Bayesian Isotope Mixing Model Research Articles

Geographic variability of carbon and nitrogen isotope ratios of nonvolant terrestrial small mammals (Rodentia) across 3 Brazilian biomes

Abstract In this study, we investigated landscape variability of the carbon and nitrogen isotopic composition of nonvolant small mammals in the 3 main Brazilian biomes (Amazon, Atlantic Forest, and Cerrado) while also considering the differential spatial distribution of C4 plants in these biomes. We compiled a subset of data on stable carbon and nitrogen isotope ratios from nonvolant small mammals of the order Rodentia and compared the aggregated isotopic variability at the biome level using classical δ13C–δ15N biplot. The concept of isotopic niche width was used to test whether different foraging attributes drive their isotopic composition, while a Bayesian isotopic mixing model was used to estimate the proportion of 3 main food categories available to these small mammals. We also developed isoscapes in the Brazilian biomes, interpolating animals δ13C and δ15N based on the geographic coordinates of each sample and “sourcescapes” considering geographic variation of the dietary sources. The findings of this study advance our understanding of the foraging ecology of small mammals in biodiversity-rich regions of the Neotropics. Classical methodologies such as stomach content confirmed dietary choices revealed by the stable isotopic composition and also highlighted the importance of C3 and C4 plants in the diet of this group of animals, especially in biomes such as the Atlantic Forest and Cerrado, where there is a predominance of highly altered landscapes. These results confirm that replacing the original vegetation with C4 crops has altered the feeding patterns of small mammals, which could lead to critical ecological changes in the trophic structure of these areas. Vertical stratification of the dietary niche and the interaction between biome and foraging lifestyle were also observed. However, in each biome, there was significant intraspecific and interspecific variation caused by preferences for ingestion of plant and animal material, leading to different degrees of omnivory. Finally, the high local variability between individuals and species requires a larger sampling design that may also contribute to improved spatial resolution of the isoscapes.

Tracing nitrogen sources and transformation characteristics in a large basin with spatially heterogeneous pollution distribution

This study used dual stable isotopes to examine nitrate sources and geographical distribution in the Liao River Basin (LRB), one of China's seven major river basins. During a normal hydrological season in April 2021, water samples were taken from the main streams of the Liao River (MLR), Shuangtaizi River (STR), Hun River (HR), Taizi River (TZR), and Daliao River (DLR). Monitoring results indicated that 93% of the water samples had a total nitrogen level exceeding the Class IV limit (1.5 mg/L) of the ‘Environmental Quality Standards (EQS) for surface water’, indicating a serious nitrogen pollution status. 71.3% of the total nitrogen on average was in the form of nitrate. The scatterplots of δD-H2O and δ18O-H2O showed that water in TZR and DLR were mainly affected by precipitation, while MLR, STR and HR were additionally impacted by evaporation and groundwater. The overall δ15N and δ18O of NO3− varied from 7.7‰ to 17.9‰ and 0.6‰–11.2‰, respectively. The correlations between δ15N-NO3- and δ18O-NO3-, along with attribution results from the Bayesian isotopic mixing model, indicated a predominant role of manure/sewage (MS) pollution in affecting river nitrate, accounting for 78% of total nitrate in MLR and 72% in DLR. A positive correlation between δ15N-NO3- and δ18O-NO3- in MLR indicated the occurrence of denitrification process. Overall, attribution results showed that the primary nitrate sources varied in different river systems within such a large basin, mainly due to spatially varied land use and human activities. Tailored nitrogen management strategies should be implemented to address the main anthropogenic pressures.

