Permanganate Index Research Articles

A River Water Quality Prediction Method Based on Dual Signal Decomposition and Deep Learning

Traditional single prediction models struggle to address the complexity and nonlinear changes in water quality forecasting. To address this challenge, this study proposed a coupled prediction model (RF-TVSV-SCL). The model includes Random Forest (RF) feature selection, dual signal decomposition (Time-Varying Filtered Empirical Mode Decomposition, TVF-EMD, and Sparrow Search Algorithm-Optimized Variational Mode Decomposition, SSA-VMD), and a deep learning predictive model (Sparrow Search Algorithm-Convolutional Neural Network-Long Short-Term Memory, SSA-CNN-LSTM). Firstly, the RF method was used for feature selection to extract important features relevant to water quality prediction. Then, TVF-EMD was employed for preliminary decomposition of the water quality data, followed by a secondary decomposition of complex Intrinsic Mode Function (IMF) components using SSA-VMD. Finally, the SSA-CNN-LSTM model was utilized to predict the processed data. This model was evaluated for predicting total phosphorus (TP), total nitrogen (TN), ammonia nitrogen (NH3-N), dissolved oxygen (DO), permanganate index (CODMn), conductivity (EC), and turbidity (TB), across 1, 3, 5, and 7-d forecast periods. The model performed exceptionally well in short-term predictions, particularly within the 1–3 d range. For 1-, 3-, 5-, and 7-d forecasts, R2 ranged from 0.93–0.96, 0.79–0.87, 0.63–0.72, and 0.56–0.64, respectively, significantly outperforming other comparison models. The RF-TVSV-SCL model demonstrates excellent predictive capability and generalization ability, providing robust technical support for water quality forecasting and pollution prevention.

An Assessment of Water Quality and Pollution Sources in a Source Region of Northwest China

China prioritizes ensuring drinking water safety, particularly in the water-scarce northwest region. This study, utilizing water quality data from 52 village and town water sources since August 2022, assesses water quality, with a specific focus on key indicators related to organic pollution sources. This study provides a scientific foundation for enhancing water quality in these sources. Employing category factor analysis for classification and grading, principal component analysis for qualitative analysis of key evaluation indicators, and the absolute principal component linear regression equation for quantitative calculation of pollution sources, this study reveals that all 52 water sources meet quality standards. Principal component analysis categorizes pollution sources as diverse types of organic compounds in surface water. Source analysis calculations highlight decay-type organic substances as major contributors to increased water color and permanganate index, with pollution contribution rates of 54.78% and 31.31%, respectively. Fecal-type organic substances dominate the increase in dissolved total solids and total coliforms, with pollution contribution rates of 56.65% and 40.16%, respectively. Additionally, high-molecular-weight organic substances exhibit lower concentrations in the water. This article presents a systematic water quality assessment methodology, which is used for the first time to qualitatively assess the types of water sources and to quantitatively trace specific sources of organic pollution in source water in northwest China. This systematic study’s results, involving initial assessment followed by traceability, recommend the adoption of a simple contact filtration and disinfection process to enhance water quality in the region.

Influence of Typical Regional Land Use/Landscape Pattern on Water TN of the Upper Yellow River

This study aimed to explore the relationship between land use landscape pattern and water quality in the upstream of the Gansu water conservation, water and soil erosion, and ecological fragile areas. Based on the land use data and water quality monitoring section in 2020 in the 200 m, 500 m, 1 km, 2 km, 50 km, and 10 km riparian buffer area, the single-factor index evaluation method, random forest regression model, and BP neural network were used to quantify the response relationship between land use and landscape pattern of the upper Yellow River in Gansu province and water quality index and to carry out the basin water quality prediction based on land use landscape index data. The results showed that： ① through the single-factor index method, the major indicators of the total nitrogen （TN） in July and September, dissolved oxygen （DO）, permanganate index, ammonia nitrogen （NH4+ -N）, total phosphorus （TP）, and other surface indexes met the surface water environment class Ⅲ water quality standard. ② The random forest regression model was used to analyze the influence of land use and landscape index on TN, and the difference in TN in different typical areas was obtained. The land use types with the highest influence on the TN index in water conservation areas, soil and soil erosion areas, and ecological fragile areas were cultivated land, grassland, and construction land, respectively. ③ The BP neural network was used to predict the water quality index based on different typical areas of land use landscape index. The result of water conservation areas was good, the error rate between the predicted value and the actual value was below 10%, and the prediction accuracy was high. The study showed that water quality prediction based on land use and landscape index/water quality quantitative relationship model had a good water quality prediction effect.

