Characterizing the river water quality in China: Recent progress and on-going challenges

Developing an Approach for Integrating Chemical Analysis and Transcriptional Changes to Assess Contaminants in Water, Sediment, and Fish

Pharmaceuticals in aquatic environments pose threats to aquatic organisms because of their continuous release and potential accumulation. Monitoring methods for these contaminants are inadequate, with targeted analyses falling short in assessing water quality's impact on biota. The present study advocates for integrated strategies combining suspect and targeted chemical analyses with molecular biomarker approaches to better understand the risks posed by complex chemical mixtures to nontarget organisms. The research aimed to integrate chemical analysis and transcriptome changes in fathead minnows to prioritize contaminants, assess their effects, and apply this strategy in Wascana Creek, Canada. Analysis revealed higher pharmaceutical concentrations downstream of a wastewater-treatment plant, with clozapine being the most abundant in fathead minnows, showing notable bioavailability from water and sediment sources. Considering the importance of bioaccumulation factor and biota-sediment accumulation factor in risk assessment, these coefficients were calculated based on field data collected during spring, summer, and fall seasons in 2021. Bioaccumulation was classified as very bioaccumulative with values >5000 L kg-1, suggesting the ability of pharmaceuticals to accumulate in aquatic organisms. The study highlighted the intricate relationship between nutrient availability, water quality, and key pathways affected by pharmaceuticals, personal care products, and rubber components. Prioritization of these chemicals was done through suspect analysis, supported by identifying perturbed pathways (specifically signaling and cellular processes) using transcriptomic analysis in exposed fish. This strategy not only aids in environmental risk assessment but also serves as a practical model for other watersheds, streamlining risk-assessment processes to identify environmental hazards and work toward reducing risks from contaminants of emerging concern. Environ Toxicol Chem 2024;43:2252-2273. © 2024 SETAC.

Can the best management practices resist the combined effects of climate and land-use changes on non-point source pollution control?

Climate and land-use changes have an overlying impact on non-point source (NPS) pollution in river basins. However, the control effect of Best Management Practices (BMPs) for NPS pollution is not yet clear under future scenarios. The Soil and Water Assessment Tool (SWAT) model was coupled with the entropy-weighted method, global climate patterns and land-use data to explore the dynamic variations in total nitrogen (TN) and total phosphorus (TP) loads in the Jing River Basin during the baseline (2000-2020) and future periods (2021-2065), evaluate the pollution reduction effectiveness of individual and combined BMPs, and propose practical BMP configurations. Results indicate that a future trend of urban land expansion, particularly in the economic scenario (LU_SSP585), leads to weakened environmental ecosystems, while the sustainable scenario (LU_SSP126) exhibits more balanced land development. The MIROC-ES2L model demonstrates higher Taylor skill scores, forecasted significant increases in precipitation, maximum, and minimum temperatures under the SSP585 scenario. Spatial heterogeneity in TN and TP loads is notable, showing an upward trajectory in the future. The interaction between land-use and climate change has complex effects on TN and TP loads, with land-use-induced TN changes being relatively small (4.6 %) and TP changes substantial (24.3 %). The spatial distribution, under overlying effects, leans towards the influence of climate change, emphasizing its dominant role in TN and TP load variations. Distinct differences exist in the reduction of NPS pollution loads among different BMPs, with combined BMPs demonstrating superior effectiveness. The environmental-cost effectiveness trends of BMPs remain consistent across various future scenarios. RG (Return agricultural land to grass), RG + TT (Terracing), and RG + FR10 (Fertilizer reduction: 10 %) + GW (Grassed waterway) + FS (Filter strip) + TT emerge as the most effective single, double, and multiple BMP combinations, respectively. The results offer valuable insights for preventing and mitigating future NPS pollution risks, optimizing land-use layouts, and enhancing watershed management decisions.

Water quality assessment in a large plateau lake in China from 2014 to 2021 with machine learning models: Implications for future water quality management

Amid the global surge of eutrophication in lakes, investigating and analyzing water quality and trends of lakes becomes imperative for formulating effective lake management policies. Water quality index (WQI) is one of the most used tools to assess water quality by integrating data from multiple water quality parameters. In this study, we analyzed the spatio-temporal variations of 11 water quality parameters in one of the largest plateau lakes, Erhai Lake, based on surveys from January 2014 to December 2021. Leveraging machine learning models, we gauged the relative importance of different water quality parameters to the WQI and further utilized stepwise multiple linear regression to derive an optimal minimal water quality index (WQImin) that required the minimal number of water quality parameters without compromising the performance. Our results indicated that the water quality of Erhai Lake typically showed a trend towards improvement, as indicated by the positive Mann-Kendall test for WQI performance (Z = 2.89, p < 0.01). Among the five machine learning models, XGBoost emerged as the best performer (coefficient of determination R2 = 0.822, mean squared error = 3.430, and mean absolute error = 1.460). Among the 11 water quality parameters, only four (i.e., dissolved oxygen, ammonia nitrogen, total phosphorus, and total nitrogen) were needed for the optimal WQImin. The establishment of the WQImin helps reduce cost in future water quality monitoring in Erhai Lake, which may also serve as a valuable framework for efficient water quality monitoring in similar waters. In addition, the elucidation of spatio-temporal patterns and trends of Erhai Lake's water quality serves as a compass for authorities, offering insights to bolster lake management strategies in the future.

Optimizing entropy weight model to accurately predict variation of pharmaceuticals and personal care products in the estuaries and coasts of the South China

Pharmaceuticals and personal care products (PPCPs) have raised increasing concern worldwide due to their continuous release and potential hazards to the ecosystem and human health. This study optimized the entropy weight model (EW-WRSR) that combines entropy weight with multi-criteria decision analysis to investigate pollution patterns of PPCPs in the coasts and estuaries. The results revealed that occurrences of PPCPs from the 1940s to the present were consistent with using PPCPs, different types of human activities, and local urban development. This helped better understand the history of PPCP contamination and evaluate the uncertainty of EW-WRSR. The model predicted hotspots of PPCPs that were consistent with the actual situation, indicating that PPCPs mainly enter the nearshore ecosystem by the form of sewage discharge and residual aquaculture. This study can provide method that identifying highly contaminated regions on a global scale.

Spatio-temporal evolution mechanism and dynamic simulation of nitrogen and phosphorus pollution of the Yangtze River economic Belt in China

Excess nitrogen and phosphorus inputs are the main causes of aquatic environmental deterioration. Accurately quantifying and dynamically assessing the regional nitrogen and phosphorus pollution emission (NPPE) loads and influencing factors is crucial for local authorities to implement and formulate refined pollution reduction management strategies. In this study, we constructed a methodological framework for evaluating the spatio-temporal evolution mechanism and dynamic simulation of NPPE. We investigated the spatio-temporal evolution mechanism and influencing factors of NPPE in the Yangtze River Economic Belt (YREB) of China through the pollution load accounting model, spatial correlation analysis model, geographical detector model, back propagation neural network model, and trend analysis model. The results show that the NPPE inputs in the YREB exhibit a general trend of first rising and then falling, with uneven development among various cities in each province. Nonpoint sources are the largest source of land-based NPPE. Overall, positive spatial clustering of NPPE is observed in the cities of the YREB, and there is a certain enhancement in clustering. The GDP of the primary industry and cultivated area are important human activity factors affecting the spatial distribution of NPPE, with economic factors exerting the greatest influence on the NPPE. In the future, the change in NPPE in the YREB at the provincial level is slight, while the nitrogen pollution emissions at the municipal level will develop towards a polarization trend. Most cities in the middle and lower reaches of the YREB in 2035 will exhibit medium to high emissions. This study provides a scientific basis for the control of regional NPPE, and it is necessary to strengthen cooperation and coordination among cities in the future, jointly improve the nitrogen and phosphorus pollution tracing and control management system, and achieve regional sustainable development.

Isolation, identification, and degradation mechanism by multi-omics of mesotrione-degrading Amycolatopsis nivea La24

Mesotrione is a herbicide used in agricultural production; however, its stability and long-term residues pose ecological risks to soil health and subsequent crops. In this research, the strain Amycolatopsis nivea La24 was identified as capable of completely degrading 50 mg∙L-1 mesotrione within 48 h. It exhibited a broad adaptability to various environment and could degrade three sulfonylurea herbicides (nicosulfuron, chlorimuron-methyl, and cinosulfuron). Non-target metabonomic and mass spectrometry demonstrated that La24 strain broke down the mesotrione parent molecule by targeting the β-diketone bond and nitro group, resulting in the production of five possible degradation products. The differentially expressed genes were significantly enriched in fatty acid degradation, amino acid metabolism, and other pathways, and the differentially metabolites in glutathione metabolism, arginine/proline metabolism, cysteine/methionine metabolism, and other pathways. Additionally, it was confirmed by heterologous expression that nitroreductase was directly involved in the mesotrione degradation, and NDMA-dependent methanol dehydrogenase would increase the resistance to mesotrione. Finally, the intracellular response of La24 during mesotrione degradation was proposed. This work provides insight for a comprehensive understanding of the mesotrione biodegradation mechanism, significantly expands the resources for pollutant degradation, and offers the potential for a more sustainable solution to address herbicide pollution in soil.

