Identification Of Nitrate Sources Research Articles

Identification of nitrate sources and transformation in karst cave water using hydrochemistry and NO 3 − isotopes (δ 15 N/δ 18 O) combined with a Bayesian mixing model

Identification of nitrate sources and transformation in karst cave water using hydrochemistry and NO ₃ ⁻ isotopes (δ ¹⁵ N/δ ¹⁸ O) combined with a Bayesian mixing model

Characterizing the impact of reservoir storage and discharge on nitrogen dynamics in an upstream wetland using a δ15N and δ18O dual-isotope approach

The identification of nitrate sources in reservoir water is important for watershed-scale surface pollution management. Significant fluctuations in river water levels arising from reservoir storage and discharge influence nitrate sources and transport processes. The Sanmenxia Reservoir, in the middle reaches of the Yellow River in China, undergoes significant water level changes (290–316 m), altering the composition of the nitrogen sources. This study employed a δ15N and δ18O dual-isotope method and MixSIAR modeling to quantify the contributions of nitrate sources. This reveals the impact of reservoir water impoundment and discharge on nitrogen dynamics in the upstream region of the wetland and the model sensitivity for each nitrate source. The results showed that the average concentrations of nitrate‑nitrogen (NO- 3-N) were elevated during the impoundment period compared to the discharge period. Nitrogen sources exhibited varying proportions in surface water, groundwater, and soil water during both the impoundment and discharge periods. The predominant sources include manure and sewage (MS), with a maximum proportion of 57.4 % in surface water. Soil nitrogen (SN) accounted for 25.8 % of groundwater nitrogen and 32.1 % of soil water nitrogen during the impoundment period, whereas, during the discharge period, soil nitrogen made up 41.4 % of surface water nitrogen, manure and sewage contributed 44.8 % of groundwater nitrogen, and manure and sewage dominated with 56.7 % of soil water nitrogen. Sensitivity analysis of the MixSIAR model revealed that the isotopic composition of the manure and sewage primary source most significantly influenced the apportionment results of the riverine nitrate source. Reservoir discharge facilitates the dissimilatory nitrate reduction to ammonium (DNRA). The migration of NO- 3 from surface water to soil water and groundwater occurred from the impoundment period to the discharge period.

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).

Hydrochemical Evolution and Nitrate Source Identification of River Water and Groundwater in Huashan Watershed, China

The combined hydrochemical analysis, factor analysis, and isotopic signals of water and nitrate were applied to explore the hydrochemical origin and identify the sources and transformation of nitrate in river water and groundwater in the Huashan watershed. Additionally, a Bayesian isotope mixing model (SIAR) was employed for quantitative assessment of the nitrate sources. The results indicated that both river water and groundwater were dominated by HCO3-Ca and HCO3-Ca·Mg types; both originated from precipitation and were influenced by evaporation. The main constituent ions in the river water and groundwater primarily originated from carbonate and silicate dissolution, with the presence of cation exchange in the groundwater. The water chemistry of river water was greatly influenced by physicochemical factors, while that of groundwater was mainly controlled by water–rock interaction. NO3− in river water was mainly influenced by soil nitrogen (SN) and manure and septic wastes (MSWs), while NO3− in groundwater was jointly affected by ammonium fertilizers (AF), SN, and MSWs. With the exception of denitrification observed in the groundwater at the watershed outlet, denitrification was absent in both groundwater in the piedmont area and in river water. The SIAR model results demonstrated that the contribution rates of atmospheric precipitation (AP), AF, SN, and MSWs to river water were 12%, 21%, 25%, and 42%, respectively, while to groundwater, they were 16%, 27%, 10%, and 47%, respectively. Overall, MSWs were the main sources of nitrate in the river water and groundwater. It is necessary to prevent the leakage of MSWs when managing water resources.

