首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到20条相似文献，搜索用时 15 毫秒
1.
The current study presents the application of selected chemometric techniques—hierarchical cluster analysis (HCA) and principal component analysis (PCA)—to evaluate the spatial variation of the water chemistry and to classify the pollution sources in the Langat River. The HCA rendered the sampling stations into two clusters (group 1 and group 2) and identified the vulnerable stations that are under threat. Group1 (LY 1 to LY 14) is associated with seawater intrusion, while group 2 (LY 15 to LY 30) is associated with agricultural and industrial pollution. PCA analysis was applied to the water datasets for group 1 resulting in four components, which explained 85 % of the total variance while group 2 extracted six components, explaining 88 % of the variance. The components obtained from PCA indicated that seawater intrusion, agricultural and industrial pollution, and geological weathering were potential sources of pollution to the study area. This study demonstrated the usefulness of the chemometric techniques on the interpretation of large complex datasets for the effective management of water resources.  相似文献   

2.
In this study, multivariate statistical approaches, namely hierarchical cluster analysis (CA) and principal component analysis (PCA), were employed to understand the impact of copper mining on surface waters located in Central-East India. The data set generated consisted of nine parameters, namely pH, dissolved oxygen (DO), alkalinity, total dissolved solids, copper, iron, manganese, zinc and fluoride, collected in forty sampling points covering all seasons. As delineated by CA, the entire data set for both the surface waters was bifurcated into groups, namely Banjar River inclusion of seepage points (BRISP) and Banjar River exclusion of seepage points (BRESP), Son River inclusion of seepage points (SRISP) and Son River exclusion of seepage points (SRESP). Four latent factors were identified, namely copper, iron, fluoride and manganese, explaining 84.7 % of variance for BRISP, 71.9 % of variance for BRESP, 66.7 % of variance for SRISP and 68 % of variance for SRESP. The extensive application of PCA on BRISP, BRESP, SRISP and SRESP reveals that the main stream of both the rivers remains unaffected by mining operations when seepage points were excluded. Additionally, iron content is considerably significant throughout the stream due to the geogenic sources and it is considered as a major factor for the depletion of DO level in the streams. This study reveals the level of contamination in the studied surface waters and the effectiveness of multivariate statistical techniques for evaluation and interpretation of complex data matrix in understanding the spatial variations and identification of pollution sources.  相似文献   

3.
In this study, spatial and seasonal variations of water quality in Haraz River Basin were evaluated using multivariate statistical techniques, such as cluster analysis, principal component analysis and factor analysis. Water quality data collected from 8 sampling stations in river during 4 seasons (Summer and Autumn of 2007, Winter and Spring of 2008) were analyzed for 10 parameters (dissolved oxygen, Fecal Coliform, pH, water temperature, biochemical oxygen demand, nitrate, total phosphate, turbidity, total solid and discharge). Cluster analysis grouped eight sampling stations into three clusters of similar water quality features and thereupon the whole river basin may be categorized into three zones, i.e. low, moderate and high pollution. The principle component analysis/factor analysis assisted to extract and recognize the factors or origins responsible for water quality variations in four seasons of the year. The natural parameters (temperature and discharge), the inorganic parameter (total solid) and the organic nutrients (nitrate) were the most significant parameters contributing to water quality variations for all seasons. Result of principal component analysis and factor analysis evinced that, a parameter that can be significant in contribution to water quality variations in river for one season, may less or not be significant for another one.  相似文献   

4.
以江西德兴大型矿区为例,进行大尺度的土壤环境调查和重金属污染评价。系统采集该区域919个土壤样品,在应用X荧光光谱仪和电感耦合等离子原子发射光谱仪为主体的分析配套方案分析土壤样品中重金属含量的基础上,采用单因子污染指数法和综合污染指数法,分别从原始含量、污染指数以及基于GIS的污染空间分析3个方面进行该矿区的土壤重金属污染评价。结果表明,矿山开采对该区的土壤环境造成了不同程度的As、Cd、Zn、Cu重金属污染。污染区域主要分布在德兴铜钼和铅锌矿区、乐安河下游乐平附近的煤矿区以及南部电化学厂附近。  相似文献   

