Optimization of River Water Quality Surveys by Multivariate Analysis of Physicochemical,Bacteriological and Ecotoxicological Data

This study aims to optimize the water quality monitoring of a polluted watercourse (Leça River, Portugal) through the principal component analysis (PCA) and cluster analysis (CA). These statistical methodologies were applied to physicochemical, bacteriological and ecotoxicological data (with the marine bacterium Vibrio fischeri and the green alga Chlorella vulgaris) obtained with the analysis of water samples monthly collected at seven monitoring sites and during five campaigns (February, May, June, August, and September 2006). The results of some variables were assigned to water quality classes according to national guidelines. Chemical and bacteriological quality data led to classify Leça River water quality as “bad” or “very bad”. PCA and CA identified monitoring sites with similar pollution pattern, giving to site 1 (located in the upstream stretch of the river) a distinct feature from all other sampling sites downstream. Ecotoxicity results corroborated this classification thus revealing differences in space and time. The present study includes not only physical, chemical and bacteriological but also ecotoxicological parameters, which broadens new perspectives in river water characterization. Moreover, the application of PCA and CA is very useful to optimize water quality monitoring networks, defining the minimum number of sites and their location. Thus, these tools can support appropriate management decisions.     

Assessment of surface water quality via multivariate statistical techniques: A case study of the Songhua River Harbin region,China

Multivariate statistical approaches, such as cluster analysis (CA) and principal component analysis/factor analysis (PCA/FA), were used to evaluate temporal/spatial variations in water quality and identify latent sources of water pollution in the Songhua River Harbin region. The dataset included data on 15 parameters for six different sites in the region over a five-year monitoring period (2005–2009). Hierarchical CA grouped the six monitored sites into three clusters based on their similarities, corresponding to regions of low pollution (LP), moderate pollution (MP) and high pollution (HP). PCA/FA of the three different groups resulted in five latent factors accounting for 70.08%, 67.54% and 76.99% of the total variance in the water quality datasets of LP, MP and HP, respectively. This indicates that the parameters responsible for water quality variation are primarily related to organic pollution and nutrients (non-point sources: animal husbandry and agricultural activities), temperature (natural), heavy metal and toxic pollution (point sources: industry) in relatively LP areas; oxygen-consuming organic pollution (point sources: industry and domestic wastewater), temperature (natural), heavy metal and petrochemical pollution (point source: industry), nutrients (non-point sources: agricultural activities, organic decomposition and geologic deposits) in MP areas; and heavy metal, oil and petrochemical pollution (point source: industry), oxygen-consuming organic pollution (point source: domestic sewage and wastewater treatment plants), nutrients (non-point sources: agricultural activities, runoff in soils) in HP areas of the Harbin region. Therefore, the identification of the main potential environmental hazards in different regions by this study will help managers make better and more informed decisions about how to improve water quality.     

Water Quality Assessment Using Multivariate Statistical Methods—A Case Study: Melen River System (Turkey)

This study is focused on water quality of Melen River (Turkey) and evaluation of 26 physical and chemical pollution data obtained five monitoring stations during the period 1995–2006. It presents the application of multivariate statistical methods to the data set, namely, principal component and factor analysis (PCA/FA), multiple regression analysis (MRA) and discriminant analysis (DA). The PCA/FA was employed to evaluate the high–low flow periods correlations of water quality parameters, while the principal factor analysis technique was used to extract the parameters that are most important in assessing high–low flow periods variations of river water quality. Latent factors were identified as responsible for data structure explaining 72–97% of the total variance of the each data sets. PCA/FA was supported with multiple regression analysis to determine the most important parameter in each factor. It examines the relation between a single dependent variable and a set of independent variables to best represent the relation in the each factor. Obtained important parameters provided us to determine the major pollution sources in Melen River Basin. So factors are conditionally named soil structure and erosion, domestic, municipal and industrial effluents, agricultural activities (fertilizer, irrigation water and livestock wastes), atmospheric deposition and seasonal effects factors. DA applied the data set to obtain the parameters responsible for temporal and spatial variations. Assessment of high–low flow period changes in surface water quality is an important aspect for evaluating temporal and spatial variations of river pollution. The aim of this study is illustration the usefulness of multivariate statistical analysis for evaluation of complex data sets, in Melen River water quality assessment identification of factors and pollution sources, for effective water quality management determination the spatial and temporal variations in water quality.     

