An integrated risk assessment framework using information theory-based coupling methods for basin-scale water quality management: A case study in the Danjiangkou Reservoir Basin, China

Spatiotemporal evolution and driving factors of agricultural non-point source pollution in the context of economic green development

Agricultural non-point source pollution (ANPSP) impacts water quality in the core water source area (CWSA) of the Middle Route of the South North Water Diversion Project. Effective governance is required to manage water pollution in this area. An inventory analysis method was used in the CSWA to estimate the ANPSP loads between 2006 and 2020. The Spatial Durbin Model was applied to identify the factors that influenced ANPSP. From 2006 to 2020, pollutant loads in the CWSA significantly decreased. The total nitrogen (TN) and total phosphorus (TP) loads in the ANPSP are decreasing. The TN and TP pollution intensity initially increased and then decreased, with the main sources shifting from livestock breeding to fertilizers. There was a significant spatial correlation of ANPSP in the CWSA, and agricultural economic growth and agglomeration exacerbated the ANPSP loads. There were regional differences in the impacts of urbanization, agricultural labor transfer, mechanization, and agricultural production efficiency on ANPSP. The adjustment of planting structure reduced ANPSP loads and produced a positive spillover effect. Finally, policy recommendations were made to support the protection of local water quality and enhance the efficiency of subsequent pollution control projects.

Characteristics, sources, and concentration prediction of endocrine disruptors in a large reservoir driven by hydrological rhythms: A case study of the Danjiangkou Reservoir

Herein, we present the first systematic investigation to clarify the effect of hydrological rhythms on the concentrations and distributions of polycyclic aromatic hydrocarbons (PAHs) and phthalate esters (PAEs) in the Danjiangkou Reservoir. The results revealed that hydrological rhythms remarkably affected the PAH and PAE concentrations and distributions in the water body, wherein the PAH concentration peaked in the flood season while the PAE concentration remarkably increased in the dry season. This study represents methodological innovation, revealing significant heterogeneity of PAHs and PAEs across different water layers. The former compounds tended to accumulate in the water body's bottom layer while the latter compounds had the highest concentration at the surface layer, which can be attributed to the different physicochemical properties and environmental transport behaviors of the two compound types. The overall concentrations of PAHs and PAEs fall within the international and domestic safety standards. The primary sources of these contaminants-coal and biomass combustion for PAHs and widespread use of plastic products for PAEs-are critical areas of regulatory focus. A machine learning model is proposed for the first time for predicting PAE concentrations in the Danjiangkou Reservoir, primarily based on the stacking model and supplemented by the random forest or XGBoost models.

Spatial and temporal distribution characteristics and source apportionment of biogenic elements using APCS-MLR model in the main inlet tributary of Danjiangkou Reservoir

Danjiangkou Reservoir has been widely concerned as the water source of the world's longest cross basin water transfer project. Biogenic elements are the foundation of material circulation and key factors affecting water quality. However, there is no comprehensive study on the biogenic elements in tributaries of Danjiangkou Reservoir, hindering a detailed understanding of geochemical cycling characteristics of biogenic elements in this region. Guanshan River, one of the main tributaries that directly enter the Danjiangkou Reservoir, was token as the research object. Spatiotemporal distribution characteristics of basic water quality parameters and biogenic elements were studied. Water quality was comprehensively evaluated through water quality index (WQI). Absolute principal component score-multiple linear regression (APCS-MLR) model was adopted to explore the main sources of biogenic elements. Results showed that, in terms of season, the concentrations of total nitrogen (TN), total phosphorus (TP), and dissolved organic carbon (DOC) were significantly higher in wet season than in dry season, while no significant differences were found for dissolved inorganic carbon (DIC) and dissolved silica (DSi). Spatially, the concentrations of dissolved carbon, DIC, TN, and TP in the middle and lower reaches were higher than that in the upstream. DOC concentration peaked in the middle reaches, while DSi showed higher concentrations in the upstream. WQI values indicated that the river water quality was between good and excellent, although the water quality in wet season was slightly worse than that in the dry season. PCA extracted five potential sources, which accounting for 84.12% of the total variance, including rock weathering, mixed source of sewage discharge and agricultural non-point source pollution, dissolved soil CO2, seasonal factor, and agricultural non-point source pollution. These sources contributed 38.96%, 12.33%, 13.54%, 23.95%, and 11.21% to river water quality parameters, respectively. Strengthening the monitoring of biogenic elements, controlling pollutant discharge, and exploring the relationship between biogenic elements and other pollutants are important for the water environment management in this basin.

