Sources and migration similarly determine nitrate concentrations: Integrating isotopic, landscape, and biological approaches

Using surface runoff to reveal the mechanisms of landscape patterns driving on various forms of nitrogen in non-point source pollution

Non-point source (NPS) pollution directly threatens river water quality, constrains sustainable economic development, and poses hazards to human health. Comprehension of the impact factors on NPS pollution is essential for scientific river water quality management. Despite the landscape pattern being considered to have a significant impact on NPS pollution, the driving mechanism of landscape patterns on NPS pollution remains unclear. Therefore, this study coupled multi-models including the Soil and Water Assessment Tool (SWAT), Random Forest, and Partial Least Squares Structural Equation Modeling (PLS-SEM) to construct the connection between landscape patterns, NPS pollution, and surface runoff. The results suggested that increased runoff during the wet season enhances the link between landscape patterns and NPS pollution, and the explained NPS pollution variation by landscape pattern increased from 59.6 % (dry season) to 84.9 % (wet season). Furthermore, from the impact pathways, we find that the sink landscape pattern can significantly and indirectly influence NPS pollution by regulating surface runoff during the wet season (0.301*). Meanwhile, the sink and source landscape patterns significantly and directly impact NPS pollution during different seasons. Moreover, we further find that the percentage of paddy land use (Pad_PLAND) and grassland patch density (Gra_PD) metrics can significantly predict the dissolved total nitrogen (DTN) and nitrate nitrogen (NO3--N) variation. Thus, controlling the runoff migration process by guiding the rational evolution of watershed landscape patterns is an important development direction for watershed NPS pollution management.

Elucidating biogeochemical characterization of nitrogen in the vadose zone integrating geochemistry, microorganism, and numerical simulation

A thorough comprehension of nitrogen biogeochemical processes in the vadose zone is crucial for the effective prevention and remediation of soil-groundwater system contamination. Despite the growing research on this subject, the full scope of nitrogen biogeochemical characterization in different geological environments remains poorly understood. This study addresses this knowledge gap by integrating geochemical, microbiological and numerical simulation approaches to gain a deeper insight into nitrogen biogeochemistry in agriculture. Our findings indicate the biogeochemical behavior of nitrogen in the vadose zone is mediated by microorganisms, driven by hydraulics, influenced by geological conditions and environmental factors. Along the groundwater flow, NH4+-N was found to be heavily accumulated in the topsoil of 0-40 cm, while NO3--N was transported and driven by hydrodynamics from both vertical and horizontal directions. Microbial diversity, species composition and functional microorganisms were significantly influenced by soil depth, rather than geomorphological types. Oxidation-reduction potential (ORP), total organic carbon (TOC), soil moisture (MOI), bicarbonate (HCO3-), and ferrous (Fe2+) were identified as the principal environmental factors that regulate nitrogen metabolism and the dominant biochemical processes, encompassing nitrogen fixation, nitrification, and denitrification. Driven by hydrodynamics, NH4+-N, NO2--N and NO3--N tend to form distinct biochemical reaction zones in the vertical vadose zone. These areas are dynamic and subject to geomorphologies. It should be noted that NO3--N can migrate towards groundwater from the clayey sand in the Alluvial Plain, which presents a potential risk of groundwater contamination. The fissure structure of loess may serve as the major transport pathway for groundwater nitrogen contamination in the Loess Tableland. This finding highlights the importance of integrating microbiology, geochemistry and hydraulics to elucidate the biogeochemical processes of nitrogen in the vadose zone with a dynamic mindset.

Riverine nitrate source identification combining δ15N/δ18O-NO3- with Δ17O-NO3- and a nitrification 15N-enrichment factor in a drinking water source region