Arrhenius equation construction and nitrate source identification of denitrification at the Lake Taihu sediment − water interface with 15 N isotope

Total nitrogen in Taihu Lake, China has gradually decreased since 2015 while the total phosphorus concentration has exhibited an increasing trend, indicating an asynchronous change. The dominant nitrogen removal process in freshwater ecosystems is denitrification which primarily occurs at the sediment-water interface. In this study, 15N isotope incubation experiments were attempted to analyze the effect of water temperature on denitrification, to construct the regional denitrification Arrhenius equations considering water temperature, and to identify the nitrate source of denitrification in Lake Taihu sediments. The results indicated that the potential N2 production rates and denitrification rates generally decreased in the west to east direction, which was significantly positively correlated with the nitrate concentration of overlying water by Pearson correlation coefficient analysis (P < 0.05). In addition, when the water temperature was lower than 30°C, the rates of the potential N2 production and denitrification were higher with an increase in water temperature, but when the water temperature was overhigh, denitrification was inhibited. The ratio of the total denitrification rate of nitrate from the water column in the sediment to the total denitrification rate during the incubation experiment was above 0.5 at each sampling site. This indicated that the denitrification in the Lake Taihu sediment primarily occurred at the expense of nitrate from the water column. The research results of Arrhenius equation construction and nitrate source identification of denitization can be applied to improve the accuracy of water quality model of Taihu Lake, which is of great significance to improve Taihu Lake water quality, and can act as a reference for the water environment treatment of other shallow eutrophic lakes in China and abroad.

Identification of nitrate sources of groundwater and rivers in complex urban environments based on isotopic and hydro-chemical evidence

Groundwater and rivers in Chinese cities suffer from severe nitrate pollution. The accurate identification of nitrate sources throughout aquatic systems is key to the water nitrate pollution management. This study investigated nitrogen components of groundwater for twelve years and analyzed the sources of nitrate in the aquatic system based on dual isotopes (δ15N-NO3− and δ18O-NO3−) in the city of Nanjing, a core city of the Yangtze River Delta region, China. Our results showed that the ratio of nitrate to the sum of ammonia and nitrate in groundwater show an increasing trend during 2010–2021. The nitrate concentration was positively correlated with the proportion of cultivated land and negatively correlated with the proportion of forest land in the buffer zone. The relationship between Cl− and NO3−/ Cl− showed that agriculture and sewage sources increased during 2010–2015, sewage sources increased during 2016–2018, agriculture sources increased during 2019–2021. Manure and sewage were the primary sources of groundwater nitrate (72 %). There was no significant difference between the developed land (78 %), cultivated land (69 %), and aquaculture area (72 %). This indicates that dense population and intensive aquaculture in the suburbs have a significant impact on nitrate pollution. The contributions of manure and sewage to the fluvial nitrate sources in the lower reaches of the Qinhuai River Basin were 61 %. The non-point sources, including groundwater N (39 %) and soil N (35 %), were 74 % over the upper reaches. This study highlights the necessity of developing different N pollution management strategies for different parts of highly urbanized watersheds and considers groundwater restoration and soil nitrogen management as momentous, long-term tasks.

Geochemistry, stable isotopes and statistic tools to estimate threshold and source of nitrate in groundwater (Sardinia, Italy)

In the European Union, nitrate vulnerable zone (NVZ) should be designed for the mitigation of nitrate (NO3−) contamination caused by agricultural practices. Before establishing new NVZ, the sources of NO3− must be recognized. A geochemical and multiple stable isotopes approach (hydrogen, oxygen, nitrogen, sulfur and boron) and statistical tools were applied to define the geochemical characteristics of groundwater (60 samples), calculate the local NO3− threshold and assess potential sources of NO3− contamination in two study areas (hereafter Northern and Southern), located in a Mediterranean environment (Sardinia, Italy). Results of the integrated approach applied to two case study, permits to highlight the strengths of integrating geochemical and statistical methods to provide nitrate source identification as a reference by decision makers to remediate and mitigate nitrate contamination in groundwater.Hydrogeochemical features in the two study areas were similar: near neutral to slightly alkaline pH, electrical conductivity in the range of 0.3 to 3.9 mS/cm, and chemical composition ranging from Ca-HCO3− at low salinity to Na-Cl− at high salinity. Concentrations of NO3− in groundwater were in the range of 1 to 165 mg/L, whereas the nitrogen reduced species were negligible, except few samples having NH4+ up to 2 mg/L. Threshold values in the studied groundwater samples were between 4.3 and 6.6 mg/L NO3−, which was in agreement with previous estimates in Sardinian groundwater.Values of δ34S and δ18OSO4 of SO42− in groundwater samples indicated different sources of SO42−. Sulfur isotopic features attributed to marine SO42− were consistent with groundwater circulation in marine-derived sediments. Other source of SO42− were recognize due to the oxidation of sulfide minerals, to fertilizers, manure, sewage fields, and SO42− derived from a mix of different sources.Values of δ15N and δ18ONO3 of NO3− in groundwater samples indicated different biogeochemical processes and NO3− sources. Nitrification and volatilization processes might have occurred at very few sites, and denitrification was likely to occur at specific sites. Mixing among various NO3− sources in different proportions might account for the observed NO3− concentrations and the nitrogen isotopic compositions. The SIAR modeling results showed a prevalent NO3− source from sewage/manure. The δ11B signatures in groundwater indicated the manure to be the predominant NO3− source, whereas NO3− from sewage was recognized at few sites. Geographic areas showing either a predominant process or a defined NO3− source where not recognize in the studied groundwater. Results indicate widespread contamination of NO3− in the cultivated plain of both areas. Point sources of contamination, due to agricultural practices and/or inadequate management of livestock and urban wastes, were likely to occur at specific sites.