Tracing sulfate sources in a tropical agricultural catchment with a stable isotope Bayesian mixing model

Sulfate (SO42−) is an essential anion in drinking water and a vital macronutrient for plant growth. However, elevated sulfate levels can impact ecosystem or human health and could be an important indicator of acid rock drainage or pollution. Therefore, monitoring SO42− sources and transport is important for water quality assessments. This study focused on exploring the sources and transformations of SO42− as well as estimating the proportional contribution of the potential SO42− pollutant sources to groundwater and surface water in a tropical river basin, the Densu River Basin. The study used major ions combined with stable sulfur and oxygen isotope compositions and a Bayesian isotope mixing model, MixSIAR. The major ion characteristics indicate that SO42− concentrations remain stable throughout the rainy and dry seasons but originate from diverse sources. The multi-isotope model (δ34SSO4, δ18OSO4) identified four potential SO42− sources: detergent, precipitation, sewage, and sulfate fertilizer. However, the δ34SSO4 and δ18OSO4 values of the fertilizer source signatures overlapped with those of precipitation and sewage. Nevertheless, the contributions from each source were disentangled using the MixSIAR model, which revealed sewage as the most dominant SO42− pollutant in the Densu Basin, accounting for ~47 % of sulfate in groundwater and ~ 56 % of sulfate in surface water. Sulfate fertilizer (~33 %) was the second most important source after sewage for groundwater, while detergent (~23 %) was the second most important source for surface water. The redox processes of bacterial sulfate reduction and sulfide oxidation were determined to have a minimal impact on the sulfur isotope fractionation within the basin. This study highlights the benefits of combining major ions, sulfur isotopes and the MixSIAR model for identifying sources of sulfate. This approach accounts for uncertainties in source contributions which allows for more robust and reliable apportionment of sulfate sources. The study emphasizes the need for effective waste management and pollution control measures to protect water quality and provides vital guidelines on how to partition sulfate sources on a large catchment scale and evidence for making pollution management decisions on water resources.

Unveiling nitrate origins in semiarid aquifers: A comparative analysis of Bayesian isotope mixing models using nitrate and boron isotopes and a Positive Matrix Factorization model

Nitrate contamination of groundwater is a pressing global concern, affecting over 80 million people worldwide, with agricultural activities being the primary contributor to nitrogen inputs into aquifers. The primary objective of this study was to identify the predominant sources of nitrate pollution and biogeochemical transformations in the semiarid region of the Meoqui-Delicias aquifer, Mexico. In this region, the uncontrolled use of chemical fertilizers and manure leads to excessive nutrient input, which accelerates the deterioration of groundwater quality. This study introduces an innovative dual-model approach (δ15NNO3 vs. δ11B) to compare the Bayesian Isotope Mixing Model (BIMM) with Positive Matrix Factorization (PMF) for nuanced source apportionment. This approach enhanced the differentiation between manure and sewage as nitrate sources. Results from the δ15NNO3 vs. δ11B model revealed that manure (52.4%) was the most significant source of nitrate pollution in the aquifer, followed by soil (37.4%), chemical fertilizers (5.5%), and sewage (4.7%). The PMF model corroborated this finding and indicated that 60.2% of the NO3–-N pollution in the aquifer was attributed to manure and sewage, confirming the superior performance of the proposed BIMM in source differentiation. Our findings enhance the understanding of nitrate dynamics in semi-arid regions and can serve as a scientific evidence base for targeted interventions in nutrient management in the study area and elsewhere.

Multi-method characterization of groundwater nitrate and sulfate contamination by karst mines in southwest China

Groundwater contamination by nitrate and sulfate in mining areas is a significant challenge. Consequently, the inputs sources of these contaminants and their evolution have received considerable attention, with the knowledge gained critical for improved management of water quality. This study integrated data on multiple stable isotopes and water chemistry data and a Bayesian isotope mixing model to investigate the relative contributions of inputs sources of sulfate and nitrate sources to bodies of water in a karst mining area in southwest China. The outcomes indicated that hydrochemical component in the water bodies of the study area is mainly derived from the dissolution of silicate rocks, carbonate rocks and sulfate minerals as well as the oxidation of sulfides. The human and agricultural wastewater, soil nitrogen, and fertilizers were the predominant inputs sources of nitrate to the mine water environment; the predominant inputs sources of sulfide were mineral oxidation, evaporite dissolution, atmospheric deposition, and sewage. Groundwater is mainly recharged from atmospheric precipitation, and surface water is closely hydraulically connected to groundwater. Nitrogen and oxygen isotope composition and water chemistry indicative of nitrification dominate the nitrogen cycle in the study area. The oxidation of pyrite and bacterial sulfate reduction (SRB) had no significant impact on the stable isotopes of groundwater. The results of this study demonstrate the inputs of different sources to nitrate and sulfate in karst mines and associated transformation processes. The results of this study can assist in the conservation of groundwater quality in mining areas and can act as a reference for future related studies.

