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
This study aims to investigate the hydrochemical characteristics of shallow aquifer in a semi-arid region situated in northwest Algeria,and to understand the major factors governing groundwater quality.The study area ...This study aims to investigate the hydrochemical characteristics of shallow aquifer in a semi-arid region situated in northwest Algeria,and to understand the major factors governing groundwater quality.The study area is suffering from recurring droughts,groundwater resource over-exploitation and groundwater quality degradation.The approach used is a combination of traditional hydrochemical analysis methods of multivariate statistical techniques,principal component analysis(PCA),and ratios of major ions,based on the data derived from 33 groundwater samples collected in February 2014.Results show that groundwater in the study area are highly mineralized and collectively has a high concentration of chloride(as Cl^(−)).The dominant water types are Na-Cl(27%),Mg-HCO_(3)(24%)and Mg-Cl(24%).According to the(PCA)approach,salinization is the main process that controls the hydrochemical variability.The PCA analysis reveal the impact of anthropogenic factor especially the agricultural activities on the groundwater quality.The PCA highlighted two types of recharge:Superficial recharge from effective rainfall and excess irrigation water distinguished by the presence of nitrate and lateral recharge or vertical leakage from carbonate formations marked by the omnipresence of HCO_(3)^(−).Additionally,three categories of samples were identified:(1)samples characterized by good water quality and receiving notable recharge from carbonate formations;(2)samples impacted by the natural salinization process;and(3)samples contaminated by anthropogenic activities.The major natural processes influencing water chemistry are the weathering of carbonate and silicate rocks,dissolution of evaporite as halite,evaporation and cation exchange.The study results can provide the basis for local decision makers to ensure the sustainable management of groundwater and the safety of drinking water.展开更多
基于多指标成分定量联合多元统计分析综合评价不同产地鹅不食草质量差异,提高鹅不食草药材的整体质量控制水平。以Waters XTerra MS C18柱为色谱柱,乙腈-0.2%磷酸为流动相,采用HPLC法同时测定鹅不食草中绿原酸、异绿原酸A、异绿原酸C、...基于多指标成分定量联合多元统计分析综合评价不同产地鹅不食草质量差异,提高鹅不食草药材的整体质量控制水平。以Waters XTerra MS C18柱为色谱柱,乙腈-0.2%磷酸为流动相,采用HPLC法同时测定鹅不食草中绿原酸、异绿原酸A、异绿原酸C、槲皮素、山柰酚、3-甲氧基槲皮素、山金车内酯D、山金车内酯C、小堆心菊素C、短叶老鹳草素A、羽扇豆醇、β-谷甾醇和蒲公英甾醇含量。对16批鹅不食草多指标成分定量检测结果进行聚类分析(cluster analysis,CA)、主成分分析(principal component analysis,PCA)和因子分析(factor analysis,FA),对不同产地鹅不食草药材进行分组和综合质量评价。利用正交偏最小二乘法-判别分析(orthogonal partial least squares-discriminant analysis,OPLS-DA)分析挖掘影响产品质量的差异性标志物。方法学验证符合中华人民共和国药典要求,13种成分在各自范围内线性关系良好(R^(2)>0.999),平均加样回收率(n=9)在96.88%~100.1%之间(RSD<2.0%)。CA结果显示16批鹅不食草聚为3类。PCA结果显示前2个主成分特征值分别为10.187和2.059,方差贡献率分别为78.361%和15.839%,表明前2个主成分代表鹅不食草94.200%信息量,对鹅不食草质量评价具有很好的代表性。FA结果显示16批鹅不食草样品主成分综合得分在-1.451~1.344,其中浙江和江苏产鹅不食草综合得分较高,排名居于前5位,湖北、湖南和广东产鹅不食草综合得分排名居中,贵州和四川产鹅不食草综合得分排名靠后。OPLS-DA结果显示短叶老鹳草素A、异绿原酸A、小堆心菊素C、槲皮素、山金车内酯C是影响鹅不食草产品质量的差异性标志物。所建立的HPLC法操作便捷,结果准确,可用于鹅不食草多指标成分定量控制;多元统计分析可用于不同产地鹅不食草的整体质量评价。展开更多
Understanding the controlling factor of groundwater quality can enhance promoting sustainable development of groundwater resources. To this end, multivariate statistical analysis（MA） and hydrochemical analysis were ...Understanding the controlling factor of groundwater quality can enhance promoting sustainable development of groundwater resources. To this end, multivariate statistical analysis（MA） and hydrochemical analysis were introduced in this work. The results indicate that the canonical discriminant function with 7 parameters was established using the discriminant analysis（DA） method, which can afford 100% correct assignation according to the 3 different clusters（good water（GW）, poor water（PW）, and very poor water（VPW）） obtained from cluster analysis（CA）. According to factor analysis（FA）, 8 factors were extracted from 25 hydrochemical elements and account for 80.897% of the total data variance, suggesting that groundwater with higher concentrations of sodium, calcium, magnesium, chloride, and sulfate in southeastern study area are mainly affected by the natural process; the higher level of arsenic and chromium in groundwater extracted from northwestern part of study area are derived by industrial activities; domestic and agriculture sewage have important contribution to copper, iron, iodine, and phosphate in the northern study area. Therefore, this work can help identify the main controlling factor of groundwater quality in North China plain so as to make better and more informed decisions about how to achieve groundwater resources sustainable development.