Water Quality Evaluation and Cause Analysis of Yaogao Reservoir Based on Multivariate Statistical Analysis
To clarify the spatial variation characteristics and main influencing factors of water quality in Yaogao Reservoir,20 sampling points were set up along the basin of Yaogao Reservoir.In June 2022,certain water samples were collected at each sampling point and various water quality indicators were analyzed.Multiple statistical analysis methods were comprehensively used,and factor analysis and comprehensive nutritional index method were used to analyze the spatial variation characteristics and main influencing factors of water quality in Yaogao Reservoir.The research results indicate that:(1)According to the comprehensive nutritional index,the water quality of Yaogao Reservoir can be clustered into four regions:the first region is located near the south side of Wabuhu Road Reservoir and the streets on both sides of the north and south;Zone 2 is located near the north side of Wabuhu Road Reservoir and the side of Taikang Street;The vertex of the concave surface of the reservoir in Zone 3;The 4th district is closest to Taikang Street;(2)The sampling points and regional water quality are moderately eutrophic,with the highest comprehensive nutrient index being in the fourth region and the lowest being in the fourth region;(3)The main pollutants causing eutrophication in the water body of Yaogao Reservoir are nitrogen,including nitrate nitrogen(NO3--N),total nitrogen(TN),total dissolved nitrogen(TDN),and ammonia nitrogen(NH4+-N),followed by phosphorus and chlorophyll a(Chl-a);(4)The concentrations of nitrogen,phosphorus,and chlorophyll-a in the water body show a gradually decreasing trend from the periphery to the middle,except for the water intake.Principal component analysis shows that human activities are an important cause of water quality deterioration in Yaogao Reservoir,and rainfall regulation also has an important impact on water quality regulation in the reservoir area.
water environmenteutrophicationcluster analysisprincipal component analysis
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