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
【目的】探究樱桃番茄Solanum lycopersicum var. cerasiforme种质资源在银川平原地区的适应性,评价适合银川平原地区新品种选育的优良樱桃番茄育种材料。【方法】以收集到的100份樱桃番茄种质资源为研究对象,对其主要表型性状进行测定...【目的】探究樱桃番茄Solanum lycopersicum var. cerasiforme种质资源在银川平原地区的适应性,评价适合银川平原地区新品种选育的优良樱桃番茄育种材料。【方法】以收集到的100份樱桃番茄种质资源为研究对象,对其主要表型性状进行测定,利用多元统计法、灰色关联度分析法和DTOPSIS法3种不同的评价方法进行适应性综合评价。基于主成分计算出综合得分,灰色关联度法计算出加权关联度,DTPOSIS法计算出相对贴近度。【结果】100份樱桃番茄的主要表型性状的变异系数在17.78%~306.46%之间,大部分性状间存在显著或极显著相关性。26个表型性状综合成了10个主成分,累计贡献率达71.901%。以3种评价方法对各种质进行排名,结果既有统一性,也有差异性,共有4份材料均排在前10名,分别是T55、T83、T42和T87,表明T55、T83、T42和T87是表现优良的种质,其中T55的表现最为优异。【结论】T55是最适宜银川平原地区栽培的种质材料,可作为重要的育种基础材料;上述3种方法对樱桃番茄的评价结果略有不同,但无巨大差异,说明方法可行,有利于种质资源评价方面的研究。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【目的】探究樱桃番茄Solanum lycopersicum var. cerasiforme种质资源在银川平原地区的适应性,评价适合银川平原地区新品种选育的优良樱桃番茄育种材料。【方法】以收集到的100份樱桃番茄种质资源为研究对象,对其主要表型性状进行测定,利用多元统计法、灰色关联度分析法和DTOPSIS法3种不同的评价方法进行适应性综合评价。基于主成分计算出综合得分,灰色关联度法计算出加权关联度,DTPOSIS法计算出相对贴近度。【结果】100份樱桃番茄的主要表型性状的变异系数在17.78%~306.46%之间,大部分性状间存在显著或极显著相关性。26个表型性状综合成了10个主成分,累计贡献率达71.901%。以3种评价方法对各种质进行排名,结果既有统一性,也有差异性,共有4份材料均排在前10名,分别是T55、T83、T42和T87,表明T55、T83、T42和T87是表现优良的种质,其中T55的表现最为优异。【结论】T55是最适宜银川平原地区栽培的种质材料,可作为重要的育种基础材料;上述3种方法对樱桃番茄的评价结果略有不同,但无巨大差异,说明方法可行,有利于种质资源评价方面的研究。