Dual nitrate isotopes (δ15N/δ18O-NO3-) are an effective tool for tracing nitrate sources in freshwater systems worldwide. However, the initial δ15N/δ18O values of different nitrate sources might be altered by isotopic fractionation during nitrification, thereby limiting the efficiency of source apportionment results. This study integrated hydrochemical parameters, site-specific isotopic compositions of potential nitrate sources, multiple stable isotopes (δD/δ18O-H2O, δ15N/δ18O-NO3- and Δ17O-NO3-), soil incubation experiments assessing the nitrification 15N-enrichment factor (εN), and a Bayesian mixing model (MixSIAR) to reduce/eliminate the influence of 15N/18O-fractionations on nitrate source apportionment. Surface water samples from a typical drinking water source region were collected quarterly (June 2021 to March 2022). Nitrate concentrations ranged from 0.35 to 3.06 mg/L (mean = 0.78 ± 0.46 mg/L), constituting ∼70 % of total nitrogen. A MixSIAR model was developed based on δ15N/δ18O-NO3- values of surface waters and the incorporation of a nitrification εN (-6.9 ± 1.8 ‰). Model source apportionment followed: manure/sewage (46.2 ± 10.7 %) > soil organic nitrogen (32.3 ± 18.5 %) > nitrogen fertilizer (19.7 ± 13.1 %) > atmospheric deposition (1.8 ± 1.6 %). An additional MixSIAR model coupling δ15N/δ18O-NO3- with Δ17O-NO3- and εN was constructed to estimate the potential nitrate source contributions for the June 2021 water samples. Results revealed similar nitrate source contributions (manure/sewage = 43.4 ± 14.1 %, soil organic nitrogen = 29.3 ± 19.4 %, nitrogen fertilizer = 19.8 ± 13.8 %, atmospheric deposition = 7.5 ± 1.6 %) to the original MixSIAR model based on εN and δ15N/δ18O-NO3-. Finally, an uncertainty analysis indicated the MixSIAR model coupling δ15N/δ18O-NO3- with Δ17O-NO3- and εN performed better as it generated lower uncertainties with uncertainty index (UI90) of 0.435 compared with the MixSIAR model based on δ15N/δ18O-NO3- (UI90 = 0.522) and the MixSIAR model based on δ15N/δ18O-NO3- and εN (UI90 = 0.442).

Non-negligible N2O emission hotspots: Rivers impacted by ion-adsorption rare earth mining

Rare earth mining causes severe riverine nitrogen pollution, but its effect on nitrous oxide (N2O) emissions and the associated nitrogen transformation processes remain unclear. Here, we characterized N2O fluxes from China's largest ion-adsorption rare earth mining watershed and elucidated the mechanisms that drove N2O production and consumption using advanced isotope mapping and molecular biology techniques. Compared to the undisturbed river, the mining-affected river exhibited higher N2O fluxes (7.96 ± 10.18 mmol m-2d-1 vs. 2.88 ± 8.27 mmol m-2d-1, P = 0.002), confirming that mining-affected rivers are N2O emission hotspots. Flux variations scaled with high nitrogen supply (resulting from mining activities), and were mainly attributed to changes in water chemistry (i.e., pH, and metal concentrations), sediment property (i.e., particle size), and hydrogeomorphic factors (e.g., river order and slope). Coupled nitrification-denitrification and N2O reduction were the dominant processes controlling the N2O dynamics. Of these, the contribution of incomplete denitrification to N2O production was greater than that of nitrification, especially in the heavily mining-affected reaches. Co-occurrence network analysis identified Thiomonas and Rhodanobacter as the key genus closely associated with N2O production, suggesting their potential roles for denitrification. This is the first study to elucidate N2O emission and influential mechanisms in mining-affected rivers using combined isotopic and molecular techniques. The discovery of this study enhances our understanding of the distinctive processes driving N2O production and consumption in highly anthropogenically disturbed aquatic systems, and also provides the foundation for accurate assessment of N2O emissions from mining-affected rivers on regional and global scales.

Identification of groundwater nitrate sources in an urban aquifer (Alborz Province, Iran) using a multi-parameter approach