A novel nonlinear direct-mapping approach for multiple time scale driving force analysis of surface water quality variations under intense human interference

Identifying the driving forces of surface water quality variations is crucial for urban environmental management, especially in densely populated regions. Statistic mapping is an approach that allows researchers to directly explore the response of surface water quality to potential drivers. Conventionally, these methods encounter a mixture of issues, including nonlinear relationships and information on multiple time-scale, caused by disparities in the influencing frequencies and degrees of driving factors. In this research, a nonlinear direct-mapping approach was developed to quantitatively analyze the driving force of surface water quality under multiple time scales. This approach separated the fluctuation and trend information from water quality data and then established a direct-mapping relationship, thereby achieving the visible multilayer structure containing both linear and nonlinear information from the time scale. Typical water pollutants including total nitrogen (TN) and total phosphorus (TP) in the Pearl River Delta (PRD), were used to verify the methodology and compare its ability to analyze driving forces with traditional statistic approaches. The results demonstrated that this approach could establish a visual multilayer mapping structure with strong interpretability, which effectively captured the contained nonlinear information, thus improving the fitting degree by 12.43% compared with traditional methods. Moreover, it successfully identified the dominant driving forces of TN and TP in the PRD as human activities related to NO2 and PM and natural factors. Its application in the changing environment demonstrated a potentially increased risk of TP in the PRD under multiple scenarios. Overall, this approach could serve as a reliable reference for pollution early warning in the short term and for industrial structure adjustment planning in the long term.

Response of hydrology and nutrient losses to different extreme rainfall conditions in a coastal watershed influenced by orchards

Global warming is altering the frequency of extreme rainfall events and introducing uncertainties for non-point source pollution (NPSP). This research centers on orchard-influenced planting areas (OIPA) in the Wulong River Watershed of Shandong Province, China, which are known for their heightened nitrogen (N) and phosphorus (P) pollution. Leveraging meteorological data from both historical (1989-2018) and projected future periods (2041-2100), this research identified five extreme rainfall indices (ERI): R10 (moderate rain), R20 (heavy rain), R50 (rainstorm), R95p (Daily rainfall between the 95th and 99th percentile of the rainfall), and R99p (>99th percentile). Utilizing an advanced watershed hydrological model, SWAT-CO2, this study carried out a comparison between ERI and average conditions and evaluated the effects of ERI on the hydrology and nutrient losses in this coastal watershed. The findings revealed that the growth multiples of precipitation in the OIPA for five ERI varied between 16 and 59 times for the historical period and 14 to 65 times for future climate scenarios compared to the average conditions. The most pronounced increases in surface runoff and total phosphorus (TP) loss were observed with R50, R95p, and R99p, showing growth multiples as high as 352 and 330 times, and total nitrogen (TN) growth multiples varied between 4.6 and 30.3 times. The contribution rates of R50 and R99p for surface runoff and TP loss in the OIPA during all periods exceeded 55%, however, TN exhibited the opposite trend, primarily due to the dominated NO3-N leaching in the sandy soil. This research revealed how the OIPA reacts to different ERI and pinpointed essential elements influencing water and nutrient losses.

Restore polder and aquaculture enclosure to the lake: Balancing environmental protection and economic growth for sustainable development

The restoration of lakes and their buffer zones is crucial for understanding the intricate interplay between human activities and natural ecosystems resulting from the implementation of environmental policies. In this study, we investigated the ecological restoration of shallow lakes and buffer zones in the Yangtze-Huaihe River Basin, specifically focusing on the removal of polder and aquaculture enclosure areas within the lakes. By examining data from eight shallow lakes and their corresponding buffer zones, encompassing lake morphology, water quality parameters, and land use/land cover (LULC) data spanning from 2008 to 2022, which shed light on the complex relationships involved. During the process of restoring polder and aquaculture enclosure areas, we observed a general decrease in the extent of polders and aquaculture enclosures within the lakes. Notably, the removal of aquaculture enclosures had a more pronounced effect (reduction rate of 83.37 %) compared to the withdrawal of polders (reduction rate of 48.76 %). Linear regression analysis revealed a significant decrease in the concentrations of seven water quality parameters, including COD, CODMn, TN, TP, NH3-N, Chl-a, and F, while pH and DO factors exhibit a distinct increasing trend. The results of redundancy analysis and Pearson correlation analysis demonstrated significant correlations between the area of polders and aquaculture enclosures and the changes in lake water quality. Encouragingly, the withdrawal of polders and the removal of aquaculture enclosures had a positive impact on the lake water quality improvement. In contrast, the LULC in the buffer zones of the lakes experienced a gradual decline owing to land degradation, resulting in a reduction in ecosystem service value (ESV). These results offer valuable support for policymakers in their endeavors to restore lake water quality, mitigate the degradation of buffer zones land, and promote the sustainable development of land and water resources.

Health impact assessment of the surface water pollution in China

China suffers from severe surface water pollution. Health impact assessment could provide a novel and quantifiable metric for the health burden attributed to surface water pollution. This study establishes a health impact assessment method for surface water pollution based on classic frameworks, integrating the multi-pollutant city water quality index (CWQI), informative epidemiological findings, and benchmark public health information. A relative risk level assignment approach is proposed based on the CWQI, innovatively addressing the challenge in surface water-human exposure risk assessment. A case study assesses the surface water pollution-related health impact in 336 Chinese cities. The results show (1) between 2015 and 2022, total health impact decreased from 3980.42 thousand disability-adjusted life years (DALYs) (95 % Confidence Interval: 3242.67-4339.29) to 3260.10 thousand DALYs (95 % CI: 2475.88-3641.35), measured by total cancer. (2) The annual average health impacts of oesophageal, stomach, colorectal, gallbladder, and pancreatic cancers added up to 2621.20 thousand DALYs (95 % CI: 2095.58-3091.10), revealing the significant health impact of surface water pollution on digestive cancer. (3) In 2022, health impacts in the Beijing-Tianjin-Hebei and surroundings, the Yangtze River Delta, and the middle reaches of the Yangtze River added up to 1893.06 thousand DALYs (95 % CI: 1471.82-2097.88), showing a regional aggregating trend. (4) Surface water pollution control has been the primary driving factor to health impact improvement, contributing -3.49 % to the health impact change from 2015 to 2022. It is the first city-level health impact map for China's surface water pollution. The methods and findings will support the water management policymaking in China and other countries suffering from water pollution.

Universal high-frequency monitoring methods of river water quality in China based on machine learning

The in-situ high-frequency monitoring of total nitrogen (TN) and total phosphorus (TP) in rivers is a challenge and key to instant water quality judgment and early warning. Based on the physical and chemical association between TN/TP and sensor-measurable predictors, we proposed a novel "indirect" measurement method for TN and TP in rivers. This method combines the timeliness of multi-sensor and the accuracy of intelligent algorithms, utilizing 188,629 data sets from 131 water monitoring stations across China. Under 5 algorithms and 4 predictor group scenarios, the results showed that: (1) extra tree regression (ETR) with 6 predictors exhibited the best precision, and the mean determination coefficient (R2) of TN and TP inversion across 131 stations reached 0.78 ± 0.25 and 0.79 ± 0.22 respectively; (2) among 6 potential predictors, the importance degrees of temperature, electrical conductivity, NH4-N, and turbidity were greater than that of pH and DO, and >80 % of stations exhibited acceptable prediction accuracy (R2 > 0.6) when the number of predictors (P) ranged from 4 to 6, which showed good tolerability to predictor variations; (3) the accurate classification rates of water quality standard (ACRws) of all stations based on TN and TP reached 90.41 ± 6.96 % and 92.33 ± 6.41 %; (4) in 9 regions/basins of China, this method showed universal application potential with no significant prediction difference. Compared with laboratory test, water quality automatic monitoring station, and remote sensing inversion, the proposed method offers high-frequency, high-precision, regional adaptability, low cost, and stable operation under rainy, cloudy, and nighttime conditions. The new method may provide important technological support for timely pollutant tracing, pre-warning, and emergency control for river pollution.

Multi-step ahead forecasting of electrical conductivity in rivers by using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model enhanced by Boruta-XGBoost feature selection algorithm

Electrical conductivity (EC) is widely recognized as one of the most essential water quality metrics for predicting salinity and mineralization. In the current research, the EC of two Australian rivers (Albert River and Barratta Creek) was forecasted for up to 10 days using a novel deep learning algorithm (Convolutional Neural Network combined with Long Short-Term Memory Model, CNN-LSTM). The Boruta-XGBoost feature selection method was used to determine the significant inputs (time series lagged data) to the model. To compare the performance of Boruta-XGB-CNN-LSTM models, three machine learning approaches-multi-layer perceptron neural network (MLP), K-nearest neighbour (KNN), and extreme gradient boosting (XGBoost) were used. Different statistical metrics, such as correlation coefficient (R), root mean square error (RMSE), and mean absolute percentage error, were used to assess the models' performance. From 10 years of data in both rivers, 7 years (2012-2018) were used as a training set, and 3 years (2019-2021) were used for testing the models. Application of the Boruta-XGB-CNN-LSTM model in forecasting one day ahead of EC showed that in both stations, Boruta-XGB-CNN-LSTM can forecast the EC parameter better than other machine learning models for the test dataset (R = 0.9429, RMSE = 45.6896, MAPE = 5.9749 for Albert River, and R = 0.9215, RMSE = 43.8315, MAPE = 7.6029 for Barratta Creek). Considering the better performance of the Boruta-XGB-CNN-LSTM model in both rivers, this model was used to forecast 3-10 days ahead of EC. The results showed that the Boruta-XGB-CNN-LSTM model is very capable of forecasting the EC for the next 10 days. The results showed that by increasing the forecasting horizon from 3 to 10 days, the performance of the Boruta-XGB-CNN-LSTM model slightly decreased. The results of this study show that the Boruta-XGB-CNN-LSTM model can be used as a good soft computing method for accurately predicting how the EC will change in rivers.

