Chemometric application in identifying sources of organic contaminants in Langat river basin

Increasing urbanization and changes in land use in Langat river basin lead to adverse impacts on the environment compartment. One of the major challenges is in identifying sources of organic contaminants. This study presented the application of selected chemometric techniques: cluster analysis (CA), discriminant analysis (DA), and principal component analysis (PCA) to classify the pollution sources in Langat river basin based on the analysis of water and sediment samples collected from 24 stations, monitored for 14 organic contaminants from polycyclic aromatic hydrocarbons (PAHs), sterols, and pesticides groups. The CA and DA enabled to group 24 monitoring sites into three groups of pollution source (industry and urban socioeconomic, agricultural activity, and urban/domestic sewage) with five major discriminating variables: naphthalene, pyrene, benzo[a]pyrene, coprostanol, and cholesterol. PCA analysis, applied to water data sets, resulted in four latent factors explaining 79.0% of the total variance while sediment samples gave five latent factors with 77.6% explained variance. The varifactors (VFs) obtained from PCA indicated that sterols (coprostanol, cholesterol, stigmasterol, β-sitosterol, and stigmastanol) are strongly correlated to domestic and urban sewage, PAHs (naphthalene, acenaphthene, pyrene, benzo[a]anthracene, and benzo[a]pyrene) from industrial and urban activities and chlorpyrifos correlated to samples nearby agricultural sites. The results demonstrated that chemometric techniques can be used for rapid assessment of water and sediment contaminations.     

Analysis of long-term water quality for effective river health monitoring in peri-urban landscapes—a case study of the Hawkesbury–Nepean river system in NSW, Australia

The Hawkesbury–Nepean River (HNR) system in South-Eastern Australia is the main source of water supply for the Sydney Metropolitan area and is one of the more complex river systems due to the influence of urbanisation and other activities in the peri-urban landscape through which it flows. The long-term monitoring of river water quality is likely to suffer from data gaps due to funding cuts, changes in priority and related reasons. Nevertheless, we need to assess river health based on the available information. In this study, we demonstrated how the Factor Analysis (FA), Hierarchical Agglomerative Cluster Analysis (HACA) and Trend Analysis (TA) can be applied to evaluate long-term historic data sets. Six water quality parameters, viz., temperature, chlorophyll-a, dissolved oxygen, oxides of nitrogen, suspended solids and reactive silicates, measured at weekly intervals between 1985 and 2008 at 12 monitoring stations located along the 300 km length of the HNR system were evaluated to understand the human and natural influences on the river system in a peri-urban landscape. The application of FA extracted three latent factors which explained more than 70 % of the total variance of the data and related to the ‘bio-geographical’, ‘natural’ and ‘nutrient pollutant’ dimensions of the HNR system. The bio-geographical and nutrient pollution factors more likely related to the direct influence of changes and activities of peri-urban natures and accounted for approximately 50 % of variability in water quality. The application of HACA indicated two major clusters representing clean and polluted zones of the river. On the spatial scale, one cluster was represented by the upper and lower sections of the river (clean zone) and accounted for approximately 158 km of the river. The other cluster was represented by the middle section (polluted zone) with a length of approximately 98 km. Trend Analysis indicated how the point sources influence river water quality on spatio-temporal scales, taking into account the various effects of nutrient and other pollutant loads from sewerage effluents, agriculture and other point and non-point sources along the river and major tributaries of the HNR. Over the past 26 years, water temperature has significantly increased while suspended solids have significantly decreased (p?<?0.05). The analysis of water quality data through FA, HACA and TA helped to characterise the key sections and cluster the key water quality variables of the HNR system. The insights gained from this study have the potential to improve the effectiveness of river health-monitoring programs in terms of cost, time and effort, particularly in a peri-urban context.     

