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.     

Delineation of Environmental Units by Multivariate Techniques in the Duero River Watershed,Michoacán,Mexico

Environmental or hydrological landscape units can integrate various environmental characteristics to support proper management of natural resources. To delineate these units, quantitative methods such as ordination, clustering, and classification of abiotic factor information are used. In the present work, environmental units were delineated in the Duero River watershed of Michoacán, Mexico. This will enhance understanding of the hydrologic landscape, which is a fundamental to natural resource management. A digital elevation model was used to generate sub-basins. Climatic data were obtained from 16 meteorological stations. Sixty-nine soil and 150 water samples were collected and analyzed in the laboratory. Geostatistical methods for spatial prediction of the environmental variables were used. Mean data for each sub-basin were obtained from the environmental variable grids, generating an abiotic factor data matrix. A multivariate analysis was conducted. Exponential, linear, spherical, and Gaussian models were fit to an empirical variogram. Spatial prediction of the environmental data was done via universal and ordinary kriging. Based on principal component analysis, abiotic factors evaporation, total nitrogen, soil pH, and sodium absorption ratio of water were selected for cluster analysis. Five environmental units were delineated in the Duero watershed. One environmental unit (number 4) provided greater than 50 % of the payment for ecosystem services. The general trend is an increase of urban area. The urban surface in 1983 and 2014 was 1724 and 4750 ha, respectively, an increase of 275 %. Environmental unit 1 showed the greatest urban area growth (1336 ha) during the latter period.     

Application of multivariate statistical methods for groundwater physicochemical and biological quality assessment in the context of public health

Three representative areas (lowland, semi-mountainous, and coastal) have been selected for the collection of drinking water samples, and a total number of 28 physical, chemical, and biological parameters per water sample have been determined and analyzed. The mean values of the physical and chemical parameters were found to be within the limits mentioned in the 98/83/EEC directive. The analysis of biological parameters shows that many of the water samples are inadequate for human consumption because of the presence of bacteria. Cluster analysis (CA) first was used to classify sample sites with similar properties and results in three groups of sites; discriminant analysis (DA) was used to construct the best discriminant functions to confirm the clusters determined by CA and evaluate the spatial variations in water quality. The standard mode discriminant functions, using 17 parameters, yielded classification matrix correctly assigning 96.97% of the cases. In the stepwise mode, the DA produced a classification matrix with 96.36% correct assignments using only ten parameters (EC, Cl^???, NO₃ ^???, HCO₃ ^???, CO₃ ^???2, Ca^?+?2, Na^?+?, Zn, Mn, and Pb). CA and factor analysis (FA) are used to characterize water quality and assist in water quality monitoring planning. CA proved that two major groups of similarity (six subclusters) between 17 physicochemical parameters are formed, and FA extracts six factors that account for 66.478% of the total water quality variation, when all samples’ physicochemical data set is considered. It is noteworthy that the classification scheme obtained by CA is completely confirmed by principal component analysis.     

Using multivariate statistical methods to assess the groundwater quality in an arsenic-contaminated area of Southwestern Taiwan

