Determination of the nitrogen isotope enrichment factor associated with ammonification and nitrification in unsaturated soil at different temperatures

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).

Diversity, composition, metabolic characteristics, and assembly process of the microbial community in sewer system at the early stage

Sewer systems play vital roles in wastewater treatment facilities, and the microbial communities contribute significantly to the transformation of domestic wastewater. Therefore, this study conducted a 180-day experiment on a sewer system and utilized the high-throughput sequencing technology to characterize the microbial communities. Additionally, community assembly analysis was performed to understand the early-stage dynamics within the sewer system. The results demonstrated that the overall diversity of microbial communities exhibited fluctuations as the system progressed. The dominant phyla observed were Chloroflexi, Bacteroidetes, Firmicutes, and Proteobacteria, accounting for over 85.4% of the total relative abundances. At the genus level, bacteria associated with fermentation displayed a high relative abundance, particularly during days 75 to 180. A random-forest machine-learning model identified a group of microbes that confirmed the substantial contribution of fermentation. During the process of fermentation, microorganisms predominantly utilized propionate formation as the main pathway for acidogenesis, followed by acetate and butyrate formation. In terms of nitrogen and sulfur cycles, dissimilatory nitrate reduction and assimilatory sulfate reduction played significant roles. Furthermore, stochastic ecological processes had a dominant effect during the experiment. Dispersal limitation primarily governed the assembly process almost the entire experimental period, indicating the strong adaptability and metabolic plasticity of microorganisms in response to environmental variations. This experiment provides valuable insights into the metabolic mechanisms and microbial assembly associated with sewer systems.

Multiple isotopes reveal the driving forces of nitrogen cycling from freshwater to brackish water

Rivers play a crucial role in global nitrogen (N) cycling, but revealing the driving mechanism of N cycling remains challenging because of the complex natural background gradients. The Qiantang River Basin provides an opportunity to elucidate the driving mechanism due to the complex climatic and hydrological conditions. In this study, the multiple stable isotopes suggested that the conservative mixing of two end members was insufficient to explain the complex behavior of N in both seasons. In-soil processes were the primary N cycling processes that controlled riverine N loading during the wet season, whereas in-stream N biological transformation processes (nitrification and assimilation) were more prevalent during the dry season. The results of MixSIAR revealed that soil sources (soil N and N fertilizer) contributed the most to NO3- during the wet season, accounting for 64.3 %, followed by manure and sewage (31.6 %) and atmospheric precipitation (4.1 %). During the dry season, manure and sewage were the predominant contributors to NO3- (52.1 %), followed by soil N (26.6 %), N fertilizer (18.8 %), and atmospheric precipitation (2.5 %). The relationships between d-excess and δ15N-NH4+ or δ15N-NO3- suggested that both climatic and hydrological conditions would be the driving forces regulating the N transportation and transformation in this basin, leading to the high spatiotemporal heterogeneity in N loading and isotopic compositions. In the wet season, precipitation patterns served as the primary driving forces regulating in-soil biological processes and soil leaching. While the hydrological conditions, especially water residence time, were the crucial factors controlling in-stream biological processes during the dry season. This study elucidates N sources, biotransformation processes, and their driving forces from freshwater to brackish water, which has applications for understanding the N fate from terrene to ocean.

Assessment of sources variability of riverine particulate organic matter with land use and rainfall changes using a three-indicator (δ13C, δ15N, and C/N) Bayesian mixing model