Spatio-temporal variations in water quality of Nullah Aik-tributary of the river Chenab, Pakistan

This study reports the spatio-temporal changes in water quality of Nullah Aik, tributary of the Chenab River, Pakistan. Stream water samples were collected at seven sampling sites on seasonal basis from September 2004 to April 2006 and were analyzed for 24 water quality parameters. Most significant parameters which contributed in spatio-temporal variations were assessed by statistical techniques such as Hierarchical Agglomerative Cluster Analysis (HACA), Factor Analysis/Principal Components Analysis (FA/PCA), and Discriminant Function Analysis (DFA). HACA identified three different classes of sites: Relatively Unimpaired, Impaired and Less Impaired Regions on the basis of similarity among different physicochemical characteristics and pollutant level between the sampling sites. DFA produced the best results for identification of main variables for temporal and spatial analysis and separated eight parameters (DO, hardness, sulphides, K, Fe, Pb, Cr and Zn) that accounted 89.7% of total variations of spatial analysis. Temporal analysis using DFA separated six parameters (E.C., TDS, salinity, hardness, chlorides and Pb) that showed more than 84.6% of total temporal variation. FA/PCA identified six significant factors (sources) which were responsible for major variations in water quality dataset of Nullah Aik. The results signify that parameters identified by statistical analyses were responsible for water quality change and suggest the possibility of industrial, municipal and agricultural runoff, parent rock material contamination. The results suggest dire need for proper management measures to restore the water quality of this tributary for a healthy and promising aquatic ecosystem and also highlights its importance for objective ecological policy and decision making process.     

Application of multivariate statistical techniques to evaluation of water quality in the Mała Wełna River (Western Poland)

The paper presents the results of determinations of physico-chemical parameters of the Ma?a We?na waters, a river situated in Wielkopolska voivodeship (Western Poland). Samples for the physico-chemical analysis were taken in eight gauging cross-sections once a month between May and November 2006. To assess the physico-chemical composition of surface water, use was made of multivariate statistical methods of data analysis, viz. cluster analysis (CA), factor analysis (FA), principal components analysis (PCA), and discriminant analysis (DA). They made it possible to observe similarities and differences in the physico-chemical composition of water in the gauging cross-sections, to identify water quality indicators suitable for characterising its temporal and spatial variability, to uncover hidden factors accounting for the structure of the data, and to assess the impact of man-made sources of water pollution.     

Characterization and source apportionment of water pollution in Jinjiang River, China

Characterizing water quality and identifying potential pollution sources could greatly improve our knowledge about human impacts on the river ecosystem. In this study, fuzzy comprehensive assessment (FCA), pollution index (PI), principal component analysis (PCA), and absolute principal component score–multiple linear regression (APCS–MLR) were combined to obtain a deeper understanding of temporal–spatial characterization and sources of water pollution with a case study of the Jinjiang River, China. Measurement data were obtained with 17 water quality variables from 20 sampling sites in the December 2010 (withered water period) and June 2011 (high flow period). FCA and PI were used to comprehensively estimate the water quality variables and compare temporal–spatial variations, respectively. Rotated PCA and receptor model (APCS–MLR) revealed potential pollution sources and their corresponding contributions. Application results showed that comprehensive application of various multivariate methods were effective for water quality assessment and management. In the withered water period, most sampling sites were assessed as low or moderate pollution with characteristics pollutants of permanganate index and total nitrogen (TN), whereas 90 % sites were classified as high pollution in the high flow period with higher TN and total phosphorus. Agricultural non-point sources, industrial wastewater discharge, and domestic sewage were identified as major pollution sources. Apportionment results revealed that most variables were complicatedly influenced by industrial wastewater discharge and agricultural activities in withered water period and primarily dominated by agricultural runoff in high flow period.     

Spatial pattern assessment of river water quality: implications of reducing the number of monitoring stations and chemical parameters