Sex-bias of core intestinal microbiota in different stocks of Chinese mitten crabs (Eriocheir sinensis)

The differences in intestinal microbiota composition are synergistically shaped by internal and external factors of the host. The core microbiota plays a vital role in maintaining intestinal homeostasis. In this study, we conducted 16S rRNA sequencing analysis to investigate the stability of intestinal microbiota and sex-bias of six stocks of Chinese mitten crabs (105 females; and 110 males). The dominant phyla in all six stocks were Proteobacteria, Tenericutes, Bacteroidetes and Firmicutes; however, their relative abundance differed significantly. Twenty-seven core operational taxonomic units (OTUs), corresponding to 18 genera, were screened. Correlation analysis revealed that OTUs of four stocks in the Yangtze River system play important roles in maintaining the stability of intestinal microbiota. Additionally, the core intestinal microbiota was significantly sex-biased, and the top three genera in terms of relative abundance (Acinetobacter, Vibrio, and Candidatus_Hepatoplasma) were significantly dominant in female crabs. Network structure analysis also confirmed gender differences in the association pattern of intestinal microbiota. The intestinal microbiota of male crabs has a higher degree of functional enrichment. This study provided a theoretical basis for further investigating exploring the shaping effect of gender and geographical factors on the intestinal microbiota of Chinese mitten crabs.

Recent Progress on the Adsorption of Heavy Metal Ions Pb(II) and Cu(II) from Wastewater

With the processes of industrialization and urbanization, heavy metal ion pollution has become a thorny problem in water systems. Among the various technologies developed for the removal of heavy metal ions, the adsorption method is widely studied by researchers and various nanomaterials with good adsorption performances have been prepared during the past decades. In this paper, a variety of novel nanomaterials with excellent adsorption performances for Pb(II) and Cu(II) reported in recent years are reviewed, such as carbon-based materials, clay mineral materials, zero-valent iron and their derivatives, MOFs, nanocomposites, etc. The novel nanomaterials with extremely high adsorption capacity, selectivity and particular nanostructures are summarized and introduced, along with their advantages and disadvantages. And, some future research priorities for the treatment of wastewater are also prospected.

Temporal drivers of tryptophan-like fluorescent dissolved organic matter along a river continuum

Tryptophan-like fluorescence (TLF) is used to indicate anthropogenic inputs of dissolved organic matter (DOM), typically from wastewater, in rivers. We hypothesised that other sources of DOM, such as groundwater and planktonic microbial biomass can also be important drivers of riverine TLF dynamics. We sampled 19 contrasting sites of the River Thames, UK, and its tributaries. Multivariate mixed linear models were developed for each site using 15 months of weekly water quality observations and with predictor variables selected according to the statistical significance of their linear relationship with TLF following a stepwise procedure. The variables considered for inclusion in the models were potassium (wastewater indicator), nitrate (groundwater indicator), chlorophyll-a (phytoplankton biomass), and Total bacterial Cells Counts (TCC) by flow cytometry. The wastewater indicator was included in the model of TLF at 89 % of sites. Groundwater was included in 53 % of models, particularly those with higher baseflow indices (0.50-0.86). At these sites, groundwater acted as a negative control on TLF, diluting other potential sources. Additionally, TCC was included positively in the models of six (32 %) sites. The models on the Thames itself using TCC were more rural sites with lower sewage inputs. Phytoplankton biomass (Chlorophyll-a) was only used in two (11 %) site models, despite the seasonal phytoplankton blooms. It is also notable that, the wastewater indicator did not always have the strongest evidence for inclusion in the models. For example, there was stronger evidence for the inclusion of groundwater and TCC than wastewater in 32 % and 5 % of catchments, respectively. Our study underscores the complex interplay of wastewater, groundwater, and planktonic microbes, driving riverine TLF dynamics, with their influence determined by site characteristics.

Keeping Nitrogen Use in China within the Planetary Boundary Using a Spatially Explicit Approach

Nitrogen (N) supports food production, but its excess causes water pollution. We lack an understanding of the boundary of N for water quality while considering complex relationships between N inputs and in-stream N concentrations. Our knowledge is limited to regional reduction targets to secure food production. Here, we aim to derive a spatially explicit boundary of N inputs to rivers for surface water quality using a bottom-up approach and to explore ways to meet the derived N boundary while considering the associated impacts on both surface water quality and food production in China. We modified a multiscale nutrient modeling system simulating around 6.5 Tg of N inputs to rivers that are allowed for whole of China in 2012. Maximum allowed N inputs to rivers are higher for intensive food production regions and lower for highly urbanized regions. When fertilizer and manure use is reduced, 45-76% of the streams could meet the N water quality threshold under different scenarios. A comparison of "water quality first" and "food production first" scenarios indicates that trade-offs between water quality and food production exist in 2-8% of the streams, which may put 7-28% of crop production at stake. Our insights could support region-specific policies for improving water quality.

High-frequency monitoring during rainstorm events reveals nitrogen sources and transport in a rural catchment

Rural areas lacking essential sewage treatment facilities and collection systems often experience eutrophication due to elevated nutrient loads. Understanding nitrogen (N) sources and transport mechanisms in rural catchments is crucial for improving water quality and mitigating downstream export loads, particularly during storm events. To further elucidate the sources, pathways, and transport mechanisms of N from a rural catchment with intensive agricultural activities during storm events, we conducted an analysis of 21 events through continuous sampling over two rainy seasons in a small rural catchment from the lower reaches of the Yangtze River. The results revealed that ammonia-N (NH4+-N) and nitrate-N (NO3--N) exhibited distinct behaviors during rainstorm events, with NO3--N accounting for the primary nitrogen loss, its load being approximately forty times greater than that of NH4+-N. Through examinations of the concentration-discharge (c-Q) relationships, the findings revealed that, particularly in prolonged rainstorms, NH4+-N exhibited source limited pattern (b = -0.13, P < 0.01), while NO3--N displayed transport limited pattern (b = -0.21, P < 0.01). The figure-eight hysteresis pattern was prevalent for both NH4+-N and NO3--N (38.1% and 52.0%, respectively), arising from intricate interactions among diverse sources and pathways. For NO3--N, the hysteresis pattern shifted from clockwise under short-duration rainstorms to counter-clockwise under long-duration rainstorms, whereas hysteresis remained consistently clockwise for NH4+-N. The hysteresis analysis further suggests that the duration of rainstorms modifies hydrological connectivity, thereby influencing the transport processes of N. These insights provide valuable information for the development of targeted management strategies to reduce storm nutrient export in rural catchments.

Coordinated dynamics of aquaculture ponds and water eutrophication owing to policy: A case of Jiangsu province, China

Aquaculture ponds (APs) are rapidly expanding globally and are considered crucial for guaranteeing the supply of food, population growth, and economic development. However, the rapid expansion of aquaculture not only brought benefits but also a series of eco-environmental issues, such as water eutrophication. To achieve sustainable development, it is essential to gain a profound understanding of the spatiotemporal evolution of APs, the drivers behind their dynamics, and their relationship with the aquatic environment. Jiangsu Province (JS) in China, a historically significant aquaculture region, encompasses two prominent river basins: the Huai River Basin (HRB) and the Yangtze River Basin (YRB). In light of the construction of an ecological civilization, JS serves as a demonstration and pioneering area for basin protection and development. Therefore, this study focuses on JS, aiming to elucidate the spatiotemporal dynamics of APs, the corresponding relationship with basin management policies, and the impact on water eutrophication. The results revealed that: (1) in 2022, APs in JS were unevenly distributed, with a total area of 3278.78 km2, of which 79 % was located in the HRB. (2) During 2016-2022, APs exhibited an initial growth trend before 2019, followed by a decrease. (3) Due to policy interventions, AP changes within different basins showed opposite trends, and the corresponding water eutrophic state aligned with AP dynamics. The findings of this study can serve as a typical case to provide scientific evidence for the formulation and implementation of policies to improve the water environment in eutrophic basins.