High concentrations of NO3̄ in water resources are detrimental to both human health and aquatic ecosystems. Identification of NO3̄ sources and biogeochemical processes is a crucial step in managing and controlling NO3̄ pollution. In this study, land use, hydrochemical data, dual stable isotopic ratios and Bayesian Stable Isotope Mixing Models (BSIMM) were integrated to identify NO3̄ sources and estimate their proportional contributions to the contamination of the Karaj Urban Aquifer (Iran). Elevated NO3̄ concentrations indicated a severe NO3̄ pollution, with 39 and 52% of groundwater (GW) samples displaying the concentrations of NO3̄ in exceedance of the World Health Organization (WHO) standard of 50 mg NO3̄ L-1 in the rainy and dry seasons, respectively. Dual stable isotopes inferred that urban sewage is the main NO3̄ source in the Karaj Plain. The diagram of NO3̄/Cl‾ versus Cl‾ confirmed that municipal sewage is the major source of NO3̄. Results also showed that biogeochemical nitrogen dynamics are mainly influenced by nitrification, while denitrification is minimal. The BSIMM model suggested that NO3̄ originated predominantly from urban sewage (78.2%), followed by soil organic nitrogen (12.2%), and chemical fertilizer (9.5%) in the dry season. In the wet season, the relative contributions of urban sewage, soil nitrogen and chemical fertilizer were 87.5, 6.7, and 5.5%, respectively. The sensitivity analysis for the BSIMM modeling indicates that the isotopic signatures of sewage had the major impact on the overall GW NO3̄ source apportionment. The findings provide important insights for local authorities to support effective and sustainable GW resources management in the Karaj Urban Aquifer. It also demonstrates that employing Bayesian models combined with multi-parameters can improve the accuracy of NO3̄ source identification.

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.

Basin-wide tracking of nitrate cycling in Yangtze River through dual isotope and machine learning

The nitrate (NO3-) input has adversely affected the water quality and ecological function in the whole basin of the Yangtze River. The protection of water sources and implementation of "great protection of Yangtze River" policy require large-scale information on water contamination. In this study, dual isotope and Bayesian mixing model were used to research the transformation and sources of nitrate. Chemical fertilizers contribute 76 % of the nitrate sources in the upstream, while chemical fertilizers were also dominant in the midstream (39 %) and downstream (39 %) of Yangtze River. In addition, nitrification process occurred in the whole basin. Four machine learning models were used to relate nitrate concentrations to explanatory variables describing influence factors to predict nitrate concentrations in the whole basin of Yangtze River. The anthropogenic and natural factors, such as rainfall, GDP and population were chosen to take as predictor variables. The eXtreme Gradient Boosting (XGBoost) model for nitrate has a better predictive performance with an R2 of 0.74. The predictive models of nitrate concentrations will help identify the nitrate distribution and transport in the whole Yangtze River basin. Overall, this study represents the first basin-wide data-driven assessment of the nitrate cycling in the Yangtze River basin.

Coupling of polyhydroxybutyrate and zero-valent iron for enhanced treatment of nitrate pollution within the Permeable Reactive Barrier and its downgradient aquifer

Permeable Reactive Barriers (PRBs) have been utilized for mitigating nitrate pollution in groundwater systems through the use of solid carbon and iron fillers that release diverse nutrients to enhance denitrification efficiency. We conduct laboratory column tests to evaluate the effectiveness of PRBs in remediating nitrate pollution both within the PRB and in the downgradient aquifer. We use an iron-carbon hydrogel (ICH) as PRB filler, which has different weight ratios of polyhydroxybutyrate (PHB) and microscale zero-valent iron (mZVI). Results reveal that denitrification in the downgradient aquifer accounts for at least 19.5 % to 32.5 % of the total nitrate removal. In the ICH, a higher ratio of PHB to mZVI leads to higher contribution of the downgradient aquifer to nitrate removal, while a lower ratio results in smaller contribution. Microbial community analysis further reveals that heterotrophic and mixotrophic bacteria dominate in the downgradient aquifer of the PRB, and their relative abundance increases with a higher ratio of PHB to mZVI in the ICH. Within the PRB, autotrophic and iron-reducing bacteria are more prevalent, and their abundance increases as the ratio of PHB to mZVI in the ICH decreases. These findings emphasize the downgradient aquifer's substantial role in nitrate removal, particularly driven by dissolved organic carbon provided by PHB. This research holds significant implications for nutrient waste management, including the prevention of secondary pollution, and the development of cost-effective PRBs.