Multi-isotopes (δD, δ18Owater, 87Sr/86Sr, δ34S and δ18Osulfate) as indicators for groundwater salinization genesis and evolution of a large agricultural drainage lake basin in Inner Mongolia, Northwest China

Groundwater salinization, a major eco-environmental problem in arid and semi-arid areas, can accelerate soil salinization, reducing crop productivity and imbalances in ecosystem diversity. This study classified water samples collected from the Ulansuhai Lake basin into five clusters using self-organizing maps (SOM). On this basis, multiple isotopes (δ18Owater, δD, 87Sr/86Sr, δ18Osulfate and δ34S) and isotopic models (Rayleigh fractionation and Bayesian isotope mixing models) were used to identify and quantify the genesis and evolution of groundwater salinization. The results showed that the samples were brackish or saline water, and the hydrochemical types were dominated by Na + K-Cl (SO4). It has been proved that the processes associated with groundwater salinization in the Ulansuhai Lake basin were dominated by water-rock interaction and human inputs. Among them, evaporite dissolution contributed substantially to groundwater salinity. Furthermore, salt inputs from human activities cannot be negligible. Based on the model calculations, evaporite dissolution accounted for the most significant proportion of all sources, with a mean value of 53 %. In addition, human inputs from regular agricultural activities (28 % from sewage and manure and 8 % from fertilizers) constituted another vital source of groundwater salinization associated with extensive agricultural activities in the study area. This study's results can deepen our understanding of the genesis of groundwater salinization and the evolution of the agricultural drainage lake basin. This knowledge will assist the Environmental Protection Department in developing effective policies for groundwater management in the Yellow River Basin.

Using sulfur isotopes to constrain the sources of sulfate in PM2.5 during the winter in Jiaozuo City

Sulfate (SO42−) is one of the main driving forces behind the explosive growth of atmospheric fine particulate matter (PM2.5), and it scatters sunlight, contributing to atmospheric cooling. Investigating the sources and transformation of SO42− is crucial for understanding PM2.5 pollution and its implications for climate change. Despite a continuous decline in PM2.5 concentrations in China since 2015, it remains unclear whether there have been changes in the sources and transformation processes of SO42−. Furthermore, previous studies that utilized isotope techniques for source identification have often overlooked the impact of isotope fractionation. This study focuses on atmospheric PM2.5 in Jiaozuo based on samples collected from December 2017 to February 2018. The analysis encompassed both the water-soluble ions and sulfur isotope compositions of SO42− (δ34S–SO42−). By employing a Bayesian isotope mixing model, quantitative analysis of SO42− sources in winter atmospheric PM2.5 in the study area was conducted. The findings revealed that the δ34S–SO42− values of winter PM2.5 ranged from −0.1‰ to 9.8‰, with an arithmetic mean of 4.9 ± 1.5‰ (n = 74). After considering sulfur isotope fractionation effects and the impact of primary SO42−, the calculated results suggested that on average, the contributions of primary SO42−, coal combustion, vehicle exhaust, biomass burning, and oil combustion to the SO42− in winter PM2.5 were 24.9 ± 15.7%, 41.2 ± 2.6%, 27.9 ± 5.3%, 4.8 ± 3.7%, and 1.2 ± 0.7%, respectively. Compared with the clean period, the contribution of the primary SO42− decreased significantly during haze episodes, while the contribution of the secondary SO42− increased. The contribution of coal combustion to the secondary SO42− increased notably by 21.9%, while the contributions from other secondary sulfate remained relatively stable. The lower δ34S values of the atmospheric PM2.5 in Jiaozuo affirm the significant impact of the anthropogenic SO2 input on the sulfur isotope composition of the atmospheric sulfate. Analysis confirmed that the decrease in the contribution of coal combustion to atmospheric SO42− in the study area, indicating the positive effects of regional air pollution control measures. Nevertheless, continued attention should be paid to the influences of coal combustion and vehicle exhaust on regional air pollution.