Comprehensive assessment of exposure and environmental risk of potentially toxic elements in surface water and sediment across China: A synthesis study

China faces a serious challenge with water pollution posed by potentially toxic elements (PTEs). Comprehensive and reliable environmental risk assessment is paramount for precise pollution prevention and control. Previous studies generally focused on a single environmental compartment within small regions, and the uncertainty in risk calculation is not fully considered. This study revealed the current exposure status of 11 PTEs in surface water and sediment across China using previously reported concentration data in 301 well-screened articles. Ecological and human health risks were evaluated and the uncertainty related to calculation parameters and exposure dataset were quantified. PTEs of high concern were further identified. Results showed Mn and Zn had the highest concentration levels, while Hg and Cd had the lowest concentrations in both surface water and sediment. Risk assessment of individual PTE showed that high-risk PTEs varied by risk receptors and environmental compartments. Nationwide, the probability of aquatic organisms being affected by Mn, Zn, Cu, and As in surface water exceeded 10 %. In sediment, Cd and Hg exhibited high and considerable risk, respectively. As was identified as the major PTE threatening human health as its carcinogenic risk was 1.45 × 10-4 through direct ingestion. Combined risk assessment showed the PTE mixture in surface water and sediment posed medium and high ecological risk with the risk quotient and potential ecological risk index of 1.76 and 558.36, respectively. Adverse health effects through incidental ingestion and dermal contact during swimming were negligible. This study provides a nationwide risk assessment of PTEs in China's aquatic environment and the robustness is verified, which can serve as a practical basis for policymakers to guide the early warning and precise management of water pollution.

Characterizing the water resource-environment-ecology system harmony in Chinese cities using integrated datasets: A Beautiful China perspective assessment

Integrated management and synergistic improvement of the water system is a topic of widespread concern. This study innovatively integrates three functions of quality assessment, synergy evaluation, and driving influence determination to establish a systematic framework assessing water system harmony. A case study of 336 Chinese cities is further performed by combining multi-scale and multi-source datasets. The results show China's water system quality has improved from 2015 to 2022. Development in the water resource, environment, and ecology subsystems have been differentiated, with 0.05 %, 4.33 %, and -1.64 % changes, respectively. The degradation of water ecology and the weak synergy with the other two subsystems have limited China's water system harmony. Water environment improvement played a contributive role in improving the water system quality. The contribution structure of water resources, environment, and ecology has shifted towards equilibrium in recent years. We found and highlighted the north-south differentiation of water system harmony in Chinese cities. The Beijing-Tianjin-Hebei and its surroundings, the Yangtze River Delta, and the middle reaches of the Yangtze River are identified as priority regions for water system harmony improvement. The primary contribution of this study is to propose an assessing concept of water resource-environment-ecology system harmony, establish well-structured assessment methods, and integrate the multiple data sources. The novel methods and findings, including the indicator system, application of data mining and decomposing methods, and the city-level water system harmony map, deconstruct and quantify the complex and diverse water system, supporting clearer and more efficient water management policymaking.

Exploring China's water scarcity incorporating surface water quality and multiple existing solutions

Water scarcity has threatened the sustainability of human life, ecosystem evolution, and socio-economic development. However, previous studies have often lacked a comprehensive consideration of the impact of water quality and existing solutions, such as inter-basin water transfer and unconventional water resources, on water scarcity. In this paper, an improved approach was proposed to quantify water scarcity levels by comprehensively considering surface water quality and multiple solutions. China's water scarcity was first assessed at a high spatial resolution on a monthly basis over the 5-year period from 2014 to 2018. Then, the driving factors including water quality and solutions were identified by a geographic detector model. Finally, an in-depth investigation was conducted to unravel the effects of water quantity solutions (i.e., inter-basin water transfer and unconventional water use), and water quality solutions (i.e., improving surface water quality) on alleviating water scarcity. Based on monthly assessments considering water quality and multiple existing solutions, the results showed that over half of the national population (∼777 million) faced water scarcity for at least one month of the year. Agricultural water use and inadequate water quality were the main driving factors responsible for China's water scarcity. Over four-fifths of the national population (∼1.10 billion) could benefit from alleviated water scarcity through a combination of water quantity and quality solutions. However, the existing solutions considered were insufficient to completely resolve water scarcity in China, especially in Northern China, persisting as a challenging issue. The results obtained from this study provided a better understanding of China's water scarcity, which could contribute to guiding future efforts aimed at alleviating water scarcity and ensuring water security in China.

Enhancing phosphorus source apportionment in watersheds through species-specific analysis

Phosphorus (P) is a pivotal element responsible for triggering watershed eutrophication, and accurate source apportionment is a prerequisite for achieving the targeted prevention and control of P pollution. Current research predominantly emphasizes the allocation of total phosphorus (TP) loads from watershed pollution sources, with limited integration of source apportionment considering P species and their specific implications for eutrophication. This article conducts a retrospective analysis of the current state of research on watershed P source apportionment models, providing a comprehensive evaluation of three source apportionment methods, inventory analysis, diffusion models, and receptor models. Furthermore, a quantitative analysis of the impact of P species on watersheds is carried out, followed by the relationship between P species and the P source apportionment being critically clarified within watersheds. The study reveals that the impact of P on watershed eutrophication is highly dependent on P species, rather than absolute concentration of TP. Current research overlooking P species composition of pollution sources may render the acquired results of source apportionment incapable of assessing the impact of P sources on eutrophication accurately. In order to enhance the accuracy of watershed P pollution source apportionment, the following prospectives are recommended: (1) quantifying the P species composition of typical pollution sources; (2) revealing the mechanisms governing the migration and transformation of P species in watersheds; (3) expanding the application of traditional models and introducing novel methods to achieve quantitative source apportionment specifically for P species. Conducting source apportionment of specific species within a watershed contributes to a deeper understanding of P migration and transformation, enhancing the precise of management of P pollution sources and facilitating the targeted recovery of P resources.

Nitrogen and phosphorus trends in lake sediments of China may diverge

The brief history of monitoring nutrient levels in Chinese lake waters limits our understanding of the causes and the long-term trends of their eutrophication and constrains effective lake management. We therefore synthesize nutrient data from lakes in China to reveal the historical changes and project their future trends to 2100 using models. Here we show that the average concentrations of nitrogen and phosphorus in lake sediments have increased by 267% and 202%, respectively since 1850. In the model projections, 2030-2100, the nitrogen concentrations in the studied lakes in China may decrease, for example, by 87% in the southern districts and by 19% in the northern districts. However, the phosphorus concentrations will continue to increase by an average of 25% in the Eastern Plain, Yunnan-Guizhou Plateau, and Xinjiang. Based on this differentiation, we suggest that nitrogen and phosphorus management in Chinese lakes should be carried out at the district level to help develop rational and sustainable environmental management strategies.

Conflict between urbanization and water environmental protection: Lessons from the Xiangjiang River Basin in China

China, the largest developing country, has experienced rapid urbanization since its reform and opening-up. However, the increasing pollution load from urban areas has deteriorated urban river water quality, contradicting the concept of sustainable and green development promoted by the Chinese government. This situation elucidates governmental shortcomings in systematic environmental protection. Our study revealed that the current wastewater treatment plant (WWTP) discharge standards in urban areas are insufficient for attaining the desired urban river water quality and thus intensify the conflict between urbanization and water environmental protection. As urbanization continues, the urban population will grow, further exacerbating pollution and conflict. Our focus was the Xiangjiang River basin in Zunyi, a typical urbanized city in China. Using a validated one-dimensional mathematical model, we compared the water quality in the Xiangjiang River between current and upgraded WWTP discharge standards. The results showed that the water quality in the Xiangjiang River falls short of the standards, with more than 60 % of the river exceeding limits. However, upgrading WWTP discharge standards significantly reduces the proportion of river sections exceeding limits, with only 0.4 % exceeding standards during specific periods. This enhancement greatly improved the Xiangjiang River's water quality, aided in restoring the entire water environment in the basin, and supported water environmental protection goals. Our research findings offer crucial support for local governments in shaping comprehensive water environmental protection policies and insights for addressing similar environmental challenges caused by rapid urbanization in other developing regions.

Profound regional disparities shaping the ecological risk in surface waters: A case study on cadmium across China

The scientific advancement of water quality criteria (WQC) stands as one of the paramount challenges in ensuring the security of aquatic ecosystem. The region-dependent species distribution and water quality characteristics would impact the toxicity of pollutant, which would further affect the derivation of WQC across regions. Presently, however, numerous countries adhere to singular WQC values. The "One-size-fits-all" WQC value for a given pollutant may lead to either "over-protection" or "under-protection" of organisms in specific region. In this study, we used cadmium(Cd) pollution in surface waters of China as a case study to shed light on this issue. This study evaluated critical water quality parameters and species distribution characteristics to modify WQC for Cd across distinct regions, thus unveiling the geographical variations in ecological risk for Cd throughout China. Notably, regional disparities in ecological risk emerged a substantial correlation with water hardness, while species-related distinctions magnified these regional variations. After considering the aforementioned factors, the variation in long-term WQC among different areas reached 84-fold, while the divergence in risk quotient extended to 280-fold. This study delineated zones of both heightened and diminished ecological susceptibility of Cd, thereby establishing a foundation for regionally differentiated management strategies.

