Spatial and temporal distributions of macrobenthic communities and their environmental driving factors in deepwater reservoirs: a case study of Yinghu Lake, China

Characterization of spatiotemporal variation of water quality is a basic environmental issue with implications for public health in China. Trends in the temporal and spatial distribution of water quality in the Huai River System (HRS) were analyzed using yearly surface water quality data collected from 1982 to 2009. Results showed that the water quality of the main stream deteriorated in the 1990s and early 2000s but has been ameliorated since 2005. The sections that were classified as severely polluted from the monitoring data were located largely in the middle reach. The water quality of HRS fluctuated during the period 1997-2009; it has improved and stabilized since 2005. In terms of spatialized frequency of serious pollution, heavily polluted regions were mostly concentrated in the area along several tributaries of the Ying, Guo and New Sui Rivers as well as the area north of Nansi Lake. These regions decreased from 1997 to 2009, especially after 2005. Our analysis indicated that water pollution in HRS had a close relation with population and primary industry during the period 1997-2009, and implied that spatiotemporal variation of surface water quality could provide a scientific foundation for human health risk assessment of the Huai River Basin.

百花湖是贵阳市重要的城市饮用水源地，并且近年来经常发生水质异常现象.本文利用2009-2018年百花湖长时间序列的监测数据，采用综合营养状态指数法和Pearson相关性分析，研究了百花湖10年间的水质变化特征和影响因素.结果表明：1）库区叶绿素a（Chl.a）、总磷（TP）、总氮（TN）、高锰酸盐指数（COD_Mn）和透明度（SD）的浓度范围分别是3.43~39.72 mg/m³、0.034~0.115 mg/L、1.200~2.759 mg/L、1.41~5.51 mg/L和0.75~2.07 m，且高氮磷比（12~63）表明百花湖是磷限制型.2）在空间上，TP、TN、氨氮、COD_Mn和Chl.a浓度沿水体流向逐渐降低，SD呈相反变化趋势.3）10年来，百花湖水质由Ⅳ类转变为Ⅲ类，综合营养状态由轻度富营养化状态转变为中营养状态，水质整体向好.4）入库支流是影响百花湖库区水质的主要因素，长期以来，东门桥河、南门河水质TP和TN等超标严重，给库区水质稳定达标带来威胁.5）百花湖Chl.a浓度与气温、水位、风速和TP等指标显著相关，是受水文、气象及营养盐因素的综合控制.未来在百花湖水环境保护治理过程中，应加大对东门桥河、南门河等重点支流的污染治理，加强对水动力学、气候变化等水文气象因素影响库区水质（藻类水华）的机制研究.;Lake Baihua is an important source of urban drinking water in Guiyang City, and water quality anomalies often occur for many years. The long-term water quality monitoring data (from 2009 to 2018), together with the comprehensive trophic level index(TLI) method and Pearson correlation analysis, are used to study the water quality change characteristics and influencing factors of Lake Baihua over the past ten years. The results show that:1) The concentrations of chlorophyll-a(Chl.a), total phosphorus(TP), total nitrogen(TN), permanganate(COD_Mn) and transparency(SD) ranged from 3.43 to 39.72 mg/m³, from 0.034 to 0.115 mg/L, from 1.200 to 2.759 mg/L, from 1.41 to 5.51 mg/L and from 0.75 to 2.07 m, respectively. The high N/P ratio (12-63) indicates that Lake Baihua is a phosphorus limited type. 2) In space, the concentrations of TP, TN, ammonia nitrogen, COD_Mn and Chl.a decreased gradually along the flow direction of the water body, and SD showed the opposite trend. 3) The water quality of Lake Baihua has gradually improved from class Ⅳ to class Ⅲ from 2009 to 2018, and TLI has changed from light-eutrophic state to mesotrophic state. 4) The inflow tributaries are the main factors affecting the water quality in the reservoir area. The water TP and TN in Dongmenqiao and Nanmen Rivers were seriously over standard, which threatens the stability of water quality in the reservoir area. 5) The concentration of Chl.a was significantly related to temperature, water level, wind speed, and total phosphorus, which is considered to be controlled by the combination of hydrological, meteorological and nutrient factors. In the future, the pollution control of Dongmenqiao, Nanmen Rivers needs to be strengthened. Meanwhile, the mechanism of water quality (algal bloom) influenced by hydrodynamics, climate change and other hydrometeorological factors also needs to be further studied.