Identification of nitrate sources in tap water sources across South Korea using multiple stable isotopes: Implications for land use and water management

Stable nitrate isotopes (δ15N-NO3 and δ18O-NO3) in conjunction with stable water isotopes (δ18O-H2O and δD-H2O) were used to identify nitrogen (N) sources and N-biogeochemical transformation in tap water sources sampled from 11 water purification plants across South Korea. The raw water sources are taken from rivers within the water supply basins, which indicates the quality of tap water is highly dependent on surrounding the land use type. We estimated the proportional contribution of the various N sources (AD: atmospheric deposition; SN: soil nitrogen; CF: chemical fertilizer; M&S: manure/sewage) using Bayesian Mixing Model. As a result, the contribution of N sources exhibited large seasonal and spatial differences, which were related to the type of land use in the water supply basins. Commonly, the M&S and SN were the dominant N source during the dry and wet seasons in almost regions, respectively. However, in the regions with high N loading ratios from urban and industrial sources, the M&S was the dominant N source during both the wet and dry seasons. In addition, the regions were characterized by high NO3− concentrations due to the decreased dilution effect of precipitation during the dry seasons. In contrast, the SN was the dominant N source in the regions with high N loading ratios from agricultural areas during both the wet and dry seasons. The NO3−-N concentration during the wet season was significantly higher than those during the dry season in these regions due to the input of non-point sources with high concentrations. Meanwhile, denitrification and nitrification were observed in the watersheds. It is important to understand the isotope fractionation due to N-biogeochemical transformation for considering the potential misinterpretations of the origin and fate NO3−. Collectively, our findings provide a basis on N source control strategies to ensure tap water quality in complex land use areas.

Delineation of Hydrochemical Characteristics and Tracing Nitrate Contamination of Groundwater Based on Hydrochemical Methods and Isotope Techniques in the Northern Huangqihai Basin, China

Hydrochemical research and identification of nitrate contamination are of great significant for the endorheic basin, and the Northern Huangqihai Basin (a typical endorheic basin) was comprehensively researched. The results showed that the main hydrochemical facies were HCO3–Mg·Ca and HCO3–Ca·Mg. Spatial variation coefficients of most indices were greater than 60%, which was probably caused by human activities. The hydrochemical evolution was mainly affected by rock weathering and also by cation exchange. The D–18O relationship of groundwater was δD = 5.93δ18O − 19.18, and the d–excess range was −1.60–+6.01‰, indicating that groundwater was mainly derived from precipitation and that contaminants were very likely to enter groundwater along with precipitation infiltration. The NO3(N) contents in groundwater exceeded the standard. Hydrochemical analyses indicated that precipitation, industrial activities and synthetic NO3 were unlikely to be the main sources of nitrate contamination in the study area. No obvious denitrification occurred in the transformation process of nitrate. The δ15N(NO3) values ranged from +0.29‰ to +14.39‰, and the δ18O(NO3) values ranged from −6.47‰ to +1.24‰. Based on the δ15N(NO3) – δ18O(NO3) dual isotope technique and hydrochemical methods, manure, sewage and NH4 fertilizers were identified to be the main sources of nitrate contamination. This study highlights the effectiveness of the integration of hydrochemical and isotopic data for nitrate source identification, and is significant for fully understanding groundwater hydrochemistry in endorheic basins and scientifically managing and protecting groundwater.

