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
Assessment of temporal and spatial variations in surface water quality is important to evaluate the health of a watershed and make necessary management decisions to control current and future pollution of receiving wa...Assessment of temporal and spatial variations in surface water quality is important to evaluate the health of a watershed and make necessary management decisions to control current and future pollution of receiving water bodies. In this work, surface water quality data for 12 physical and chemical parameters collected from 10 sampling sites in the Nenjiang River basin during the years(2012-2013) were analyzed. The results show that river water quality has significant temporal and spatial variations. Hierarchical cluster analysis(HCA) grouped 12 months into three periods(LF, MF and HF) and classified 10 monitoring sites into three regions(LP, MP and HP) based on the similarity of water quality characteristics. The principle component analysis(PCA)/factor analysis(FA) was used to recognize the factors or origins responsible for temporal and spatial water quality variations. Temporal and spatial PCA/FA revealed that the Nenjiang River water chemistry was strongly affected by rock/water interaction, hydrologic processes and anthropogenic activities. This work demonstrates that the application of HCA and PCA/FA has achieved meaningful classification based on temporal and spatial criteria.展开更多
通过富集培养和分离纯化的方法,从天津大港油田石油污染土壤和渤海海上钻井平台洗油污水中分离出6株石油降解细菌,生理生化试验及16S r DNA序列分析鉴定表明,它们分别属于Bacillus芽胞杆菌属(S1、S2、S3、S4)、Pseudomonas假单胞菌属(W1...通过富集培养和分离纯化的方法,从天津大港油田石油污染土壤和渤海海上钻井平台洗油污水中分离出6株石油降解细菌,生理生化试验及16S r DNA序列分析鉴定表明,它们分别属于Bacillus芽胞杆菌属(S1、S2、S3、S4)、Pseudomonas假单胞菌属(W1)和Ochrobactrum苍白杆菌属(W2),其中,S3具有最高的烷烃(41.3%)和芳烃(30.9%)降解率,从石油污染场地中筛选出的内源微生物对本油田石油的降解效果优于外源物种.对4株芽胞杆菌属菌株构建微生物组进行石油降解实验,结果表明,由S1和S4构成的微生物组F3具有最高的烷烃(50.5%)和芳烃(54.0%)降解率,比单菌降解率分别提高了69.9%和156.1%,同时比最优降解单菌S3的降解率分别高出22.1%和74.6%,而由S2和S3构成的微生物组F4对烷烃和芳烃的降解率最低,分别为18.5%和18.9%,比单菌降解率降低了55.3%和39.0%,实验表明同菌属微生物种间对石油的降解同时存在协同促进和拮抗抑制作用,芽胞杆菌属内亲缘性近的菌株之间对石油降解主要表现为促进作用.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.
基金Project(2012ZX07501002-001)supported by Major Science and Technology Program for Water Pollution Control and Treatment of the Ministry of Science and Technology,China
文摘Assessment of temporal and spatial variations in surface water quality is important to evaluate the health of a watershed and make necessary management decisions to control current and future pollution of receiving water bodies. In this work, surface water quality data for 12 physical and chemical parameters collected from 10 sampling sites in the Nenjiang River basin during the years(2012-2013) were analyzed. The results show that river water quality has significant temporal and spatial variations. Hierarchical cluster analysis(HCA) grouped 12 months into three periods(LF, MF and HF) and classified 10 monitoring sites into three regions(LP, MP and HP) based on the similarity of water quality characteristics. The principle component analysis(PCA)/factor analysis(FA) was used to recognize the factors or origins responsible for temporal and spatial water quality variations. Temporal and spatial PCA/FA revealed that the Nenjiang River water chemistry was strongly affected by rock/water interaction, hydrologic processes and anthropogenic activities. This work demonstrates that the application of HCA and PCA/FA has achieved meaningful classification based on temporal and spatial criteria.
文摘通过富集培养和分离纯化的方法,从天津大港油田石油污染土壤和渤海海上钻井平台洗油污水中分离出6株石油降解细菌,生理生化试验及16S r DNA序列分析鉴定表明,它们分别属于Bacillus芽胞杆菌属(S1、S2、S3、S4)、Pseudomonas假单胞菌属(W1)和Ochrobactrum苍白杆菌属(W2),其中,S3具有最高的烷烃(41.3%)和芳烃(30.9%)降解率,从石油污染场地中筛选出的内源微生物对本油田石油的降解效果优于外源物种.对4株芽胞杆菌属菌株构建微生物组进行石油降解实验,结果表明,由S1和S4构成的微生物组F3具有最高的烷烃(50.5%)和芳烃(54.0%)降解率,比单菌降解率分别提高了69.9%和156.1%,同时比最优降解单菌S3的降解率分别高出22.1%和74.6%,而由S2和S3构成的微生物组F4对烷烃和芳烃的降解率最低,分别为18.5%和18.9%,比单菌降解率降低了55.3%和39.0%,实验表明同菌属微生物种间对石油的降解同时存在协同促进和拮抗抑制作用,芽胞杆菌属内亲缘性近的菌株之间对石油降解主要表现为促进作用.
