Assessment of seasonal variations of chemical characteristics in surface water using multivariate statistical methods

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.     

Evaluation of spatial and seasonal variations in surface water quality using multivariate statistical techniques

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.     

Water quality assessment of the Jinshui River (China) using multivariate statistical techniques

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.     

Assessment of variations in water quality using statistical techniques: a case study of Işıklı Lake, Çivril/Denizli,Turkey

A study of the hydrochemical evaluation of waters in the I??kl? Lake and surrounding area was carried out with the objective of identifying the geochemical processes and their relation with water quality in the region. The multivariate statistical techniques were used in the hydrochemical evaluation of waters. Statistical analysis of water quality parameters was made to seeing the interrelationship between different variables in order to explain the water quality and pollution status of study area. For this purpose, water samples were taken from lake, river, stream, and springs which are represented by investigated area and water qualities were evaluated. Generally, Ca²⁺, Mg²⁺, and Cl^?, HCO₃ ^? ions are dominant within surface water and water sources. Arsenic concentration increase is determined in I??kl? spring and Kufi stream water samples. Also, aluminum concentration is high level in the Kufi stream water samples. This increase was related to igneous rocks as geogenic origin. Also, geogenic contamination was identified in R-mode factor and cluster analyses. There is high correlation between electrical conductivity and major ions of waters.     

Water pollution sources assessment by multivariate statistical methods in the Tahtali Basin,Turkey

In this study, multivariate statistical methods including factor, principal component and cluster analysis were applied to surface water quality data sets obtained from the Tahtali River Basin, Turkey. Factor and principal components analysis results revealed that surface water quality was mainly controlled by agricultural uses and domestic discharges. Cluster analysis generated two clusters. Based on the locations of the sites consisted by each cluster and variable concentrations at these stations, it was concluded that agricultural discharges strongly affected north and northeast part of the region. These methods are believed to assist water managers to understand complex nature of water quality issues and determine priorities to improve water quality.     

Monitoring the seasonal dynamics of physicochemical parameters from Atoyac River basin (Puebla), Central Mexico: multivariate approach

Spatiotemporal variations of ten physicochemical parameters in the water quality of Atoyac River basin, Central Mexico, were obtained from 22 sampling sites (66 samples in total) located all along the basin for three different seasons (dry, rainy and winter). Multivariate statistical techniques such as correlation matrix, factor analysis (FA) and cluster analysis (CA) were used as a tool to understand the process. Physicochemical parameters such as temperature (T), pH, conductivity (λ), dissolved oxygen (DO), spectral absorption coefficient (SAC), oxidation–reduction potential (ORP), turbidity, 5-day biochemical oxygen demand (BOD₅), chemical oxygen demand (COD) and total suspended solids (TSS) were analyzed. Extremely high values of pH (10.24), conductivity (1870 µS/cm) and reduced redox potential (?370.1 mV) were observed in the dry season, whereas elevated TSS of 2996 mg/L was detected during the rainy season. The results elucidated high influence from the adjoining industrial, agricultural and urban zones, making the river unsuitable for life. FA generated varifactors, which accounted for cumulative % of 75.04 (dry), 76.22 (rainy) and 79.96 (winter) clearly grouping the external factors responsible for these significant values indicating the source of contamination. Cluster analysis facilitated the ease of classifying the sampling sites based on the similarities of physicochemical parameters. This study carried out in different seasons using multivariate statistical techniques would definitely prove to be an efficient tool for the restoration and establishing the real-time monitoring stations along this important river basin of Mexico.     

Assessment of surface water quality using multivariate statistical analysis techniques: a case study from Ghrib dam,Algeria

This study explores the water quality status and pollution sources in Ghrib Dam, Algeria. It allows us to obtain more accurate information on water quality by applying a series of multivariate statistical techniques, including principal component analysis (PCA)/factor analysis (FA), hierarchical cluster analysis (CA), and multiple regression analysis (MRA). On 19 physicochemical parameters dataset over 5 years and from 6 different sites located in and around the lake. One-way analysis of variance (ANOVA) was used to investigate the statistically considerable spatial and seasonal differences. The results of ANOVA suggest that there exist a statistically significant temporal variation in the water quality of the dam for all parameters. On the other hand, only organic matter has a statistically significant spatial variation. In the multiple linear models, an association between organic and inorganic parameters was found; their origin comes from the mechanical erosion process of agricultural lands in the watershed. The PCA/FA identifies five dominant factors as responsible of the data structure, explaining more than 94.96% of the total variance in the water quality dataset. This suggests that the variations in water compounds’ concentration are mainly related to the multiple anthropogenic activities, as well as natural processes. The results of cluster analysis demonstrate that the sampling stations were divided in two similar groups, which indicates spatial homogeneity. While seasonal grouping has showed that the source of pollution was related to the level of runoff in the seasons.     