5.
Spatial variations of the water quality in the Haicheng River during April and October 2009 were evaluated for the national monitoring program on water pollution control and treatment in China. The spatial autocorrelation analysis with lower Moran’s I values displayed the spatial heterogeneity of the 12 physicochemical parameters among all the sampling sites of the river. The one-way ANOVA showed that all variables at different sampling sites had significant spatial differences (p < 0.01). Based on the similarity of water quality characteristics, cluster analysis grouped the 20 sampling sites into three clusters, related with less polluted, moderately polluted and highly polluted sites. The factor analysis extracted three major factors explaining 76.4 % of the total variance in the water quality data set, i.e., integrated pollution factor, nitrogen pollution factor and physical factor. The results revealed that the river has been severely polluted by organic matter and nitrogen. The major sources leading to water quality deterioration are complex and ascribed to anthropogenic activities, e.g., domestic and industrial wastewater discharges, agricultural runoff, and animal rearing practices.  相似文献   

6.
The increasing interest of the construction aggregates industry in reducing production costs and the costs resulting from improper use of construction materials leads to the question whether it is possible to statistically identify some rock variants by their reflectivity of near-infrared and mid-infrared light. Infrared spectroscopy allows quantitative and qualitative analysis of minerals in a reliable manner, whereas the classification of rocks is complicated by the fact that the optic behavior of minerals forming the rock often appears muted. In addition, minor constituents may dominate the spectrum. Furthermore the relevant spectra form high dimensional data, which are extremely difficult to analyse statistically, especially when curves are very similar. Common methods of multivariate statistics for this type of data, used in chemometric studies, followed by linear discriminant analysis, do not lead to acceptable classification error rates. In this paper wavelets are used in order to reduce dimensionality. As wavelets are better able to mirror local behavior of curves, they are more suitable for selecting characteristic features. The approximation is analyzed in terms of its classification properties using Mahalanobis distance or flexible discriminant analysis.  相似文献   

7.
Geochemical data obtained during mineral exploration often are biased by systematic as well as random errors; these may result in failures when usual methods of evaluation are used. This is true particularly in soil surveys carried out in regions where a long history of prospecting and mining activity has occurred and/or where aerial chemical pollution is likely to have occurred.A satisfactory evaluation of geochemical data even in such an unfavorable case requires sampling on a relatively dense grid and utilization of all available knowledge of types of mineralization. The evaluation procedure proposed consists of five consecutive phases: (1). Dividing the area of interest into subareas of a relatively homogeneous geological nature. (2). Processing by multivariate methods (factor analysis, in particular) without consideration of geographic relations. (3). A preliminary interpretation and search for a geochemical explanation of factors. (4). Processing of individual factors in two-dimensional geographic space by directional and frequency linear filtering methods. (5). Final interpretation and construction of a geochemical model. The procedure is illustrated by an example from a geochemical exploration survey in the vicinity of Píbram (Middle Bohemia).  相似文献   

8.
Heavy metal contamination is of great concern in rapidly urbanizing areas. A basin-basis study on the impacts of urbanization on the heavy metal contamination in surface sediments from the Qinhuai River, Eastern China, was conducted, focusing on the spatial variation and source appointments. All of the sampling sites can be divided into three groups based on the hierarchical cluster analysis (HCA) results, which correspond well to the pollution levels of the studied heavy metals in the sediments of the rural, suburban, and urban sections of the Qinhuai River. The relationship between the heavy metal and the Al/Si ratio of sediments varied distinctly with the metal species and urbanization degree of the river sections. Correlation analysis and HCA highlighted that zinc appeared to be a fairly efficient geochemical signature of urban-related heavy metal contamination. The contributions derived from urban activities ranged from 35.9% for Ni to 96.1% for Cu, as estimated by a multilinear regression of the absolute principal component score method (MLR-ACPS). Agricultural activities had a clear impact on As, Pb, and Cu contamination of the sediment. Lithologic sources contributed a significant portion of Ni, Cr, and As to the sediment.  相似文献   

