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Riverine nitrogen pollution is ubiquitous and attracts considerable global attention. Nitrate is commonly the dominant total nitrogen (TN) constituent in surface and ground waters; thus, stable isotopes of nitrate (δ15N/δ18O-NO3-) are widely used to differentiate nitrate sources. However, δ15N/δ18O-NO3- approach fails to present a holistic perspective of nitrogen pollution for many coastal-plain river networks because diverse nitrogen species contribute to high TN loads. In this study, multiple isotopes, namely, δ15N/δ18O-NO3-, δ18O-H2O, δ15N-NH4+, δ15N-PN, and δ15Nbulk/δ18O/SP-N2O in the Wen-Rui Tang River, a typical coastal-plain river network of Eastern China, were investigated to identify transformation processes and sources of nitrogen. Then, a stable isotope analysis in R (SIAR) model-TN source apportionment method was developed to quantify the contributions of different nitrogen sources to riverine TN loads. Results showed that nitrogen pollution in the river network was serious with TN concentrations ranging from 1.71 to 8.09 mg/L (mean ± SD: 3.77 ± 1.39 mg/L). Ammonium, nitrate, and suspended particulate nitrogen were the most prominent nitrogen components during the study period, constituting 45.4 %, 28.9 %, and 19.9 % of TN, respectively. Multiple hydrochemical and isotopic analysis identified nitrification as the dominant N cycling process. Biological assimilation and denitrification were minor N cycling processes, whereas ammonia volatilization was deemed negligible. Isotopic evidence and SIAR modeling revealed municipal sewage was the dominant contributor to nitrogen pollution. Based on quantitative estimates from the SIAR model, nitrogen source contributions to the Wen-Rui Tang River watershed followed: municipal sewage (40.6 %) ≈ soil nitrogen (39.5 %) > nitrogen fertilizer (9.7 %) > atmospheric deposition (2.8 %) during wet season; and municipal sewage (59.1 %) > soil nitrogen (30.4 %) > nitrogen fertilizer (4.1 %) > atmospheric deposition (1.0 %) during dry season. This study provides a deeper understanding of nitrogen dynamics in eutrophic coastal-plain river networks, which informs strategies for efficient control and remediation of riverine nitrogen pollution.

Microplastics are an emerging and persistent pollutant due to their threat to global ecological systems and human health. Recent studies showed that microplastics have infiltrated the remote Third Pole - the Tibetan Plateau. Here, we summarize the current evidence for microplastic pollution in the different environments (rivers/lakes, sediment, soil, ice/snow and atmosphere) of the Tibetan Plateau. We assess the spatial distribution, source, fate, and potential ecological effects of microplastics in this broad plateau. The integrated results show that microplastics were pervasive in biotic and abiotic components of the Tibetan Plateau, even at the global highest-altitude, Mt. Everest. Although the concentration of microplastics in the Tibetan Plateau was far below that found in the densely populated lowlands, it showed a higher concentration than that in the ocean system. Tourist populations are identified as a substantial source of anthropogenic plastic input rather than local residents due to the rapid development of the tourism industry. In the sparsely inhabited remote area of the Tibetan Plateau, long-range atmospheric transport facilitates allochthonous microplastic diffusion. Robust solar radiation in the Tibetan Plateau might enhanced production of secondary microplastics by weathering (UV-photooxidation) of abandoned plastic waste. A rough estimation showed that the microplastic export flux from melting glaciers was higher than that measured in most of the world's largest rivers, which affects local and downstream areas. Since the Tibetan Plateau is vital for Asian water supply and numerous endangered wildlife, the potential human and ecological risk of microplastics to these fragile ecosystems needs to be fully evaluated within the context of climate-change impacts.