Nutrient variations and environmental relationships of lakes and reservoirs before and after the COVID-19 epidemic public lockdown policy elimination: A nationwide comparative view in China

The continuous impact of COVID-19 on aquatic environments has attracted considerable attention, primarily focusing on short-term water quality effects during lockdown, while studies on changes following the lifting of restrictions are relatively limited. Following adjustments to China's pandemic public policy in December 2022, the effects on water quality and nutrient status in lakes and reservoirs remain unclear. In this study, we collected national environmental monitoring data comprising 15 indicators of water quality, meteorology, soil, and economic factors, from 86 lakes and reservoirs across China between March 2021 and December 2023. Total nitrogen (TN), total phosphorus (TP), the mass TN/TP ratio (TN/TP), and ammonia-nitrogen (NH3-N) were selected as representative nutrient indicators. The water quality index (WQI) and multivariate statistical techniques were employed to comprehensively assess national water quality and identify the drivers of nutrient variations in sub-regions. The results show that during the monitoring period from 2021 to 2023, Chinese national water quality consistently fell within the 'good (61-80)' or 'excellent (81-100)' categories, with the lowest water quality observed in the summer of each year. The summer of 2021 recorded the lowest WQI value among all seasons at 75.01. Following the elimination of the COVID-19 epidemic public lockdown policy, concentrations of TN, TP, and NH3-N declined. These findings indicate a general improvement in the water quality of lakes and reservoirs nationwide. Mantel test and multiple stepwise linear regression models revealed significant correlations between nutrients and human activity indicators in central, eastern, and northern China. In northern China, TP showed a significant positive correlation with GDP (0.2 < Mantel's r < 0.5, P < 0.05), with the beta value increasing from 0.27 to 0.38 after the elimination of the COVID-19 epidemic public lockdown policy. In these regions, the influence of rainfall, wind speed, NDVI, surface soil moisture, and water temperature on nutrients shifted from significant to insignificant effects after the elimination of the COVID-19 epidemic public lockdown policy, indicating that human activities have overshadowed natural factors. This study examines the water quality and nutrient status of lakes and reservoirs in China after the elimination of the COVID-19 epidemic public lockdown policy, highlighting the long-term impacts and spatial variations of the pandemic. These findings will inform environmental governance and promote sustainable water resource management in the post-pandemic era.

Identifying environmental impacts on planktonic algal proliferation and associated risks: a five-year observation study in Danjiangkou Reservoir, China

Understanding the risks of planktonic algal proliferation and its environmental causes is crucial for protecting water quality and controlling ecological risks. Reservoirs, due to the characteristics of slow flow rates and long hydraulic retention times, are more prone to eutrophication and algal proliferation. Chlorophyll-a (Chl-a) serves as an indicator of planktonic algal biomass. Exploring the intricate interactions and driving mechanisms between Chl-a and the water environment, and the potential risks of algal blooms, is crucial for ensuring the ecological safety of reservoirs and the health of water users. This study focused on the Danjiangkou Reservoir (DJKR), the core water source of the Middle Route of the South-to-North Water Diversion Project of China (MRSNWDPC). The multivariate statistical methods and structural equation modeling were used to explore the relationships between chlorophyll-a (Chl-a) contents and water quality factors and understand the driving mechanisms affecting Chl-a variations. The Copula function and Bayesian theory were combined to analyze the risk of changes in Chl-a concentrations at Taocha (TC) station, which is the core water source intake point of the MRSNWDPC. The results showed that the factors driving planktonic algal proliferation were spatially heterogeneous. The main factors affecting Chl-a concentrations in Dan Reservoir (DR) were water physicochemical factors (water temperature, dissolved oxygen, pH value, and turbidity) with a total contribution rate of 60.18%, whereas those in Han Reservoir (HR) were nutrient factors (total nitrogen, total phosphorus, and ammonia nitrogen) with a total contribution rate of 73.58%. In TC, the main factors were water physicochemical factors (turbidity, pH, and water temperature) and nutrient factors (total phosphorus) with total contribution rates of 39.76% and 45.78%, respectively. When Chl-a concentrations in other areas of the DJKR ranged from the minimum to the uppermost quartile, the probabilities that Chl-a concentrations at the TC station exceeded 3.4 μg/L (the benchmark value of Chl-a for lakes in the central-eastern lake area of China) owing to the influence of these areas were all less than 10%. Thus, the risk of planktonic algal proliferation at the MRSNWDPC intake point is low. This study developed an integrated framework to investigate spatiotemporal changes in algal proliferation and their driving factors in reservoirs, which can be used to support water quality management in mega hydro projects.