Herein, cluster analysis was applied to evaluate the spatiotemporal variations in water quality variables of a river. The analysis was performed using the data obtained from 15 monitoring stations during 2007-2018 in the Yeongsan River, Republic of Korea. The spatiotemporal analysis successfully clustered the annual water quality variables temporally into years of poor water quality (2007-2012) and good water quality (2013-2018), and spatially into stations observing bad water quality (midstream) and good water quality (upstream and downstream). For the spatial cluster analysis results before and after a large river engineering project, the water quality was grouped into four clusters according to regional effects and water pollutant sources. The clustering analysis results clearly reflected changes in the water quality along the river due to the project. Overall, this study demonstrates that cluster analysis can be effectively used for evaluating spatiotemporal variations in river water quality.

In the present study, statistical analyses (descriptive and principal component analysis) were applied to Ghaggar River surface water data set monitored in the month of June 2006 and 2007 to check spatio-temporal variations in water quality. From these, two summer observations were taken into consideration because in summer season, high concentrations are observed for different water constituents. The various physico-chemical constituents like pH, total dissolved solids (TDS), electrical conductivity (EC), temperature, Cl - , HCO 3 - , CO 3 2- , Na + , K + , Ca 2+ , Mg 2+ , F - , SO 4 2- and PO 4 3- were analyzed. Statistical results revealed that water quality variables were totally different during the two summer observations. Keywords : Principal component analysis, spatio-temporal variations, water quality, summer season.

This research investigated the spatiotemporal variation of water quality in the Gilgel Gibe reservoir, Ethiopia, using physicochemical water quality parameters. Nonparametric tests and multivariate statistical techniques were used to evaluate data sets measured during dry and rainy seasons. Electrical conductivity (EC), pH, biochemical oxygen demand (BOD5), total phosphorus (TP), total nitrogen (TN), nitrate (NO3-), total dissolved solids (TDSs), and total suspended solids (TSSs) were all significantly different among seasons (Mann-Whitney U test, p < 0.01). In addition, principal component analysis distinguished dry season samples from wet season samples. The dry season was positively associated with EC, pH, TP, TN, NO3-, TDS, and TSS and negatively associated with BOD5. The wet season was in contrast associated with high values of turbidity, soluble reactive phosphorus (SRP), water temperature, and dissolved oxygen (DO). Within the reservoir, spatial variation was observed for some of the water quality parameters, with significant difference at p = < 0.05. Overall, high nutrient concentrations suggest eutrophic conditions, likely due to high nutrient loading from the watershed. Levels of TSS, attributed to inputs from tributaries, have been excessive enough to inhibit light penetration and thus have a considerable impact on the aquatic food web. Our findings indicate that the reservoir is at high risk of eutrophication and siltation, and hence, urgent action should target the planning and implementation of integrated watershed management for this and similar reservoirs in the region.

Understanding the spatiotemporal patterns of water quality is crucial because it provides essential information for water pollution control. The spatiotemporal variations in water quality for the Nanxi River in the Taihu watershed of China were evaluated by a water quality index (WQI) and multivariate statistical techniques; additionally, the potential sources of contamination were identified. The data set included 22 water quality parameters collected during the monitoring period from 2015 to 2020 for 14 monitoring stations. WQI assessment revealed that approximately 85% of monitoring stations were classified as “medium-low” water quality, and most showed continuous improvement in water quality. Cluster analysis divided the 14 monitoring stations into three clusters (low contamination, medium contamination and high contamination). Discriminant analysis identified pH, petroleum, volatile phenol, chemical oxygen demand, total phosphorus, F, S, fecal coliform, SO4, Cl, NO3-N, total hardness, NO2-N and NH3 as important parameters affecting spatial variations. Factor analysis identified four potential contamination source types: nutrient, organics, feces and oil. This study demonstrated the usefulness of multivariate statistical techniques in assessing large data sets, identifying contamination source types, and better understanding spatiotemporal variations in water quality to restore and protect water resources.