Comparative Study of Water Quality Prediction Methods Based on Different Artificial Neural Network

The prediction of future data using existing data is an effective tool for regional planning and watershed management. The back propagation neural network （BPNN） and convolutional neural network （CNN） were used to construct a prediction model based on the water quality index of Hengyang in Xiangjiang River Basin from April to May 2022 and the results of permanganate index prediction by different models were compared. The prediction results displayed by BPNN could predict the water quality； however, overfitting occurred during the prediction. BPNN modified by particle swarm optimization （PSO） could avoid overfitting, which improved the parameter selection method of the BPNN mode. The CNN model had a better prediction effect, which had a more complex structure and a more scientific fitting method to avoid the model falling into the local extreme value during the fitting process and improve the accuracy of the model prediction results. The evaluation parameters including root-mean-square error （RMSE）, coefficient of determination （R2）, and mean absolute error （MAE） were used to predict the accuracy of the network. Compared with that of the traditional BPNN model, PSO-BPNN reduced the RESM of the test set from 0.278 2 mg·L-1 to 0.210 9 mg·L-1, reduced the MAE of the test set from 0.222 3 mg·L-1 to 0.153 7 mg·L-1 and increased the R2 of the test set from 0.864 0 to 0.921 8, which indicated that PSO-BPNN had more stable fitting ability. RMSE, MAE, and R2 of the test set in the CNN model were 0.122 0 mg·L-1, 0.092 7 mg·L-1, and 0.970 5, respectively, which showed that CNN had a better fitting and prediction effect than that of BPNN.

Environmental Assessment and Restoration of the Hunjiang River Basin Based on the DPSIR Framework

The Hunjiang River, a vital water system in northeastern China, has suffered severe ecological damage due to overexploitation. This study analyzes the basin’s environmental conditions from 2016 to 2020, identifies key restoration factors, and examines practical restoration projects. Investigating five major pollutants (permanganate index, chemical oxygen demand (COD), biochemical oxygen demand, ammonia nitrogen, total phosphorus) in eight sections, the study finds the Xicun section most polluted, mainly from Baishan City’s industrial and domestic discharges. The ammonia nitrogen concentration at the Zian section also shows deterioration. Using a DPSIR (Driving forces, Pressures, State, Impacts, Responses) framework, the study elucidates the relationship between environmental and socio-economic issues. Results indicate that population changes, industrial development, and water resource management have complex ecological impacts. Evaluating the urban water resource carrying capacity with the entropy weight method and correlation coefficient weighting method, the study finds that increasing forest coverage, improving wastewater treatment efficiency, and reducing COD emissions are crucial. Quantitative assessment of integrated protection and restoration projects involving mountains, rivers, forests, farmlands, lakes, and grasslands demonstrates their positive impact. This research reveals the interplay between the ecological environment and social factors, proposes practical restoration measures, and clarifies project effects, providing reliable decision-making schemes for policymakers.

Synergizing Convergent Cross-Mapping and machine learning for reliable daily forecasting of riverine chlorophyll-a concentration

River algal blooms pose a global ecological and environmental problem, resulting in serious consequences for watershed ecosystems and human health. However, current research has yet to fully identify factors influencing river algal blooms or accurately predict chlorophyll-a concentration, a key indicator, at various lead-times using high-frequency data. Here, we employed Empirical Dynamic Modeling (EDM) and machine learning techniques to forecast daily chlorophyll-a concentration in the Han River of China. The Convergent Cross-Mapping (CCM) analysis revealed the causal relationships between chlorophyll-a concentration and eight variables: pH, total nitrogen, water level in the Han River, water level in the Yangtze River, water temperature, permanganate index, sunlight duration and total phosphorus in the Han River. Subsequent multivariate EDM models with three lead-times (i.e., 1-day, 5-day, and 10-day) only showed acceptable performance in model training (R2 = 0.19–0.73, MAE = 2.76–9.17 μg/L), while exhibited poor predictive performance in model testing (R2 = − 0.50–0, MAE = 9.64–13.09 μg/L). However, Gradient Boosting Machine (GBM) and Random Forest (RF) models with three lead-times exhibited robust performance in both model training (R2 ≥ 0.8, MAE < 4 μg/L) and testing (R2 > 0.6, MAE < 6 μg/L), demonstrating that machine learning models were more suitable than multivariate EDM models for reliably predicting algal blooms. Our study contributes valuable tools for predicting daily chlorophyll-a concentration. The methods presented herein hold broad applicability and offer insights into predicting the entire process of river algal blooms based on daily monitoring data.