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.

Integrated simulation-surrogate-optimization modeling framework for multiple tradeoffs among socioeconomic and ecological targets in reservoir operations

Integrated reservoir water quantity and quality management is significant for water supply security and river ecosystem health. However, the spatiotemporal heterogeneity of water quality and the nonuniform response of multiple indicators to operation changes make it difficult to determine optimal operation schedules. This study proposes a coupled simulation-surrogate-optimization modeling approach for compromising multiple water quantity and quality targets in reservoir operations. The Environmental Fluid Dynamics Code (EFDC) was used to simulate spatiotemporal reservoir water quality dynamics. Subsequently, an ecological damage assessment method was established, accounting for the spatiotemporal heterogeneity of multiple water quality indicators and the nonlinear relationship between the water quality deterioration and ecological damage. To quickly simulate the ecological damage, a surrogate model was developed using the nonlinear autoregressive network with exogenous inputs (NARX). Finally, the surrogate model was integrated into a reservoir operation optimization model for compromising socioeconomic and ecological targets. By applying the methods to China's Danjiangkou Reservoir as a case, it was shown that more even nutrient distribution in the reservoir increased water eutrophication area while reducing concentration peak values, which helped decrease the ecological damage. Operation changes could lead to opposite effects on in-reservoir and downstream ecological targets, increasing operation optimization complexity. Both ecological and socioeconomic benefits significantly increased (by 9.4%-16.4%) during dry years under the optimized operation scheme, implying that synergies were obtained. This study offers implications and a management tool for reservoir operations to address the multiple tradeoffs among socioeconomic and ecological benefits.

Assessing and predicting water quality index with key water parameters by machine learning models in coastal cities, China

The water quality index (WQI) is a widely used tool for comprehensive assessment of river environments. However, its calculation involves numerous water quality parameters, making sample collection and laboratory analysis time-consuming and costly. This study aimed to identify key water parameters and the most reliable prediction models that could provide maximum accuracy using minimal indicators. Water quality from 2020 to 2023 were collected including nine biophysical and chemical indicators in seventeen rivers in Yancheng and Nantong, two coastal cities in Jiangsu Province, China, adjacent to the Yellow Sea. Linear regression and seven machine learning models (Artificial Neural Network (ANN), Self-Organizing Maps (SOM), K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF), Extreme Gradient Boosting (XGB) and Stochastic Gradient Boosting (SGB)) were developed to predict WQI using different groups of input variables based on correlation analysis. The results indicated that water quality improved from 2020 to 2022 but deteriorated in 2023, with inland stations exhibiting better conditions than coastal ones, particularly in terms of turbidity and nutrients. The water environment was comparatively better in Nantong than in Yancheng, with mean WQI values of approximately 55.3-72.0 and 56.4-67.3, respectively. The classifications "Good" and "Medium" accounted for 80 % of the records, with no instances of "Excellent" and 2 % classified as "Bad". The performance of all prediction models, except for SOM, improved with the addition of input variables, achieving R2 values higher than 0.99 in models such as SVM, RF, XGB, and SGB. The most reliable models were RF and XGB with key parameters of total phosphorus (TP), ammonia nitrogen (AN), and dissolved oxygen (DO) (R2 = 0.98 and 0.91 for training and testing phase) for predicting WQI values, and RF using TP and AN (accuracy higher than 85 %) for WQI grades. The prediction accuracy for "Medium" and "Low" water quality grades was highest at 90 %, followed by the "Good" level at 70 %. The model results could contribute to efficient water quality evaluation by identifying key water parameters and facilitating effective water quality management in river basins.

Intensive ammonium fertilizer addition activates iron and carbon conversion coupled cadmium redistribution in a paddy soil under gradient redox conditions