Application of multivariable statistical techniques in plant-wide WWTP control strategies analysis.

The main objective of this paper is to present the application of selected multivariable statistical techniques in plant-wide wastewater treatment plant (WWTP) control strategies analysis. In this study, cluster analysis (CA), principal component analysis/factor analysis (PCA/FA) and discriminant analysis (DA) are applied to the evaluation matrix data set obtained by simulation of several control strategies applied to the plant-wide IWA Benchmark Simulation Model No 2 (BSM2). These techniques allow i) to determine natural groups or clusters of control strategies with a similar behaviour, ii) to find and interpret hidden, complex and casual relation features in the data set and iii) to identify important discriminant variables within the groups found by the cluster analysis. This study illustrates the usefulness of multivariable statistical techniques for both analysis and interpretation of the complex multicriteria data sets and allows an improved use of information for effective evaluation of control strategies.     

Evaluation and Quality Control of Environmental Analytical Data from the Niagara River Using Multiple Chemometric Methods

The use of artificial neural networks (ANN), principal component analysis (PCA) and universal process modelling (UPM) to identify the source of water samples based on the variation of chemical data from these samples has been investigated. Chromatographic data sets generated from three locations on the Niagara River were used in this research. The concentrations of target organic compounds were chromatographically determined and used as classification features. Chromatographic variation between three sampling sites was determined over a one-year period and included 149 separate samples. Variation within sampling sites was evaluated over a seven-year period. ANN and UPM techniques correctly identified the source of 95% of the water samples based on minor differences in the chromatographic data. PCA and UPM gave direct visualization of differences within chemical data sets. PCA and UPM were also found to be useful tools for the detection of chromatographic outliers from within sampling sites. The correlation between target compounds and surrogates are discussed. The results show that these methods are useful for the determination of the variation of target organic compounds over time both within and between sampling sites. The potential of these systems for monitoring analytical quality control based on entire data sets is also presented.     

Water quality assessment of a tropical coastal lake system using multivariate cluster,principal component and factor analysis

Statistical techniques represent a reliable tool for classifying, modelling and interpreting surface water quality monitoring data, particularly for lakes. The complexity associated with the analysis of a large number of measured variables, however, is a major problem in water quality assessments. Multivariate analysis, such as cluster analysis and factor analysis (FA), was utilized in this study for the analysis of water quality data (including water discharges and 28 water quality parameters) for Akkulam–Veli Lake, a tropical coastal lake system in Kerala, India. This lake is partially divided into two sub‐systems, namely Veli Lake and Akkulam Lake. Akkulam Lake exhibits freshwater characteristics, in contrast to Veli Lake, which exhibits saline water characteristics because of its close proximity to the sea. Thus, studying this lake provides insights into water quality variations in both a freshwater and saline water lake in a tropical region. Water quality patterns and variations in Akkulam–Vela Lake over three seasons, including pre‐monsoon (PRM), monsoon (MON) and post‐monsoon (POM), also were studied, utilizing multivariate techniques. The organic pollution factor played a significant role on lake water quality during PRM. The influence of organic pollution tends to decrease during MON and POM, a particular situation faced by urban lakes in tropical regions. Polluted stretches in a lake system during different seasons can easily be ascertained by hierarchical cluster analysis. Further, the factors affecting a lake system as a whole, as well as for a particular sampling site, can easily be identified by FA. Improved water quality can be observed during POM. Akkulam and Vela lakes exhibit a wide variation in water quality during all seasons, a finding that corroborates a water flow obstruction from Akkulam Lake to Veli Lake because of the bund existing between the two lakes. The location of the bund is identified as the major reason for different hydrochemical processes in A–V Lake.     