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,

Identifying the sources and transformations of riverine nitrate plays a critical role in mitigating nitrogen enrichment of river networks. Several previous studies have used δ18O-NO3− to quantitatively assess riverine nitrate contributed by atmospheric nitrate and terrestrial sources, but their results have great uncertainty due to the wide range of δ18O-NO3− values and isotopic fractionation during nitrogen-cycling processes in terrestrial environment. The nitrate 17O anomaly (△17O-NO3−), as an unambiguous tracer of atmospheric nitrate, is a promising tool to effectively separate atmospheric nitrate from microbially produced nitrate. However, to our knowledge, △17O-NO3− approach has not been previously applied to identify nitrate pollution sources in plain river networks of eastern China. In this study, we used a multiple isotope approach (δD/δ18O-H2O and δ15N/δ18O/△17O-NO3−) for the first time to quantitatively identify sources and transformations of riverine nitrate in a hypereutrophic coastal plain river network namely Wen-Rui Tang River located in eastern China, which is a region receiving high inputs of atmospheric nitrogen deposition. The △17O-NO3− values in precipitation and river water during the study period (April–June of 2021) varied from 14.83‰ to 31.39‰ and from − 2.82‰ to 9.66‰, respectively. The δD/δ18O-H2O values revealed that river water mainly originated from recent precipitation with little evaporation. Moreover, the δ15N/δ18O-NO3− values indicated that microbial nitrification, not denitrification, was the predominant nitrogen-cycling process in the watershed. Based on a Bayesian mixing model (Stable Isotope Analysis in R, SIAR) using δ15N/△17O-NO3−, municipal sewage was identified as the dominant nitrate source (50.5 ± 11.7%), followed by soil nitrogen (23.8 ± 13.7%), atmospheric nitrate deposition (14.3 ± 2.9%), and nitrogen fertilizer (11.4 ± 8.7%). Finally, an uncertainty analysis for nitrate source apportionment demonstrated that the greatest uncertainty was associated with soil nitrogen, followed by municipal sewage, nitrogen fertilizer, and atmospheric nitrate deposition. This study provides important scientific information on riverine nitrate source apportionment to guide pollution control/remediation strategies and highlights the benefits of utilizing △17O-NO3− to enhance nitrate source apportionment.

Sinking of microplastics (MPs) after biofouling is considered an important mechanisms responsible for the downward transport/sedimentation of MPs in the ocean and freshwaters. Previous studies demonstrated MP sinking caused by an increase in the composite density of MPs after biofouling, while MPs with smaller size or shapes with higher surface area to volume ratios (SA:V), such as films, are speculated to sink faster. In this study, we designed an in situ microcosm to simulate the ambient environmental conditions experienced by floating MPs to elucidate the biofouling and sinking of polyethylene (PE), polypropylene (PP), and expanded-polystyrene (EPS) MPs of various sizes and shapes. Our results showed smaller PE and PP MP granules sank faster than large ones. Even EPS granules of 100 μm diameter, having a much lower density (0.02 mg/mm3) than water, started to sink after 2 weeks of biofouling. Moreover, PE film and fiber MPs with higher SA:V did not sink faster than PE MP granules of the same mass, implying that mechanisms other than SA:V, such as fouling contact area and drag coefficient, play a role in the regulation of biofouling and sinking of MPs.

Currently, the industrial application of bioelectrochemical systems (BESs) that are incubated with natural electrochemically active microbes (EABs) is limited due to inefficient extracellular electron transfer (EET) by natural EABs. Notably, recent studies have identified several novel living biomaterials comprising highly efficient electron transfer systems allowing unparalleled proficiency of energy conversion. Introduction of these biomaterials into BESs could fundamentally increase their utilization for a wide range of applications. This review provides a comprehensive assessment of recent advancements in the design of living biomaterials that can be exploited to enhance bioelectrocatalytic performance. Further, modular configurations of abiotic and biotic components promise a powerful enhancement through integration of nano-based artificial mediators and synthetic biology. Herein, recent advancements in BESs are synthesized and assessed, including heterojunctions between conductive nanomaterials and EABs, in-situ hybrid self-assembly of EABs and nano-sized semiconductors, cytoprotection in biohybrids, synthetic biological modifications of EABs and electroactive biofilms. Since living biomaterials comprise a broad range of disciplines, such as molecular biology, electrochemistry and material sciences, full integration of technological advances applied in an interdisciplinary framework will greatly enhance/advance the utility and novelty of BESs. Overall, emerging fundamental knowledge concerning living biomaterials provides a powerful opportunity to markedly boost EET efficiency and facilitate the industrial application of BESs to meet global sustainability challenges/goals.

