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

Yihang Wu,

Qianzhu Zhang,

Yuan Luo

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Дек. 18, 2024

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

Язык: Английский

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

Jing Xu,

Yuming Mo,

Senlin Zhu

и другие.

Heliyon, Год журнала: 2024, Номер 10(13), С. e33695 - e33695

Опубликована: Июнь 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.

Язык: Английский

Процитировано

5
Assessment and prediction of Water Quality Index (WQI) by seasonal key water parameters in a coastal city: application of machine learning models DOI

Yuming Mo,

Jing Xu,

Chanjuan Liu

и другие.

Environmental Monitoring and Assessment, Год журнала: 2024, Номер 196(11)

Опубликована: Окт. 3, 2024

Язык: Английский

Процитировано

4
Spatial and temporal distribution characteristics and source apportionment of biogenic elements using APCS-MLR model in the main inlet tributary of Danjiangkou Reservoir DOI

Yihang Wu,

Qianzhu Zhang,

Yuan Luo

и другие.

Environmental Science and Pollution Research, Год журнала: 2025, Номер unknown

Опубликована: Янв. 20, 2025

Язык: Английский

Процитировано

0
Spatio-temporal characteristics and influencing factors of water quality in Xiangxi Bay under the operation of the three gorges reservoir DOI Creative Commons
Aihui Jiang,

Dongsheng Wang,

Zhen Ning

и другие.

Journal of Contaminant Hydrology, Год журнала: 2025, Номер 270, С. 104518 - 104518

Опубликована: Фев. 16, 2025

Язык: Английский

Процитировано

0
Using multiple machine learning algorithms to optimize the water quality index model and their applicability DOI Creative Commons
Fei Ding, Shilong Hao, Wenjie Zhang

и другие.

Ecological Indicators, Год журнала: 2025, Номер 172, С. 113299 - 113299

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
Groundwater Storage Response to Extreme Hydrological Events in Poyang Lake, China’s Largest Fresh-Water Lake DOI Creative Commons
Xiaoxi Yu, Chengpeng Lu, Edward Park

и другие.

Remote Sensing, Год журнала: 2025, Номер 17(6), С. 988 - 988

Опубликована: Март 12, 2025

Groundwater systems are important for maintaining ecological balance and ensuring water supplies. However, under the combined pressures of shifting climate patterns human activities, their responses to extreme events have become increasingly complex. As China’s largest freshwater lake, Poyang Lake supports critical resources, health, adaptation efforts. Yet, relationship between groundwater storage (GWS) hydrological in this region remains insufficiently studied, hindering effective management. This study investigates GWS response by downscaling Gravity Recovery Climate Experiment (GRACE) data validating it with five years observed daily levels. Using GRACE, Global Land Data Assimilation System (GLDAS), ERA5 data, a convolutional neural network (CNN)–attention mechanism (A)–long short-term memory (LSTM) model was selected downscale high resolution (0.1° × 0.1°) estimate recovery times return baseline. Our analysis revealed seasonal fluctuations that phase precipitation, evapotranspiration, runoff. durations flood (2020) drought (2022) ranged from 0.8 3.1 months 0.2 4.8 months, respectively. A strong correlation meteorological droughts, while agricultural significantly weaker. These results indicate precipitation runoff more sensitive than evapotranspiration influencing changes. findings highlight significant sensitivity GWS, despite improved management

Язык: Английский

Процитировано

0
Spatiotemporal evolution of environmental factors in representative tributaries of the Yellow River: insights from a decade of monitoring data DOI
Siyi Chen,

Yanyun Luo,

Yuhao Qiu

и другие.

Environmental Geochemistry and Health, Год журнала: 2025, Номер 47(5)

Опубликована: Апрель 5, 2025

Язык: Английский

Процитировано

0
Water quality evaluation in Liaoning Province large reservoirs: a new method integrating random forest-TOPSIS and Monte Carlo simulation DOI Creative Commons

Chong Zhang,

Mo Chen, Yi Wang

и другие.

Applied Water Science, Год журнала: 2025, Номер 15(5)

Опубликована: Апрель 7, 2025

Язык: Английский

Процитировано

0
Quantifying seasonal variations in pollution sources with machine learning-enhanced positive matrix factorization DOI Creative Commons

Yaotao Xu,

Peng Li,

Minghui Zhang

и другие.

Ecological Indicators, Год журнала: 2024, Номер 166, С. 112543 - 112543

Опубликована: Авг. 29, 2024

Язык: Английский

Процитировано

3
Water Quality Changes in the Xingkai (Khanka) Lake, Northeast China, Driven by Climate Change and Human Activities: Insights from Published Data (1990–2020) DOI Open Access
Yaping Wu,

Dan Chen,

Yu Liu

и другие.

Water, Год журнала: 2024, Номер 16(21), С. 3080 - 3080

Опубликована: Окт. 28, 2024

Water quality degradation and eutrophication of lakes are global ecological environmental concerns, especially shallow lakes. This study collected hydrochemical data from 2935 samples the Chinese part Xingkai (Khanka) Lake, based on 40 published papers spanning period 2001 to 2023. Using water index (WQI), improved geo-accumulation (Igeo), redundancy analysis (RDA), we analyzed overall contamination characteristics environment in Lake. Additionally, explored impact climate change human activities lake’s quality. The results showed that annual WQI for Lake ranged 47.3 72, with a general downward trend, indicating improving Notably, average May total nitrogen (TN) content decreased significantly, signaling further improvement concentration TN sediments was 1401.3 mg/kg, reflecting mild contamination. Igeo values heavy metals Hg Cr were greater than 1, moderate contamination, while Cd Pb between 0 which is range uncontaminated moderately contaminated. Land use (average temperature precipitation) key factors influencing quality, cumulative explanatory ratios 67.3% 50.1%. utilized land-use as metric activities, highlighting potential impacts It offers vital insights sustainable management provides valuable references into similar transboundary

Язык: Английский

Процитировано

3