Multivariate analysis of the sediment quality of a tropical coastal lake system

Accurate knowledge of sediment quality is essential because it affects the magnitude and trends of water quality constituents. There are only a few analyses of sediment quality characteristics using multivariate analysis tools. This study utilizes hierarchical cluster analysis (HCA), factor analysis (FA) and multiple regression analysis (MRA) to demonstrate the usefulness of these techniques to analyse sediment quality for Akkulam–Veli Lake, a tropical coastal lake system in Kerala, India. The variation of sediment quality patterns during the premonsoon (PRM), monsoon (MON) and postmonsoon (POM) periods were assessed with cluster analysis. Factor analysis was used to identify prominent factors influencing sediment quality, while the factors influencing heavy metal partitioning in the sediment and overlying water were identified using multiple regression analysis. The study results indicated the sediment in the upstream portion of the lake was polluted during PRM, with the prominent factors being the ‘heavy metal factor’ and the ‘organic pollution factor’, followed by the ‘phosphorus pollution factor’ and the ‘cadmium pollution factor’. The ‘heavy metal factor’ and the ‘organic pollution factor’ are the prominent factors during MON, whereas the ‘heavy metal factor’, ‘organic pollution factor’ and ‘salinity factor’ were prominent POM factors. The salinity of the overlying water above the sediments plays an important role during PRM and POM, whereas the dissolved oxygen content was important during MON.     

南水北调中线总干渠水质变化趋势及污染源分析

为了解和掌握南水北调中线总干渠水质状况,依据总干渠26个常规水质断面监测数据,分析了总干渠沿程水质变化趋势。结果表明,主要污染指标(CODMn、氨氮、总氮和总磷)呈现出沿程增加趋势。根据影响总干渠水质的潜在影响因素,分析了7类污染源(直接水污染、大气粉尘沉降、桥面雨水污染、底泥污染、周边垃圾场淋溶液污染、雨洪污染、受污染地下水)及其可能对干渠水质产生的影响程度,并针对不同污染源提出了防控对策,供管理部门决策参考。     

引滦入津工程水质时空演化规律分析

采用1986~2006年潘家口水库水质监测资料,分析水质时空变化规律。结果表明水库水体高锰酸盐指数较早期没有增加,但存在一定的增加趋势,总氮和总磷较早期有显著增加,富营养化趋势明显。采用引滦入津工程沿程8个监测断面的监测资料,分析水质空间变化规律,发现潘家口水库上游污染严重,下游水质较好。从水文变化和人类活动两方面,分析水质演化原因,据此提出了引滦入津工程水资源保护建议。     

ASTER卫星影像在太湖水质空间分异分析中的应用

利用ASTER卫星影像针对太湖的部分水域水质进行研究,首先根据夏季与影像同期太湖水体主要为竺山湖水域和梅梁湖水域中的水质实测数据,进行聚类分析和主成分分析,发现太湖水体主要受到悬浮物和藻类物质的污染,其他污染指标与它们之间存在着紧密的联系,所以针对水质的遥感分析也以这两类污染指标为主。对太湖的部分水域水质的遥感影像进行处理,用水体指数掩膜将水体从背景中分离,监督分类将水体按污染物成分与含量不同分成6类:近岸水(相对干净水体)、泥沙污染(泥沙较多)、泥沙和藻类混合、混沙水(泥沙少量)、混藻水(藻类少量)和藻类污染(藻类较多)。分类的总精度为84.796 5%,Kappa系数为0.817 4,统计出各污染类型水域的面积,发现太湖的污染物主要为泥沙类,其次为藻类。在太湖沿岸水域受泥沙污染较严重,且具有一定的扩散趋势;太湖中、东部受藻类的污染较严重。用NDVI提取藻类污染区,结果与监督分类的相符。最后结合遥感图像水体周围状况以及实际统计资料对太湖水质的污染成因作了分析。     

Identifying Homogeneous Water Quality Regions in the Nile River Using Multivariate Statistical Analysis

