificial neural network were performed. Two inversion models, based on input of the measured salinity and field spectral data and just the field spectral data, were investigated. After comparing the accuracy of the two models, it was considered that the nitrate model with salinity and field spectral data as input performed best. Finally, the selected model was applied to MODIS data to achieve inversion of the nitrate concentrations in the estuary waters and obtain the temporal distribution of nitrate concentrations as a means to monitor and evaluate water quality in the estuary.

Nitrogen Mass Balance and Pressure Impact Model Applied to an Urban Aquifer

The assurance of drinking water supply is one of the biggest emerging global challenges, especially in urban areas. In this respect, groundwater and its management in the urban environment are gaining importance. This paper presents the modeling of nitrogen load from the leaky sewer system and from agriculture and the impact of this pressure on the groundwater quality (nitrate concentration) in the urban aquifer located beneath the City of Ljubljana. The estimated total nitrogen load in the model area of 58 km2 is 334 ton/year, 38% arising from the leaky sewer system and 62% from agriculture. This load was used as input into the groundwater solute transport model to simulate the distribution of nitrate concentration in the aquifer. The modeled nitrate concentrations at the observation locations were found to be on average slightly lower (2.7 mg/L) than observed, and in general reflected the observed contamination pattern. The ability of the presented model to relate and quantify the impact of pressures from different contamination sources on groundwater quality can be beneficially used for the planning and optimization of groundwater management measures for the improvement of groundwater quality.

Distribution and Source Analysis of Nitrate in Surface Waters of China

The nitrate pollution of surface water has attracted worldwide attention, and it is not optimistic in China. To identify the distribution, sources, and transformation mechanisms of nitrate in China's surface waters, the nitrate data of 71 major rivers from 7 regions were systematically collected. The spatial distribution of nitrate concentrations in surface water was analyzed, and the main nitrate pollution sources were revealed based on nitrogen and oxygen isotopes of nitrate. The results show that approximately 7.83% of rivers in China exceeded the Chinese drinking water standard for nitrate (45 mg·L-1). The concentrations of nitrate in Mudanjiang, Haihe River, and the estuary of Yangtze River was even more than 90 mg·L-1, which indicates a serious pollution phenomenon. The isotopic compositions of surface water in China ranged from -23.5‰ to 26.99‰ for δ15N-NO3 and from -12.7‰ to 83.5‰ for δ18O-NO3. The main sources of nitrate are domestic sewage in Northeast, Central, and East China, while those are inorganic fertilizers and domestic sewage in Southwest and South China. The nitrate sources of surface water in Northwest and North China are complex, mainly from domestic sewage, inorganic fertilizer, and nitration of soil organic matter. Through correlation analysis, it is found that the nitrate concentrations of surface water have a positive relationship with population, wastewater discharge, agricultural nitrogen fertilizer application, and GDP per capita in China. It is urgent to solve the problem of pollution and prevent the further pollution of China's surface waters. The new "10-Point Water Plan" issued by the Chinese government solved the previous problems, but it will take decades to control and repair the polluted surface waters. In Northwest, North China, Southwest, and South China, not only the scale of sewage treatment plants in cities and counties should be increased but also the use of agricultural fertilizers should be controlled and managed by the government. Northeast, Central, and East China need to further control point source pollution and reduce the discharge of industrial wastewater and domestic sewage into rivers.