Spatial-temporal differentiation and control strategies of nitrogen environmental loss in China's coastal regions based on flow analysis

Nitrogen pollution emissions from human production and living activities in coastal regions are important topics in the management of environmental pollution in coastal waters. However, to date, there has been relatively little research systematically assessing the environmental loss of nitrogen (NEL) from human activities that negatively affect marine ecosystems. This study categorised emission sources into five subsystems, namely livestock, farming, aquatic, industrial, and residential. Through flow analysis, the anthropogenic emissions of nitrogen in the gas, liquid, and solid phases from 11 coastal provinces in China in 2011, 2015, and 2020 were determined. A nitrogen cost index was constructed by combining the social indicators of each province. The effectiveness of nitrogen emission control since the land-sea coordination and the future challenges for the coastal region were discussed from various perspectives. The results of the study showed that the total NEL that poses a potential threat to marine ecosystems in coastal areas of China has decreased from 18.93 TgN to 14.66 TgN since the proposal of land-sea coordination, with livestock systems and aquatic systems emitting the most. The Bohai and Yellow Seas area were most threatened by nitrogen pollution. Among the three oceanic pathways, liquid-phase nitrogen discharge from each subsystem was effectively controlled, and the control of gas-phase nitrogen emissions is still the most numerous NEL state, although it has had a significant effect. The results of the correlation analysis suggest that NEL flow can characterize the regional management of nutrient-based organic pollutants. Past management tools and environmental investments in China have been more effective in controlling emissions from point and line sources involving artificial facilities, but less direct effect on mariculture. How to control surface source pollution from livestock and aquaculture will be an important challenge to reduce reactive nitrogen emissions in the future.

Identification and apportionment of groundwater nitrate sources in Chakari Plain (Afghanistan)

The Chakari alluvial aquifer is the primary source of water for human, animal, and irrigation applications. In this study, the geochemistry of major ions and stable isotope ratios (δ2H-H2O, δ18O-H2O, δ15N-NO3̄, and δ18O-NO3̄) of groundwater and river water samples from the Chakari Plain were analyzed to better understand characteristics of nitrate. Herein, we employed nitrate isotopic ratios and BSIMM modeling to quantify the proportional contributions of major sources of nitrate pollution in the Chakari Plain. The cross-plot diagram of δ15N-NO3̄ against δ18O-NO3̄ suggests that manure and sewage are the main source of nitrate in the plain. Nitrification is the primary biogeochemical process, whereas denitrification did not have a significant influence on biogeochemical nitrogen dynamics in the plain. The results of this study revealed that the natural attenuation of nitrate in groundwater of Chakari aquifer is negligible. The BSIMM results indicate that nitrate originated mainly from sewage and manure (S&M, 75‰), followed by soil nitrogen (SN, 13‰), and chemical fertilizers (CF, 9.5‰). Large uncertainties were shown in the UI90 values for S&M (0.6) and SN (0.47), whereas moderate uncertainty was exhibited in the UI90 value for CF (0.29). The findings provide useful insights for decision makers to verify groundwater pollution and develop a sustainable groundwater management strategy.

Unexpectedly high nitrate levels in a pristine forest river on the Southeastern Qinghai-Tibet Plateau

River nitrate (NO₃^-) pollution is a global environmental issue. Recently, high NO₃^- levels in some pristine or minimally-disturbed rivers were reported, but their drivers remain unclear. This study integrated river isotopes (δ¹⁸O/δ¹⁵N-NO₃^- and δD/¹⁸O-H₂O), ¹⁵N pairing experiments, and qPCR to reveal the processes driving the high NO₃^- levels in a nearly pristine forest river on the Qinghai-Tibet Plateau. The river isotopes suggested that, at the catchment scale, NO₃^- removal was prevalent in summer, but weak in winter. The pristine forest soils contributed more than 90 % of the riverine NO₃^-, indicating the high NO₃^- backgrounds. The release of soil NO₃^- to the river was "transport-limited" in both seasons, i.e., the NO₃^- production/stock in the soils exceeded the capacity of hydrological NO₃^- leaching. In summer, this regime and the NO₃^--plentiful conditions in the soils associated with the strong NO₃^- nitrification led to the high riverine NO₃^- levels. While the in-soil nitrification was weak in winter, the leaching of legacy NO₃^- resulted in the consistently high NO₃^- levels. This study provides insights into the reasons for high NO₃^- levels in pristine or minimally-disturbed rivers worldwide and highlights the necessity to consider NO₃^- backgrounds when evaluating anthropogenic NO₃^- pollution in rivers.