Petroleum pollution and evolution of water quality in the Malian River Basin of the Longdong Loess Plateau,Northwestern China

The possible sources of water pollution and the evolution of water quality in the Malian River Basin of China’s Longdong Loess Plateau were studied based on chemical analysis. Concentrations of major chemicals in the river were related to the distance downstream from the source of the river and the surface water in the upstream had good quality, while the river from Southern Huanxian County to Qingcheng County was seriously polluted. Groundwater quality was generally good in the phreatic aquifer beneath the loess plateau, with bicarbonate and calcium dominant, but salinity was high and petroleum pollution was present in many parts of the study area. Both surface water and groundwater quality have declined rapidly during the past 20 years, leading to much of the water unsafe to drink and unsuitable for use in irrigation. The substances released into the water included industrial wastes, domestic sewage, organic and inorganic fertilizers, and pesticides, as well as a range of other substances that are harmful to humans. However, petroleum contamination caused by the oil industry in the Longdong oilfield is the largest source of pollution that infiltrates surface water and groundwater, leading to deterioration of water quality. It is recommended that oilfield management must be improved and the petroleum products and wastes must be disposed carefully to reduce the risk of further spills and pollution.     

晋城境内河流污染现状分析及防治对策

以2003年枯水期化验资料为基础,对现状条件下的晋城市地表水环境质量进行了分析评价,并采用综合污染指数法定量地对河流污染程度给予了分析评价,发现地表水体大部分严重污染、地下水也不同程度受到污染,分析了污染原因并提出防治措施.     

Determination of aquifer susceptibility to pollution using statistical analysis

 The use of statistical techniques in studying the causes of geochemical variations in aquifers can provide important results which cannot be derived in other ways. In this study, data from the Wadi Shueib catchment area in Jordan is evaluated, using principle component factor and multivariate factor analysis in order to better understand the variablility in groundwater chemistry and evaluate the sources of pollution and the susceptibility of these aquifers to the different sources of pollution. This study clearly demonstrates that these statistical techniques can help determine the various mechanisms causing chemical variation in the aquifers and the relative susceptibility of each aquifer to different types of pollution. Received: 13 March 1996 · Accepted: 18 June 1996     

滹沱河冲洪积扇地下水硝酸盐的污染特征及污染源解析

为研究滹沱河冲洪积扇地区地下水硝酸盐污染机制,对滹沱河冲洪积扇地区地下水和地表水进行了采样监测,运用环境健康风险评价模型对研究区硝酸盐进行评价,采用水化学和多元统计方法研究了滹沱河冲洪积扇地区地下水硝酸盐污染问题。结果表明:研究区地表水NO-3污染较轻,NO-3均值为19.54 mg/L,所有水样均未超出我国地表水环境质量标准（45 mg/L）;但是,地下水已经受到了NO-3的严重污染,NO-3均值为75.84 mg/L,且有30.43%水样超出我国地下水质量标准（88. 6 mg/L）。研究区3个水文地质单元地下水硝酸盐的平均个人年健康风险分别为4.94×10-8、1.99×10-8和2.61×10-9,低于国际辐射防护委员会（ICRP）推荐的最大可接受风险水平（5.0×10-5/a）,因此,认为不会对人群构成严重危害。水文地质单元和地下水埋深对硝酸盐污染有显著影响,但是,土地利用类型对硝酸盐浓度的影响不显著。滹沱河冲洪积扇地区地下水硝酸盐的主要污染来源是生活污水和化肥。此外,强烈开采地下水也是该地区NO-3污染的诱因。     

Water and sediment quality assessment in the mid-Black Sea coast of Turkey using multivariate statistical techniques