Zhari Namco is situated in the alpine grassland belt of northwestern Xizang with a fragile ecological environment. As the third-largest lake in Xizang, there has been a long-term lack of research data concerning its basin water environment. In an effort to elucidate the surface water environment characteristics of the basin and the factors influencing them, an extensive investigation was conducted from August 2021 to June 2022, encompassing periods of high flow, low flow, and base flow. Further, the study also involved comprehensive assessments of the water chemistry characteristics and spatial-temporal variation in lake sampling sites of the basin that were not significant by using mathematical statistics, hydrochemical analysis, correlation analysis, and principal component analysis. The findings revealed the following： ① The water in the Zhari Namco Basin exhibited an alkaline nature, with dominant ionic compositions in the lake comprising Na+, SO42-, and Cl-, whereas the rivers were primarily characterized by Ca2+, HCO3-, and SO42-. ② The main pollutants exceeding established standards included sulfates, arsenic, chlorides, and total phosphorus. The study identified significant spatiotemporal variations in water quality. Temporally, the exceedance of sulfates, arsenic, and total phosphorus was most pronounced during high-flow periods, followed by that during low-flow and base flow periods, with chloride levels showing less temporal variation. Spatially, river water quality surpassed that of the lakes, with arsenic, total phosphorus, TDS, sulfate, chloride, K+, and Na+ concentrations in lakes 1 to 2 orders of magnitude higher than those in rivers. Water qualities exceeding the established standard were primarily found in the lake, with less spatial variations within the lake itself. ③ Hydrochemical processes within the basin were found to be primarily influenced by natural phenomena, including evaporation-concentration and rock weathering. Various elements entered the lakes via surface runoff, where they continuously accumulated under the influence of evaporation-concentration processes, ultimately leading to exceedances. ④ Temporal variations in water quality were primarily attributed to increased elemental loss and intensified evaporation during high-flow periods. The spatial discrepancies in water quality were predominantly a consequence of the differing hydrodynamic conditions between flowing water bodies and enclosed water bodies.

Spatio-temporal changes in surface water quality and sediment phosphorus content of a large reservoir in Turkey

Based on 116-phase Landsat satellite remote sensing data between 1984 and 2021, this paper inverted the long-term distribution of water quality parameters such as dissolved oxygen (DO), oxidation-reduction potential (ORP), and chlorophyll-a (Chl-a) of the water in Baiyangdian Lake, and analyzed the spatiotemporal distribution characteristics and variations of water quality in Baiyangdian Lake over 37 years. In terms of temporal scale, the inter-annual variation of DO shows certain stability, and the images with the proportion of pollution-free area reaching 90% or more account for 88.7% of the total, showing no pollution in terms of DO; In terms of ORP, the images with the proportion of pollution-free area reaching 90% or more account for 81.2% of the total, showing no pollution to light pollution; the inter-annual variation of Chl-a concentration shows certain volatility, and the overall performance is light-moderate pollution, but the pollution level has been alleviated in recent years; the pollution status of water quality in Baiyangdian Lake in terms of Chl-a and ORP has a certain correlation. In terms of spatial scale, the spatial distribution pattern of DO and ORP is stable, presenting the characteristic that most areas are pollution-free, and a few areas with more frequent human activities show light and moderate pollution.