Effect of hydrogen isotopic offset and input data on the isotope-based estimation of plant water sources

The stable isotope of water has been widely used to investigate plant water resources with the assumption that root water uptake and plant water transport is a non-isotopic fractionation process. However, recent research revealed that significant isotopic offsets between xylem water and potential water sources, which may result in errors when estimating plant water sources. We test five different input data modes in Bayesian isotope mixing model [only δ18O (M−O), only δD (M−D), δD after subtracting the deviation with respect to the soil water line (SW-excess) from the δD of xylem samples (M−SD), δD and δ18O (M−OD), and δD and δ18O after subtracting the SW-excess from the δD of xylem samples (M−OSD)] to identify sources of water absorbed by Chinese wolfberries (Lycium chinense Miller). The results showed that there was significant difference (p < 0.05) between the M−O and M−D modes. The evaluation indexes (RESE, N, MaxE, MinE, MAPE) performed well in the M−SD, M−O, and M−OSD modes. Furthermore, the M−SD, M−OSD, and M−O had consistent contribution proportions for the three soil layers. In addition, the corrected δD values in the dual and single isotope modes performed better than the original δD in quantifying water source contribution. This study provides critical insights into choosing a suitable method for isotope-based identification of plant water sources.

Distinguishing the contributions of different smelting emissions to the spatial risk footprints of toxic elements in soil using PMF, Bayesian isotope mixing models, and distance-based regression

Toxic element pollution of soils emanating from smelting operations is an escalating global concern due to its severe impact on ecosystems and human health. In this study, soil samples were collected and analyzed to quantify the risk contributions and delineate the spatial risk footprints from smelting emissions for 8 toxic elements. A comprehensive health risk contribution and delineation framework was utilized, consisting of Positive matrix factorization (PMF), spatial interpolation, an advanced Bayesian isotope mixing model via Mixing Stable Isotope Analysis in R (MixSIAR), and distance-based regression. The results showed that the mean concentrations of As, Cd, Cu, Hg, Pb, and Zn exceeded the background levels, indicating substantial contamination. Three sources were identified using the PMF model and confirmed by spatial interpolation and MixSIAR, with contributions ranked as follows: industrial wastewater discharge and slag runoff from the smelter site (48.9 %) > natural geogenic inputs from soil parent materials (26.7 %) > atmospheric deposition of dust particles from smelting operations (24.5 %). Among the identified sources, smelter runoff posed the most significant risk, accounting for 97.9 % of the non-carcinogenic risk (NCR) and 59.9 % of the carcinogenic risk (CR). Runoff also drove NCR and CR exceedances at 7.8 % and 4.7 % of sites near the smelter, respectively. However, atmospheric deposition from smelting emissions affected soils across a larger 0.8 km radius. Although it posed lower risks, contributing just 1.1 % to NCR and 22.6 % to CR due to the limited elevation of toxic elements, deposition reached more distant soils. Spatial interpolation and distance-based regression delineated high NCR and CR exposure hotspots within 1.4 km for runoff and 0.8 km for deposition, with exponentially diminishing risks at further distances. These findings highlight the need for pathway-specific interventions that prioritize localized wastewater containment and drainage controls near the smelter while implementing broader regional air pollution mitigation measures.