The Tamsui River basin is located in Northern Taiwan and encompasses the most metropolitan city in Taiwan, Taipei City. The Taiwan Environmental Protection Administration (EPA) has established 38 water quality monitoring stations in the Tamsui River basin and performed regular river water quality monitoring for the past two decades. Because of the limited budget of the Taiwan EPA, adjusting the monitoring program while maintaining water quality data is critical. Multivariate analysis methods, such as cluster analysis (CA), factor analysis (FA), and discriminate analysis (DA), are useful tools for the statistically spatial assessment of surface water quality. This study integrated CA, FA, and DA to evaluate the spatial variance of water quality in the metropolitan city of Taipei. Performing CA involved categorizing monitoring stations into three groups: high-, moderate-, and low-pollution areas. In addition, this categorization of monitoring stations was in agreement with that of the assessment that involved using the simple river pollution index. Four latent factors that predominantly influence the river water quality of the Tamsui River basin are assessed using FA: anthropogenic pollution, the nitrification process, seawater intrusion, and geological and weathering processes. We plotted a spatial pattern using the four latent factor scores and identified ten redundant monitoring stations near each upstream station with the same score pattern. We extracted five significant parameters by using DA: total organic carbon, total phosphorus, As, Cu, and nitrate, with spatial variance to differentiate them from the polluted condition of the group obtained by using CA. Finally, this study suggests that the Taiwan EPA can adjust the surface water-monitoring program of the Tamsui River by reducing the monitoring stations to 28 and the measured chemical parameters to five to lower monitoring costs.     

Water quality of a tributary of the Pearl River, the Beijiang, Southern China: implications from multivariate statistical analyses

Water quality information of Beijiang River, a tributary of Pearl River in Guangdong, China, was analyzed to provide an overview of the hydrochemical functioning of a major agricultural/rural area and an industrial/urban area. Eighteen water quality parameters were surveyed at 13 sites from 2005 to 2006 on a monthly basis. A bivariate correlation analysis was carried out to evaluate the regional correlations of the water quality parameters, while the principal component analysis (PCA) technique was used to extract the most influential variables for regional variations of river water quality. Six principal components were extracted in PCA which explained more than 78% and 84% of the total variance for agricultural/rural and industrial/urban areas, respectively. Physicochemical factor, organic pollution, sewage pollution, geogenic factor, agricultural nonpoint source pollution, and accumulated pesticide usage were identified as potential pollution sources for agricultural/rural area, whereas industrial wastewaters pollution, mineral pollution, geogenic factor, urban sewage pollution, chemical industrial pollution, and water traffic pollution were the latent pollution sources for industrial/urban area. A multivariate linear regression of absolute principal component scores (MLR-APCS) technique was used to estimate contributions of all identified pollution sources to each water quality parameter. High coefficients of determination of the regression equations suggested that the MLR-APCS model was applicable for estimation of sources of most water quality parameters in the Beijiang River Basin.     

Use of water quality index and multivariate statistical techniques for the assessment of spatial variations in water quality of a small river

Rapid urban development has led to a critical negative impact on water bodies flowing in and around urban areas. In the present study, 25 physiochemical and biological parameters have been studied on water samples collected from the entire section of a small river originating and ending within an urban area. This study envisaged to assess the water quality status of river body and explore probable sources of pollution in the river. Weighted arithmetic water quality index (WQI) was employed to evaluate the water quality status of the river. Multivariate statistical techniques namely cluster analysis (CA) and principal component analysis (PCA) were applied to differentiate the sources of variation in water quality and to determine the cause of pollution in the river. WQI values indicated high pollution levels in the studied water body, rendering it unsuitable for any practical purpose. Cluster analysis results showed that the river samples can be divided into four groups. Use of PCA identified four important factors describing the types of pollution in the river, namely (1) mineral and nutrient pollution, (2) heavy metal pollution, (3) organic pollution, and (4) fecal contamination. The deteriorating water quality of the river was demonstrated to originate from wide sources of anthropogenic activities, especially municipal sewage discharge from unplanned housing areas, wastewater discharge from small industrial units, livestock activities, and indiscriminate dumping of solid wastes in the river. Thus, the present study effectively demonstrates the use of WQI and multivariate statistical techniques for gaining simpler and meaningful information about the water quality of a lotic water body as well as to identify of the pollution sources.     