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This study aims to investigate the hydrochemical characteristics of shallow aquifer in a semi-arid region situated in northwest Algeria,and to understand the major factors governing groundwater quality.The study area is suffering from recurring droughts,groundwater resource over-exploitation and groundwater quality degradation.The approach used is a combination of traditional hydrochemical analysis methods of multivariate statistical techniques,principal component analysis(PCA),and ratios of major ions,based on the data derived from 33 groundwater samples collected in February 2014.Results show that groundwater in the study area are highly mineralized and collectively has a high concentration of chloride(as Cl^(−)).The dominant water types are Na-Cl(27%),Mg-HCO_(3)(24%)and Mg-Cl(24%).According to the(PCA)approach,salinization is the main process that controls the hydrochemical variability.The PCA analysis reveal the impact of anthropogenic factor especially the agricultural activities on the groundwater quality.The PCA highlighted two types of recharge:Superficial recharge from effective rainfall and excess irrigation water distinguished by the presence of nitrate and lateral recharge or vertical leakage from carbonate formations marked by the omnipresence of HCO_(3)^(−).Additionally,three categories of samples were identified:(1)samples characterized by good water quality and receiving notable recharge from carbonate formations;(2)samples impacted by the natural salinization process;and(3)samples contaminated by anthropogenic activities.The major natural processes influencing water chemistry are the weathering of carbonate and silicate rocks,dissolution of evaporite as halite,evaporation and cation exchange.The study results can provide the basis for local decision makers to ensure the sustainable management of groundwater and the safety of drinking water.
基金supported by the Major State Basic Research Development Program (No. 2010CB428800)the Geological Survey Projects Foundation of Institute of Hydrogeology and Environmental Geology (No. SK201308)
文摘Understanding the controlling factor of groundwater quality can enhance promoting sustainable development of groundwater resources. To this end, multivariate statistical analysis（MA） and hydrochemical analysis were introduced in this work. The results indicate that the canonical discriminant function with 7 parameters was established using the discriminant analysis（DA） method, which can afford 100% correct assignation according to the 3 different clusters（good water（GW）, poor water（PW）, and very poor water（VPW）） obtained from cluster analysis（CA）. According to factor analysis（FA）, 8 factors were extracted from 25 hydrochemical elements and account for 80.897% of the total data variance, suggesting that groundwater with higher concentrations of sodium, calcium, magnesium, chloride, and sulfate in southeastern study area are mainly affected by the natural process; the higher level of arsenic and chromium in groundwater extracted from northwestern part of study area are derived by industrial activities; domestic and agriculture sewage have important contribution to copper, iron, iodine, and phosphate in the northern study area. Therefore, this work can help identify the main controlling factor of groundwater quality in North China plain so as to make better and more informed decisions about how to achieve groundwater resources sustainable development.