Assessment of river water quality has been attracting a great deal of attention because of its important implications for the living environment of human beings and aquatic organisms. River water quality is commonly assessed using dozens of different water quality parameters. However, different parameters may contain redundant information, which could lead to the waste of monitoring efforts. Thus, this study constructed a novel cost-effective assessment model of river water quality using the 1-year monitoring data collected from 23 sampling stations in the water control zone of Tolo Harbour and Channel in Hong Kong. First, the spatio-temporal variations of water quality parameters and the overall status of river water quality were analyzed based on all 19 parameters using Kruskal-Wallis test, hierarchical cluster analysis, and the water quality index (WQI). The results indicated that most water quality parameters and overall water quality status varied significantly over space, but did not exhibit obvious seasonal differences; and 99.27% of water samples were identified to be in good or excellent status of overall WQI. Then, using principal component analysis (PCA)/factor analysis (FA) and Pearson's correlation analysis, eight parameters, including 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), ammonia-nitrogen (NH3-N), nitrate-nitrogen (NO3-N), chlorophyll-a (Chl-a), fluoride (F-), total suspended solids (TSS), and arsenic (As), were verified to be responsible for the greatest contributions to water quality, the assessment of overall water quality status. These eight crucial parameters were further employed to establish six cost-effective water quality assessment models. Using the overall WQI as the benchmark, the results of linear regression analysis demonstrated that the cost-effective model constructed based on BOD5, COD, NH3-N, NO3-N, F-, TSS, and As were the optimal water quality assessment model, which can achieve the most reliable results with reduced parameters.

Assessing the spatio-temporal variations of surface water quality is important for water environment management. In this study, surface water samples are collected from 2008 to 2015 at 17 stations in the Ying River basin in China. The two pollutants i.e. chemical oxygen demand (COD) and ammonia nitrogen (NH3-N) are analyzed to characterize the river water quality. Cluster analysis and the seasonal Kendall test are used to detect the seasonal and inter-annual variations in the dataset, while the Moran’s index is utilized to understand the spatial autocorrelation of the variables. The influence of natural factors such as hydrological regime, water temperature and etc., and anthropogenic activities with respect to land use and pollutant load are considered as driving factors to understand the water quality evolution. The results of cluster analysis present three groups according to the similarity in seasonal pattern of water quality. The trend analysis indicates an improvement in water quality during the dry seasons at most of the stations. Further, the spatial autocorrelation of water quality shows great difference between the dry and wet seasons due to sluices and dams regulation and local nonpoint source pollution. The seasonal variation in water quality is found associated with the climatic factors (hydrological and biochemical processes) and flow regulation. The analysis of land use indicates a good explanation for spatial distribution and seasonality of COD at the sub-catchment scale. Our results suggest that an integrated water quality measures including city sewage treatment, agricultural diffuse pollution control as well as joint scientific operations of river projects is needed for an effective water quality management in the Ying River basin.

This study is an effort to trace the spatiotemporal variation in water at Narmada estuarine region through solute concentration. A total of 72 water samples were collected and analyzed from three sampling points along with in situ measurement of tidal height at monthly basis for 2 years. Result shows that spatiotemporal variation of water quality occurs because of the following main mechanisms, i.e., carbonate weathering, dilution and seawater–freshwater mixing. Firstly, points situated toward inland showing the simple dilution effect on receiving high amount of monsoonal precipitation. Secondly, tidal fluctuation pattern has a strong influence on the water quality taken from the point located in near proximity to the coast. Finally, it can be concluded that water quality shows a different response, in accordance with the different tidal phase and the distance from the sea.