Sources of NH4+ in PM2.5 and Their Seasonal Variations in Urban Tianjin China: New Insights From the Seasonal δ15N Values of NH3 Source

AbstractThe stable nitrogen isotopic composition (δ15N) has been widely used to quantify sources of ammonium (NH4+) in PM2.5. However, the overlap and uncertainty in δ15N values from different NH3 sources, coupled with their seasonal variability, hinder accurate identification of NH4+ source. Here, the δ15N values of various NH3 source samples collected by the active sampler were determined. Subsequently, we measured the δ15N values of NH4+ in PM2.5, which were collected seasonally in Tianjin. We found that the combustion‐related NH3 (c‐NH3) exhibiting higher δ15N values compared to volatile NH3 (v‐NH3), but all δ15N values was fell within the range reported by previous studies. Furthermore, inconsistent seasonal variations were observed in the δ15N‐NH3 values originating from emissions of agricultural soil and human excreta. The application of the Bayesian isotope mixing model (MixSIAR model) revealed a significant increase in the contribution of v‐NH3 to NH4+ when incorporating current source data, as opposed to previous data, for δ15N of NH3 source. Notably, the contribution of v‐NH3 (53.1%) to NH4+ was almost equivalent to that of c‐NH3 (46.9%) when considering the seasonal δ15N signatures of NH3 source. Additionally, the estimated contribution of v‐NH3 to NH4+ exhibited significant seasonal variability, which is more reasonable than in the non‐seasonal scenario. This study demonstrated that v‐NH3 and c‐NH3 contributed to NH4+ in PM2.5 in Tianjin almost equally, and it is highlighted that the seasonal δ15N values of NH3 sources should be considered when estimating the contributions of different NH3 sources to NH4+ in PM2.5 by the MixSIAR model.

Assessment major NOx sources to nitrate of TSP around the Danjiangkou reservoir using isotopes and a Bayesian isotope mixing model

Nitrate (NO3−) is a key component of atmospheric particulate matter. However, studies quantifying NO3− formation and sources in water source areas remain scarce. This study employs NO3− concentration, dual isotopic analyses (δ15N, δ18O), and SIAR and PSCF models to elucidate NO3− formation mechanisms and apportion sources in the Danjiangkou Reservoir area. The result showed that the concentration of NO3− (0.52 μg m−3-2.39 μg m−3), δ15N–NO3- (−2.3‰-4.2‰) and δ18O–NO3- (64.2‰–96.6‰) values varied seasonally in total suspended particulate matter (TSP). The contribution of the NO2 + ·OH pathway was the highest in summer (84%), while the main contributions came from the N2O5 + H2O and NO3 + VOCs pathways in winter (75%). The SIAR model revealed that the contributions of coal combustion and traffic emissions were 34% and 23%, respectively, indicating that fossil fuel release contributed most. Moreover, the contribution of biomass combustion sources was 31%, and soil emission sources had the least (12%). Considering liquefied petroleum gas, gasoline, and diesel vehicles as traffic emission sources, their contribution to NO3− in TSP increased to 50%. The PSCF analysis indicated that external input areas were predominantly from central Henan Province, northern Hubei Province, and central Shaanxi Province. This study quantitatively analyzed the sources of NO3− in atmospheric particulate matter in water sources for the first time, indicating that controlling the emissions of nitrogen oxides from gasoline cars, heavy-duty diesel trucks, and ships could help alleviate haze pollution and enhance air quality in the Danjiangkou reservoir area.

Research progress on nitrate isotope coupled multi-tracer tracing groundwater nitrate pollution