Detecting homogeneous regions in the Nile River is essential in carrying mathematical modelling. The aim of this paper is to indentify homogenous regions with respect to water quality. Eight years data were subjected to principal components analysis (PCA) to define the parameters responsible for the variability in water quality. The PCA produced three variates (or principal components). For the Nile stem, variates are related to bacterial pollution, organic pollution and then agricultural/nutrients. As for the Nile branches, variables group as coming from bacterial and organic sources, while the agricultural/nutrients stamp is more visible in summer. Then, cluster analysis (CA) was performed to verify whether the observations could be grouped into spatially coherent patterns. CA grouped sampling sites into three homogenous regions: upper, middle and lower Nile stem. To interpret the subdivision, CA was performed on municipal and demographic data coming from Nile governorates, such as potable water consumption, sewage collection, cultivated areas and population data. The cultivated areas group similarly to nutrients water quality data and the percentages of uncollected sewage group similarly to bacterial data. The consecutive use of PCA and CA enabled to determine the main sources of pollution and to identify homogeneous regions with respect to water quality variables.     

辽河流域地表水水质达标评价及水质变化分析

本文根据多年地表水水质监测资料,系统地对辽河流域地表水进行了水质达标评价和水质变化趋势分析,并确定了严重污染河流名单.结果表明:辽河流域地表水水质污染以有机污染为主,点源污染重于面源污染.水质达标率不高,地表水污染有上升的趋势.     

基于多元统计分析的闽江水质时空变化特征

为全面评估闽江水质状况,识别水质时空变化特征,采用自组织映射神经网络(SOM)聚类对其19个监测断面8项水质指标进行时空聚类,并通过主成分分析(PCA)方法识别时空维度中主要污染物,分析时空背景下的闽江水质变化特征。分析结果显示:SOM网络将闽江水质变化周期划分为4月至11月和12月至次年3月2个时段,前时段水质优于后时段。将流域19个断面聚类为S1、S2和S3三类,S1代表闽江三大支流上游区域,水质优良;S2代表各支流中下游区域,作为主要的农林生产基地,水质主要受非点源污染影响;S3代表闽江下游区域,水质主要受城市生活污水和工业废水影响。PCA解析表明闽江中下游水质主要受氮磷控制。     

辽河流域水环境时空差异性评价

采用聚类分析和主成分分析等多元统计方法对辽河流域的水环境时空差异性进行分析评价.依据流域内4条主要河流(辽河、浑河、太子河和大辽河)的25个监测点2000-2009年资料,监测点分为重度污染区、中度污染区和水质清洁区,反映不同采样点的水质水平和污染程度.通过重度污染区和中度污染区的各个站点主成分综合得分的计算,得到辽河...     

Classification of Drinking Water Quality Index and Identification of Significant Factors

Water pipes are considered to be one of responsible sources for the water pollution. Among these sources of water supply, the water pipes are the only source of carrying out fresh or processed water into lakes, ponds and streams etc. In Pakistan, knowledge on the condition of water pipes is scarce as deterioration of water pipes are hardly inspected due to high cost. The aim of the current research was to examine the quality of water pipelines of eight districts of South-Punjab, namely, Mianwali, Khushab, Layyah, Bhakkar, Dera Ghazi Khan, Muzaffargarh, Rajanpur and Rahim Yar Khan. Selected sampling stations were analyzed for physio-chemical parameters such as pH, Total Dissolve Solids (TDS), Sulfate (SO₄), Chlorine (Cl), Calcium (Ca), Magnesium (Mg), Hardness, Nitrate (NO₃), Fluoride (F) and Iron (Fe). The data pertaining water monitoring contain different parameters and seem difficult work for the interpretation of water quality by managing different parameters separately. For this purpose, National Sanitation Foundation Water Quality Index (NSF-WQI) was determined to communicate the quality of water in a simple form. Besides this, groups comprising of similar sampling sites based on water quality characteristics were identified using unsupervised technique. Factor Analysis (FA) has been performed for extracting the latent pollution sources that may cause the more variance in large and complex data. The calculated values of WQI from 1600 sampling stations ranging from 20.73 to 223.74 are divided into five groups; Excellent to Unsuitable class of waters with the average value 62.09 described as good limit for drinking water. Further sampling stations are divided into five optimal clusters selected with suitable k value obtained from Silhouette coefficient. Results of k-means clustering are also verified with natural groups made by WQI. Analysis of multivariate techniques showed several factors to be responsible for the water quality deterioration. It is found out from the FA that three latent factors such as organic pollution, agriculture run-off and urban land use caused 83.30 % of the total variation. Hence, water quality management and control of these latent factors are strongly recommended.     