Water quality assessment and source identification of Daliao river basin using multivariate statistical methods

Multivariate statistical methods, such as cluster analysis (CA), discriminant analysis (DA) and principal component analysis (PCA), were used to analyze the water quality dataset including 13 parameters at 18 sites of the Daliao River Basin from 2003-2005 (8424 observations) to obtain temporal and spatial variations and to identify potential pollution sources. Using Hierarchical CA it is classified 12 months into three periods (first, second and third period) and the 18 sampling sites into three groups (groups A, B and C). Six significant parameters (temperature, pH, DO, BOD(5), volatile phenol and E. coli) were identified by DA for distinguishing temporal or spatial groups, with close to 84.5% correct assignment for temporal variation analysis, while five parameters (DO, NH(4)(+)-N, Hg, volatile phenol and E. coli) were discovered to correctly assign about 73.61% for the spatial variation analysis. PCA is useful in identifying five latent pollution sources for group B and C (oxygen consuming organic pollution, toxic organic pollution, heavy metal pollution, fecal pollution and oil pollution). During the first period, sites received more oxygen consuming organic pollution, toxic organic pollution and heavy metal pollution than those in the other two periods. For group B, sites were mainly affected by oxygen consuming organic pollution and toxic organic pollution during the first period. The level of pollution in the second period was between the other two periods. For group C, sites were mainly affected by oil pollution during the first period and oxygen consuming organic pollution during the third period. Furthermore, source identification of each period for group B and group C provided useful information about seasonal pollution. Sites were mainly affected by fecal pollution in the third period for group B, indicating the character of non-point source pollution. In addition, all the sites were also affected by physical-chemistry pollution. In the second and third period for group B and second period for group C sites were also affected by natural pollution.     

Assessment of anthropogenic sources of water pollution using multivariate statistical techniques: a case study of the Alqueva’s reservoir, Portugal

Multivariate statistical techniques were applied to evaluate spatial/temporal variations, and to interpret water quality data set obtained at Alqueva reservoir (south of Portugal). The water quality was monitored at nine different sites, along the water line, over a period of 18 months (from January 2006 to May 2007) using 26 water quality parameters. The cluster analysis allowed the formation of five different similarity groups between sampling sites, reflecting differences on the water quality at different locations of the Alqueva reservoir system. The PCA/FA identified six varifactors, which were responsible for 64% of total variance in water quality data set. The principal parameters, which explained the variability of quality water, were total phosphorus, oxidability, iron, parameters that at high concentrations indicate pollution from anthropogenic sources, and herbicides indicative of an intensive agricultural activity. The spatial analysis showed that the water quality was worse in the north of the reservoir.     

Water quality assessment, by statistical analysis, on rural and urban areas of Chocancharava River (Río Cuarto), Córdoba, Argentina

Water quality has degraded dramatically in the Chocancharava River (Río Cuarto, Córdoba, Argentina) due to point and non-point sources. This paper aims to assess spatial and temporal variations of physical and chemical parameters of the river. Six sampling sites and six sampling campaigns were developed. During the period 2007–2008, wet and dry seasons were included. A statistical analysis was carried out with 23 physical and chemical variables. Then, a new statistical analysis was carried out including the Riparian Corridors Quality Index and the physical and chemical variables (24 variables). Considering a multivariate system, analysis of variance, principal component analysis and cluster analysis were used. From the statistical analysis, the river was divided into two zones with different degrees of contamination. The most polluted zone is due to pollution inputs of urban, industrial and agricultural sources. This area showed a remarkable deterioration in water quality, mainly due to wastewater discharges. According to Riparian Quality, better results were found in sections of poor water quality, due to the fact that the river bank forest was less degraded downstream of the sewage discharge.     

农业面源污染防治的监测问题分析

近年来,农业面源污染已成为许多国家和地区水环境质量改善的主要影响因素,开展农业面源污染监测将为深入打好污染防治攻坚战提供重要支撑。该文系统分析当前我国农业面源污染监测存在的主要问题,综合考虑国内外经验,提出如下建议:采取空间嵌套式的布局模式优化地表水环境监测点位,充分发挥环境监测的预测预报和风险评估功能;建立包括污染源、产排污系数和空间传输过程的农业面源污染全过程监测网络;定期开展土壤氮、磷养分含量评估和地下水硝酸盐氮测定和评估;建立完善数据整合与共享机制。     

太浦河界标断面水质达标综合治理对策

简述了太浦河界标断面2020年水质考核目标以及区域水污染现状。指出,界标断面水质主要超标因子为DO和TP,农业面源污染、工业污染、生活污水处理相对滞后、内源污染、部分黑臭河道河段清淤不彻底是导致区域水污染的主要因素。提出,要推进农业面源污染治理,强化工业污染源治理,加强区域性污染物控制以及生活污染源整治,促进河湖生态系统恢复,加强自动监测站管理工作。     

