Multivariate statistical techniques,such as cluster analysis(CA),discriminant analysis(DA),principal component analysis(PCA) and factor analysis(FA),were applied to evaluate and interpret the surface water quality dat...Multivariate statistical techniques,such as cluster analysis(CA),discriminant analysis(DA),principal component analysis(PCA) and factor analysis(FA),were applied to evaluate and interpret the surface water quality data sets of the Second Songhua River(SSHR) basin in China,obtained during two years(2012-2013) of monitoring of 10 physicochemical parameters at 15 different sites.The results showed that most of physicochemical parameters varied significantly among the sampling sites.Three significant groups,highly polluted(HP),moderately polluted(MP) and less polluted(LP),of sampling sites were obtained through Hierarchical agglomerative CA on the basis of similarity of water quality characteristics.DA identified p H,F,DO,NH3-N,COD and VPhs were the most important parameters contributing to spatial variations of surface water quality.However,DA did not give a considerable data reduction(40% reduction).PCA/FA resulted in three,three and four latent factors explaining 70%,62% and 71% of the total variance in water quality data sets of HP,MP and LP regions,respectively.FA revealed that the SSHR water chemistry was strongly affected by anthropogenic activities(point sources:industrial effluents and wastewater treatment plants;non-point sources:domestic sewage,livestock operations and agricultural activities) and natural processes(seasonal effect,and natural inputs).PCA/FA in the whole basin showed the best results for data reduction because it used only two parameters(about 80% reduction) as the most important parameters to explain 72% of the data variation.Thus,this work illustrated the utility of multivariate statistical techniques for analysis and interpretation of datasets and,in water quality assessment,identification of pollution sources/factors and understanding spatial variations in water quality for effective stream water quality management.展开更多
探究城市洪灾韧性与生态服务价值之间耦合协调关系,对城市防洪与生态文明建设具有重要意义。研究以吉林省松花江流域为例,基于“压力-状态-响应”模型(Pressure-State-Response,PSR)结合客观权重赋权法(Criteria Importance Though Inte...探究城市洪灾韧性与生态服务价值之间耦合协调关系,对城市防洪与生态文明建设具有重要意义。研究以吉林省松花江流域为例,基于“压力-状态-响应”模型(Pressure-State-Response,PSR)结合客观权重赋权法(Criteria Importance Though Intercrieria Correlation,CRITIC)-熵权法组合权重计算吉林省松花江流域城市洪灾韧性并探究其时空分布;采用生态系统服务和权衡综合评估(Integrated Valuation of Ecosystem Services and Trade-offs,InVEST)模型分析生态系统服务中水土保持量与水源涵养量,并在县区尺度上对其进行冷热点分析及变化趋势分析;通过耦合协调度模型对城市洪灾韧性与两种生态系统服务之间的关系进行探究。研究结果显示:2010—2022年吉林省松花江流域城市洪涝韧性呈西低东高的分布特征,整体韧性由一般提升为较高水平;生态系统服务空间异质性显著,热点区域分布于植被覆盖度较高的东部与南部,冷点区域分布于西部与北部,研究区中部生态系统服务下降,南部及西北部生态系统服务提升;城市洪灾韧性与生态系统服务耦合协调性一般,其中城市洪灾韧性与水土保持量耦合协调性轻微失调,与水源涵养量初级协调,且均有继续下降的风险,生态系统服务的下降已开始制约城市洪灾韧性的提高,需及时采取措施。展开更多
基金Project (2012ZX07501002-001) supported by the Ministry of Science and Technology of China
文摘Multivariate statistical techniques,such as cluster analysis(CA),discriminant analysis(DA),principal component analysis(PCA) and factor analysis(FA),were applied to evaluate and interpret the surface water quality data sets of the Second Songhua River(SSHR) basin in China,obtained during two years(2012-2013) of monitoring of 10 physicochemical parameters at 15 different sites.The results showed that most of physicochemical parameters varied significantly among the sampling sites.Three significant groups,highly polluted(HP),moderately polluted(MP) and less polluted(LP),of sampling sites were obtained through Hierarchical agglomerative CA on the basis of similarity of water quality characteristics.DA identified p H,F,DO,NH3-N,COD and VPhs were the most important parameters contributing to spatial variations of surface water quality.However,DA did not give a considerable data reduction(40% reduction).PCA/FA resulted in three,three and four latent factors explaining 70%,62% and 71% of the total variance in water quality data sets of HP,MP and LP regions,respectively.FA revealed that the SSHR water chemistry was strongly affected by anthropogenic activities(point sources:industrial effluents and wastewater treatment plants;non-point sources:domestic sewage,livestock operations and agricultural activities) and natural processes(seasonal effect,and natural inputs).PCA/FA in the whole basin showed the best results for data reduction because it used only two parameters(about 80% reduction) as the most important parameters to explain 72% of the data variation.Thus,this work illustrated the utility of multivariate statistical techniques for analysis and interpretation of datasets and,in water quality assessment,identification of pollution sources/factors and understanding spatial variations in water quality for effective stream water quality management.
文摘探究城市洪灾韧性与生态服务价值之间耦合协调关系,对城市防洪与生态文明建设具有重要意义。研究以吉林省松花江流域为例,基于“压力-状态-响应”模型(Pressure-State-Response,PSR)结合客观权重赋权法(Criteria Importance Though Intercrieria Correlation,CRITIC)-熵权法组合权重计算吉林省松花江流域城市洪灾韧性并探究其时空分布;采用生态系统服务和权衡综合评估(Integrated Valuation of Ecosystem Services and Trade-offs,InVEST)模型分析生态系统服务中水土保持量与水源涵养量,并在县区尺度上对其进行冷热点分析及变化趋势分析;通过耦合协调度模型对城市洪灾韧性与两种生态系统服务之间的关系进行探究。研究结果显示:2010—2022年吉林省松花江流域城市洪涝韧性呈西低东高的分布特征,整体韧性由一般提升为较高水平;生态系统服务空间异质性显著,热点区域分布于植被覆盖度较高的东部与南部,冷点区域分布于西部与北部,研究区中部生态系统服务下降,南部及西北部生态系统服务提升;城市洪灾韧性与生态系统服务耦合协调性一般,其中城市洪灾韧性与水土保持量耦合协调性轻微失调,与水源涵养量初级协调,且均有继续下降的风险,生态系统服务的下降已开始制约城市洪灾韧性的提高,需及时采取措施。