Human-health risks from microplastics have attracted considerable attention, but little is known about human-exposure pathways and intensities. Recent studies posited that inhalation of atmospheric microplastics was the dominant human-exposure pathway. Herein, our study identified that atmospheric microplastics ingested from deposition during routine dining/drinking activities represent another important exposure pathway. We measured abundances of atmospheric-deposited microplastics of up to 105 items m-2 d-1 in dining/drinking venues, with 90% smaller than 100 µm and a dominance of amorphous fragments rather than fibers. Typical work-life scenarios projected an annual ingestion of 1.9 × 105 to 1.3 × 106 microplastics through atmospheric deposition on diet, with higher exposure rates for indoor versus outdoor dining/drinking settings. Ingestion of atmospheric-deposited microplastics through diet was similar in magnitude to presumed inhalation exposure, but 2-3 orders of magnitude greater than direct ingestion from food sources. Simple mitigation strategies (e.g., covering and rinsing dishware) can substantially reduce the exposure of atmospheric deposition microplastics through diet.

Microplastics (MPs) in marine and terrestrial environments have been intensively studied, but the dynamics of airborne MPs remains limited. Existing studies on atmospheric MPs are mostly derived from collection of atmospheric deposition, whereas direct measurements of airborne MPs are scarce. However, the abundance of airborne MPs is more relevant for evaluating human inhalation exposure risk. Herein, airborne MPs in indoor and outdoor environments from urban and rural areas of a coastal city in eastern China were investigated. MP concentrations (mean±SD) in indoor air (1583 ± 1180 n/m3) were an order of magnitude higher than outdoor air (189 ± 85 n/m3), and airborne MP concentrations in urban areas (224 ± 70 n/m3) were higher than rural areas (101 ± 47 n/m3). MPs smaller than 100 µm dominated airborne MPs, and the predominant shape of airborne MPs was fragments, as opposed to fibers. The larger MP size fractions contained a higher proportion of fibers, whereas the smaller size fractions were nearly exclusively composed of fragments. The health risk caused by ubiquitous airborne MPs should not be discounted as the maximum annual outdoor exposure of airborne MPs can reach 1 million/year, while indoor exposure may be even higher due to higher indoor airborne MP concentrations.

Nitrate is a prominent pollutant in surface and groundwater bodies worldwide. Isotopes in nitrate provide a powerful approach for tracing nitrate sources and transformations in waters. Given that analytical techniques for determining isotopic compositions are generally time-consuming, laborious and expensive, alternative methods are warranted to supplement and enhance existing approaches. Hence, we developed a support vector regression (SVR) model and explored its feasibility to predict nitrogen isotopic composition of nitrate (δ15N-NO3-) in a rural-urban river system in Southeastern China. A total of 16 easily obtained hydro-chemical variables were measured in the wet season (September 2019) and dry season (January 2020) and used to develop the SVR prediction model. The grading method utilized ~75% (35) of the samples for model building while the remaining 11 samples assessed model performance. Principal component analysis (PCA) extracted 7 principal components for SVR model inputs as PCA reduces superfluous variables. We optimized tuning parameters in the SVR model using a grid search technique coupled with V-fold cross-validation. The optimized SVR model provided accurate δ15N-NO3- predictions with a determination coefficient (R2) of 0.88, Nash-Sutcliffe (NS) of 0.87, and mean square error (MSE) of 0.53‰ in the testing step, and performed much better than the corresponding multivariate linear regression model (R2 = 0.60, NS = 0.58 and MSE = 1.76‰) and general regression neural network model (R2 = 0.66, NS = 0.65 and MSE = 1.45‰). Overall, the SVR model provides a potential indirect method to predict environmental isotope values for water quality management that will complement and enhance the interpretation of direct measurements of δ15N-NO3-.

