Identifying nitrate sources and transformations in an agricultural watershed in Northeast China: Insights from multiple isotopes

Groundwater hydrochemical signatures, nitrate sources, and potential health risks in a typical karst catchment of North China using hydrochemistry and multiple stable isotopes

Nitrate pollution in aquatic ecosystems has received growing concern, particularly in fragile karst basins. In this study, hydrochemical compositions, multiple stable isotopes (δ2H-H2O, δ18Ο-Η2Ο, δ15Ν-ΝΟ3-, and δ18Ο-ΝΟ3-), and Bayesian stable isotope mixing model (MixSIAR) were applied to elucidate nitrate pollution sources in groundwater of the Yangzhuang Basin. The Durov diagram identified the dominant groundwater chemical face as Ca-HCO3 type. The NO3- concentration ranged from 10.89 to 90.45 mg/L (average 47.34 mg/L), showing an increasing trend from the upstream forest and grassland to the downstream agricultural dominant area. It is worth noting that 47.2% of groundwater samples exceeded the NO3- threshold value of 50 mg/L for drinking water recommended by the World Health Organization. The relationship between NO3-/Cl- and Cl- ratios suggested that most groundwater samples were located in nitrate mixed endmember from agricultural input, soil organic nitrogen, and manure & sewage. The Self-Organizing Map (SOM) and Pearson correlations analysis further indicated that the application of calcium fertilizer, sodium fertilizer, and livestock and poultry excrement in farmland elevated NO3- level in groundwater. The output results of the MixSIAR model showed that the primary sources of NO3- in groundwater were soil organic nitrogen (55.3%), followed by chemical fertilizers (28.5%), sewage & manure (12.7%), and atmospheric deposition (3.4%). Microbial nitrification was a dominant nitrogen conversion pathway elevating NO3- levels in groundwater, while the denitrification can be neglectable across the study area. The human health risk assessment (HHRA) model identified that about 88.9%, 77.8%, 72.2%, and 50.0% of groundwater samples posing nitrate's non-carcinogenic health hazards (HQ > 1) through oral intake for infants, children, females, and males, respectively. The findings of this study can offer useful biogeochemical information on nitrogen pollution in karst groundwater to support sustainable groundwater management in similar human-affected karst regions.

Hydrogeochemical characterization, quality assessment, and potential nitrate health risk of shallow groundwater in Dongwen River Basin, North China

Assessing groundwater geochemical formation processes and pollution circumstances is significant for sustainable watershed management. In the present study, 58 shallow groundwater samples were taken from the Dongwen River Basin (DRB) to comprehensively assess the hydrochemical sources, groundwater quality status, and potential risks of NO3- to human health. Based on the Box and Whisker plot, the cation's concentration followed the order of Ca2+  > Mg2+  > Na+  > K+, while anions' mean levels were HCO3-  > SO42-  > NO3-  > Cl-. The NO3- level in groundwater samples fluctuated between 4.2 and 301.3 mg/L, with 67.2% of samples beyond the World Health Organization (WHO) criteria (50 mg/L) for drinking. The Piper diagram indicated the hydrochemical type of groundwater and surface water were characterized as Ca·Mg-HCO3 type. Combining ionic ratio analysis with principal component analysis (PCA) results, agricultural activities contributed a significant effect on groundwater NO3-, with soil nitrogen input and manure/sewage inputs also potential sources. However, geogenic processes (e.g., carbonates and evaporite dissolution/precipitation) controlled other ion compositions in the study area. The groundwater samples with higher NO3- values were mainly found in river valley regions with intense anthropogenic activities. The entropy weight water quality index (EWQI) model identified that the groundwater quality rank ranged from excellent (70.7%) and good (25.9%) to medium (3.4%). However, the hazard quotient (HQ) used in the human health risk assessment (HHRA) model showed that above 91.38% of groundwater samples have a NO3- non-carcinogenic health risk for infants, 84.48% for children, 82.76% for females, and 72.41% for males. The findings of this study could provide a scientific basis for the rational development and usage of groundwater resources as well as for the preservation of the inhabitants' health in DRB.