The interaction between water quality and meteorological factors on chlorophyll-a concentration in Honghu Lake: based on PiecewiseSEM-generalized additive model coupling model.

In order to explore the interactive effects of environmental factors on the chlorophyll-a (Chl-a) concentration variation in Honghu Lake, this study was based on the monitoring data of Chl-a mass concentration and water quality factors (water temperature, pH, dissolved oxygen, permanganate index, total nitrogen, total phosphorus) and meteorological factors (evaporation, precipitation, sunshine hours, average wind speed) at three research sites (Dakou, Chatan Island, Lantian) in Honghu Lake from January 2010 to December 2019. Time series analysis, piecewise structural equation model (PiecewiseSEM), and generalized additive model (GAM) were used to quantitatively study the spatial and temporal changes of different environmental factors and their interaction with chlorophyll-a concentration in Honghu Lake. The results showed that the effects of TN and DO on Chl-a at Dakou and Chatan Island were more significant than other environmental meteorological factors, while the effects of DO and CODMn on Chl-a at Lantian were more obvious. At the same time, it was found that Chl-a had a non-linear relationship with TN and DO at Dakou and Chatan Island, a non-linear relationship with DO at Lantian, and a linear relationship with CODMn. The interaction effect of dominant environmental meteorological factors on Chl-a was significantly higher than that of a single factor, and the explanation rates were 80.6%, 72.8%, and 64.6%, respectively. In conclusion, based on the Piecewise SEM and GAM model, it not only can reveal the influence of the interaction of influencing factors on the change of Chl-a concentration, but also has important significance for the early warning and control of lake eutrophication.

Spatiotemporal Changes in the Quantity and Quality of Water in the Xiao Bei Mainstream of the Yellow River and Characteristics of Pollutant Fluxes

The Xiao Bei mainstream, located in the middle reaches of the Yellow River, plays a vital role in regulating the quality of river water. Our study leveraged 73 years of hydrological data (1951–2023) to investigate long-term runoff trends and seasonal variations in the Xiao Bei mainstream and its two key tributaries, the Wei and Fen Rivers. The results indicated a significant decline in runoff over time, with notable interannual fluctuations and an uneven distribution of runoff within the year. The Wei and Fen Rivers contributed 19.75% and 3.59% of the total runoff to the mainstream, respectively. Field monitoring was conducted at 11 locations along the investigated reach of Xiao Bei, assessing eight water quality parameters (temperature, pH, dissolved oxygen (DO), chemical oxygen demand (COD), ammonia nitrogen (NH3-N), total phosphorus (TP), permanganate index (CODMn), and 5-day biochemical oxygen demand (BOD5)). Our long-term results showed that the water quality of the Xiao Bei mainstream during the monitoring period was generally classified as Class III. Water quality parameters at the confluence points of the Wei and Fen Rivers with the Yellow River were higher compared with the mainstream. After these tributaries merged into the mainstream, local sections show increased concentrations, with the water quality parameters exhibiting spatial fluctuations. Considering the mass flux process of transmission of the quantity and quality of water, the annual NH3-N inputs from the Fen and Wei Rivers to the Yellow River accounted for 11.5% and 67.1%, respectively, and TP inputs accounted for 6.8% and 66.18%. These findings underscore the critical pollutant load from tributaries, highlighting the urgent need for effective pollution management strategies targeting these tributaries to improve the overall water quality of the Yellow River. This study sheds light on the spatiotemporal changes in runoff, water quality, and pollutant flux in the Xiao Bei mainstream and its tributaries, providing valuable insights to enhance the protection and management of the Yellow River’s water environment.