Groundwater is a major water resource in Southwestern Taiwan; hence, long-term monitoring of water quality is essential. The study aims to assess the hydrochemical characteristics of water in the arsenic-contaminated aquifers of Choushui River alluvial fan and Chianan Plain, Taiwan using multivariate statistical methods, namely, factor analysis (FA), cluster analysis (CA), and discriminant analysis (DA). Factor analysis is applied to reveal the processes controlling the hydrochemistry of groundwater. Cluster analysis is applied to spatially categorize the collected water samples based on the water quality. Discriminant analysis is then applied to elucidate key parameters associated with the occurrence of elevated As concentration (>10 μg L(-1)) in groundwater. Major water types are characterized as Na-Ca-Cl and Na-Mg-Cl in the Choushui River alluvial fan and Chianan Plain, respectively. Inorganic species of arsenic (As), particularly As(III), prevail in these two groundwater catchments, and their levels are higher in the Chianan Plain than in the Choushui River alluvial fan. Through FA, three factors, namely, the degree of salination, As reduction, and iron (Fe) reduction, are determined and denoted irrespective of some differences between the factorial compositions. Spatial distribution patterns of factors As reduction and Fe reduction imply that the redox zonation is delineated by As- and Fe-dominance zones separately. The results of CA demonstrate that three main groups can be properly explained by the factors extracted via FA. Three- (Fe(2+), Fe(3+), and NH (4) (+) ) and four-parameters (Fe(2+), Fe(3+), NH (4) (+) , and Ca(2+)) derived from discriminant analysis for Choushui River alluvial fan and Chianan Plain are elucidated as key parameters affecting the distribution of As-contained groundwater. The analytical results indicate that the reductive dissolution of Fe minerals is prerequisite for the mobilization of As, whereas the shift of redox condition from Fe- to As-reducing leads to the accumulation of dissolved As in this area.     

Polycyclic aromatic hydrocarbons in effluents from wastewater treatment plants and receiving streams in Tianjin, China

Surface water, suspended particulate matter, pore water, and sediment samples were collected and analyzed for polycyclic aromatic hydrocarbons (PAHs) in Yongding New River, South Drainage Canal and North Drainage Canal, which receive most of wastewater from industrial city of Tianjin. PAH concentrations in effluent samples of wastewater treatment plants (WTP) discharging into the South Drainage Canal and North Drainage Canal were quantified for the first time. The results showed that the discharge of the WTPs recently only contributed to the PAH contamination in the canals near the outlets of the WTPs. PAH levels in sediments of the streams were greatly higher than those in soils by riverbank probably due to receiving large amounts of untreated wastewater. Unusually high benz[a] anthracene concentration strongly influenced the seasonal and spatial variation of total PAH concentrations in South Drainage Canal. Paired samples t test of ??Nap, Fl, Phe, Fluo and ??Nap, Phe, Fluo, Chry concentrations, which were dominant components in the air samples from non-heating and heating season, respectively, in the suspended particulate matters from the streams showed that PAH source from air deposition was more important for Yongding New River than that for South Drainage Canal and North Drainage Canal. Source apportionment based on PAH profiles indicated that coal combustion was the major PAH contamination source, and coke oven sources and wood combustion also contributed to the PAH contamination of the streams. This was further indicated by organic petrography analysis.     

Spatial water quality assessment of Langat River Basin (Malaysia) using environmetric techniques

This study investigates the spatial water quality pattern of seven stations located along the main Langat River. Environmetric methods, namely, the hierarchical agglomerative cluster analysis (HACA), the discriminant analysis (DA), the principal component analysis (PCA), and the factor analysis (FA), were used to study the spatial variations of the most significant water quality variables and to determine the origin of pollution sources. Twenty-three water quality parameters were initially selected and analyzed. Three spatial clusters were formed based on HACA. These clusters are designated as downstream of Langat river, middle stream of Langat river, and upstream of Langat River regions. Forward and backward stepwise DA managed to discriminate six and seven water quality variables, respectively, from the original 23 variables. PCA and FA (varimax functionality) were used to investigate the origin of each water quality variable due to land use activities based on the three clustered regions. Seven principal components (PCs) were obtained with 81% total variation for the high-pollution source (HPS) region, while six PCs with 71% and 79% total variances were obtained for the moderate-pollution source (MPS) and low-pollution source (LPS) regions, respectively. The pollution sources for the HPS and MPS are of anthropogenic sources (industrial, municipal waste, and agricultural runoff). For the LPS region, the domestic and agricultural runoffs are the main sources of pollution. From this study, we can conclude that the application of environmetric methods can reveal meaningful information on the spatial variability of a large and complex river water quality data.     