Nitrate pollution in groundwater has become a global concern. One of the most important issues in controlling the nitrate pollution of groundwater is to identify the pollution source quickly and accurately. In this review, we firstly summarized the isotopic background values of potential sources of nitrate pollution in groundwater in 17 provinces (cities, autonomous regions) and 29 study areas in China, which could provide the fundamental database for subsequent research. Secondly, we reviewed the research progress of nitrate isotopes combined with multiple tracers for tracing nitrate in groundwater, and discussed their applicable conditions, advantages, and disadvantages. We found that halides and microorganisms combined with nitrate isotopes could accurately trace the pollution sources of domestic sewage, excrement and agricultural activities. The combination of Δ17O and nitrate isotopes could effectively distinguish the source of atmospheric deposition of nitrate in groundwater. The combination of groundwater age and nitrate isotopes could further determine the time scale of nitrate pollution. In addition, we summarized the application cases and compared the characteristics of mass balance mixing model, IsoSource model, Bayesian isotope mixing model, and EMMTE model for quantitative identification of nitrate pollution in groundwater. For the complexity and concealment of groundwater pollution sources, the coupling of nitrate isotopes with other chemical and biological tracing methods, as well as the application of nitrate isotope quantitative models, are effective tools for reliably identifying groundwater nitrate sources and transformation processes.

Tracing the impacts of ecological water replenishment on the sources and transformation of groundwater nitrate through isotope and microbial analysis

Ecological water replenishment (EWR) changes the recharge conditions, flow fields, and physicochemical properties of regional groundwater. However, the resulting impacts on mechanisms regulating the sources and transformation of groundwater nitrate remain unclear. This study investigated how EWR influences the sources and transformation processes of groundwater nitrate using an integrated approach of Water chemistry analysis and stable isotopes (δ15N-NO3− and δ18O-NO3−) along with microbial techniques. The results showed that groundwater NO3-N decreased from 12.98 ± 7.39 mg/L to 7.04 ± 8.52 mg/L after EWR. Water chemistry and isotopic characterization suggested that groundwater nitrate mainly originated from sewage and manure. The Bayesian isotope mixing model (MixSIAR) indicated that EWR increased the average contribution of sewage and manure sources to groundwater nitrate from 46 % to 61 %, whereas that of sources of chemical fertilizer decreased from 43 % to 21 %. Microbial community analysis revealed that EWR resulted in a substantial decrease in the relative abundance of Pseudomonas spp denitrificans, from 13.7 % to 0.6 %. Both water chemistry and microbial analysis indicated that EWR weakened denitrification and enhanced nitrification in groundwater. EWR increases the contribution of nitrate to groundwater by promoting the release of sewage and feces in the unsaturated zone. However, the dilution effect caused by EWR was stronger than the contribution of sewage and fecal sources to groundwater nitrate. As a result, EWR helped to reduce groundwater nitrate concentrations. This study showed the effectiveness of integrated isotope and microbial techniques for delineating the sources and transformations of groundwater nitrate influenced by EWR.

Evolution of groundwater hydrochemical characteristics and formation mechanism during groundwater recharge: A case study in the Hutuo River alluvial–pluvial fan, North China Plain

A pilot project for groundwater recharge from rivers is currently being carried out in North China Plain. To investigate the influence of river recharge on groundwater hydrochemical characteristics, dynamic monitoring and analysis of groundwater samples were conducted at a typical recharge site in the Hutuo River alluvial-pluvial fan in the North China Plain from 2019 to 2021. Hydrochemical, isotopic, and multivariate statistical analyses were used to systematically reveal the spatiotemporal variation of groundwater chemistry and its driving factors during groundwater recharge process. The results showed that the groundwater hydrochemical types and characteristics in different recharge areas and recharge periods exhibited obvious spatiotemporal differences. The groundwater type varied from HCO3·SO4-Na·Mg to HCO3·SO4-Ca·Mg in an upstream ecological area, while the groundwater type changed from SO4·HCO3-Mg·Ca to HCO3·SO4-Ca·Mg in the downstream impacted by reclaimed water. Changes in the contents of Ca2+, Mg2+ and HCO3− were mostly controlled by the water–rock interactions and mixing-dilution of recharge water, while the increases in Na+, NO3−, Cl−, SO42− and NO3− contents were mainly due to the infiltration of reclaimed water. Nitrogen and oxygen isotope (δ15N and δ18O) tests and the Bayesian isotope mixing model results further demonstrated that nitrate pollution mainly originated from anthropogenic sources, and the major contribution came from manure and sewage, with an average proportion of 64.6 %. Principal component analysis indicated that water–rock interactions, river-groundwater mixing and redox environment alternation were dominant factors controlling groundwater chemical evolution in groundwater recharge process.