Nitrate dynamics and source identification of rainwater in Beijing during rainy season: Insight from dual isotopes and Bayesian model

Anthropogenic reactive nitrogen emissions have a significant impact on atmospheric chemical composition and earth surface ecosystem. As one of the most important sinks of atmospheric nitrogen, the wet deposition of nitrate (rainwater NO3−) has been widely concerned. Yet, the sources and transformation processes of wet deposited NO3− were not well revealed in megacity during rainy season in the context of global climate change. Here, we investigated the concentrations of nitrogen components and dual isotopes of rainwater nitrate collected in Beijing during July to August 2021 (rainy season). The main findings showed that the concentrations of NH4+-N, NO3−-N, and NO2−-N ranged 0.5– 6.7 mg L−1, 0.3– 4.5 mg L−1, and 0.05– 0.18 mg L−1, respectively, with the average relative percentages of 69 %, 29 %, and 2 %. The stoichiometry analysis of characteristic ion ratios indicated that the contribution of municipal wastes and agricultural sources to rainwater NH4+-N is relatively significant, while traffics were the major contributor of NO3−-N instead of the fixed emission. Rainwater δ15N-NO3− and δ18O-NO3− presented slightly 15N-depleted characteristic compared to previous studies with the average values of −3.9 ± 3.1 ‰ and 58.7 ± 12.6 ‰. These isotope compositions suggesting an origin of rainwater NO3− from the mixing of multi-sources and was mainly generated via the pathway of OH radical oxidization. Further source apportionment of rainwater NO3− by Bayesian mixing model evaluated that traffic (30.3 %) and soil (30.3 %) emissions contributed mostly to NO3−, while the contribution of biomass burning (18.8 %) and coal combustion (20.6 %) were relatively lower. This study highlighted the important role of dual isotopes in rainwater nitrate source identification and formation processes in megacity.

Identification of Nitrate Sources in Tap Water Across South Korea Using Multiple Stable Isotopes: Implications for Land Use and Water Management

Identification of Nitrate Sources in Tap Water Across South Korea Using Multiple Stable Isotopes: Implications for Land Use and Water Management

Identification of nitrate sources in the Jing River using dual stable isotopes, Northwest China.

Nitrate (NO3-) contamination has become a dominant international problem in the aquatic environment, so identifying the sources and transformations of NO3- is the basis for improving water quality. Since the Jing River is the largest tributary of the Wei River, to understand its water quality, this study collected surface water samples from the Shaanxi section of the Jing River during the dry season. The potential sources of NO3- were analyzed by hydrochemical and bi-isotopic methods, and the SIAR model was used to estimate the proportional contribution of each source. Results indicated that NO3--N was the main form of inorganic nitrogen in this area, and the average total nitrogen content was 10.23 mg·L-1, which showed that nitrogen pollution was highly serious; the transformation process of nitrogen in this study area was mainly nitrification; The results of Bayesian model showed that manure and sewage contributed to the most NO3- (64.39%) in the dry season, followed by soil nitrogen, which was 26.35%. These results help to adopt better nitrogen management measures to meet the national environmental quality standards for surface water.

Sources of Nitrate in Ground Water Aquifers of the Semiarid Region of Tanzania

Nitrate isotopic values are often used as a tool to identify sources of nitrate in order to effectively manage ground water quality. In this study, the concentrations of NO3−, NO2−, and NH4+ from 50 boreholes and shallow wells in the Singida and Manyoni Districts were analyzed during the dry and wet seasons, followed by identification of nitrate sources using the hydrochemical method (NO3−/Cl−) and stable isotope (δ15N and δ18O) techniques. Results showed that NO2− and NH4+ concentrations were very low in both seasons due to the nitrification process. The concentrations of NO3− ranged from 2.4 ppm to 929.6 ppm with mean values of 118.5 ppm ± 118.5 ppm , during the dry season and from 2.4 ppm to 1620.0 ppm with mean values of 171.6 ppm ± 312.3 ppm , during the wet season. The higher NO3− contamination observed in the wet season could be due to rainfall which accelerated the surface runoff that collects different materials from various settings into the ground water sources. Nitrate source identification through hydrochemical technique revealed that most nitrates originated from sewage effluents and/or organic wastes such as manure. Likewise, the mean values of δ15N-NO3− ( + 20.90 ‰ ± 5.17 ‰ and + 18.30 ‰ ± 6.33 ‰ ) and the mean values of δ18O-NO3−( + 13.86 ‰ ± 3.18 ‰ and + 13.69 ‰ ± 3.97 ‰ ) suggest that 80% of boreholes and 52% of shallow wells were dominated with nitrate from sewage effluents and/or manure as most ground water sources were situated in densely populated areas with congested and poorly constructed onsite sanitation facilities such as pit latrines and manure. Therefore, to reduce nitrate pollution in the study area, a central sewer must be constructed to treat the discharged wastes. Also, groundwater harvesting should consider the proper principles for groundwater harvesting recommended by the respective authority to minimize chances of contamination and hence prevention of health risk.