9.
Water pollution has become a growing threat to human society and natural ecosystems in the recent decades. Assessment of seasonal changes in water quality is important for evaluating temporal variations of river pollution. In this study, seasonal variations of chemical characteristics of surface water for the Chehelchay watershed in northeast of Iran was investigated. Various multivariate statistical techniques, including multivariate analysis of variance, discriminant analysis, principal component analysis and factor analysis were applied to analyze river water quality data set containing 12 parameters recorded during 13 years within 1995–2008. The results showed that river water quality has significant seasonal changes. Discriminant analysis identified most important parameters contributing to seasonal variations of river water quality. The analysis rendered a dramatic data reduction using only five parameters: electrical conductivity, chloride, bicarbonate, sulfate and hardness, which correctly assigned 70.2 % of the observations to their respective seasonal groups. Principal component analysis / factor analysis assisted to recognize the factors or origins responsible for seasonal water quality variations. It was determined that in each season more than 80 % of the total variance is explained by three latent factors standing for salinity, weathering-related processes and alkalinity, respectively. Generally, the analysis of water quality data revealed that the Chehelchay River water chemistry is strongly affected by rock water interaction, hydrologic processes and anthropogenic activities. This study demonstrates the usefulness of multivariate statistical approaches for analysis and interpretation of water quality data, identification of pollution sources and understanding of temporal variations in water quality for effective river water quality management.  相似文献   

10.
As the major water catchment in Hobart city, the River Derwent provides water services to Hobart residents; however, water quality of the River Derwent is becoming unreliable. The aims of this paper are to identify the major water issues in the river and to reveal its impacts on Hobart residents and ecosystem. A methodology of secondary data analysis has been involved; which covers a wide range of existing dissertations. Through all the analysis of data, heavy metals, contaminated sediment and overload nitrogen can be regarded as three main causes of the water pollution. Moreover, the impacts of the water pollution are proved to be significant and perennial. On the basis of the analysis result, water pollution tends to be a tough issue that requires a great amount of time and efforts to deal with.  相似文献   

11.
Multivariate statistical techniques have been widely utilized to assess water quality and evaluate aquatic ecosystem health. In this study, cluster analysis, discriminant analysis, and factor analysis techniques are applied to analyze the physical and chemical variables in order to evaluate water quality of the Jinshui River, a water source area for an interbasin water transfer project of China. Cluster analysis classifies 12 sampling sites with 22 variables into three clusters reflecting the geo-setting and different pollution levels. Discriminant analysis confirms the three clusters with nine discriminant variables including water temperature, total dissolved solids, dissolved oxygen, pH, ammoniacal nitrogen, nitrate nitrogen, turbidity, bicarbonate, and potassium. Factor analysis extracts five varifactors explaining 90.01% of the total variance and representing chemical component, oxide-related process, natural weathering and decomposition processes, nutrient process, and physical processes, respectively. The study demonstrates the capacity of multivariate statistical techniques for water quality assessment and pollution factors/sources identification for sustainable watershed management.  相似文献   

12.
江西德兴矿集区水系沉积物重金属污染的时空对比   总被引：8，自引：0，他引：8  
对比研究矿山开采一定时段内区域环境污染的时空变化特征,对于动态监测矿山开采对区域环境质量的影响情况、污染趋势等,具有重要的科学意义和实际价值。本文在利用2004年野外采集与测试分析的330个水系沉积物样品重金属含量数据基础上,充分利用矿山开采早期(1989)的1:20万乐平幅(As、Hg、Cd、Cr、Zn、Cu、Pb)水系沉积物地球化学图,通过数据预处理,分别对样品采集点重金属元素含量的统计分析和地质累积指数法评价的基础上,采用GIS的三维空间分析功能,对比研究德兴地区水系沉积物重金属污染时空变化。研究结果表明,十几年来,矿山开采对区域水系沉积物已造成了严重的污染,主要集中分布在是德兴铅锌和铜钼矿区德兴河与大坞河流域及其周边地区、乐安河下游沿岸局部地区、西北煤矿区以及乐安河下游乐平附近的煤矿区。  相似文献   