Polycyclic aromatic hydrocarbons in water, sediment and soil of the Songhua River Basin, China

The Songhua River is the third largest river in China and the primary source of drinking and irrigation water for northeastern China. The distribution of 16 priority polycyclic aromatic hydrocarbons (PAHs) in water [dissolved water (DW) and suspended particulate matter (SPM)], sediment, and soil in the river basin was investigated, and the associated risk of cancer from these PAHs was also assessed. The total concentration of PAHs ranged from 13.9 to 161 ng L^?1 in DW, 9.21 to 83.1 ng L^?1 in SPM, 20.5 to 632 ng g^?1 dw (dry weight) in sediment, and from 30.1 to 870 ng g^?1 dw in soil. The compositional pattern of PAHs indicated that three-ring PAHs were predominant in DW and SPM samples, while four-ring PAHs dominated in sediment and soil samples. The spatial distribution of PAHs revealed some site-specific sources along the river, with principal component analysis indicating that these were from pyrogenic sources (such as coal and biomass combustion, and vehicle emissions) and coke oven emission distinguished as the main source of PAHs in the Songhua River Basin. Based on the ingestion of PAH-contaminated drinking water from the Songhua River, cancer risk was quantitatively estimated by combining the Incremental Lifetime Cancer Risk assessment model and BaP-equivalent concentration for five age groups of people (adults, teenagers, children, toddlers, and infants). Overall, the results suggest that the estimated integrated lifetime cancer risk for all groups was in acceptable levels. This study is the first attempt to provide information on the cancer risk of PAHs in drinking water from the Songhua River.     

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.     

Partitioning and sources of PAHs in wastewater receiving streams of Tianjin,China

Polycyclic aromatic hydrocarbons (PAHs) partitioning among dissolved phase, suspended particulate matter, pore water, and sediment was studied in one moderately contaminated river (Yongding New River) and two highly contaminated drainage canals (South Drainage Canal and North Drainage Canal) of Tianjin, China. PAHs concentrations in sediment (ranged from 0.2 to 195 μg/g) showed positive relations with both total organic carbon contents (ranged from 0.7% to 31.1%, dw) and black carbon contents (ranged from 0.1% to 2.1%, dw) in the sediments. Moreover, most of the measured organic carbon normalized partition coefficients of PAHs in the three streams were 0.76 to 1.54 log units higher than the predicted values. These indicated that strong and nonlinear sorption of PAHs by carbonaceous geosorbents such as black carbon (BC) existed in the streams, and BC was an important part of the carbonaceous particles controlling the partitioning of PAHs in the sediments of this study. PAH component ratio analyses suggested that PAHs in the three streams, effluent samples from wastewater treatment plants, and soil samples by the riverbank had similar main sources, which is coal/petroleum combustion. We suggested the transportation and transformation of both carbonaceous particles and PAHs during wastewater treatment process, surface runoff, etc, should be studied further in order to make decisions on PAHs controlling measures.     

Spatial distribution and seasonal variation of polycyclic aromatic hydrocarbons (PAHs) contaminations in surface water from the Hun River, Northeast China

The objectives of this study were to investigate the levels, dispersion patterns, seasonal variation, and sources of 16 priority polycyclic aromatic hydrocarbons (16 EPA-PAHs) in the Hun River of Liaoning Province, China. Samples of surface water were collected from upstream to downstream locations, and also from the main tributaries of the Hun River in dry period, flood period, and level period, respectively. After appropriate preparation, all samples were analyzed for 16 EPA-PAHs. Total PAHs concentrations varied from 124.55 to 439.27 ng l^?1 in surface water in dry period, 1,615.75 to 5,270.04 ng l^?1 in flood period, and 2,247.42 to 7,767.9 ng l^?1 in level period. The 16 EPA-PAHs concentrations were significantly increased in the order of level period > flood period > dry period. The composition pattern of PAHs in surface water was dominated by low molecular weight PAHs, in particular two- to three-ring PAHs. In addition, two-ring PAH accounted for 39.33 to 88.27 % of the total PAHs in level period. Low molecular weight PAHs predomination together with higher levels of PAHs in flood and level period suggested a relatively recent local source of PAHs. Special PAHs ratios such as phenanthrene/anthracene and fluoranthene/pyrene indicated that under dry weather season conditions, the PAHs found in surface water were primarily from petrogenic source, while under wet weather season conditions they were from mixed source of both petrogenic inputs and combustion sources. The comparison of PAHs contamination among different types of areas in China suggested that atmospheric depositions might be the most important approaches of PAHs into water system. Although the Hun River exists low PAHs ecological risk now, potential toxic effects will be existed in the future especially in flood and level period.     