Evaluation of baffle-plate bioreactor coupling with adsorption column for the remediation of aquaculture wastewater

In this study, a baffle-plate bioreactor was designed and coupled with an adsorption column for the remediation of aquaculture wastewater. Specifically, baffle-plate was installed in the bioreactor to improve the removal of carbon (C) and nitrogen (N), while the ceramsite prepared from steel slag and river sediment was filled into the column for the adsorptive removal of phosphorus (P). To evaluate the performance of the bioreactor, the operation conditions (e.g., hydraulic retention time (HRT) and C/N ratio) were tentatively optimized. Under the optimal operation conditions, the effects of sulfamethoxazole (SMX) on the performance of the bioreactor and the microbial community were comprehensively investigated. Satisfactorily, the removal rates of NH4+-N, NO3−-N and permanganate index (IMn) were all above 88 % before adding SMX. Moreover, neither IMn nor NH4+-N was influenced by the SMX shock, whereas the NO3−-N removal was significantly deteriorated owing to the inhibitory effect of SMX on denitrifying bacteria. As revealed by the analysis of microbial communities, the microbial abundance obviously increased in the aerobic (e.g., Azohydromonas and Chitinophagaceae) and anaerobic (e.g., Alkaliflexus) zone as compared to the initially inoculated sludge. Nevertheless, the abundance of most denitrifying genera decreased after adding SMX, leading to the inefficient denitrification. In addition, the ceramsite prepared under the optimum conditions had great void fraction (55.2 %) and water absorbency (29.5 %). The average removal rate of phosphorus by the adsorption column was 71.6 %. Overall, these findings may shed new lights into the coupling of bioreactor and adsorption column for the remediation of aquaculture wastewater.

Watershed landscape characteristics and connectivity drive river water quality under seasonal dynamics

Landscape characteristics and connectivity are pivotal determinants of river water quality within the context of water quality management. They play particularly important roles in the spread of non-point source pollution. However, previous research has not investigated their combined effects, especially in the topographically and ecologically volatile Loess Plateau, China. This study applied machine learning and positive matrix factorization techniques to water quality monitoring data for the Wuding River basin, China, from 2017 to 2021 to assess the seasonal impacts of environmental factors on river water quality. The results showed that there were spatial and seasonal variations in pollution sources, and specific thresholds for the key landscape indices that influence water quality were identified. Landscape composition and configuration have the greatest influence on the water quality parameters during the dry season, whereas connectivity and landscape configuration are more critical during the wet season. The connectivity contributions to chemical oxygen demand, the potassium permanganate index, and total phosphorus in the wet season were 19.07%, 27.47%, and 5.08% greater than in the dry season, respectively. Connectivity also made a significant contribution to the composite water quality index, accounting for 33.46% in the dry season and 36.22% in the wet season. The positive matrix factorization model identified agricultural non-point sources and mixed pollutants as the main factors affecting water quality during the dry season, whereas erosion and mixed pollution sources dominated the wet season. Critical thresholds were established for the landscape indices that affected water quality, such as a landscape shape index below 18.5 and a grassland proportion above 65%. The results can be used to develop more effective water quality management strategies and illustrate the essential role played by connectivity when tailoring water quality management strategies to seasonal variations.

Effect of the ten-year fishing ban on change of phytoplankton community structure: Insights from the Gan River.

The Yangtze River is one of the largest riverine ecosystems in the world and is a biodiversity hotspot. In recent years, this river ecosystem has undergone rapid habitat deterioration due to anthropogenic disturbances, leading to a decrease in freshwater biodiversity. Owing to these anthropogenic impacts, the Chinese government imposed a "Ten-year fishing ban" (TYFB) in the Yangtze River and its tributaries. Ecological changes associated with this decision have not been documented to evaluate the level of success. This study evaluates the success of the TYFB by analyzing the changes in phytoplankton communities and comparing them to periods before the TYFB was imposed. A total of 325 phytoplankton species belonging to 7 phyla and 103 genera dominated by Chlorophyceae and Bacillariophyceae were identified. Species diversity according to the Shannon-Wiener ranged between 1.19 and 3.00. The results indicated that phytoplankton diversity increased, while both density and biomass decreased after the TYFB. The health of the aquatic ecosystem appeared to have improved after the TYFB, as indicated by the phytoplankton-based index of biotic integrity. Significant seasonal and spatial differences were found among the number of species, density, and biomass of phytoplankton, where these differences were correlated with pH, water depth, chlorophyll-a, permanganate index, transparency, copper, ammonia nitrogen, and total phosphorus based on redundancy analysis. Results from this study indicate that the TYFB played an important role in the restoration of freshwater ecosystem in the Yangtze River and its tributaries.