13C dicarboxylic acid signatures indicate temporal shifts in catchment sediment sources in response to extreme winter rainfall

Rainfall and land-use interactions drive temporal shifts in suspended sediment sources, yet the magnitude of such changes remains poorly understood due to the lack of land-use specific source tracers. We investigated α,ω-dicarboxylic fatty acid root-specific biomarkers, as diagnostic tracers for apportioning sources of time-integrated suspended sediment samples collected from a grassland dominated agricultural catchment in the southwest of England during the wet winter period. Applying fatty acids-specific stable carbon isotope analysis and a Bayesian isotope mixing model, we show that stream banks contributed most of the sediment in the early winter, i.e. October–December, while winter cereal-dominated arable land contributed more than half of the sediment during the late winter, i.e. January–March. The dominant sediment source shifted in conjunction with a period of prolonged consecutive rainfall days in the later period suggesting that intervention required to mitigate soil erosion and sediment delivery should adapt to changing rainfall patterns. Our novel findings demonstrate that isotopic signatures of α,ω-dicarboxylic fatty acids are promising tracers for understanding the resistance of agricultural soils to water erosion and quantifying the interactive effects of extreme rainfall and land use on catchment sediment source dynamics.

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.

Large Seasonal Variation in Nitrogen Isotopic Abundances of Ammonia Volatilized from a Cropland Ecosystem and Implications for Regional NH3 Source Partitioning.

Ammonia (NH3) volatilization from agricultural lands is a main source of atmospheric reduced nitrogen species (NHx). Accurately quantifying its contribution to regional atmospheric NHx deposition is critical for controlling regional air nitrogen pollution. The stable nitrogen isotope composition (expressed by δ15N) is a promising indicator to trace atmospheric NHx sources, presupposing a reliable nitrogen isotopic signature of NH3 emission sources. To obtain more specific seasonal δ15N values of soil NH3 volatilization for reliable regional seasonal NH3 source partitioning, we utilized an active dynamic sampling technique to measure the δ15N-NH3 values volatilized from maize cropping land in northeast China. These values varied from -38.0 to -0.2‰, with a significantly lower rate-weighted value observed in the early period (May-June, -30.5 ± 6.7‰) as compared with the late period (July-October, -8.5 ± 4.3‰). Seasonal δ15N-NH3 variations were related to the main NH3 production pathway, degree of soil ammonium consumption, and soil environment. Bayesian isotope mixing model analysis revealed that without considering the seasonal δ15N variation in soil-volatilized NH3 could result in an overestimate by up to absolute 38% for agricultural volatile NH3 to regional atmospheric bulk ammonium deposition during July-October, further demonstrating that it is essential to distinguish seasonal δ15N profile of agricultural volatile NH3 in regional source apportionment.

Using live videography observation and Bayesian isotope mixing model to identify food composition and dietary contribution to inorganic mercury and methylmercury intake by songbird nestlings

Mercury (Hg) exposure is increasing in terrestrial birds; however, studies on its sources are scarce. In the present study, we elucidated the food composition of green-backed tit nestlings from three urban forest parks (CPL, AHL, and LCG) using live videography observation (LVO). Furthermore, the daily dietary intakes of inorganic Hg (IHg) (MDIIHg) and methylmercury (MeHg) (MDIMeHg) were determined using the Bayesian isotope mixing model (BIMM) to uncover the nestlings' specific dietary Hg contribution. Both LVO and BIMM indicated that Lepidoptera (primarily caterpillar) constituted the primary food source for the nestlings in the three forests, accounting for approximately 60% of their diet in all three forest parks. The estimated MDI of Hg revealed that lepidopterans and spiders primarily contributed to IHg exposure, with a co-contribution ratio of 71.8%–97.7%. Unexpectedly, dietary MeHg was mostly derived from spiders; the highest contribution ratio of 93.6% was recorded at CPL, followed by another peak ratio of 92.9% at LCG. However, the dietary exposure was primarily IHg, accounting for 69.8% (AHL), 62.0% (LCG), and 61.3% (CPL) of the nestlings. Our study findings highlight the importance of dietary IHg transfer in evaluating the effects of Hg in nestlings. LVO, coupled with BIMM, is an effective tool for determining the food compositions of songbird nestlings and estimating the contribution of specific diets.