Identification of nitrate accumulation mechanism of surface water in a mining-rural-urban agglomeration area based on multiple isotopic evidence

The accumulation of nitrate (NO3-) in surface waters resulting from mining activities and rapid urbanization has raised widespread concerns. Therefore, it is crucial to develop a nitrate transformation information system to elucidate the nitrogen cycle and ensure sustainable water quality management. In this study, we focused on the main river and subsidence area of the Huaibei mining region to monitor the temporal and spatial variations in the NO3- content. Multiple isotopes (δD, δ18O-H2O, δ15N-NO3-, δ18O-NO3-, and δ15N-NH4+) along with water chemistry indicators were employed to identify the key mechanisms responsible for nitrate accumulation (e.g., nitrification and denitrification). The NO3- concentrations in surface water ranged from 0.28 to 7.50 mg/L, with NO3- being the predominant form of nitrogen pollution. Moreover, the average NO3- levels were higher during the dry season than during the wet season. Nitrification was identified as the primary process driving NO3- accumulation in rivers and subsidence areas, which was further supported by the linear relationship between δ15N-NO3- and δ15N-NH4+. The redox conditions and the relationship between δ15N-NO3- and δ18O-NO3-, and lower isotope enrichment factor of denitrification indicated that denitrification was weakened. Phytoplankton preferentially utilized available NH4+ sources while inhibiting NO3- assimilation because of their abundance. These findings provide direct evidence regarding the mechanism underlying nitrate accumulation in mining areas, while aiding in formulating improved measures for effectively managing water environments to prevent further deterioration.

Nitrate transformation and source tracking of rivers draining into the Bohai Sea using a multi-tracer approach combined with an optimized Bayesian stable isotope mixing model

Excessive levels of NO3- can result in multiple eco-environmental issues due to potential toxicity, especially in coastal areas. Accurate source tracing is crucial for effective pollutant control and policy development. Bayesian models have been widely employed to trace NO3- sources, while limited studies have utilized optimized Bayesian models for NO3- tracing in the coastal rivers. The Bohai Rim is highly susceptible to ecological disturbances, particularly N pollution, and has emerged as a critical area. Therefore, identification the N fate and understanding their sources contribution is urgent for pollution mitigation efforts. In addition, understanding the influenced key driven factors to source dynamic in the past ten years is also implication to environmental management. In this study, water samples were collected from 36 major river estuaries that drain into the Bohai Sea of North China. The main transformation processes were analyzed and quantified the sources of NO3- using a Bayesian stable isotope mixing model (MixSIAR) with isotopic approach (δ15N-NO3- and δ18O-NO3-). The overall isotopic composition of δ15N-NO3- and δ18O-NO3- in estuary waters ranged from -0.8-19.3‰ (9.3 ± 4.6‰) and from -7.1-10.5‰ (5.0 ± 4.3‰), respectively. The main sources of nitrate in most river estuaries were manure & sewage, and chemical fertilizer, while weak denitrification and mixed processes were observed in Bohai Rim region. A temporal decrease in the nitrogen load entering the Bohai Sea indicates an improvement in water quality in recent years. By incorporating informative priors and utilizing the calculated coefficients, the accuracy of sourcing results was significantly improved. This study highlighted the optimized MixSIAR model enhanced its accuracy for sourcing analysis and providing valuable insights for policy formulation. Future efforts should focus on improving management strategies to reduce nitrogen into the bay.

Distribution, sources and main controlling factors of nitrate in a typical intensive agricultural region, northwestern China: Vertical profile perspectives

Nitrate (NO3-) pollution of groundwater is a global concern in agricultural areas. To gain a comprehensive understanding of the sources and destiny of nitrate in soil and groundwater within intensive agricultural areas, this study employed a combination of chemical indicators, dual isotopes of nitrate (δ15N-NO3- and δ18O-NO3-), random forest model, and Bayesian stable isotope mixing model (MixSIAR). These approaches were utilized to examine the spatial distribution of NO3- in soil profiles and groundwater, identify key variables influencing groundwater nitrate concentration, and quantify the sources contribution at various depths of the vadose zone and groundwater with different nitrate concentrations. The results showed that the nitrate accumulation in the cropland and kiwifruit orchard at depths of 0-400 cm increased, leading to subsequent leaching of nitrate into deeper vadose zones and ultimately groundwater. The mean concentration of nitrate in groundwater was 91.89 mg/L, and 52.94% of the samples exceeded the recommended grade III value (88.57 mg/L) according to national standards. The results of the random forest model suggested that the main variables affecting the nitrate concentration in groundwater were well depth (16.6%), dissolved oxygen (11.6%), and soil nitrate (10.4%). The MixSIAR results revealed that nitrate sources vary at different soil depths, which was caused by the biogeochemical process of nitrate. In addition, the highest contribution of nitrate in groundwater, both with high and low concentrations, was found to be soil nitrogen (SN), accounting for 56.0% and 63.0%, respectively, followed by chemical fertilizer (CF) and manure and sewage (M&S). Through the identification of NO3- pollution sources, this study can take targeted measures to ensure the safety of groundwater in intensive agricultural areas.