Application of chemometrics in understanding the spatial distribution of human pharmaceuticals in surface water

The growing interest in the environmental occurrence of veterinary and human pharmaceuticals is essentially due to their possible health implications to humans and ecosystem. This study assesses the occurrence of human pharmaceuticals in a Malaysian tropical aquatic environment taking a chemometric approach using cluster analysis, discriminant analysis and principal component analysis. Water samples were collected from seven sampling stations along the heavily populated Langat River basin on the west coast of peninsular Malaysia and its main tributaries. Water samples were extracted using solid-phase extraction and analyzed using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) for 18 pharmaceuticals and one metabolite, which cover a range of six therapeutic classes widely consumed in Malaysia. Cluster analysis was applied to group both pharmaceutical pollutants and sampling stations. Cluster analysis successfully clustered sampling stations and pollutants into three major clusters. Discriminant analysis was applied to identify those pollutants which had a significant impact in the definition of clusters. Finally, principal component analysis using a three-component model determined the constitution and data variance explained by each of the three main principal components.     

Assessment of surface water quality using a growing hierarchical self-organizing map: a case study of the Songhua River Basin,northeastern China,from 2011 to 2015

The analysis of a large number of multidimensional surface water monitoring data for extracting potential information plays an important role in water quality management. In this study, growing hierarchical self-organizing map (GHSOM) was applied to a water quality assessment of the Songhua River Basin in China using 22 water quality parameters monitored monthly from 13 monitoring sites from 2011 to 2015 (14,782 observations). The spatial and temporal features and correlation between the water quality parameters were explored, and the major contaminants were identified. The results showed that the downstream of the Second Songhua River had the worst water quality of the Songhua River Basin. The upstream and midstream of Nenjiang River and the Second Songhua River had the best. The major contaminants of the Songhua River were chemical oxygen demand (COD), ammonia nitrogen (NH₃-N), total phosphorus (TP), and fecal coliform (FC). In the Songhua River, the water pollution at downstream has been gradually eased in years. However, FC and biochemical oxygen demand (BOD₅) showed growth over time. The component planes showed that three sets of parameters had positive correlations with each other. GHSOM was found to have advantages over self-organizing maps and hierarchical clustering analysis as follows: (1) automatically generating the necessary neurons, (2) intuitively exhibiting the hierarchical inheritance relationship between the original data, and (3) depicting the boundaries of the classification much more clearly. Therefore, the application of GHSOM in water quality assessments, especially with large amounts of monitoring data, enables the extraction of more information and provides strong support for water quality management.     

Integrated estuary management for diffused sediment pollution in Dapeng Bay and neighboring rivers (Taiwan)