In intensive agricultural watersheds, riverine particulate organic matter (POM) may be transported from many sources such as rice paddies, crop uplands, forests, and livestock farming areas during rainy seasons. However, the impacts of land-use and rainfall changes on the POM sources are not well understood. In this study, changes in the sources of riverine POM were investigated in an agricultural area of Korea between 2014 and 2020/21. During this period, land-use and rainfall patterns changed dramatically. The δ¹³C, δ¹⁵N, and C/N of the POM sources as well as those of riverine POM were analyzed, and a stable isotope analysis in R (SIAR) model was utilized for source apportionment. There were differences in δ¹³C, δ¹⁵N, and C/N among the sources. For example, manure had higher δ¹³C (-22.6 ± 3.3‰) and δ¹⁵N (+10.6 ± 5.9‰) than soils (from -28.0 ± 0.8‰ to -25.1 ± 1.2‰ for δ¹³C and +3.6 ± 1.7‰ to +9.8 ± 1.4‰ for δ¹⁵N). For soils, the δ¹³C and δ¹⁵N were higher for upland soils, while C/N was greater for forest soils than for others. For riverine POM, the δ¹⁵N marginally changed; however, the δ¹³C and C/N increased from -26.1 ± 0.9‰ to -20.8 ± 5.3‰ and from +7.7 ± 1.7 to +18.8 ± 8.3 between 2014 and 2020/21, respectively. The SIAR model showed that the contributions of paddy (from 41.0% to 14.9%) and upland fields (from 48.1% to 23.7%) to riverine POM decreased between the periods due to decreased paddy area and the implementation of best management practice on upland fields, respectively. However, the contribution of forests (from 3.5% to 28.0%) and manure (from 7.4% to 33.5%) increased probably due to improper management of forest clear-cutting sites and livestock manure storage sites. The contributions of agricultural soils to riverine POM decreased in drier years. Our study suggests that land management rather than land-use area is critical in riverine POM management, particularly in wetter years.

APCS-MLR model: A convenient and fast method for quantitative identification of nitrate pollution sources in groundwater

Nitrate (NO₃^-) contamination in groundwater has diverse sources and complicated transformation processes. To effectively control NO₃^- pollution in groundwater systems, quantitative and accurate identification of NO₃^- sources is critical. In this work, we applied hydrochemical characteristics and isotope analysis to determine NO₃^- source apportionment. For the first time, the NO₃^- source contributions were calculated using hydrochemical indicators combined with multivariate statistical model (PCA-APCS-MLR). The results interpret that chemical fertilizers (58.11%) and natural sources (22.69%) were the primary NO₃^- sources in the vegetable cultivation area (VCA) which were rather close to the estimation by Bayesian isotope mixing model (SIAR). In particular, the contributions of chemical fertilizers in the VCA differed by only 3.79% between the two methods. Compared with previous approaches e.g. SIAR, the key advantage of the proposed PCA-APCS-MLR model is that it only requires the hydrochemical indicators which can be easily measured. A series of complicated experiments including measurement of isotope data of NO₃^- in groundwater, monitoring of in-situ pollution source information and calculation of isotopic enrichment factor can be simply avoided. The PCA-APCS-MLR model offers a much more convenient and faster method to determine the contribution rates of NO₃^- pollution sources in groundwater.

Isotope fractionation (δ13C, δ15N) and microbial community response in degradation of petroleum hydrocarbons by biostimulation in contaminated soil

This study investigated the isotope effects of δ¹³C and δ¹⁵N and microbial response during biodegradation of hydrocarbons by biostimulation with nitrate or compost in the petroleum-contaminated soil. Compost and KNO₃ amendments promoted the total petroleum hydrocarbon (TPH) removal accompanied by a significant increase of Actinobacteria and Firmicutes phyla. Soil alpha diversity decreased after 90 days of biostimulation. An inverse significant carbon isotope effect (ε_c = 16.6 ± 0.8‰) and strong significant nitrogen isotope effect (ε_N = -24.20 ± 9.54‰) were shown by the KNO₃ supplementation. For compost amendment, significant carbon and nitrogen isotope effect were ε_c = 38.8 ± 1.1‰ and ε_N = -79.49 ± 16.41‰, respectively. A clear difference of the carbon and nitrogen stable isotope fractionation was evident by KNO₃ or compost amendment, which indicated that the mechanisms of petroleum degradation by adding compost or KNO₃ may be different.