13.
北京市PM_(2.5)中主要重金属元素污染特征及季节变化分析   总被引：3，自引：0，他引：3  
利用2005年4月18日—2008年9月27日北京市中国地质大学(东门)采样点的PM2.5质量浓度变化与重金属Cd、Pb、As、Cu及Zn等污染特征,结合最新发布的《环境空气质量标准》(GB3095—2012),初步分析了近4年时间里北京市单点PM2.5的污染水平及主要重金属污染元素的变化特征,得出了一些有意义的认识。2005年春季—2008年春季期间PM2.5质量浓度为13.1～171μg/m3之间变化,平均浓度为65.6μg/m3,超过最新环境空气质量标准制定的PM2.5年平均浓度限值35μg/m3,北京市PM2.5污染形势依然严峻。奥运会及残奥会期间PM2.5的24 h质量浓度平均值为40.7μg/m3,没有超标。北京市PM2.5中的重金属元素含量及富集特征随着不同年份不同季节差别较大,典型的城市污染元素As在冬季质量浓度最高。对比环境空气质量标准的参考浓度限值发现,As元素的质量浓度在研究期间的年均值均超过了年平均浓度参考限值0.006μg/m3。化学分析结果显示人为污染是PM2.5中Cu、Cd、Pb、Zn、As重金属污染的主要来源,其中As污染需要引起足够重视。研究结果对于北京市大气污染防治具有一定的借鉴意义。  相似文献   

14.
Concentrations and sources of polycyclic aromatic hydrocarbons (PAHs) were investigated in surface sediments of the Yellow River Estuary (YRE). The isobath-parallel tidal and residual currents play important roles in the variation of PAH distribution, such that the contamination level of PAHs in fine-grained sediments is significantly higher than in the relatively coarse grain size sediments. Both diagnostic ratios and principal component analysis (PCA) with multivariate linear regression (MLR) were used to apportion sources of PAHs. The results revealed that pyrogenic sources are important sources of PAHs. Further analysis indicated that the contributions of coal combustion, traffic-related pollution and mixed sources (spills of oil products and vegetation combustion) were 35, 29 and 36 %, respectively, using PCA/MLR. Pyrogenic sources (coal combustion and traffic-related pollution) contribute 64 % of anthropogenic PAHs in sediments, which indicates that energy consumption could be a predominant factor in PAH pollution of YRE. Acenaphthylene and acenaphthene are the two main species of PAHs with more ecotoxicological concern in YRE.  相似文献   

15.
基于多元统计方法的河流水质空间分析   总被引：15，自引：0，他引：15       下载免费PDF全文
基于聚类分析和判别分析探讨了河流水质空间分析方法,旨在识别采样点的空间相似性与差异性,从而为水质监测网络优化提供支持。该方法首先利用kurtosis和Skewness检验数据分布特征和进行数据对数转化与标准化处理;然后利用聚类分析进行空间相似性分析,确定空间尺度分类情况;最后利用判别分析识别显著性污染指标,以此反映上述空间尺度分类的差异性。以香港后海湾水质管制区为例,结果表明:①通过对数转化显著改善数据分布特征,使绝大部分污染指标呈正态或接近正态分布;②该区域采样点在个案链锁距离与最大链锁距离之比(Dlink/Dmax)×100<35处明显分为3类,它们分别代表轻度、中度、重度污染3种类型,且后两者属于采样点主要属于营养盐和重金属污染类型,需要控制其生活污水、畜牧污染、工业污染和地表径流污染;③后退式判别分析具有良好的指标降维能力,仅需7个显著性污染指标(pH,NH3-N,NO3-N,F.coil,Fe,Ni和Zn)可以反映整体水质的空间差异性,且具有90.65%的正确判别能力;④归纳起来,从3类采样点中选择一个或多个、监测7个显著性污染指标即可全面反映后海湾水质管制区的水质空间特征,实现水质监测网络优化。  相似文献   