While over-fertilization and nitrogen deposition can lead to the enrichment of nitrogen in soil, its effects on heavy metal fractions under gradient moisture conditions remains unclear. Here, the effect of intensive ammonium (NH4+) addition on the conversion and interaction of cadmium (Cd), iron (Fe) and carbon (C) was studied. At relatively low (30-80 %) water hold capacity (WHC) NH4+ application increased the carbonate bound Cd fraction (F2Cd), while at relatively high (80-100 %) WHC NH4+ application increased the organic matter bound Cd fraction (F4Cd). Iron‑manganese oxide bound Cd fractions (F3Cd) and oxalate-Fe decreased, but DCB-Fe increased in NH4+ treatments, indicating that amorphous Fe was the main carrier of F3Cd. The variations in F1Cd and F4Cd observed under the 100-30-100 % WHC treatment were similar to those observed under low moisture conditions (30-60 % WHC). The C=O/C-H ratio of organic matter in soil decreased under the 30-60 % WHC treatment, but increased under the 80-100 % WHC treatment, which was the dominant factor influencing F4Cd changes. The conversion of NH4+ declined with increasing soil moisture content, and the impact on oxalate-Fe was greater at 30-60 % WHC than at 80-100 % WHC. Correspondingly, genetic analysis showed the effect of NH4+ on Fe and C metabolism at 30-60 % WHC was greater than at 80-100 % WHC. Specifically, NH4+ treatment enhanced the expression of genes encoding extracellular Fe complexation (siderophore) at 30-80 % WHC, while inhibiting genes encoding Fe transmembrane transport at 30-60 % WHC, indicating that siderophores simultaneously facilitated Cd detoxification and Fe complexation. Furthermore, biosynthesis of sesquiterpenoid, steroid, butirosin and neomycin was significantly correlated with F4Cd, while glycosaminoglycan degradation metabolism and assimilatory nitrate reduction was significantly correlated with F2Cd. Overall, this study gives a more comprehensive insight into the effect of NH4+ on activated Fe and C conversion on soil Cd redistribution under gradient moisture conditions.

Sustainable governance of drinking water conservation areas based on adaptive thresholds

Drinking water quality is integral to the Sustainable Development Goals framework. At the present, China's drinking water conservation faces a number of challenges that are partially brought on by strict conservation measures that don't fully take into account human-land conflict and sustainable development. Taking the idea of adaptive governance, this study seeks to identify adaptive thresholds and adaptive solutions for compatible drinking water conservation and local development. Pressure and resistance to drinking water quality in its status, future potential, and adaptive thresholds were explored to identify sustainable governance for the Baimei Conservation Area, Fujian Province. Field research, local governance forums, and the Soil and Water Assessment Tool (SWAT) model were utilized to explore the drinking water quality pressure and resistance to drinking water quality. In order to uncover potential future changes in pressure and resistance, suitability analyses and multi-scenario simulations were used to examine the status quo, pressure, and resistance scenarios. Adaptive thresholds were then identified through SWAT modeling of each scenario to guarantee the drinking water quality is greater than Class II in the Core Conservation Area and Class Ⅲ in 2nd-grade Conservation Area, respectively. The research finds that construction land development and farming are the key pressures on drinking water quality, and forests and wetlands are the primary resistances. The expansion of construction lands and the increased wetlands was centered on potential future scenarios because farming has no room for growth and forests are already heavily covered. The adaptive threshold of construction land expansion is identified to be 10% without new wetlands but can be 20% by adding 10% wetlands in subbasins, 5, 8, and 9. This study confirms the potential of adaptive sustainability for drinking water conservation areas. A similar analysis procedure can also be adapted to enhance adaptive governance for the sustainability of other conservation areas nationally and globally.

A new framework for water quality forecasting coupling causal inference, time-frequency analysis and uncertainty quantification

Accurate forecasting of water quality variables in river systems is crucial for relevant administrators to identify potential water quality degradation issues and take countermeasures promptly. However, pure data-driven forecasting models are often insufficient to deal with the highly varying periodicity of water quality in today's more complex environment. This study presents a new holistic framework for time-series forecasting of water quality parameters by combining advanced deep learning algorithms (i.e., Long Short-Term Memory (LSTM) and Informer) with causal inference, time-frequency analysis, and uncertainty quantification. The framework was demonstrated for total nitrogen (TN) forecasting in the largest artificial lakes in Asia (i.e., the Danjiangkou Reservoir, China) with six-year monitoring data from January 2017 to June 2022. The results showed that the pre-processing techniques based on causal inference and wavelet decomposition can significantly improve the performance of deep learning algorithms. Compared to the individual LSTM and Informer models, wavelet-coupled approaches diminished well the apparent forecasting errors of TN concentrations, with 24.39%, 32.68%, and 41.26% reduction at most in the average, standard deviation, and maximum values of the errors, respectively. In addition, a post-processing algorithm based on the Copula function and Bayesian theory was designed to quantify the uncertainty of predictions. With the help of this algorithm, each deterministic prediction of our model can correspond to a range of possible outputs. The 95% forecast confidence interval covered almost all the observations, which proves a measure of the reliability and robustness of the predictions. This study provides rich scientific references for applying advanced data-driven methods in time-series forecasting tasks and a practical methodological framework for water resources management and similar projects.