The aim of this study was to investigate the water and sediment quality in the mid-Black Sea coast of Turkey. The samples were collected from six stations during 2007. Investigated parameters were total carbon (TC), total inorganic carbon (TIC), total organic carbon (TOC), ammonium-nitrogen (NH₄-N), nitrate-nitrogen (NO₃-N), nitrite-nitrogen (NO₂-N), total phosphorus (TP), sulphate, total hardness, methylene blue active substances (MBAS), phenol, adsorbable organic halogens (AOX), dissolved oxygen (DO), pH and electrical conductivity (EC) in water samples and TC, TIC, TOC, TP, pH, electrical conductivity (EC), redox potential (Eh) and water content (WC) in sediment samples. Different multivariate statistical techniques were used to evaluate variations in surface water and sediment quality. Principal component analysis helped in identifying the factors or sources responsible for water and sediment quality variations. Five factors were found responsible for 87.63% of the total variance in the surface waters. In sediments, three factors explained 84.73% of the observed total variance. Cluster analysis classified the monitoring sites into two groups based on similarities of water and sediment quality characteristics.     

Using the VIKOR method to evaluate the design of a water quality monitoring network in a watershed

Water quality monitoring stations play critical roles for analyzing the information of water quality conditions in a watershed. The understanding of water quality conditions of a river system is very important for water resource management. This study applied the VIKOR method, a commonly used method in multiple criteria analysis, to evaluate the design of the water quality monitoring network in the Taipei Water Resource Domain (TWRD). Five criteria were selected for multiple criteria analysis: non-point source pollution, green cover ratio, landslide area ratio, over-utilization area ratio of hillsides, and density of water quality monitoring stations. This study recommended several areas where there is a need for sufficient monitoring records of water quality. The result shows that more monitoring stations are needed in the Bei-Shih Creek Basin. Although the Nan-Shih Creek Basin covers larger area than the Bei-Shih Creek Basin and the Sin-Dian Creek Basin, it can accept less density of water quality monitoring stations.     

基于多元统计方法的地下水水化学特征分析:以沈阳市李官堡傍河水源地为例

为研究沈阳市李官堡傍河水源地的水化学变化特征与污染来源,在对该水源地开展野外污染调查、水化学样品采集等工作的基础上,结合聚类分析和因子分析两种多元统计分析方法对研究区地下水环境进行系统分析。结果表明:研究区平水期和丰水期的地下水均可划分为Ⅰ、Ⅱ两类具有不同水化学特征的两个区域。Ⅰ区主要分布在浑河沿岸,主要特征为Eh值偏低,pH值偏高,处于还原环境中,NH4+污染主要来源于浑河下渗与农业活动;Ⅱ区主要分布在研究区西北部,主要特征为Eh值偏高,pH值偏低,处于氧化环境中,NO3-污染主要来源于居民日常生活和工业活动等地表污染的垂向入渗。     

汉江流域硫铁矿区厚子河支流水质评价研究

为查明厚子河不同区域河水受硫铁矿污染的程度,在厚子河支流4个典型河流断面采集水样,开展水质全分析测试,并采用单因子评价法和综合水质标识指数法进行评价分析。研究结果表明: (1)运用单因子评价法对硫铁矿区厚子河水质评价的结果无差异,均为劣Ⅴ类水; 而运用综合水质标识指数法的评价结果显示,距离硫铁矿开采区由近及远的4个断面HZ001、HZ002、HZ003、HZ004的综合水质标识指数逐渐下降,水质类别分别为劣Ⅴ类且黑臭、劣Ⅴ类但不黑臭、Ⅳ类、Ⅲ类,符合现场调查的实际情况,能够反映距离污染源越远时河水水质趋好的总体态势; (2)综合水质标识指数法不仅可以客观反映同一河流各个断面所处的综合水质类别、同一水质类别中受污染的不同程度以及与水环境功能区类别的比较结果,而且能够实现对劣V类水质污染程度的精细划分,相较于单因子评价法更加客观、科学、全面,适用于矿山开采等重污染区域河流断面水质污染情况的研判。     

汉江流域硫铁矿区厚子河支流水质评价研究

为查明厚子河不同区域河水受硫铁矿污染的程度,在厚子河支流4个典型河流断面采集水样,开展水质全分析测试,并采用单因子评价法和综合水质标识指数法进行评价分析。研究结果表明: (1)运用单因子评价法对硫铁矿区厚子河水质评价的结果无差异,均为劣Ⅴ类水; 而运用综合水质标识指数法的评价结果显示,距离硫铁矿开采区由近及远的4个断面HZ001、HZ002、HZ003、HZ004的综合水质标识指数逐渐下降,水质类别分别为劣Ⅴ类且黑臭、劣Ⅴ类但不黑臭、Ⅳ类、Ⅲ类,符合现场调查的实际情况,能够反映距离污染源越远时河水水质趋好的总体态势; (2)综合水质标识指数法不仅可以客观反映同一河流各个断面所处的综合水质类别、同一水质类别中受污染的不同程度以及与水环境功能区类别的比较结果,而且能够实现对劣V类水质污染程度的精细划分,相较于单因子评价法更加客观、科学、全面,适用于矿山开采等重污染区域河流断面水质污染情况的研判。     