16.
《Applied Geochemistry》1999,14(4):423-432
The chemical characteristics of the mineral fractions of aerosol and precipitation collected in Sardinia (NW Mediterranean) are highlighted by means of two multivariate statistical approaches. Two different combinations of classification and statistical methods for geochemical data are presented. It is shown that the application of cluster analysis subsequent to Q-Factor analysis better distinguishes among Saharan dust, background pollution (Europe-Mediterranean) and local aerosol from various source regions (Sardinia). Conversely, the application of simple cluster analysis was able to distinguish only between aerosols and precipitation particles, without assigning the sources (local or distant) to the aerosol. This method also highlighted the fact that crust-enriched precipitation is similar to desert-derived aerosol. Major elements (Al, Na) and trace metal (Pb) turn out to be the most discriminating elements of the analysed data set. Independent use of mineralogical, granulometric and meteorological data confirmed the results derived from the statistical methods employed.  相似文献   

17.
分析珠江三角洲腹地佛山顺德区208个蔬菜地表层土样Cu、Ni、Cr、As、Pb、Zn、Cd和Hg等8种重金属的全量,结果表明,8种重金属的平均浓度高于广东省土壤背景值。Cd和Hg的最高浓度和变异系数分别为6.54mg/kg、115%和4.82mg/kg、151%,暗示Cd和Hg的人为来源。多元统计与傅立叶和谱分析的结合,解释了Cr、Ni和Cu的自然来源,Pb、Zn、As、Cd和Hg的人为来源;傅立叶和谱分析进一步阐释了Zn与Cu的双重来源,并推断土壤Hg来源于大气沉降。研究区内大约21.7%的土壤受重金属污染,表明需要调整该区的农业生产活动。  相似文献   

18.
Selecting the Liuyanghe River watershed as an example, using monitoring data of water quality of nearly 10 years and the improved synthesis pollution index method to evaluate the water quality, the research studied the temporal and spatial characteristics of surface water quality of a typical basin in the red soil hilly region, and analyzed reasons for the surface water quality change. The results indicated the improved synthesis pollution index had a better serviceability than other methods, such as, Pollution Index method, Fuzzy Evaluation method, Grey-System method etc. As for the temporal characteristic, because of no-point source pollution, the water quality of Liuyanghe River watershed had become a more and more serious problem over a ten-year period. The spatial characteristic indicated that the pollution degree increased from upstream to downriver. Water quality upstream was better, and the content of the heavy metals was higher in the middle of the river, and the pollution of ammonia nitrogen intensified downriver. The result suggested the improved universal pollution index could be used in the assessment of the water environment.  相似文献   

19.
A large data set obtained by a 1-year monthly determination of water quality from Sanya Bay, South China Sea, was treated by three-way principal component analysis aimed at exploring the spatial and temporal patterns of water quality in Sanya Bay. Tucker3 model of optimum complexity (2, 2, 1) explaining 33.18% of the data variance, allowed interpretation of the data information in three modes. The model explained spatial and temporal variation trends in terms of water quality variables during the study period. Water quality in sampling station (S2) Sanya River was mainly influenced by Sanya River, and water quality in other stations (S1, S3–S10) were mainly influenced by the waters in South China Sea. The results delineated the mouth of Sanya River as critical from pollution point of view. The dry season from October to the next April and rainy season from May to September have different influences on water quality in Sanya Bay. The information extracted by the three-way models would be very useful to regional agencies in developing a strategy to carry out scientific plans for resource use based on marine system functions.  相似文献   

20.
In this paper, the surface water quality of the Sakarya River in Turkey is assessed by using multivariate statistical techniques. These techniques were applied to the chemical parameters obtained from the five different surface water quality observation stations. Factor and principal component analysis results reveal that the agricultural, anthropogenic and domestic pollution caused differences in terms of water quality. Cluster analysis revealed two different clusters of similarities between the stations, reflecting different chemical properties and pollution levels in the studied river. Surface water quality downstream of the river was different from the water quality upstream. Thus, this study shows the usefulness of multivariate statistical techniques for analysis and interpretation in the surface water quality problem.  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号