Analysis of the Water Quality Status and Its Historical Evolution Trend in the Mainstream and Major Tributaries of the Yellow River Basin

The Yellow River basin, an area of extreme water scarcity, has faced significant challenges in water quality management due to rapid economic and social development since the 1980s. This study analyzes the water quality evolution over nearly 40 years, focusing on primary pollutants like chemical oxygen demand (COD), ammonia nitrogen (NH3-N), and permanganate index (CODMn). In the 1990s, sections of the river were severely polluted, with some areas failing to meet the lowest national standards. In 2000, 32% of the river water was classified as inferior Class V. However, enhanced water resource management and stricter pollutant regulations introduced after 2000 have significantly improved water quality. By 2010, water quality reached its nadir, with 16% of water classified as inferior Class V and 25% as Class IV–V. By 2020, water quality showed marked improvement, with a significant reduction in segments classified as inferior Class V and Class IV–V. Recent years have seen water quality stabilize, with COD meeting Class I standards and NH3-N and CODMn meeting Class II standards based on national criteria. The study also highlights discrepancies in water quality between the mainstream and tributaries of the Yellow River. While the mainstream generally maintains good water quality, many tributaries remain severely polluted. In 2022, 85% of the water in tributaries was classified as Class I to III, 12.3% as Class IV to V, and only 2.7% as Class V. However, all water in the mainstream reached Class I–III, with 86% achieving Class II and 14% achieving Class I. A detailed analysis of the Huayuankou section over the past three decades shows a general decline in pollution indicators. Seasonal water quality fluctuations, correlated with flow rates and temperatures, were observed, often exhibiting normal distribution patterns. These findings underscore the effectiveness of sustained pollution control and the need for continuous, adaptive management strategies to improve and maintain water quality in the Yellow River basin.

Relationships between water quality of a long-distance inter-basin water diversion project and air pollution emissions along the canal: Distributions, lag effects, and nonlinear responses

Understanding and quantifying the influences and contributions of air pollution emissions on water quality variations is critically important for surface water quality protection and management. To address this, we created a five-year daily data matrix of six water quality indicators—permanganate index (CODMn), NH3-N, pH, turbidity, conductivity, and dissolved organic matter (DOM)—and six air pollution indicators—O3, CO, NO2, SO2, 2.5 μm particulate matter (PM2.5), and inhalable particles (PM10)—using data from seven national monitoring stations along the world's longest water-diversion project, the Middle Route of the South-to-North Water Diversion Project in China (MR-SNWD). Multivariate techniques (Mann–Kendall, Spearman's correlation, lag correlation, and Generalized Additive Models [GAMs]) were applied to examine the nonlinear relationships and lag effects of air pollution on water quality. Air pollution and water quality exhibited marked spatial heterogeneity along the MR-SNWD, with all water quality parameters meeting Class I or II national standards and the air pollution indicators exceeding those thresholds. Except for CODMn and DOM, the other water quality and air pollution indicators exhibited significant seasonal differences. Air pollution exhibited significant lag effects on water quality at the northern stations, with NO2, SO2, PM2.5, and PM10 being highly correlated with changes in pH, with an average lag of 17 d. Based on the GAMs, lag effects enhanced the significant nonlinear relationships between air pollution and water quality, increasing the average deviance explained for CODMn, NH3-N, and pH by 93%, 24%, and 41%, respectively. These findings provide a scientific basis for protecting water quality along the long-distance inter-basin water-diversion project under anthropogenic air pollution.

Spatial-temporal distribution characteristics of surface water pollutants and their potential sources in Ngari, China.