Identification of source of faecal pollution of Tirumanimuttar River, Tamilnadu, India using microbial source tracking

Efficient management of deteriorating water bodies can be achieved by determining the sources of faecal pollution. Resourceful techniques for discrimination of the sources of Escherichia coli in surface water have recently been developed, including the use of river water to facilitate faecal indicator surveillance, identification of sources of faecal contamination and employing relevant management practices to maintain water quality. This study was conducted to employ microbial source tracking (MST) techniques for the determination of the sources of faecal pollution based on a water quality investigation of the physico-chemical characteristics and coliform count point of the Tirumanimuttar River. To accomplish this, an MST library-based antibiotic resistance analysis, serotyping and the genomic tool rep-PCR techniques were applied, and the obtained results were analysed statistically. Among 135 and 70 E. coli isolates present in the library and water samples collected from the river and nearby well water sources, respectively, most showed intrinsic, high or moderate resistance to antibiotics. Isolates from human and pig faecal sources were 92% homologous with the samples from the river, whereas isolates from sewage and dairy cattle showed 89% and 80% homology, respectively. These findings indicated that the Tirumanimuttar River is subjected to stress from anthropogenic activities and runoff contaminated with agricultural and human faecal contamination. The sources of faecal pollution identified in this study may facilitate the monitoring and management of the Tirumanimuttar River.     

基于主成分分析-多元线性回归的松花江水体中多环芳烃源解析

对松花江全流域14个监测断面的16种美国环保局优先控制的多环芳烃(PAHs)的主要来源及其贡献率应用主成分因子分析-多元线性回归模型(PCA-MLR)进行了来源解析。结果表明:松花江全流域为化石和石油燃料的复合PAHs污染,水体环境中PAHs首要污染源为化石燃料燃烧和交通污染,合计贡献率为63.1%,第二大污染源为工业和民用燃煤污染,合计贡献率为36.9%,沿江的石化、石油基地、大型焦化厂、电厂都是PAHs的主要来源。     

Spatial and seasonal characteristics of river water chemistry in the Taizi River in Northeast China

Anthropogenic activities have led to water quality deterioration in many parts of the world, especially in Northeast China. The current work investigated the spatiotemporal variations of water quality in the Taizi River by multivariate statistical analysis of data from the 67 sampling sites in the mainstream and major tributaries of the river during dry and rainy seasons. One-way analysis of variance indicated that the 20 measured variables (except pH, 5-day biological oxygen demand, permanganate index, and chloride, orthophosphate, and total phosphorus concentrations) showed significant seasonal (p?≤?0.05) and spatial (p?<?0.05) variations among the mainstream and major tributaries of the river. Hierarchical cluster analysis of data from the different seasons classified the mainstream and tributaries of the river into three clusters, namely, less, moderately, and highly polluted clusters. Factor analysis extracted five factors from data in the different seasons, which accounted for the high percentage of the total variance and reflected the integrated characteristics of water chemistry, organic pollution, phosphorous pollution, denitrification effect, and nitrogen pollution. The results indicate that river pollution in Northeast China was mainly from natural and/or anthropogenic sources, e.g., rainfall, domestic wastewater, agricultural runoff, and industrial discharge.     

Agriculture and elevation are the main factors for Pampasic stream habitat and water quality

Streams of the Pampasic plain in Southeastern South America are ecosystems affected by both water pollution and habitat alteration mainly due to agricultural activity. Water quality is influenced by the quality of habitats and both depend on land use and watershed morphology. The objective of this study was to determine the relationship between the variables of four factors: (1) the morphology of the watershed, (2) land use in the watershed, (3) river habitat, and (4) water quality of wadeable streams in Uruguay, as well as to determine the most representative variables to quantify such factors. We studied 28 watersheds grouped into three ecoregions and four principal activities, which generated seven zones with three to five streams each. Correlations between the variables of each factor allowed reducing the total number of variables from 57 to 32 to perform principal component analyses (PCA) by factor, reducing the number of variables to 18 for a general PCA. The first component was associated with water quality and elevation. The second was associated with the stream and watershed size, the third with habitat quality, and the fourth to the use of neighboring soils and objects in the channel. Our results indicate that agricultural intensity and elevation are the main factors associated with the habitat and water quality of these lowland streams. These factors must be especially considered in the development of water quality monitoring programs.     