Assessing the impact of water-sediment factors on water quality to guide river-connected lake water environment improvement

The substantial impacts of exogenous pollutants on lake water quality have been extensively reported. Water-sediment factors, which are essential for regulating water quality in river-connected lakes, have not been studied in depth under different hydrological conditions. This study has combined a 31-year water environmental dataset (1991-2021) regarding Dongting Lake and a vector autoregression model (VAR) in order to investigate the impulse response characteristics and contributions of water quality caused by water-sediment factors across different periods. Our analysis suggests that total nitrogen (TN) exhibited a significant increasing trend, whereas total phosphorus (TP) increased to 0.17 mg/L, and then decreased to 0.07 mg/L from 1991 to 2021. The inflow of suspended sediment discharge (SSD) decreased significantly during the study period, mainly because of the decrease in SSD in the three channels (TC). In the pre-Three Gorges Dam (TGD) period, water discharge (WD) and SSD were the Granger causes of TN and TP. In the post-TGD periods this relationship disappeared because of the construction of the TGD, which reduced the inflow of SSD and WD into the lake. Water quality indicators showed an instant response to the shock from themselves with high values, whereas the impulse response of the water quality to water-sediment factors exhibited lagged variations. This meant that the water quality indicators displayed a high impact by themselves across the different periods, with values varying from 67 % to 95 %. Water level (WL) and SSD were the predominant water-sediment factors for TP in the pre-TGD period, with the impact on TP changes accounting for 11 % and 9 %, respectively, whereas the contribution of SSD decreased to 2 % in the post-TGD period. WL was the most crucial water-sediment factor for CODMn during the different periods, with contributions varying from 17 % to 20 %. To improve the water quality of Dongting Lake, in addition to the implementation of strict controls on excessive external nutrient loading, regulating water-sediment factors according to the hydrological features of Dongting Lake during different periods is vital.

Decoding China's anthropogenic typical pollutant discharge patterns: Long-term dynamics and hotspot transitions driven by population, diet, and sanitation

Human activities have led to an alarming increase in pollution, resulting in widespread water contamination. A comprehensive understanding of the quantitative relationship between anthropogenic pollutant discharges and the escalating anthropogenic disturbances and environmental efforts is crucial for effective water quality management. Here we establish a Model for Estimating Anthropogenic pollutaNts diScharges (MEANS) and simulate the long-term dynamics of various types of anthropogenic discharges in China based on an unprecedented spatio-temporal dynamic parameter dataset. Our findings reveal that from 1980 to 2020, anthropogenic discharges exhibited an overall trend of initially increasing and subsequently decreasing, with the peak occurring around 2005. During this period, the dominant pollution sources in China shifted from urban to rural areas, thereby driving the transition of hotspot pollutants from nitrogen to phosphorus in the eastern regions. The most significant drivers of anthropogenic pollutant discharges gradually shifted from population size and dietary structure to wastewater treatment and agricultural factors. Furthermore, we observed that a significant portion of China's regions still exceed the safety thresholds for pollutant discharges, with excessive levels of total phosphorus (TP) being particularly severe. These findings highlight the need for flexible management strategies in the future to address specific pollution levels and hotspots in different regions. Our study underscores the importance of considering the complex interplay between anthropogenic disturbances, environmental efforts, and long-term anthropogenic pollutant discharges for effective water pollution control.

Neonicotinoids Persisting in the Sea Pose a Potential Chronic Risk to Marine Organisms: A Case from Xiangshan Bay, China (2015-2019)

Neonicotinoid insecticides (neonics) are extensively employed in agriculture and pervade various environmental matrices. However, few studies have documented the occurrence and potential chronic ecological risks of these chemicals in the marine environment. We collected 720 seawater samples from Xiangshan Bay during 2015-2019 and the integrated concentrations of seven neonics were determined using the relative potency factor method. Trend analyses using the Mann-Kendall test in time series, along with the estimation of the flux of neonics into the sea, were conducted. At last, the ecological risk of neonics was evaluated by water quality criteria derivation based on species sensitivity distribution. Our findings revealed that 47.6% of samples contained at least one neonic, with the integrated concentration of neonics ranging from 63.30 to 1684.14 ng/L. Imidacloprid and dinotefuran exhibited the highest frequency of detection in the analysis. The significance level of the Mann-Kendall test ranged from 2.16 × 10-10 to 1.21 × 10-5 (S > 0), indicating all neonics behaved with sharply increasing trends. Approximately 8.47 × 10-2 tons of neonics were discharged into Xiangshan Bay. Notably, the integrated concentrations of neonics represented a potential chronic ecological risk to marine organisms. This study provided novel insights into the spatial distribution, source, and migration of neonic species and their impacts on marine ecosystems.

Atmospheric Nitrogen Pollution Control Benefits the Coastal Environment

Nitrogen is an essential nutrient and a major limiting element for the ocean ecosystem. Since the preindustrial era, substantial amounts of nitrogen from terrestrial sources have entered the ocean via rivers, groundwater, and atmospheric deposition. China serves as a key hub in the global nitrogen cycle, but the pathways, sources, and potential mitigation strategies for land-ocean nitrogen transport are unclear. By combining the CHANS, WRF-Chem, and WNF models, we estimated that 8 million tonnes (Tg) of nitrogen was transferred into the ocean in 2017 in China, with atmospheric deposition contributing 1/3. About half variation of the offshore chlorophyll concentration was explained by atmospheric deposition. The Bohai Sea was the hot spot of nitrogen input, estimated at 214 kg N ha-1, while other areas were around 25-51 kg N ha-1. The largest contributors are agricultural systems (4 Tg, 55%), followed by domestic sewage (2 Tg, 21%). Abatement measures could reduce nitrogen export to the ocean by 43%, and mitigating ammonia and nitrogen oxide emissions accounts for 33% of this reduction, highlighting the importance of addressing air pollution in resolving ocean pollution. The cost-benefit analysis suggests the priority of nitrogen reduction in cropland and transport systems for the ocean environment.

Decoupling the heterogeneity of sediment microbial communities along the urbanization gradients: A Bayesian-based approach

Comprehending the response of microbial communities in rivers along urbanization gradients to hydrologic characteristics and pollution sources is critical for effective watershed management. However, the effects of complex factors on riverine microbial communities remain poorly understood. Thus, we established a bacteria-based index of biotic integrity (Ba-IBI) to evaluate the microbial community heterogeneity of rivers along an urbanization gradient. To examine the response of Ba-IBI to multiple stressors, we employed a Bayesian network based on structural equation modeling (SEM-BN) and revealed the key control factors influencing Ba-IBI at different levels of urbanization. Our findings highlight that waterborne nutrients have the most significant direct impact on Ba-IBI (r = -0.563), with a particular emphasis on ammonia nitrogen, which emerged as the primary driver of microbial community heterogeneity in the Liuyang River basin. In addition, our study confirmed the substantial adverse effects of urbanization on river ecology, as urban land use had the greatest indirect effect on Ba-IBI (r = -0.460). Specifically, the discharge load from wastewater treatment plants (WWTP) was found to significantly negatively affect the Ba-IBI of the entire watershed. In the low urbanized watersheds, rice cultivation (RC) and concentrated animal feeding operations (CAFO) are key control factors, and an increase in their emissions can lead to a sharp decrease in Ba-IBI. In moderately urbanized watersheds, the Ba-IBI tended to decrease as the level of RC emissions increased, while in those with moderate RC emissions, an increase in point source emissions mitigated the negative impact of RC on Ba-IBI. In highly urbanized watersheds, Ba-IBI was not sensitive to changes in stressors. Overall, our study presents a novel approach by integrating Ba-IBI with multi-scenario analysis tools to assess the effects of multiple stressors on microbial communities in river sediments, providing valuable insights for more refined environmental decision-making.

Cyanobacterial bioreporter of nitrate bioavailability in aquatic ecosystems

The water eutrophication, resulting from the discharge of industrial and agricultural wastewater, leads to ecological degradation. However, to date, how to assess and manage the risks of water pollution, especially nitrogen pollution, remains a particularly noteworthy issue. Nitrate, the most important nitrogen compound, has become a bottleneck restricting total nitrogen management. The development of bioreporters monitoring nitrate pollution contributes to the estimation of water quality, especially the availability of nutrients. In this study, we obtained 9 bioreporters from 40 cyanobacterial derivatives which were constructed based on different hosts, copy numbers, and sensing elements and evaluated the performance of bioreporters. The results showed that single-celled Synechocystis was more sensitive to nitrate than filamentous Anabaena, that the reporter gene luxABCDE responded faster than sfgfp in most bioreporters, and that relatively medium-copy plasmid improved the performance of sensing elements. Nine bioreporters performed well in bioavailable nitrate detection, of which AD-AS-X and AR-NI-X, activated by nitrate repletion, had the shortest response time (2 h) and the widest response range (20-800 μM), respectively. Moreover, SR-GLN-SG, activated by nitrate deficiency, exhibited the best linear response (R2 = 0.998). After parameter optimization, exponential growth phase bioreporters, culture temperature of 30 °C, sample volume of 200 μL were determined as optimal monitoring conditions. We found that common water contaminants (copper, cadmium, and phosphorus) had no impact on the performance of bioreporters, indicating the stability of bioreporters. Six out of 9 bioreporters, especially the SR-NB-X, were highly effective in detecting the bioavailable nitrate in wastewater sample. This study provides valuable references for developing more cyanobacterial bioreporters and their practical application in nitrate detection.