基于SOM和 PCA的闽江流域地表水水质综合评价

根据2014年1月至2015年11月闽江流域19个断面的水质月均值监测数据,采用自组织特征映射网络(SOM)和主成分分析(PCA)法研究了闽江流域水质时空变化特征,并用水质指数对闽江水质进行了综合评价。SOM分析将水质样本分为3个空间群组,其中水质变化周期分2个阶段:4—11月、12月至次年3月。PCA法分析表明,春冬季沙溪和富屯溪支流以及闽江下游福州城区河段营养盐水平偏高,在春夏季上游部分河段和下游闽江口有机污染水平偏高。水质指数评价结果显示,闽江流域整体水质较好,其中三大子流域及闽江下游水质评价从优至劣顺序为:富屯溪流域、建溪流域、沙溪流域、闽江下游流域。     

湘江干流衡阳段水质变化

为研究湘江衡阳段水质变化,在湘江衡阳段干流设置7个监测断面,经2001~2005年监测分析表明:湘江衡阳段干流重金属污染逐步减轻,营养型污染有所增加,污染逐渐由上游向下游转移,水质污染都有不同程度减轻,整体水质呈改善趋势。分析总结了水质污染变化原因,指出衡阳市养殖业的发展成为衡阳段有机污染的重要来源之一,城镇人口增加是营养型污染增加的主要原因。     

Water Quality in the Río Lerma,Mexico: An Overview of the Last Quarter of the Twentieth Century

The Río Lerma basin is the most important watershed in the Central Plateau of Mexico. Major urban, industrial, agricultural and livestock regions are located in its catchment area. Regarded as a center of endemism for its fish fauna diversity, it is also the most polluted watercourse in Mexico. This study assesses spatial and long temporal variations in water quality over the last 25 years with two approaches: the use of a water quality index multiplicative and weighted (WQI) and a principal component analysis (PCA). The general rating scale for WQI range on a 0–100 with 100 indicating highest water quality. WQI scores ranging from 26.53 to 67.44 denote Rio Lerma water is not fit for drinking, requires treatment for most industrial and crop uses, and is suitable for coarse fish only. Navigation is impracticable and inexistent. PCA shows the monitoring stations arrayed along a set of environmental parameter gradients. Several endemic fish species have been lost: two silver side food fishes are extinct, two more species (one of them a food fish) are endangered, and another three are threatened.     

宝象河雨季径流pH值及其对环境因子响应研究

水体pH值是水体水化学特征的综合反映,是评价水质的重要指标之一,对水体酸化和富营养化等都有最直接响应。为弄清城市纳污河雨季径流pH值变化机理及其与水环境因子的响应关系,选取滇池第二大入湖河流宝象河为研究对象,结合流域土地利用类型,对雨季宝象河径流pH值及主要水环境指标展开系统监测和研究。研究结果表明:宝象河径流pH值空间特征为上游呈弱酸性,下游呈弱碱性,城市化程度越高,pH值越高;时间特征为雨季期间自上游向下游,人为干扰越明显,变化幅度越大;滇池宝象河径流水质参数指标和营养盐指标总体呈现出自河源到入湖口不断积累的过程;雨季滇池宝象河径流pH值与温度、溶解氧呈非常显著正相关,与BOD5呈显著正相关,与氧化还原电位呈非常显著负相关。研究结果可为快速城市化区域水资源及水环境保护提供参考和依据。