This work investigated sediment samples collected from Dapeng Bay and three neighboring rivers (Kaoping River, Tungkang River, and Lingbeng River) in southwestern Taiwan, Republic of China. Multivariate statistical analysis techniques, i.e., factor analysis, cluster analysis, and canonical discriminant analysis were used for the evaluation of spatial variations to determine the types of pollution and to identify pollutant sources from neighboring rivers. Factor analysis results showed that the most important latent factors in Dapeng Bay are soil texture, heavy metals, organic matter, and nutrients factors. Contour maps incorporating the factor scores showed heavy metals accumulate along the lakesides, especially on the southeastern banks of the lakes. A cluster analysis was performed using factor scores computed from these latent factors. We then classified these areas into five distinct classes using sampling stations, and we illustrate that in the three river classes, the sediment properties are influenced by industrial and domestic wastewater and agricultural activities (including livestock rearing and farm activities). However, in Dapeng Bay, the rivers were influenced more by complicated biogeochemical processes; these could be identified as a type of pollution. Canonical discriminant analysis illustrated that two constructed discriminant functions made a marked contribution to most of the discriminant variables, and the significant parameters of porosity and Cd, Cr, Al, and Pb content were combined as the ??heavy metal factor??. The recognition capacities of the two discriminant functions were 82.6% and 17.4%, respectively. It is also likely that the annual mean of the water exchange rate is insufficient (taking about 7 days to eliminate pollutants) and therefore has significantly influenced the carbon and nutrient biogeochemical processes and budgets in the semi-enclosed ecosystem. Thus, the sediment properties are not similar between the lagoon and the neighboring rivers. Our results yield useful information concerning estuary recovery and water resources management and may be applicable to other basins with similar characteristics that are experiencing similar coastal environmental issues.     

The assessment and prediction of temporal variations in surface water quality—a case study

In order to optimize the processes of sampling, monitoring, and management, the initial aim of this paper was to develop a model for the definition and prediction of temporal changes of water quality. In the case of the Morava River Basin (Serbia), the patterns of temporal changes have been recognized by applying different multivariate statistical techniques. The results of the conducted cluster analysis are the indicators of the existence of the three monitoring periods: the low-water, transitional, and high-water periods, which is in accordance with changes in the water flow in the analyzed river basin. A possibility of reducing the initial data set and recognizing the main pollution sources was examined by carrying out the principal component/factor analysis. The results indicate that the natural factor has a dominant influence in temporal groups. In order to recognize the discriminatory water quality parameters, a discriminant analysis (DA) was carried out. Conducting the DA enabled a significant reduction in the data set by the extraction of two parameters (the water temperature and electrical conductivity). Furthermore, the artificial neural network technique was used for testing the possibility of predicting changes in the values of the discriminant factors in the monitoring periods. The reliability of this method for the prediction of temporal variations of both extracted parameters within all temporal clusters has been proven.     

Evaluation of surface water quality by using Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI) method and discriminant analysis method: a case study Coruh River Basin

In this study, the water quality of the Coruh River Basin, which is located in the Eastern Black Sea Region of Turkey, was evaluated. The water quality data measurement results obtained by the State Hydraulic Works 26th Regional Directorate from four different sites over a course of 4 years between the years 2011 and 2014 in the Coruh River Basin were used as the data. In this study, the water quality was evaluated by using the Canadian Council of Ministers of the Environmental Water Quality Index (CCME WQI) method and discriminant analysis (DA). The water quality of the Coruh River Basin was calculated as 30.4 and 71.35 by using the CCME WQI and classified as “poor,” “marginal,” and “fair”. These values show that the water of the Coruh River Basin is degraded and under threat and its overall quality is not close to natural or desired levels. The monitoring sites were divided into two groups by the cluster analysis (CA). DA is a multivariate analysis technique used to divide individuals or objects into different groups and assign them into predetermined groups. As a result of DA, calcium (Ca) and sulfate (SO₄) were determined to be significant parameters in the determination of the water quality of the Coruh River Basin. The success of DA depends on the percentage of correct classification. As a result of the analysis, 23% of the parameters in the first measurement point, 69.2% of the parameters in the second and third measurement points, and 76.9% of the parameters in the fourth measurement point were classified correctly. Since the second measurement point is the discharge point of a copper mine, it can be said that the water quality parameters measured may provide accurate results in detecting pollution at this point.     