Guiding the landscape patterns evolution is the key to mitigating river water quality degradation

Consensus has emerged that landscape pattern evolution significantly impacts the river environment. However, there remains unclear how the landscape pattern evolves possible to achieve a balance between land resource use and water conservation. Thus, simulating future landscape patterns under different scenarios to predict river eutrophication level is critical to propose targeted landscape planning programs and alleviate river water quality degradation. Here, we coupled five water quality parameters (TOC, TN, NO3--N, NH4+-N, TP), collected from October 2020 to September 2021, to construct the river eutrophication index (EI) to assess river water quality. Meanwhile, based on redundancy analysis, patch-generating land use simulation model, and stepwise multiple linear regression model comprehensively analyze the Fengyu River watershed landscape patterns evolution and their impact on river eutrophication. Results indicated that current rivers reach eutrophic levels, and EI reaches 40.7. The landscape patterns explain 88.2 % of river eutrophication variation, while the LPI_Con metric is critical and individually explained 21.5 %. Furthermore, eutrophication in the watershed will increase in 2040 under the natural development (ND) scenario, and the EI will reach 44.4. In contrast, farmland protection (FP) scenarios and environmental protection (EP) scenarios contribute to mitigating eutrophication, the EI values are 38.2 and 38.1, respectively. The results provide a potential mechanistic explanation that river eutrophication is a consequence of unreasonable landscape pattern evolution. Guiding the landscape patterns evolution based on critical driver factors from a planning perspective is conducive to mitigating river water quality degradation.

Assessment of the Wanyu River (China) based on a water, sediment and hydrobiont framework

Due to the striving for the development of economy and agriculture, anthropogenic activities in many countries dramatically alter natural hydrology. These activities are primarily responsible for river deterioration. Thus, we need to assess the river environment and take measures for remediation. According to the survey data, the study identified the critical factors causing water quality deterioration and evaluated the aquatic biodiversity in the Wanyu River. First, based on the monitoring data of water (dissolved oxygen (DO), chemical oxygen demand (COD), total phosphorus (TP), and ammonia nitrogen (NH3-N)), sediment (copper (Cu), zinc (Zn), lead (Pb), arsenic (As), nickel (Ni), mercury (Hg), cadmium (Cd), and chromium (Cr)), and aquatic biodiversity (fish and hydrophyte), the study identified the critical factors causing river quality deterioration. Second, the study provided some recommendations that would consolidate the restoration efforts. Consequently, because of the government's efforts in building the municipal sewage treatment plant, dredging, and other measures, the river environment improved during the 2020-2021 period. The maximum concentrations of COD, NH3-N, and TP in water were reduced by 17.76%, 26.17%, and 20.93%, respectively. The sediment had no risk of heavy metal pollution in the past 2 years. And we could utilize sludge as garden soil or compost resource. However, reducing agricultural pollution, internal nutrient loading, and cost-effective restoration and evaluation represent significant challenges in the efforts to recover the river ecosystem.

Legacy phosphorus delays the accomplishment of expected phosphorus management object

Legacy phosphorus (P) in watersheds continuously affects the water quality. The time lag between anthropogenic P input and algal bloom has made P dynamics prediction in aquatic ecosystems more challenging. Whether the legacy P in the Yangtze River Watershed (YRW) exceeds its storage threshold remains unknown, and the continuous impact of legacy P on the water quality has not been analyzed. This study aimed to evaluate variation trends (1970-2018) and influencing factors for accumulated P in the YRW under different economic development periods, quantitatively identify the watershed P storage threshold based on the two split line models and estimate the time required for the return of legacy P to the baseline level using an exponential decay process. The results showed that the P storage threshold of the YRW was surpassed due to intense anthropogenic activities, and the residual P still had an impact on aquatic ecosystems for a long time. The dissolved total P loadings may become the top priority to achieve better P management goals. The time lags for the legacy P restoration would require for about 1000 years to be exhausted. The legacy P in the YRW would continuously undermine the restoration efforts of the water quality. The combined effects of watershed P surplus reductions and depletion of residual P may become essential to better manage P in the future. We still need to strengthen our efforts to make soil legacy P more absorbed by crops and improve sewage treatment capacity to achieve sustainable development of YRW.

Human and natural activities regulate organic matter transport in Chinese rivers

Rivers connect terrestrial and aquatic ecosystems and export approximately 55.47 % of the net terrestrial carbon fixation. However, due to unavailable high-frequency monitoring data, litter is known about diurnal variation in riverine carbon transport on a national scale. Based on daily measurements between March 2021 and February 2022 at 1491 stations across China, this study clarified the spatiotemporal variations in riverine organic matter indicated by chemical oxygen demand (COD). Spatially, COD content showed a spatial pattern with high values in the northwest (p < 0.05), and COD flux was determined by water discharge (84.01 %). Human activities explained 73.20 % of the spatial variations in riverine COD content; in particular, agricultural planting significantly elevated riverine COD (r = 0.73, p < 0.01). Seasonally, 95.53 % of stations showed significant seasonal variations in COD contents (p < 0.05); 69.72 % (25.81 %) were identified as Type II (III) typically had the maximum (minimum) COD in summer (autumn). Moreover, except for human activities (41.08 ± 22.94 %), natural factors also contributed 47.41 ± 24.04 % to the seasonal variations. In summer, high temperatures increased COD by promoting algal proliferation at Type II stations; however, heavy precipitation diluted COD contents at Type III stations. In these cases, seasonal measurements were essential for estimating riverine organic matter transport, especially the values measured in spring and winter. This study has significant implications for managing the aquatic environment, estimating riverine organic matter transport, and balancing the global carbon budget.

Sewage treatment decreased organic carbon resources in Hong Kong waters during 1986-2020

Riverine organic carbon (OC) transport plays a role in regulating terrestrial and marine carbon pools and deteriorating coastal water quality. However, long-term OC transport in Asian rivers and its diffusion in marginal seas have remained unreported. This study reported the spatiotemporal variations in OC resources for Hong Kong waters, China, based on monthly monitoring data collected at 82 river stations and 94 ocean sites during 1986-2020. The station-based riverine OC varied spatially and was generally high, with a mean value of 1.4-52.0 mg/L. Moreover, along with improving water quality, OC at 97.6% of the river stations decreased during 1986-2020; overall, sewage treatment accounted for 83.4% of the exponential decrease in riverine OC (R2 = 0.68, p < 0.01). However, the reduction in riverine OC accounted for only 10.4% of the reduction in the marine five-day biochemical oxygen demand (BOD5), which occurred at 70.2% of the ocean sites, especially those closest to the shore. The linear reduction in the marine BOD5 (R2 = 0.24, p < 0.01) was mainly attributed to reduced OC input from the adjacent Pearl River (61.9%) and decreases in phytoplankton growth (19.0%). These results indicated that sewage treatment improved water quality and decreased OC resources in Hong Kong waters, which can serve as a sustainable development model for other coastal cities. This study has important implications for mitigating organic pollution in the context of human efforts to manage the water environment.

Occurrence, stability and cytotoxicity of halobenzamides: A new group of nitrogenous disinfection byproducts in drinking water

Exploring disinfection byproducts (DBPs) with adverse health effects in drinking water is a constant challenge. Halobenzamides (HBZAMs) are suspected to be a new group of nitrogenous DBPs but have not been reported in drinking water to date. In this study, by coupling SPE and UPLC‒MS/MS, a sensitive method was established to detect eight HBZAMs in drinking water with recoveries and limits of detection of 80-103% and 0.01-0.04 ng/L, respectively. Subsequently, distinct fragments of HBZAMs were extended to the development of a pseudotargeted method for the analysis of the fourteen HBZAMs that were speculated and lack chemical standards. Using the developed method, eight HBZAMs were quantified in ten drinking water samples with concentrations ranging from 2.4 to 7.2 ng/L and a detection frequency of 100%, among which five HBZAMs were stable with half-lives over 72 h under real chlorine levels. Twelve HBZAMs without standards were identified in three to ten drinking water samples with comparable levels. The cytotoxicity of eight quantified HBZAMs in CHO-K1 cells varied with disparity, in which the cytotoxicity of 3,5-DBBZAM was over 10-fold higher than that of aliphatic dichloroacetamide. Considering their diversity, toxicity and stability, the occurrence of HBZAMs in drinking water deserves attention.

Vertical distribution analysis and total mass estimation of nitrogen and phosphorus in large shallow lakes

Analysing the vertical distribution of nutrient salts and estimating the total mass of lake nutrients is helpful for the management of lake nutrient status and the formulation of drainage standards in basins. However, studies on nitrogen (N) and phosphorus (P) in lakes have focused on obtaining measures of N and P concentrations, but no understanding exists on the vertical distribution of N and P in the entire water column. The present study proposes algorithms for estimating the total masses of N/P per unit water column (ALGO-TNmass/ALGO-TPmass) for shallow eutrophic lakes. Using Lake Taihu as an example, the total masses of nutrients in Lake Taihu in the historical period were obtained, and the algorithm performance was discussed. The results showed that the vertical distribution of nutrients decreased with increasing depth and exhibited a quadratic distribution. Surface nutrients and chlorophyll-a concentrations play important roles in the vertical distribution of nutrients. Based on conventional surface water quality indicators, algorithms for the vertical nutrient concentration in Lake Taihu were proposed. Both algorithms had good accuracy (ALGO-TNmass R2 > 0.75, RMSE <0.57; ALGO-TPmass R2 > 0.80, RMSE ≤0.50), the ALGO-TPmass had better applicability than the ALGO-TNmass, and had good accuracy in other shallow lakes. Therefore, deducing the TPmass using conventional water quality indicators in surface water, which not only simplifies the sampling process but also provides an opportunity for remote sensing technology to monitor the total masses of nutrients, is feasible. The long-term average total mass of N was 11,727 t, showing a gradual downward trend before 2010, after which it stabilised. The maximum and minimum intra-annual total N masses were observed in May and November, respectively. The long-term average total mass of P was 512 t, showing a gradual downward trend before 2010, and a slow upward trend thereafter. The maximum and minimum intra-annual total masses of P occurred in August and February or May, respectively. The correlation between the total mass of N and meteorological conditions was not obvious, whereas some influence on the total mass of P was evident, particularly water level and wind speed.