The Ngari region has many important rivers and is critical to water resource security and water resource continuity in China and even in adjoining Asian countries. However, the spatial distribution and monthly variation in local water quality have been poorly understood until recently. In this study, the spatial-temporal variations of 12 water quality parameters, including pH, dissolved oxygen (DO), permanganate index (IMn), chemical oxygen demand (COD), five-day biochemical oxygen demand (BOD5), ammonia nitrogen (NNH3), total nitrogen (Ntotal), total phosphorus (Ptotal), copper (Cu), fluoride (F), arsenic (As) and cadmium (Cd), were determined from samples collected monthly at 22 water cross-sectional sites in the Ngari region in 2020. The surface water pollution in the southern Ngari region was the most serious, and the water pollution level in winter was higher than that in the other seasons. As (0.0781 ~ 0.6154 mg/L) and F (1.05 ~ 4.64 mg/L) were the main exceedance factors derived from the recharge of high arsenic and fluoride geothermal water and weathering of As and F-bearing minerals. The hazard quotient and carcinogenic risk for As and F at the five contaminated sampling sites indicated potential health risks and even carcinogenicity to local populations. The hydrochemistry types of the lakes and rivers in the Ngari region were mainly chloride water and carbonate water. The results from this study can provide a scientific basis for the prevention and control of surface water pollution in the Ngari region and contribute to subsequent research on the ecology of water bodies.

Phosphorus prediction in the middle reaches of the Yangtze river based on GRA-CEEMDAN-CNLSTM-DBO

Accurate and rapid prediction of water quality is crucial for the protection of aquatic ecosystems. This study aims to enhance the prediction of total phosphorus (TP) concentrations in the middle reaches of the Yangtze River by integrating advanced modeling techniques. Using operational and discharge data from the Three Gorges Reservoir (TGR), along with water quality parameters from downstream sections, we used Grey Relational Analysis (GRA) to rank the factors contributing to TP concentrations. The analysis identified turbidity, permanganate index (CODMn), total nitrogen (TN), water temperature, chlorophyll a, upstream water level variation, and discharge from the Three Gorges Dam (TGD) as the top contributors. Subsequently, a coupled neural network model was established, incorporating these key contributors, to predict TP concentrations under the dynamic water level control during flood periods in the TGR. The proposed GRA-CEEMDAN-CN1D-LSTM-DBO model was compared with conventional models, including BP, LSTM, and GRU. The results indicated that the GRA-CEEMDAN-CN1D-LSTM-DBO model significantly outperformed the others, achieving a correlation coefficient (R) of 0.784 and a root mean square error (RMSE) of 0.004, compared to 0.58 (R) and 0.007 (RMSE) for the LSTM model, 0.576 (R) and 0.007 (RMSE) for the BP model, and 0.623 (R) and 0.006 (RMSE) for the GRU model. The model's accuracy and applicability further validated in two sections: YC (Yunchi) in Yichang City and LK (Liukou) in Jingzhou City, where it performed satisfactorily in predicting TP in YC (R = 0.776, RMSE = 0.007) and LK (R = 0.718, RMSE = 0.007). Additionally, deep learning analysis revealed that as the distance away from dam increased, prediction accuracy gradually decreased, indicating a reduced impact of TGR operations on downstream TP concentrations. In conclusion, the GRA-CEEMDAN-CN1D-LSTM-DBO model demonstrates superior performance in predicting TP concentration in the middle reaches of the Yangtze River, offering valuable insights for dynamic water level control during flood seasons and contributing of smart to the advancement of water management in the Yangtze River.