Quantifying and assessing nitrogen sources and transport in a megacity water supply watershed: Insights for effective non-point source pollution management with mixSIAR and SWAT models

The expansion and intensification of human activities have resulted in excessive nitrogen (N) in rivers, causing worldwide concern. To effectively manage agricultural non-point source pollution and ensure a safe drinking water supply in watersheds, it is crucial to trace and quantify the primary sources and spatial distribution patterns of N. In response to the challenge of unclear sources and agricultural non-point source pollution, this study utilised the Bayesian isotope mixing model and Soil and Water Assessment Toolmodel to identify the dominant nitrate sources and transformation processes. These models were employed to quantify N retention by the mainstream and tributaries in the Bai River Basin, which directly impacts the safety of drinking water in Beijing. Total N (TN) concentrations in the Yunzhou Reservoir (4.22 ± 0.04 mg.L˗1) and Miyun Reservoir (2.88 ± 0.62 mg.L˗1) inlets in autumn and winter were V class (2 mg.L˗1) category as per GB 3838–2002 standards. This implies higher risks of eutrophication and algal blooms exceeding the standards at individual points and seasons in the Bai River. Fertilisers were the main nitrate source in the Bai River Basin, contributing 44.6% during the dry season and 62.9% during the wet season, suggesting that nitrate is more susceptible to leaching and runoff during rainy periods. Rainfall was not a major contributor, with only 3.2% and 2.9% originating from the Hebei and Beijing areas of the Bai River, respectively. Annually, the Bai River Basin exports loads of approximately 629.3 t.a˗1 of TN and 433.7 t.a˗1 of organic N (Org-N) from the Bai River Basin land to the river. The TN and Org-N loads at the final destination in the Miyun Reservoir were 521.3 t.a˗1 and 100.3 t.a˗1, respectively. Of the exported TN and Org-N, 17.16% and 76.87%, respectively, were retained in the river network. Consequently, N transformation occurred in the Bai River, with nitrification–denitrification being particularly dominant. Nitrification was more evident in the nitrate-enriched river.

Primary Sulfate Is the Dominant Source of Particulate Sulfate during Winter in Fairbanks, Alaska.

Within and surrounding high-latitude cities, poor air quality disturbs Arctic ecosystems, influences the climate, and harms human health. The Fairbanks North Star Borough has wintertime particulate matter (PM) concentrations that exceed the Environmental Protection Agency's (EPA) threshold for public health. Particulate sulfate (SO4 2-) is the most abundant inorganic species and contributes approximately 20% of the total PM mass in Fairbanks, but air quality models underestimate observed sulfate concentrations. Here we quantify sulfate sources using size-resolved δ34S(SO4 2-), δ18O(SO4 2-), and Δ17O(SO4 2-) of particulate sulfate in Fairbanks from January 18th to February 25th, 2022 using a Bayesian isotope mixing model. Primary sulfate contributes 62 ± 12% of the total sulfate mass on average. Most primary sulfate is found in the size bin with a particle diameter < 0.7 μm, which contains 90 ±5% of total sulfate mass and poses the greatest risk to human health. Oxidation by all secondary formation pathways combined contributes 38 ± 12% of total sulfate mass on average, indicating that secondary sulfate formation is inefficient in this cold, dark environment. On average, the dominant secondary sulfate formation pathways are oxidation by H2O2 (13 ± 6%), O3 (8 ± 4%), and NO2 (8 ± 3%). These findings will inform mitigation strategies to improve air quality and public health in Fairbanks and possibly other high-latitude urban areas during winter.