铜陵矿区主要河流水质分析与污染评价

铜陵矿区是长江下游重要的铜铁资源基地,也是典型的含硫多金属矿区,矿山酸性废水是矿业开发活动不可避免的环境问题,对地表水体有很大影响。本文以铜陵矿区主要河流为研究对象,通过野外调查采样和室内测试分析,从常规理化性质、矿山酸性废水和重金属元素三方面分析了的水质现状,采用单因子指数和内梅罗水质指数法进行了污染评价。结果表明:(1)矿区河流污染成分以有机污染和矿山酸性废水污染为主,其次是重金属污染。(2)在检测的46个河段中, 按综合污染指数大小分级,共计有93.48%的河段受到不同程度的污染,其中,水质严重污染的河段占 4.35%,水质重污染的河段占13.04%,水质污染的河段占 65.22%,水质轻污染的河段占10.87%;共计有6.52%的河段水质较好,均为清洁状态。(3)3条河流按污染程度大小依次为新桥河>顺安河>红星河,除顺安河外,其他均受到了矿山酸性废水污染。今后应重点关注矿山酸性废水的污染机理与风险评估,加强矿区水环境保护与恢复治理工作。     

Hydrochemical model and probable pollution in the water of Euphrates River (Qaem–Falluja)

The study of hydrochemical models and probable pollution in Euphrates River from Qaem–Al-Falluja was performed through regime observation in ten water points. The hydrochemical properties of Euphrates River waters are determined by using the analysis results of 21 physico-chemical variables during the water year (2008). The interpretation of the hydrochemical phenomena is achieved in accordance with the statistical results of Polynomial Regression Statistic, calculating the coefficient of variation among the physico-chemical components of the water terminating by 14 models. The results of monitoring during the water year 2008 indicate a pollution case by SO₄ and Cd in Ramadi and downstream stations; TDS, Mg, Na, Cl, Mn, and CO₂ in Ramadi station; TDS, Ca, Na, Cl, and Fe in Baghdadi station; and Ca and H _T in Qaem station and Mg in Obaydi station. The research suggests the best locations for hydrochemical monitoring as continuous hydrologic stations used for the long-term monitoring. These stations are in Qaem city as inlet location and Al-Baghdadi and Ramadi cities terminating in Falluja city as outlet location. Furthermore, daily record system is recommended for the other stations in each city to complete the regime observation of Euphrates water type. From the configuration approach of hydrochemical models with the pollution phenomena, there are potential point sources of pollution such as municipal effluent pipes and reused water from mining process for building materials in the first sector of river, which extends between Qaem and Haditha dam. Also groundwater seepages and springs discharge of mineralized water mixed with sewage water from cities act as potential point sources of pollution on the river water in the second sector extended between Haditha and Ramadi scheme. The third sector of the river is affected by all reasons in the first and second sectors as well as the effectiveness of agricultural activities throughout drainage canals and irrigation projects extended between Ramadi and Falluja cities.     

渭河流域地表水可利用量分析计算

地表水可利用量是流域和区域水资源规划的重要指标.本文应用断面过流量减去河道内生态需用水量与河道外难控制利用弃水量,并扣除二者间重复计算量的算法,利用华县、状头两个水文站断面实测和统计数据,分析计算渭河多年平均地表水可利用量为54.91亿～66.33亿m~3/a.     

东营黄河三角洲水质污染特征及其物源分析

简要介绍了东营黄河三角洲主要纳污河流,并分析了河流的水质特征及部分水质离子含量高的原因;取5个河流断面样品,分析化学需氧量、铜、铅、镁、汞、六价铬、镉、砷、氰化物、挥发酚及油类含量,发现油类污染最为严重,超标率为100%。河流水质污染主要来源于采油、炼化,其等标污染负荷比分别为74.85%和17.36%;主要污染企业有5个,按等标污染负荷比对其进行了排序,提出了一系列水污染控制措施。