Microbial communities, as critical functional components of riverine ecosystems, play a pivotal role in biogeochemical cycles and water quality regulation. The South-to-North Water Diversion Middle Route Project (SNWD-MRP) is a major cross-basin water transfer initiative, and bacteria are essential for the stability of water quality in the project. This study employed environmental DNA (eDNA) metabarcoding targeting the 16S rRNA gene to investigate spatiotemporal variations in water quality and bacterial communities along the SNWD-MRP during summer and winter. Integrated analyses, including redundancy analysis (RDA), Mantel tests, and ecological network modeling, were applied to unravel the driving mechanisms of microbial succession. The water quality along the SNWD-MRP is generally classified as Grade I, with significant seasonal variations in water quality parameters and microbial community composition. In the summer, higher temperatures lead to an increased abundance of cyanobacteria. In contrast, during the winter, lower water temperatures and higher dissolved oxygen levels result in the dominance of Pseudomonas and Bacillota species. RDA identified the permanganate index as the primary driver of microbial composition across seasons, with total phosphorus and total nitrogen having a greater influence in winter. Mantel tests highlighted significant correlations between Cyanobacteria and total phosphorus during winter. Ecological network analysis revealed that the complexity and connectivity of the winter network increased, likely due to suitable nutrient levels rendering the microbial network more complex and stable. These findings underscore the synergistic effects of temperature and nutrient availability on microbial succession, providing actionable insights for optimizing water quality management and ecological stability in large-scale water diversion systems.

The aim of this study was to investigate the spatio-seasonal variations in water quality and suitability of the Shitalakhya river, an economically important and ecologically critical urban river in Bangladesh, along with associated influencing factors and possible sources of water pollution. Therefore, surface water samples were collected monthly from five sampling sites, and fourteen water quality parameters were evaluated. The results showed that some studied water quality parameters, e.g., temperature, TDS, TA, TH, NO2 –, and NO3 –, exceeded the maximum allowable limit, whereas statistically significant (p < .05) variations were observed among pre-monsoon (February–May), monsoon (June–September), and post-monsoon (October–January) seasons. The values of water quality index (WQI) exhibited that the water quality was found to be very poor to unsuitable for drinking, fisheries, or aquatic environment. The principal component analysis (PCA) extracted two PCs explaining 91.092% of the total variance, which suggested that the variations in water quality are attributed mainly to point and nonpoint sources of contamination including municipal and industrial wastewater discharge, and agricultural runoff of inorganic fertilizers. The cluster analysis (CA) also showed relative spatial and seasonal variations in river water quality, indicating the influence of hydrological changes and pollution sources. The study revealed that the water of the Shitalakhya river is highly polluted and potentially hazardous for human uses, and thus more attention should be given to safeguard such an important urban river.

Water quality deterioration is a prominent issue threatening water security worldwide. As the largest river in China, the Yangtze River Basin is facing severe water pollution due to intense human activities. Analyzing water quality trends and identifying the corresponding driver factors are important components of sustainable water quality management. Thus, spatiotemporal characteristics of the water quality from 2008 to 2020 were analyzed by using a Mann-Kendall test and rescaled range analysis (R/S). In addition, multi-statistical analyses were used to determine the main driving factors of variation in the permanganate index (CODMn), ammonia nitrogen (NH3-N) concentration, and total phosphorus (TP) concentration. The results showed that the mean concentrations of NH3-N and TP decreased from 0.31 to 0.16mg/L and 0.16 to 0.07mg/L, respectively, from 2008 to 2020, indicating that the water quality improved during this period. However, the concentration of CODMn did not reduce remarkably. Based on R/S analysis, the NH3-N concentration was predicted to continue to decrease from 2020 to 2033, whereas the CODMn concentration was forecast to increase, highlighting an issue of great concern. In terms of spatial distribution, water quality in the upstream was better than that of the mid-downstream. Multi-statistical analyses revealed that the temporal variation in water quality was predominantly influenced by tertiary industry (TI), the nitrogen fertilizer application rate (N-FAR), the phosphate fertilizer application rate (P-FAR), and the irrigation area of arable land (IAAL), with contribution rates of 15.92%, 14.65%, 3.46%, and 2.84%, respectively. The spatial distribution of CODMn was mainly influenced by TI, whereas that of TP was primarily determined by anthropogenic activity factors (e.g., N-FAR, P-FAR). This study provides deep insight into water quality evolution in the Yangtze River Basin that can guide water quality management in this region.