Microplastics (MPs) are an environmental problem of growing concern. Aquatic sediments are considered as a final sink for MPs, but dredging can remobilize sedimentary MPs into both aquatic and terrestrial ecosystems. Although dredging is globally used for waterway deepening and ecological restoration, the environmental impacts of dredging on MP pollutants has not been previously assessed. In this study, Nile Red staining combined with micro-FTIR methods showed sediments containing high MP concentrations (6060-37610 n/kg·DW) from urban/suburban segments of a plain river network in Southeast China. The dredged sediments were stored in piles on farmlands, whereby MPs were subsequently dispersed to surrounding soils and surface waters while awaiting a permanent disposal option. MP concentrations in the soils surrounding the pile were higher in the dry season (wind dispersion), while MP concentrations in waters downstream of the piles were higher in the wet season (rainfall/runoff erosion). Whether dredge sediments are finally used to fertilize farmland, as fill material for coastal land reclamation or dumped into the ocean, MPs have the potential for remobilization into the environment causing concerns with aquatic food webs, agricultural production and human health. Therefore, disposal of dredge sediments containing MPs requires careful assessment to minimize potential environmental impacts.

This study examined the role of intermittent illumination/dark conditions coupled with MnO2-ammendments to regulate the mobility of As and Fe in flooded arsenic-enriched soils. Addition of MnO2 particles with intermittent illumination led to a pronounced increase in the reductive-dissolution of Fe(III) and As(V) from flooded soils compared to a corresponding dark treatments. A higher MnO2 dosage (0.10 vs 0.02 g) demonstrated a greater effect. Over a 49-day incubation, maximum Fe concentrations mobilized from the flooded soils amended with 0.10 and 0.02 g MnO2 particles were 2.39 and 1.85-fold higher than for non-amended soils under dark conditions. The corresponding maximum amounts of mobilized As were at least 92 % and 65 % higher than for non-amended soils under dark conditions, respectively. Scavenging of excited holes by soil humic/fulvic compounds increased mineral photoelectron production and boosted Fe(III)/As(V) reduction in MnO2-amended, illuminated soils. Additionally, MnO2 amendments shifted soil microbial community structure by enriching metal-reducing bacteria (e.g., Anaeromyxobacter, Bacillus and Geobacter) and increasing c-type cytochrome production. This microbial diversity response to MnO2 amendment facilitated direct contact extracellular electron transfer processes, which further enhanced Fe/As reduction. Subsequently, the mobility of released Fe(II) and As(III) was partially attenuated by adsorption, oxidation, complexation and/or coprecipitation on active sites generated on MnO2 surfaces during MnO2 dissolution. These results illustrated the impact of a semiconducting MnO2 mineral in regulating the biogeochemical cycles of As/Fe in soil and demonstrated the potential for MnO2-based bioremediation strategies for arsenic-polluted soils.

Minerals are ubiquitous in the natural environment and have close contact with microorganisms. In various scenarios, microorganisms that harbor extracellular electron transfer (EET) capabilities have evolved a series of beneficial strategies through the mutual exchange of electrons with extracellular minerals to enhance survival and metabolism. These electron exchange interactions are highly relevant to the cycling of elements in the epigeosphere and have a profound significance in bioelectrochemical engineering applications. In this review, we summarize recent advances related to the effects of different minerals that facilitate the EET process and discuss the underlying mechanisms and outlooks for future applications. The promotional effects of minerals arise from their redox-active ability, electrical conductivity and photocatalytic capability. In mineral-promoted EET processes, various responses have concurrently arisen in microorganisms, such as stretching of electrically conductive pili (e-pili), upregulated expression of outer-membrane cytochromes (Cyts) and production of specific enzymes, and secretion of extracellular polymeric substances (EPSs). This review synthesizes the understanding of electron exchange mechanisms between microorganisms and minerals and highlights potential applications in development of renewable energy production and pollutant remediation, which are topics of particular significance to future exploitation of biotechnology.

