The dynamic changes in phytoplankton and environmental factors within Dongping Lake (China) before and after the South-to-North Water Diversion Project

Environmental Research, Journal Year: 2024, Volume and Issue: 246, P. 118138 - 118138

Published: Jan. 7, 2024

Language: Английский

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Spatiotemporal comprehensive evaluation of water quality based on enhanced variable fuzzy set theory: A case study of a landfill in karst area

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 450, P. 141882 - 141882

Published: March 29, 2024

Language: Английский

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Spatial variability, source identification, and partitioning of groundwater constituents in a typical lakeside plain on Yungui Plateau

Process Safety and Environmental Protection, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 1, 2024

Language: Английский

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Water quality management of micro swamp wetland based on the “source-transfer-sink” theory: A case study of Momoge Swamp Wetland in Songnen Plain, China

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 446, P. 141450 - 141450

Published: Feb. 24, 2024

Language: Английский

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An integrated framework consisting of spatiotemporal evolution and driving force analyses for early warning management of water quality

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 462, P. 142628 - 142628

Published: May 19, 2024

Language: Английский

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Unraveling the impacts of multiscale landscape patterns and socioeconomic development on water quality: A case study of the National Sustainable Development Agenda Innovation Demonstration Zone in Lincang City, Southwest China

Journal of Hydrology Regional Studies, Journal Year: 2024, Volume and Issue: 51, P. 101660 - 101660

Published: Jan. 19, 2024

National Sustainable Development Agenda Innovation Demonstration Zone in Lincang City, Southwest China Revealing the current water quality status rivers and reservoirs its drivers is crucial to achieving United Nations Goal 6 (SDG 6). We assessed spatial-temporal dynamics influence of natural socioeconomic factors on index (WQI) parameters using redundancy analysis (RDA) partial least squares path model (PLSPM). The results indicate following. (1) annual average values WQI City from 2018 2020 were 92.26, 92.06, 92.45, respectively. spring, summer, autumn, winter 92.48, 90.38, 92.68, 93.49, seasonal levels good or higher. However, spatial heterogeneity exists for some City. (2) highly complex. landscape composition, configuration, pollutant discharges are key affecting annually seasonally, a scale dependence observed. (3) Chemical physical directly affect WQI, particularly at small scales (100 m 500 buffer zones). have strong inhibitory effect (−0.79, −0.78) (−0.56, −0.72), whereas weak promoting (0.05–0.13). Landscape composition configuration indirectly WQI. In contrast, pollution discharge impact through their chemical factors. These findings demonstrate that social interact multiple ways, impacting reservoirs. This interaction depends scale. Therefore, it consider appropriate distance future land use planning, design planning Moreover, controlling wastewater industrial agricultural activities domestic usage vital ensuring high quality.

Language: Английский

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Water quality assessment and pollution evaluation of surface water sources: The case of Weishan and Luoma Lakes, Xuzhou, Jiangsu Province, China

Environmental Technology & Innovation, Journal Year: 2023, Volume and Issue: 32, P. 103397 - 103397

Published: Oct. 11, 2023

Water pollution is a pressing concern in China as well other countries around the world. Despite escalating water quality issues associated with sources of Xuzhou, Jiangsu Province, China—namely Weishan and Luoma Lakes—a dearth scientific systematic guidance regarding environmental management remains. This study investigated potential Lakes through hydrochemical analysis, correlation analysis (CA), principal component (PCA), analyses Quality Index (WQI) evaluation index (PEI). Results showed that average values some components, such turbidity, total dissolved solids (TDS), nitrogen (TN), hardness (TH), alkalinity (ALK), were higher than standard limits. The CA results revealed heterogeneity pathways pollutants ions. PCA was used to identify five key indicators for Lakes, explaining cumulative variance 85.26–86.64% 83.56–85.64%, respectively. In Lake, anthropogenic industrial primary contributors pollution, whereas natural source, followed by agricultural sources. general, WQI PEI indicated Lake classified "good" during period, despite deterioration both lakes. Overall, implementing robust plan maintaining aquatic environment these two lake areas necessary.

Language: Английский

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

Heliyon, Journal Year: 2024, Volume and Issue: 10(13), P. e33695 - e33695

Published: June 28, 2024

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

Language: Английский

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Assessment and prediction of Water Quality Index (WQI) by seasonal key water parameters in a coastal city: application of machine learning models

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 196(11)

Published: Oct. 3, 2024

Language: Английский

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Source apportionment and influencing factors of surface water pollution through a combination of multiple receptor models and Geodetector

Environmental Research, Journal Year: 2024, Volume and Issue: 263, P. 120168 - 120168

Published: Oct. 17, 2024

Language: Английский

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