Source identification of nitrate in the upper aquifer system of the Wadi Shueib catchment area in Jordan based on stable isotope composition

Groundwater forms the main freshwater supply in arid and semi-arid areas, and contamination of this precious resource is complicated by the slow rate of recharge in these areas. Nitrate contamination of groundwater is a global water quality problem, as it entails threat to human health as well as aquatic ecosystems. Source identification of contamination is the cornerstone and a prerequisite for any effective management program of water quality. Stable isotope composition of the dissolved nitrate ( $${{\rm{\delta}}^{15}}{\rm{N - N}}{{\rm{O}}_{{3^ - }}}$$ and $${{\rm{\delta}}^{18}}{\rm{N - N}}{{\rm{O}}_{{3^ - }}}$$ ) has been applied to identify $${\rm{N}}{{\rm{O}}_{{3^ - }}}$$ sources and the main transformation processes in the upper aquifer system (A1/2, A4, and B2/A7 aquifers) in the Wadi Shueib catchment area, Jordan. Moreover, the stable isotope compositions of the groundwater (δ2H-H2O and δ18O-H2O) in conjunction with the groundwater hydrochemistry were integrated to investigate the origin and evolution of the groundwater. Results revealed that groundwater in the study area is fresh and hard-very hard water, and mainly a Ca-Mg-Cl type. $${\rm{N}}{{\rm{O}}_{{3^ - }}}$$ concentration was in the range of 7.0–74.0 mg/L with an average of 37.0 mg/L. Most of the samples showed concentration higher than the natural background concentration of $${\rm{N}}{{\rm{O}}_{{3^ - }}}$$ (5.0–10.0 mg/L). The δ2H-H2O and δ18O-H2O values indicated that the groundwater is meteoric, and of Mediterranean origin, with a strong evaporation effect. The $${{\rm{\delta}}^{15}}{\rm{N - N}}{{\rm{O}}_{{3^ - }}}$$ values ranged between 6.0‰ and 11.3‰ with an average of 8.7‰, and the $${{\rm{\delta}}^{18}}{\rm{N - N}}{{\rm{O}}_{{3^ - }}}$$ values ranged between 1.6‰ and 5.9‰ with an average of 3.4‰. These values are in conformity with the stable isotope composition of nitrate derived the nitrification of wastewater/manure, and soil NH4. Nitrification and denitrification are the main transformation processes affecting nitrogen species. Statistical analysis revealed no significant differences in the δ2H-H2O and δ18O-H2O values, and $${{\rm{\delta}}^{15}}{\rm{N - N}}{{\rm{O}}_{{3^ - }}}$$ and $${{\rm{\delta}}^{18}}{\rm{N - N}}{{\rm{O}}_{{3^ - }}}$$ values for the three aquifers (A1/2, A4, and B2/A7), indicating that the groundwater of these aquifers has the same origin, and a common source of pollution.

A critical assessment of widely used techniques for nitrate source apportionment in arid and semi-arid regions

The assessment of nitrate pollution origin using stable isotope techniques is a fundamental prerequisite for the application of sustainable groundwater management plans. Although nitrate pollution is a worldwide groundwater quality problem, existing knowledge on the origin of nitrate pollution in arid and semi-arid regions is still scarce. Using the example of the Grombalia aquifer (NE Tunisia), this work summarizes the main strengths and constraints of multi-isotope techniques targeting at nitrate source identification and apportionment The results highlighted that, even in the case of well-established methodologies, like those of isotope hydrogeochemistry (δ15NNO3, δ 18ONO3 and δ 11B) and mixing modelling for source apportionment, it is fundamental to take into account regional and local end-members to avoid biased data interpretation and to fully exploit the potential of such accurate tools.