Effects of land use patterns on seasonal water quality in Chinese basins at multiple temporal and spatial scales

Diverse land use patterns exhibit varying effects on water quality across different seasons and spatial scales. However, current studies on the correlation between land use and water quality in single small-scale basins no longer meet the needs of regional coordinated development. Simultaneous comparative analysis of multiple large-scale basins can promote water environmental protection in different basins, but there is currently limited relevant research. In this study, the data from 86 sampling points across seven major river basins in China were analyzed. Multivariate statistical analysis and the redundancy analysis (RDA) were employed to investigate the influence of land use patterns on water quality at different seasons and spatial scales. The results indicated notable differences in water quality across various seasons and locations. Except for higher pH and permanganate index (COD) concentrations in the wet season in the Songhua River Basin and higher COD concentrations in the Pearl River Basin, the concentrations of all parameters in other basins are higher in the dry season. PH and COD exhibited considerable spatial variations within basins, while dissolved oxygen (DO) and ammonia nitrogen (NH4+-N) showed smaller variations. RDA showed that land use had a more pronounced effect on water quality during the dry season in the Yangtze, Liao and Pearl River Basins, while the impact was greater in the wet season in the four basins of the Yellow, Huai, Hai and Songhua River Basins. At the spatial scale, the 2000 m buffer zone had the most significant impact within the Yangtze, Hai and Liao River Basins, while the 1000 m buffer zone had the greatest impact in the Huai and Pearl River Basins. For the Yellow and Songhua River Basins, the buffer zone was 500 m during the wet season and 1000 m and 2000 m during the dry season, respectively. The research findings can offer a scientific foundation for the development of basin-specific land management policies and water quality protection measures in different basins from a multi-scale perspective.

Characteristics and Factors Influencing Summer Phytoplankton Community Structures of Typical Lakes in Southeast Hubei, China

Phytoplankton plays a key role in the stabilization of aquatic ecosystems. However, there is a lack of research on the structure of phytoplankton communities and their influencing factors in shallow lakes in Southeast Hubei. In this study, four typical lakes were selected in the summer of 2019, and the phytoplankton community structure and its drivers were investigated for each lake. The results showed that the number of phytoplankton species, dominant species, biomass, and abundance varied among lakes. A comprehensive trophic level index (TLI) assessment showed that Lake Xiandao was oligotrophic, Lake Baoan was light eutrophic, and Lake Wang and Lake Ci were moderate eutrophic. The Shannon–Wiener, Margalef, and Pielou indices showed that the phytoplankton communities of Lake Wang and Lake Ci are less stable, and the water bodies are more severely contaminated. In contrast, the communities of the Lake Baoan and Lake Xiandao are more stable and have superior water quality. A redundancy analysis (RDA) indicated that the primary variables influencing phytoplankton community structures were pH and water temperature (WT) (Lake Ci); permanganate index (CODMn) and pH (Lake Xiandao); WT and total nitrogen (TN) (Lake Wang); and WT and total phosphorus (TP) (Lake Baoan).

Temporal and spatial variation in atmospheric wet deposition of nutrients and organic matter at the southern and northern foothills of the Qinling Mountains

ABSTRACT Wet deposition significantly impacts the basin ecosystem and water quality of the Qinling Mountains (QMs). However, few research focused on the difference in wet deposition between the northern (QN) and southern (QS) foothills due to the barrier effect of the QMs. This two-year investigation studied nutrient and organic matter deposition in QN and QS during summer and autumn. Results showed higher concentrations of nitrogen (N), phosphorus (P), dissolved organic carbon (DOC), and permanganate index (CODMn) in QN's rainwater, particularly in autumn. The CODMn in QN ranged 0.59–7.67 mg/L, and DOC ranged 0.64–4.45 mg/L. For QS, the CODMn ranged 0.71–3.25 mg/L, and DOC ranged 0.28–2.62 mg/L. Backward trajectory analysis revealed the accumulation of pollutants in QN originating from Northern China, intensified by autumn heating and straw burning. Tyrosine-like and humic-like components in rainwater DOM suggested that they primarily originated from autochthonous sources. Rainwater N:P mass ratios range from 39:1 to 145:1, highlighting a higher N input than P in QMs' wet deposition. The findings underscore the importance of atmospheric wet deposition in nutrient and organic matter input to the QMs and establish a foundation for exploring the ecological effects of wet deposition.