Source partitioning using phosphate oxygen isotopes and multiple models in a large catchment

Excessive phosphorus (P) loadings cause major pollution concerns in large catchments. Quantifying the point and nonpoint P sources of large catchments is essential for catchment P management. Although phosphate oxygen isotopes (δ18O(PO4)) can reveal P sources and P cycling in catchments, quantifying multiple P sources in a whole catchment should be a research focus. Therefore, this study aimed to quantitatively identify the proportions of multiple potential end members in a typical large catchment (the Yangtze River Catchment) by combining the phosphate oxygen isotopes, land use type, mixed end-element model, and a Bayesian model. The δ18O(PO4) values of river water varied spatially from 4.9‰ to18.3‰ in the wet season and 6.0‰ to 20.9‰ in the dry season. Minor seasonal differences but obvious spatial changes in δ18O(PO4) values could illustrate how human activity changed the functioning of the system. The results of isotopic mass balance and the Bayesian model confirmed that controlling agricultural P from fertilizers was the key to achieving P emission reduction goals by reducing P inputs. Additionally, the effective rural domestic sewage treatment, development of composting technology, and resource utilization of phosphogypsum waste could also contribute to catchment P control. P sources in catchment ecosystems can be assessed by coupling an isotope approach and multiple-models.

Virus-pathogen interactions improve water quality along the Middle Route of the South-to-North Water Diversion Canal

Bacterial pathogens and viruses are the leading causes of global waterborne diseases. Here, we discovered an interesting natural paradigm of water "self-purification" through virus-pathogen interactions over a 1432 km continuum along the Middle Route of the South-to-North Water Diversion Canal (MR-SNWDC) in China, the largest water transfer project in the world. Due to the extremely low total phosphorus (TP) content (ND-0.02 mg/L) in the MR-SNWDC, the whole canal has experienced long-lasting phosphorus (P) limitation since its operation in 2015. Based on 4443 metagenome-assembled genomes (MAGs) and 40,261 nonredundant viral operational taxonomic units (vOTUs) derived from our recent monitoring campaign, we found that residential viruses experiencing extreme P constraints had to adopt special adaptive strategies by harboring smaller genomes to minimize nucleotide replication, DNA repair, and posttranslational modification costs. With the decreasing P supply downstream, bacterial pathogens showed repressed environmental fitness and growth potential, and a weakened capacity to maintain P acquisition, membrane formation, and ribonucleotide biosynthesis. Consequently, the unique viral predation effects under P limitation, characterized by enhanced viral lytic infections and an increased abundance of ribonucleotide reductase (RNR) genes linked to viral nuclear DNA replication cycles, led to unexpectedly lower health risks from waterborne bacterial pathogens in the downstream water-receiving areas. These findings highlighted the great potential of water self-purification associated with virus-pathogen dynamics for water-quality improvement and sustainable water resource management.

Identifying the influence of natural and human factors on seasonal water quality in China: current situation of China's water environment and policy impact

Agricultural production, urbanization, and other anthropogenic activities, the major causes of surface water pollution in China, have dramatically altered hydrological processes and nutrient cycles. Identifying and quantifying the key factors affecting water quality are essential for the better prevention and management of water pollution. However, due to the limitations of traditional statistical analysis methods, it is difficult to evaluate the spatial changes and interactions of influencing factors on water quality. In addition, research on a national scale is difficult, as it involves large-scale and long-term water quality monitoring work. In this study, we collected and collated the monthly average concentrations of four water quality parameters, dissolved oxygen, ammonia nitrogen, chemical oxygen demand, and total phosphorous, based on data from 1547 water quality monitoring stations in China. The combined pollution level of the water quality was assessed using the water quality index. Based on the water quality characteristics, water quality monitoring sites in the dry and wet seasons were grouped using k-means clustering. Eleven environmental factors were evaluated using geodetector software, including six human factors and five natural factors. The results showed that there are high-risk areas for water quality pollution in the eastern and southeastern coastal regions of China in both the dry and wet seasons and that surface water pollution in China is highly spatial heterogenous in both the dry and wet seasons. Among the anthropogenic factors, urban land area is the main factor of water quality pollution in the dry season, and the explanation rate of spatial heterogeneity of integrated water quality pollution index is 20.3%. The number of poultry farms and the area of farmland explained 12.4% and 12.1% of the integrated water quality pollution index in the wet season. The nonlinear relationship between these three anthropogenic and natural factors and their interaction exacerbated water quality pollution. Based on this analysis, we identified the key factors affecting surface water quality in China during the dry and wet seasons, evaluated the achievements of the water environmental protection policies in China in recent years, and proposed future management measures for the effective prevention and control of water quality pollution in high-risk areas.

Assessing water quality in the Pearl River for the last decade based on clustering: Characteristic, evolution and policy implications

The Pearl River (PR) is China's second-largest river, playing a crucial role in regulating and supplying water in the southeast. However, for the last decade, the PR has been experiencing water quality deterioration due to population growth, rapid economic development, and diverse human activities, particularly in its delta areas. This study analyzed the characteristics and evolution of eight water quality variables, including pH values (pH), water temperature (WT), dissolved oxygen (DO), five-day biochemical oxygen demand (BOD5), permanganate index (PI), total phosphorus (TP), ammonia nitrogen (NH3N), and fluoride (F-), which were monitored monthly at 16 water quality monitoring stations from January 2009 to August 2019. Overall, annual average BOD5 and F- concentrations met Class I water quality standards, while TP and NH3N conformed to lower standards. The cluster results showed noticeable differences for parameter grouping (DO-organic parameters-nutrient and solutes), seasonal variation (wet and dry), and water quality status (contaminated-remediating-fine). The Water Quality Index (WQI) ranged from 8.3 ("very poor") to 91.7 ("excellent") in the entire basin from 2009 to 2019, and NH3N-DO based WQImins were identified using the All-Subsets Linear Regression method. The fitting results of the Generalized Additive Models displayed that the deviance explained by natural factors ranged from 37.2% to 61.3%, while the socioeconomic explanation exceeded 70%. The WQImin component evolution (2003-2019) of Shenzhen River estuary, the most important part of the PR estuary, agreed with key parameter variations in heterogeneous clusters in the entire basin. Moreover, Shenzhen's water quality remediation applications indicated that reasonable-efficient-powerful efforts and support from governments could accelerate recovery. For the management departments, consistent measures should be strictly enforced, and elaborate regionalized management based on clusters could be attempted to maintain excellent water quality.

Bioremediation of n-alkanes, polycyclic aromatic hydrocarbons, and heavy metals from wastewater using seaweeds

The removal of n-alkanes, polycyclic aromatic hydrocarbons, and heavy metals from wastewater using three dried seaweeds Ulva intestinalis Linnaeus (green alga), Sargassum latifolium (Turner) C.Agardh (brown alga), and Corallina officinalis Kützing (red alga) has been shown to evaluate their potential usage as inexpensive adsorbents. Under natural environmental conditions, numerous analytical methods, including zeta potential, energy dispersive X-ray spectroscopy (EDX), SEM, and FT-IR, are used in this study. The results showed that n-alkanes and polycyclic aromatic hydrocarbons adsorption increased with increasing contact time for all three selected algae, with a large removal observed after 15 days, while the optimal contact time for heavy metal removal was 3 h. S. latifolium dry biomass had more potential as bioadsorbent, followed by C. officinalis and then U. intestinalis. S. latifolium attained removal percentages of 65.14%, 72.50%, and 78.92% for light n-alkanes, heavy n-alkanes, and polycyclic aromatic hydrocarbons (PAHs), respectively, after 15 days. Furthermore, it achieved removal percentages of 94.14, 92.62, 89.54, 87.54, 82.76, 80.95, 77.78, 73.02, and 71.62% for Mg, Zn, Cu, Fe, Cr, Pb, Cd, Mn, and Ni, respectively, after 3 h. Carboxyl and hydroxyl from FTIR analysis took part in wastewater treatment. The zeta potentials revealed that algal cells have a negatively charged surface, and the cell surface of S. latifolium has a more negative surface charge than U. intestinalis and C. officinalis. Our study suggests that seaweeds could play an important role in wastewater treatment and thus help as an economical, effective, and ecofriendly bioremediation system for ecological health and life protection.