Identification of Nitrate Sources in Rivers in a Complex Catchment Using a Dual Isotopic Approach

Excessive nutrient input to surface water, including nitrate, exacerbates water eutrophication. Clarifying the proportions of different nitrate sources in the aquatic environment is critical for improving the polluted water. However, nitrate sources in river basins are very complex and not clearly understood. In this study, nitrogen concentrations and nitrate isotopic compositions were determined to estimate the spatiotemporal variation in nitrate sources in the Yuntaishan River basin, Nanjing, East China, from March 2019 to January 2020. The results showed that the concentrations of total nitrogen (TN), ammonium (NH4+-N), and nitrate (NO3−-N) changed in the ranges of 0.53–18.0 mg/L, 0.01–15.4 mg/L, and 0.06–9.3 mg/L, respectively, wherein NO3−-N was the main nitrogen form. Higher nitrogen concentrations appeared in winter and in the downstream parts of the river. In the entire river basin, the NO3−-N mainly originated from sewage (67%) and soil (26%), with clear spatial variations. NO3−-N in the Yunba sub-watershed was mainly derived from sewage (78%), which was higher than that in other tributaries, i.e., Shengli River (44%) and Yangshan River (49%). This was due to the fact that that Shengli and Yangshan sub-watersheds were covered by urban areas and were equipped with a complete sewage treatment system. In addition, the contributions of sewage to NO3−-N rose from 60% upstream to 86% downstream, suggesting the increasing influence of the point source of sewage. The results showed that 53% of NO3−-N in the basin outlet originated from the point source of sewage near the M4 site. Sewage contributed 75% of NO3−-N in the rainy season and 67% of NO3−-N in the dry season, suggesting the weakly temporal variation. Our results highlight the spatiotemporal variations in sources of NO3−-N. These results will aid in the development of measures needed to control nitrogen pollution in river basins.

Non-carcinogenic health risk assessment with source identification of nitrate and fluoride polluted groundwater of Wardha sub-basin, central India

In order to understand the pollution status of groundwater with geochemical evolution and appraisal of its probable public health risk due to nitrate (NO3–) and fluoride (F–), a total of 93 groundwater samples were collected during pre-monsoon (May) period from Wardha sub-basin, central India. By employing Piper plot, transition from Ca–HCO3 type water (recharge waters) to Na–Cl (saline water) type water through mixed Ca–Na–HCO3, mixed Ca–Mg–Cl (reverse ion exchange waters) and Ca–Cl types (leachate waters), were observed. The Geogenic processes such as silicate, dolomite, halite and carbonate weathering along with calcite precipitation and ion exchange process were identified as major controlling factors for evolution and alteration of groundwater chemistry. The Saturation index highlighted that the groundwater in the area is oversaturated with respect to the mineral calcite and dolomite, and under saturated with gypsum, fluorite and halite. The high NO3– and F– concentration overpassing the permissible limit were found in 54.8% and 18.5% of samples. The plot of F– with Na+/Ca2+, Na+/Mg2+ and F–/Cl– established fluoride bearing rock weathering is responsible for F– contamination. Based on the cluster analysis, the groundwater was grouped into Cluster-I Ca–Na–HCO3 type (61.3%) and Cluster-II Na–Ca–HCO3–Cl type (30.1%). The total hazard index (HI) based on human health risk assessment (HHRA) model for cumulative NO3– and F– toxicity through oral and dermal pathways were computed as 100%, 97.85% and 96.77% for children, female and male populations respectively. The HQ(nitrate) > 1 through ingestion pathway were in 84.95%, 68.82% and 62.37%, and HQ(fluoride) > 1 in 83.87%, 62.37% and 43.01% of the groundwater samples were recorded for children, female and male population respectively. The risk assessment study highlighted very high toxicity and severe health impact of ingestion of contaminated groundwater on public health.

Combining multi-isotopic and molecular source tracking methods to identify nitrate pollution sources in surface and groundwater