Distribution, relationship, and environmental driving factors of chlorophyll-a and algal cell density: A national view of China

Chlorophyll-a (Chl-a) and algal cell density (ACD) are vital for assessing algal proliferation and eutrophication in aquatic ecosystems. Although Chl-a is often used as a proxy for ACD, its accuracy requires validation, and studies on their linear correlation are scarce. Additionally, Chl-a and ACD are influenced by various factors, including nutritional, economic, biochemical, physicochemical, and meteorological factors. However, the relative importance of these factors on Chl-a and ACD remains insufficiently studied. This study analyzed data from 57 lakes and reservoirs across China from March 2021 to February 2023, investigating the distribution patterns of Chl-a and ACD across different regions and seasons. The study employed a linear regression model to explore the correlation between Chl-a and ACD in various regions and seasons throughout China. Furthermore, Mantel test and generalized linear model were used to evaluated the relative importance of nutritional factors (total nitrogen (TN), total phosphorus (TP), TN/TP ratio (TN/TP), and ammonia nitrogen (NH3-N)), economic factors (gross domestic product), meteorological factors (surface pressure, net solar radiation, air temperature, wind speed (WS), and rainfall (RF)), as well as biochemical and physicochemical factors (turbidity (TUR), pH value, water temperature (WT), permanganate index, and dissolved oxygen) on Chl-a and ACD. Results showed that the average concentrations of Chl-a and ACD in South China were highest, at 12 μg/L and 19.5 × 106 cells/L, respectively. Seasonally, Chl-a peaked in spring and was lowest in winter, while ACD peaked in summer and was lowest in winter. Significant seasonal and regional variations in the Chl-a and ACD correlation were observed, with spring showing the strongest relationship. In Central China, Chl-a and ACD were significantly correlated throughout the four seasons, whereas correlations were less distinct in Eastern and Western regions. Therefore, Chl-a as a proxy for ACD requires caution. Nutrient factors (TN, TP, TN/TP, NH3-N) were identified as the primary drivers of Chl-a and ACD. Meteorological factors (WS, RF), along with biochemical and physicochemical factors (WT, TUR) also emerged as critical predictors of Chl-a and ACD, with spatial variations. This study validates Chl-a as a proxy for ACD, analyzes their spatiotemporal distribution, and assesses the influence of various factors on Chl-a and ACD, enhancing our understanding of algal dynamics in Chinese lakes and reservoirs.

Analysis of Spatiotemporal Variation and Driving Factors of Water Quality in the Xiangjiang River Basin from 1990 to 2016

In order to explore the spatiotemporal variation characteristics and driving mechanism of water quality in the Xiangjiang River Basin, the data of 16 water quality parameters at 113 stations over 26 years from 1990 to 2016 in the Xiangjiang River Basin were collected for synthetically assessing the water quality and identifying its main pollutants through the water quality index and other methods. The causal mechanism of water quality, especially the driving effect of water level and land use pattern, was analyzed. The results showed that： ① The overall water quality grade of the Xiangjiang River Basin during the study period was "good." However, the water quality deteriorated first （from 1990 to 2003） and then improved （from 2004 to 2016）. The season variation in water quality was not obvious, but the water quality fluctuation of the wet season was larger. The water pollution load of the main stream decreased successively from the middle reaches, downstream reaches, and upstream reaches. The upstream tributaries had the best water quality, whereas areas with poor water quality were mainly distributed at the mouth of the middle and downstream tributaries, especially in areas where multiple tributaries converged. ② Toxic heavy metals had the characteristics of toxicity, persistence, and bioaccumulation. If they exceeded a certain concentration in water, they were difficult to purify, posing great harm to the natural environment and human health. The toxic metal index （CI1） was the leading factor affecting water quality, in which Hg and Cd were the main parameters affecting the overall water quality of the Xiangjiang River Basin. ③ The water level had a positive impact on the water quality of the Xiangjiang River by diluting environmental parameters. Land type had little effect on heavy metal concentration, whereas forest land could improve water quality. Grassland had a negative correlation with permanganate index over a large scale range （≥ 5 km）. The increase in water bodies, arable land, and impermeable surface areas within the watershed increased the probability of high fecal coliform concentration in the water body. ④ With the increase in buffer distance, the water quality explained by the land use pattern increased. On the scale of 10 km buffer zone in the riparian zone, the explanation degree by land use pattern on water quality was the highest, which was an effective scale for water quality control of the Xiangjiang River. This research showed that the driving factors of heavy metal pollution and other pollution were different, but their regional differences were all obvious in the Xiangjiang River Basin. Therefore, pollution control should be classified and taken according to local conditions.