Impact of intensive horticulture practices on groundwater content of nitrates, sodium, potassium, and pesticides

A monitoring program of nitrate, nitrite, potassium, sodium, and pesticides was carried out in water samples from an intensive horticulture area in a vulnerable zone from north of Portugal. Eight collecting points were selected and water-analyzed in five sampling campaigns, during 1 year. Chemometric techniques, such as cluster analysis, principal component analysis (PCA), and discriminant analysis, were used in order to understand the impact of intensive horticulture practices on dug and drilled wells groundwater and to study variations in the hydrochemistry of groundwater. PCA performed on pesticide data matrix yielded seven significant PCs explaining 77.67% of the data variance. Although PCA rendered considerable data reduction, it could not clearly group and distinguish the sample types. However, a visible differentiation between the water samples was obtained. Cluster and discriminant analysis grouped the eight collecting points into three clusters of similar characteristics pertaining to water contamination, indicating that it is necessary to improve the use of water, fertilizers, and pesticides. Inorganic fertilizers such as potassium nitrate were suspected to be the most important factors for nitrate contamination since highly significant Pearson correlation (r = 0.691, P < 0.01) was obtained between groundwater nitrate and potassium contents. Water from dug wells is especially prone to contamination from the grower and their closer neighbor's practices. Water from drilled wells is also contaminated from distant practices.     

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 heavy metal concentration in the Khoshk River water and sediment, Shiraz, Southwest Iran

Heavy metal contents and contamination characteristics of the water and sediment of the Khoshk River, Shiraz, Southwest Iran were investigated. The abundance of heavy metals decreases as Zn > Mn > Cr > Ni >Pb > Cu > Cd in water samples and Mn > Cr > Pb > Ni > Zn > Cu > Cd in sediments, respectively. Based on the enrichment factor and geoaccumulation index values, sediments were loaded with Cr, Zn, Pb, Cu, and Cd. Pearson correlation matrix as well as cluster and principal components analyses and analysis of variance were implemented on data from sampling sites. Based on the locations of sampling sites in clusters and variable concentrations at these stations, it was concluded that municipal, industrial, and domestic discharges in the Shiraz urban area strongly affected heavy metals concentrations in the Khoshk River water and sediment. Results obtained from principal components analysis of sediment samples showed that the high concentration of Ni was mainly from natural origin, related to the composition of parent rocks, while the elevated values of Cr, Zn, Pb, Cd, and Cu were due to anthropogenic activities.     

Polycyclic aromatic hydrocarbons in surface water and bed sediments of the Hungarian upper section of the Danube River

This study was performed to elucidate the distribution, concentration trend and possible source of polycyclic aromatic hydrocarbons (PAHs) in surface water and bed sediments of the Hungarian upper section of the Danube River and the Moson Danube branch. A total of 217 samples (water and sediments) were collected from four different sampling sites in the period of 2001–2010 and analysed for the 16 priority US Environmental Protection Agency PAHs. Concentrations of total 16 PAHs (∑PAHs) in water samples ranged from 25 to 1,208 ng/L, which were predominated by two- and three-ring PAHs. The ∑PAH concentrations in sediments ranged from 8.3 to 1,202.5 ng/g dry weight. Four-ring PAHs including fluoranthene and pyrene were the dominant species in sediment samples. A selected number of concentration ratios of specific PAH compounds were calculated to evaluate the possible sources of PAH contamination. The ratios reflected a pattern of pyrogenic input as a major source of PAHs. The levels of PAHs determined were compared with other sections of the Danube and other regions of the world.     

Multivariate assessment of polycyclic aromatic hydrocarbons in surface sediments of the Beijiang, a tributary of the Pearl River in Southern China

To estimate the severity of polycyclic aromatic hydrocarbon (PAH) contamination in the upper sediment of the Beijiang River, 42 sediment samples were analyzed for the presence of 16 key PAHs using gas chromatography–mass spectrometry. The concentrations of PAH in the sediment ranged from 44 to 8,921 ng g^?1 dry weight. The four- to six-ring PAHs, contributing >50 % to PAHs in 34 of the 42 sites, were the dominant species. Based on a principal component analysis, combined with multivariate linear regression, it became clear that the most important contributors of PAH were fossil fuel combustion (48 %), diesel emissions plus oil spillage (33 %), and coke combustion (19 %). The surface sediments of Beijiang River were grossly contaminated by PAHs mainly derived from combustion.