Intimate coupling of microbial extracellular electron transfer (EET) and photoelectrochemical processes is an emerging research area with great potential to circumvent many disadvantages associated with traditional techniques that depend on independent microbial or photocatalysis treatment. Microbial EET processes involve microorganism oxidation of extracellular electron donors for respiration and synchronous reduction of extracellular electron acceptors to form an integrated respiratory chain. Coupled microbial EET-photoelectrochemical technologies greatly improve energy conversion efficiency providing both economic and environmental benefits. Among substitutes for semiconductor photocatalysts, cadmium sulfide nanoparticles (CdS NPs) possess several attractive properties. Specifically, CdS NPs have suitable electrical conductivity, large specific surface area, visible light-driven photocatalysis capability and robust biocompatibility, enabling them to promote hybrid microbial-photoelectrochemical processes. This review highlights recent advances in intimately coupled CdS NPs-microbial extracellular electron transfer systems and examines the mechanistic pathways involved in photoelectrochemical transformations. Finally, the prospects for emerging applications utilizing hybrid CdS NPs-based microbial-photoelectrochemical technologies are assessed. As such, this review provides a rigorous fundamental analysis of electron transport dynamics for hybrid CdS NPs-microbial photoelectrochemical processes and explores the applicability of engineered CdS NPs-biohybrids for future applications, such as in environmental remediation and clean-energy production.

It is crucial to quantitatively track riverine nitrate (NO3-) sources and transformations in drinking water source watersheds for preventing current and future NO3- pollution, and ensuring a safe drinking water supply. This study identified the significant contributors to riverine NO3- in Zhaoshandu reservoir watershed of Zhejiang province, southeast China. To achieve this goal, we used hydrochemistry parameters and stable isotopes of NO3- (δ15N-NO3- and δ18O-NO3-) accompanied with a Markov Chain Monte Carlo mixing model to estimate the proportional contributions of riverine NO3- inputs from atmospheric deposition (AD), chemical nitrogen fertilizer (NF), soil nitrogen (SN), and manure and sewage (M&S). Results indicated that the main form of riverine nitrogen in this region was NO3-, constituting ~60% of the total nitrogen mass on average (total organic nitrogen ~37% & ammonium ~3%). Variations in the isotopic signatures of NO3- demonstrated that microbial nitrification of NF, SN and M&S was the primary nitrogen transformation process within the Zhaoshandu reservoir watershed, whereas denitrification was minimal. A classical dual isotope bi-plot incorporating chloride concentrations suggested NF, SN and M&S were the major contributors of NO3- to the river. Riverine NO3- source apportionment results were further refined using the Markov Chain Monte Carlo mixing model, which revealed that AD, NF, SN and M&S contributed 7.6 ± 4.1%, 22.5 ± 12.8%, 27.4 ± 14.5% and 42.5 ± 11.3% of riverine NO3- at the watershed outlet, respectively. Finally, uncertainties associated with NO3- source apportionment were quantitatively characterized as: SN > NF > M&S > AD. This work provides a comprehensive approach to distinguish riverine NO3- sources in drinking water source watersheds, which helps guide implementation of management strategies to effectively control NO3- contamination and protect drinking water quality. SUMMARY OF THE MAIN FINDING FROM THIS WORKS (CAPSULE): We utilized NO3- stable isotope analysis and a Markov Chain Monte Carlo mixing model to quantify riverine nitrate pollution sources in a drinking water source watershed in Zhejiang province, southeast China. Markov Chain Monte Carlo mixing model output showed that NF, SN and M&S were the dominant sources of riverine NO3- during the sampling period in Zhaoshandu watershed. Uncertainty analysis characterized the variation strength associated with contributions of individual nitrate sources and indicated the greatest uncertainty for SN, followed by NF, M&S and AD.