Evaluation of surface water quality characteristics by using multivariate statistical techniques: A case study of the Euphrates river basin, Turkey

The surface water quality of the Euphrates river basin in Turkey are evaluated by using the multivariate statistical techniques known as factor analysis (FA) and multidimensional scaling (MDS) analysis. When FA was applied to the water quality data obtained from the 15 different surface water quality monitoring stations, two factors were identified, which were responsible from the 86.02% of the total variance of the water quality in the Euphrates river basin. The first factor called the urban land use factor explained 44.20% of the total variance and the second factor called the agricultural use factor explained 41.81% of the total variance. MDS technique showed that electrical conductivity (EC), percent sodium (Na%) and total salt are the most important variables causing difference in the water quality analysis.     

Analysis of spatial and temporal water pollution patterns in Lake Dianchi using multivariate statistical methods

Various multivariate statistical methods including cluster analysis (CA), discriminant analysis (DA), factor analysis (FA), and principal component analysis (PCA) were used to explain the spatial and temporal patterns of surface water pollution in Lake Dianchi. The dataset, obtained during the period 2003–2007 from the Kunming Environmental Monitoring Center, consisted of 12 variables surveyed monthly at eight sites. The CA grouped the 12 months into two groups, August–September and the remainder, and divided the lake into two regions based on their different physicochemical properties and pollution levels. The DA showed the best results for data reduction and pattern recognition in both temporal and spatial analysis. It calculated four parameters (TEMP, pH, COD_Mn, and Chl-a) to 85.4% correct assignment in the temporal analysis and three parameters (BOD, NH 2009/11/20Heavy metals (Cd, Cu, Fe, Mn and Zn) concentrations were determined in different tissues (muscle, kidney, liver, brain, gonads, heart and feathers) of Glaucous Gulls (Larus hyperboreus) from Bjørnøya and Jan Mayen. The age and spatial dependent variations in heavy metals were quantified and interpreted in view of the three chemometric techniques, i.e. non-parametric Mann–Whitney U test, redundancy gradient analysis and detrended correspondence analysis. The Glaucous Gulls from Bjørnøya contained significantly higher (p?<?0.05) levels of Cd, Cu and Zn than those inhabited Jan Mayen. Adult birds were characterized by greater (p?<?0.01) concentration of muscle, hepatic and renal heavy metals in comparison to chicks. Insignificantly higher slope constant Zn/Cd for the liver than for the kidney may reflect insignificant Cd exposure. Estimate of transfer factor (TF) allows us to assess variations in heavy metal concentrations during the individual development of Glaucous Gulls. It may be stated that there is a distinct increase of bioaccumulation of all the studied metals during subsequent stages of the bird life.     

Surface water quality assessment of the Vatinsky Egan River catchment, West Siberia

Stream water chemistry were analyzed across Vatinsky Egan River Catchment (West Siberia). The objective of the study is to reveal the spatial and seasonal variations of the water quality and to assess the anthropogenic chemical inputs into the river system. Stream chemistry were monitored in 24 sampling sites for a period extended from January 2002 to December 2005. Spatial distribution of constituents in the Vatinsky Egan River basin indicated pollution from non-point sources associated with oil development. Data revealed that ion concentrations of river waters are usually negatively correlated with stream discharge. The major spatial variations of the hydrochemistry are related to the salinity. Chloride exhibited wide and high concentration range. A comparison with another rivers of West Siberia reveals that Vatinsky Egan River is the most saline and regional impacts further out in the watershed. The salinity of the river water increases substantially as it crosses Samotlor oil field. Many Cl(-) concentrations in the middle and lower parts of the catchment exceed the world average river values by one or more orders of magnitude. For 38% of sampling events, total petroleum hydrocarbons (TPH) concentrations were above the recommended water quality standards.