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).

Inward-to-outward assembly of amine-functionalized carbon dots and polydopamine to Shewanella oneidensis MR-1 for high-efficiency, microbial-photoreduction of Cr(VI).

A novel photosensitized living biohybrid was fabricated by inward-to-outward assembly of amine-functionalized carbon dots (NCDs) and polydopamine (PDA) to Shewanella oneidensis MR-1 and applied for high-efficiency, microbial-photoreduction of Cr(VI). Within a 72 h test period, biohybrids achieved a pronounced catalytic reduction capacity (100%) for 100 mg/L Cr(VI) under visible illumination, greatly surpassing the poor capacity (only 2.5%) displayed by the wild strain under dark conditions. Modular configurations of NCDs and PDA afforded biohybrids with a large electron flux by harvesting extracellular photoelectrons generated from illuminated NCDs and increasing reducing equivalents released from an enlarged intracellular NADH/NAD+ pool. Further, increased production of intracellular c-type cytochromes and extracellular flavins resulting from the modular configuration enhanced the biohybrid electron transport ability. The enhancement of electron transport was also attributed to more conductive conduits at NCDs-PDA junction interfaces. Moreover, because NCDs are highly reductive, the enhanced Cr(VI) reduction was also attributed to direct reduction by the NCDs and the direct Cr(VI) reduction by sterile NCDs-assembled biohybrid was up to 20% in the dark. Overall, a highly efficient strategy for removal/transformation of Cr(VI) by using NCD-assembled photosensitized biohybrids was proposed in this work, which greatly exceeded the performance of Cr(VI)-remediation strategies based on conventional microbial technologies.

Nitrate pollution source apportionment, uncertainty and sensitivity analysis across a rural-urban river network based on δ15N/δ18O-NO3- isotopes and SIAR modeling.

Nitrate pollution is of considerable global concern as a threat to human health and aquatic ecosystems. Nowadays, δ15N/δ18O-NO3- combined with a Bayesian-based SIAR model are widely used to identify riverine nitrate sources. However, little is known regarding the effect of variations in pollution source isotopic composition on nitrate source contributions. Herein, we used δ15N/δ18O-NO3-, SIAR modeling, probability statistical analysis and a perturbing method to quantify the contributions and uncertainties of riverine nitrate sources in the Wen-Rui Tang River of China and to further investigate the model sensitivity of each nitrate source. The SIAR model confirmed municipal sewage (MS) as the major nitrate source (58.5-75.7%). Nitrogen fertilizer (NF, 8.6-20.9%) and soil nitrogen (SN, 7.8-20.1%) were also identified as secondary nitrate sources, while atmospheric deposition (AD, <0.1-7.9%) was a minor source. Uncertainties associated with NF (UI90 = 0.32) and SN (UI90 = 0.30) were high, whereas those associated with MS (UI90 = 0.14) were moderate and AD low (UI90 = 0.0087). A sensitivity analysis was performed for the SIAR modeling and indicated that the isotopic composition of the predominant source (i.e., MS in this study) had the strongest effect on the overall riverine nitrate source apportionment results.