Regression-based methods are commonly used for riverine constituent concentration/flux estimation, which is essential for guiding water quality protection practices and environmental decision making. This paper developed a multivariate adaptive regression splines model for estimating riverine constituent concentrations (MARS-EC). The process, interpretability and flexibility of the MARS-EC modelling approach, was demonstrated for total nitrogen in the Patuxent River, a major river input to Chesapeake Bay. Model accuracy and uncertainty of the MARS-EC approach was further analysed using nitrate plus nitrite datasets from eight tributary rivers to Chesapeake Bay. Results showed that the MARS-EC approach integrated the advantages of both parametric and nonparametric regression methods, and model accuracy was demonstrated to be superior to the traditionally used ESTIMATOR model. MARS-EC is flexible and allows consideration of auxiliary variables; the variables and interactions can be selected automatically. MARS-EC does not constrain concentration-predictor curves to be constant but rather is able to identify shifts in these curves from mathematical expressions and visual graphics. The MARS-EC approach provides an effective and complementary tool along with existing approaches for estimating riverine constituent concentrations.

Quantitative assessment of metal sources in sediments is essential for implementation of source control and remediation strategies. This study investigated metal contamination in sediments to assess potential ecological risks and quantify pollutant sources of metals (Cu, Zn, Pb, Cd, Cr, Co and Ni) in the Wen-Rui Tang River watershed. Total and fraction analysis indicated high pollution levels of metals. Zinc and Cd posed high ecological risk based on the risk assessment code, with the highest ecological risk found in the southwestern of the watershed. The positive matrix factorization (PMF) model was highly effective in predicting total metal concentrations and identified three contributing metal sources. An agricultural source (factor 1) contributed highly to Cu (74.1%) and Zn (42.5%), and was most prominent in the west and south-central portions of the watershed. Cd (93.5%) showed a high weighting with industrial sources (factor 2) with a hot spot in the southwest. Factor 3 was identified as a mixed natural and vehicle traffic source that showed large contribution to Cr (65.2%), Ni (63.9%) and Pb (50.7%). Spatial analysis indicated a consistent pattern between PMF-identified factors and suspected metal sources at the watershed scale demonstrating the efficacy of the PMF modeling approach for watershed analysis.

Accurate quantification of dissolved oxygen (DO) is critically important for managing water resources and controlling pollution. Artificial intelligence (AI) models have been successfully applied for modeling DO content in aquatic ecosystems with limited data. However, the efficacy of these AI models in predicting DO levels in the hypoxic river systems having multiple pollution sources and complicated pollutants behaviors is unclear. Given this dilemma, we developed a promising AI model, known as support vector machine (SVM), to predict the DO concentration in a hypoxic river in southeastern China. Four different calibration models, specifically, multiple linear regression, back propagation neural network, general regression neural network, and SVM, were established, and their prediction accuracy was systemically investigated and compared. A total of 11 hydro-chemical variables were used as model inputs. These variables were measured bimonthly at eight sampling sites along the rural-suburban-urban portion of Wen-Rui Tang River from 2004 to 2008. The performances of the established models were assessed through the mean square error (MSE), determination coefficient (R 2), and Nash-Sutcliffe (NS) model efficiency. The results indicated that the SVM model was superior to other models in predicting DO concentration in Wen-Rui Tang River. For SVM, the MSE, R 2, and NS values for the testing subset were 0.9416 mg/L, 0.8646, and 0.8763, respectively. Sensitivity analysis showed that ammonium-nitrogen was the most significant input variable of the proposal SVM model. Overall, these results demonstrated that the proposed SVM model can efficiently predict water quality, especially for highly impaired and hypoxic river systems.