Nitrate (NO3-) pollution adversely impacts surface and groundwater quality. In recent decades, many countries have implemented measures to control and reduce anthropogenic nitrate pollution in water resources. However, to effectively implement mitigation measures at the origin of pollution,the source of nitrate must first be identified. The stable nitrogen and oxygen isotopes of NO3- (ẟ15N and ẟ18O) have been widely used to identify NO3- sources in water, and their combination with other stable isotopes such as boron (ẟ11B) has further improved nitrate source identification. However, the use of these datasets has been limited due to their overlapping isotopic ranges, mixing between sources, and/or isotopic fractionation related to physicochemical processes. To overcome these limitations, we combined a multi-isotopic analysis with fecal indicator bacteria (FIB) and microbial source tracking (MST) techniques to improve nitrate origin identification. We applied this novel approach on 149 groundwater and 39 surface water samples distributed across Catalonia (NE Spain). A further 18 wastewater treatment plant (WWTP) effluents were also isotopically and biologically characterized. The groundwater and surface water results confirm that isotopes and MST analyses were complementary and provided more reliable information on the source of nitrate contamination. The isotope and MST data agreed or partially agreed in most of the samples evaluated (79 %). This approach was especially useful for nitrate pollution tracing in surface water but was also effective in groundwater samples influenced by organic nitrate pollution. Furthermore, the findings from the WWTP effluents suggest that the use of literature values to define the isotopic ranges of anthropogenic sources can constrain interpretations. We therefore recommend that local sources be isotopically characterized for accurate interpretations. For instance, the detection of MST inferred animal influence in some WWTP effluents, but the ẟ11B values were higher than those reported in the literature for wastewater. The results of this study have been used by local water authorities to review uncertain cases and identify new vulnerable zones in Catalonia according to the European Nitrate Directive (91/676/CEE).

Identification of nitrate sources and transformations in basin using dual isotopes and hydrochemistry combined with a Bayesian mixing model: Application in a typical mining city

The external nitrogen load input caused by human activities exacerbates the eutrophication process of aquatic ecosystems in mining areas, causing water quality problems. However, knowledge of the sources and environmental behavior of nitrate in the surface water of mining areas is still very limited. This study investigated the nitrate content and spatiotemporal variation characteristics of surface water in the Linhuan mining area, identified the sources and transformation processes of nitrate using isotopes and hydrochemistry, and evaluated the contribution rates of different potential nitrate sources based on a Bayesian mixing model. The nitrogen pollution in the surface water in the mining area seriously exceeded class Ⅴ of the Environmental Quality Standard of Surface Water of China (GB3838-2002). The NO3− content ranged from 0.87 to 3.41 mg/L, showing obvious seasonal and spatial differences. Isotope and NO3−/Cl− analysis indicated that nitrate in the subsidence area water (SAW) was mainly derived from chemical fertilizer (NF) and soil organic nitrogen (NS), while nitrate in the mainstream of the Huihe River water (HRW) was mainly derived from manure/sewage (MS). The nitrate in the tributary of the Baohe River water (BRW) was mainly derived from soil NS, and nitrification was a nitrogen conversion pathway in the soil. The results of the Bayesian mixing model showed that the main sources of nitrate in the BRW, HRW and SAW were NF (34.5%), MS (68.8%) and NF (40.8%) in the wet season, and NS (33.4%), MS (70.9%) and NF (58.1%) in the dry season, respectively. The results of this study provide a new integrated method for the identification of nitrate pollution sources in mining areas, and this method can be used to improve the biogeochemical information of nitrogen in the aquatic ecosystems of mining areas and help formulate relevant measures to reduce water nitrogen pollution.

Combined use of stable nitrogen and oxygen isotopes to constrain the nitrate sources in a karst lake

Nitrate is a highly concerned pollutant in global aquatic ecosystem, resulting in eutrophication and water quality deterioration. As a result of the dissolved inorganic carbon fertilization effect, lake ecosystem is expected to respond more sensitively to nitrogen (N) addition in karst region than in non-karst region. Identifying accurately the sources of nitrate in lake system is an important prerequisite for formulating effective strategies on reducing nitrate and restoring water ecosystem. Quantitative identification of nitrate sources to lakes in karst region is limited until now. In this study, δ15N-NO3− and δ18O-NO3− were jointly used to identify the nitrate sources in Caohai Lake, a typical karst lake. The δ15N-NO3− and δ18O-NO3− of lake water displayed significant seasonal variations. The average δ15N-NO3− values during normal, dry and wet seasons were 6.6‰, 12.7‰ and 0.9‰, respectively. Accordingly, the average δ18O-NO3− values were 11‰, 13‰ and 16‰, respectively. The average contribution percentages of nitrate from agricultural activities, precipitation and sewage were 42 %, 41 % and 17 %, respectively. Strict measures should be taken to prohibit unreasonable agricultural activities and to improve nutrient use efficiency through optimized fertilization and irrigation management. In view of the sensitivity of karst lake ecosystem to N/P addition, higher discharge standard requirement is necessary for restoring water ecosystem and maintaining good water quality. This study proved that the combined use of δ15N-NO3− and δ18O-NO3− is a promising method for identifying quantitatively the nitrate sources in karst lake system.