Control mechanisms of water chemistry based on long-term analyses of the Yangtze River

Long-term series data can provide a glimpse of the influence of natural and anthropogenic factors on water chemistry. However, few studies have been conducted to analyze the driving forces of the chemistry of large rivers based on long-term data. This study aimed to analyze the variations and driving mechanisms of riverine chemistry from 1999 to 2019. We compiled published data on major ions in the Yangtze River, one of the three largest rivers in the world. The results showed that Na+ and Cl- concentrations decreased with increasing discharge. Significant differences in riverine chemistry were found between the upper and middle-lower reaches. Major ion concentrations in the upper reaches were mainly controlled by evaporites, especially Na+ and Cl- ions. In contrast, major ion concentrations in the middle-lower reaches were mainly affected by silicate and carbonate weathering. Furthermore, human activities were the drivers of some major ions, notably SO42- ions associated with coal emissions. The increased major ions and total dissolved solids in the Yangtze River in the last 20 years were ascribed to the continuous acidification of the river and the construction of the Three Gorges Dam. Attention should be given to the impact of anthropogenic activities on the water quality of the Yangtze River.

Spatial responses of water quality to river density and connectivity alterations on the Taihu Plain

With the advancement of urbanization, the structure and connectivity of river networks have been changed by the interference of human activities, resulting in a series of water environment problems. Numerous studies have indicated that river networks are associated with water quality; unfortunately, few studies have revealed the contributions of the structure and connectivity of river networks to variations in water quality. Taking one water conservancy region with dense and braided rivers on the Taihu Plain as an example, we depicted the spatial aggregations of water quality using the Getis-Ord Gi* index, quantified the variations in polluted regions using the standard deviational ellipse method, and quantified the influence of river density and connectivity on water quality during the different seasons. The results showed that (1) the water quality during the flood season was better than that during the non-flood season, especially in the western region; (2) the spatial aggregations of most water quality indicators were higher and the polluted regions increased in size during the flood period compared to the non-flood period; and (3) the relative contribution rates of the river density and connectivity exhibited mean values of 62.5% (61.2%) and 37.5% (38.8%) in the flood (non-flood) period. Our results provide theoretical support for enhancing water environment management.

A raster-based estimation of watershed phosphorus load and its impacts on surrounding rivers based on process-based modelling

Quantifying phosphorus (P) load from watersheds at a fine scale is crucial for studying P sources in lake or river ecosystems; however, it is particularly challenging for mountain-lowland mixed watersheds. To address this challenge, we proposed a framework to estimate the P load at the grid scale and assessed its risk to surrounding rivers in a typical mountain-lowland mixed watershed (Huxi Region in Lake Taihu Basin, China). The framework coupled three models: the Phosphorus Dynamic model for lowland Polder systems (PDP), the Soil and Water Assessment Tool (SWAT), and the Export Coefficient Model (ECM). The coupled model performed satisfactory for both hydrological and water quality variables (Nash-Sutcliffe efficiency >0.5). Our modelling practice revealed that polder, non-polder, and mountainous areas had P load of 211.4, 437.2, and 149.9 t yr^-1, respectively. P load intensity in lowlands and mountains was 1.75 and 0.60 kg ha^-1 yr^-1, respectively. A higher P load intensity (>3 kg ha^-1 yr^-1) was mainly observed in the non-polder area. In lowland areas, irrigated cropland, aquaculture ponds and impervious surfaces contributed 36.7%, 24.8%, and 25.8% of the P load, respectively. In mountainous areas, irrigated croplands, aquaculture ponds, and impervious surfaces contributed 28.6%, 27.0%, and 16.4% of the P load, respectively. Rivers with relatively high P load risks were mainly observed around big cities during rice season, owing to a large contribution of P load from the non-point source pollution of urban and agricultural activities. This study demonstrated a raster-based estimation of watershed P load and their impacts on surrounding rivers using coupled process-based models. It would be useful to identify the hotspots and hot moments of P load at the grid scale.

Environmental ammonia analysis based on exclusive nitrification by nitrifying biofilm screened from natural bioresource

Biofilm-based biological nitrification is widely used for ammonia removal, while hasn't been explored for ammonia analysis. The stumbling block is the coexist of nitrifying and heterotrophic microbes in real environment resulting in non-specific sensing. Herein, an exclusive ammonia sensing nitrifying biofilm was screened from natural bioresource, and a bioreaction-detection system for the on-line analysis of environmental ammonia based on biological nitrification was reported. The nitrifying microbes were aggregated into a nitrifying biofilm through a result-oriented bioresource enrichment strategy. The predominant nitrifying population and progressive surface reaction in the plug flow bioreactor led to the exclusive and exhaustive ammonia biodegradation for the establishment of a novel analytical method. The on-line ammonia monitoring prototype achieved complete biodegradation for determining ammonium nitrogen within 5 min and showed exceptional reliability in long-term real sample measurements without frequent calibration. This work offers a low-threshold natural screening paradigm for developing sustainable bioresource-based analytical technologies.

Impact of COVID-19 lockdown on water quality in China during 2020 and 2022: two case surges

The COVID-19 severely affected the world in 2020. Taking the two outbreaks in China in 2020 and 2022 as examples, the spatiotemporal changes in surface water quality levels and COD_Mn and NH₃-N concentrations were analyzed, and the relationships between the variations in the two pollutants and environmental and social factors were evaluated. The results showed that during the two lockdowns, due to the total water consumption (including industrial, agricultural, and domestic water) decreased, the proportion of good water quality increased by 6.22% and 4.58%, and the proportion of polluted water decreased by 6.00% and 3.98%, the quality of water environment has been improved significantly. However, the proportion of excellent water quality decreased by 6.19% after entering the unlocking period. Before the second lockdown period, the average COD_Mn concentration exhibited a "falling, rising, and falling" trend, while the average NH₃-N concentration changed in the opposite direction. The correlation analysis revealed that the increasing trend of pollutant concentrations was positively correlated with longitude and latitude, and weakly correlated with DEM and precipitation. A slight decrease trend in NH₃-N concentration was negatively correlated with the population density variation and positively correlated with the temperature variation. The relationship between the change in the number of confirmed cases in provincial regions and the change in pollutant concentrations was uncertain, with positive and negative correlations. This study demonstrates the impact of lockdowns on water quality and the possibility of improving water quality through artificial regulation, which can provide a reference basis for water environmental management.

Monitor water quality through retrieving water quality parameters from hyperspectral images using graph convolution network with superposition of multi-point effect: A case study in Maozhou River

Quantitative prediction by unmanned aerial vehicle (UAV) remote sensing on water quality parameters (WQPs) including phosphorus, nitrogen, chemical oxygen demand (COD), biochemical oxygen demand (BOD), and chlorophyll a (Chl-a), total suspended solids (TSS), and turbidity provides a flexible and effective approach to monitor the variation in water quality. In this study, a deep learning-based method integrating graph convolution network (GCN), gravity model variant, and dual feedback machine involving parametric probability analysis and spatial distribution pattern analysis, named Graph Convolution Network with Superposition of Multi-point Effect (SMPE-GCN) has been developed to calculate concentrations of WQPs through UAV hyperspectral reflectance data on large scale efficiently. With an end-to-end structure, our proposed method has been applied to assisting environmental protection department to trace potential pollution sources in real time. The proposed method is trained on a real-world dataset and its effectiveness is validated on an equal amount of testing dataset with respect to three evaluation metrics including root of mean squared error (RMSE), mean absolute percent error (MAPE), and coefficient of determination (R²). The experimental results demonstrate that our proposed model achieves better performance in comparison with state-of-the-art baseline models in terms of RMSE, MAPE, and R². The proposed method is applicable for quantifying seven various WQPs and has achieved good performance for each WQP. The resulting MAPE ranges from 7.16% to 10.96% and R² ranges from 0.80 to 0.94 for all WQPs. This approach brings a novel and systematic insight into real-time quantitative water quality monitoring of urban rivers, and provides a unified framework for in-situ data acquisition, feature engineering, data conversion, and data modeling for further research. It provides fundamental support to assist environmental managers to efficiently monitor water quality of urban rivers.

A novel framework to improve the consistency of water quality attribution from natural and anthropogenic factors

One critical question for water security and sustainable development is how water quality responses to the changes in natural factors and human activities, especially in light of the expected exacerbation in water scarcity. Although machine learning models have shown noticeable advances in water quality attribution analysis, they have limited interpretability in explaining the feature importance with theoretical guarantees of consistency. To fill this gap, this study built a modelling framework that employed the inverse distance weighting method and the extreme gradient boosting model to simulate the water quality at grid scale, and adapted the Shapley additive explanation to interpret the contributions of the drivers to water quality over the Yangtze River basin. Different from previous studies, we calculated the contribution of features to water quality at each grid within river basin and aggregated the contribution from all the grids as the feature importance. Our analysis revealed dramatic changes in response magnitudes of water quality to drivers within river basin. Air temperature had high importance in the variability of key water quality indicators (i.e. ammonia-nitrogen, total phosphorus, and chemical oxygen demand), and dominated the changes of water quality in Yangtze River basin, especially in the upstream region. In the mid- and downstream regions, water quality was mainly affected by human activities. This study provided a modelling framework applicable to robustly identify the feature importance by explaining the contribution of features to water quality at each grid.