Assessment of the natural and anthropogenic sources of chemical elements in alluvial soils from the Drava River using multivariate statistical methods

The objectives of this study are as follows: (a) an assessment of the geochemical background signature of the Drava Valley before the industrial revolution; (b) an evaluation of anthropogenic geochemical influences on the alluvial plains and river terraces in the valley; and (c) a determination of the spatial distribution of trace elements in the alluvial soils of the Drava River downstream of the Austrian–Slovenian border to the confluence of Mura and Drava Rivers.     

Source and distribution of metals in urban soil of Bombay,India, using multivariate statistical techniques

Simplification of a complex system of geochemical variables obtained from the soils of an industrialized area of Bombay is attempted by means of R-mode factor analysis. Prior to factor analysis, discriminant analysis was carried out taking rock and soil chemical data to establish the anthropogenic contribution of metals in soil. Trace elements (Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, and Zn) are expressed in terms of three rotated factors. The factors mostly indicate anthropogenic sources of metals such as atmospheric fallout, emission from different industrial chimneys, crushing operations in quarries, and sewage sludges. Major elements (Na, Mg, Al, Si, P, K, Ca, Ti, Mn, and Fe) are also expressed in terms of three rotated factors indicating natural processes such as chemical weathering, presence of clay minerals, and contribution from sewage sludges and municipal refuse. Summary statistics (mean, standard deviation, skewness, and kurtosis) for the particle size distribution were interpreted as moderate dominance of fine particles. Mineralogical studies revealed the presence of montmorillonite, kaolinite, and illite types of clay minerals. Thus the present study provides information about the metal content entering into the soil and their level, sources, and distribution in the area.     

Polycyclic aromatic hydrocarbons (PAHs) in Xuzhou urban street dust: concentration and sources

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous organic pollutants in urban environments and are considered as the priority pollutants. The main objectives of this study were (1) to assess the contamination extent of 16 PAHs compounds in urban street dust and (2) to identify the anthropogenic sources of contamination. Forty-nine samples were analysed for 16 PAHs by gas chromatography/mass spectrometry. Results showed that total PAHs concentrations varied from 2174 to 24,499 ng/g with an average value of 6616 ng/g. Total PAHs concentrations showed a significant correlation with the total organic carbon (TOC) content (r = 0.599, p < 0.0001), suggesting likely that TOC can influence the distribution of PAHs in Xuzhou street dust. Source identification indices suggested that the primary source of PAHs was automobile exhausts. A factor analysis was performed, and three factors were extracted from the available dataset, explaining a total variance of approximately 84.7%. Results from the factor analysis could be indicative of PAHs with different origins.     

基于多元统计和傅立叶和谱分析的土壤重金属的来源解析及其风险评价

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

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.     

Assessment of groundwater quality using multivariate statistical techniques in Hashtgerd Plain, Iran

Multivariate statistical techniques, such as cluster analysis (CA), factor analysis (FA), principal component analysis (PCA), and discriminant analysis (DA), were applied for the evaluation of variations and the interpretation of a large complex groundwater quality data set of the Hashtgerd Plain. In view of this, 13 parameters were measured in groundwater of 26 different wells for two periods. Hierarchical CA grouped the 26 sampling sites into two clusters based on the similarity of groundwater quality characteristics. FA based on PCA, was applied to the data sets of the two different groups obtained from CA, and resulted in three and five effective factors explaining 79.56 and 81.57% of the total variance in groundwater quality data sets of the two clusters, respectively. The main factors obtained from FA indicate that the parameters influencing groundwater quality are mainly related to natural (dissolution of soil and rock), point source (domestic wastewater) and non-point source pollution (agriculture and orchard practices) in the sampling sites of Hashtgerd Plain. DA provided an important data reduction as it uses only three parameters, i.e., electrical conductivity (EC), magnesium (Mg²⁺) and pH, affording more than 98% correct assignations, to discriminate between the two clusters of groundwater wells in the plain. Overall, the results of this study present the effectiveness of the combined use of multivariate statistical techniques for interpretation and reduction of a large data set and for identification of sources for effective groundwater quality management.     

Identifying sources of salinization using hydrochemical and isotopic techniques, Konarsiah, Iran

Konarsiah salt diapir is situated in the Simply Folded Zone of the Zagros Mountain, south Iran. Eight small permanent brine springs emerge from the Konarsiah salt body, with average total dissolved solids of 326.7 g/L. There are numerous brackish to saline springs emerging from the alluvial and karst aquifers adjacent to the diapir. Concerning emergence of Konarsiah diapir in the study area, halite dissolution is the most probable source of salinity in the adjacent aquifers. However, other sources including evaporation and deep brines through deep Mangerak Fault are possible. The water samples of the study area were classified based on their water-type, salinity, and the trend of the ions concentration curves. The result of this classification is in agreement with the hydrogeological setting of the study area. The hydrochemical and isotopic evaluations show that the groundwater samples are the result of mixing of four end members; Gachsaran sulfate water, Sarvak and Asmari carbonate fresh waters, and diapir brine. The molar ratios of Na/Cl, Li/Cl, Br/Cl, and SO₄/Cl; and isotopic signature of the mixed samples justify a groundwater mixing model for the aquifers adjacent to the salt diapir. The share of brine in each adjacent aquifer was calculated using Cl mass balance. In addition, concentrations of 34 trace elements were determined to characterize the diapir brine and to identify the possible tracers of salinity sources in the mixed water samples. B, Mn, Rb, Sr, Cs, Tl, and Te were identified as trace elements evidencing contact of groundwater with the salt diapir.     

Detection of nitrate sources in urban groundwater by isotopic and chemical indicators,Hangzhou City,China

The sources of nitrate in the shallow groundwater at urban area, Hangzhou City, China were investigated using chemical and isotopic techniques. Groundwater electric conductivity was high in the study area and ranged from 369–1,544 S/cm. Most of the groundwater was weakly alkaline and the groundwater chemistry was seriously influenced by land use in the city. There was a high frequency of groundwater (43% of total) with nitrate above World Heath Organizations standards of 10 mg NO₃/L. The different land-use areas had different nitrate concentrations (0.04–34.41 mgN/L) and ¹⁵N _NO3 values (7.8~35.5). Domestic wastewater was the major nitrate source of shallow groundwater in the urban area, and the point source (septic tank) still existed in residential settings. At agricultural settings manure was the major NO₃ sources. High ¹⁵N _NO3 value (>20) and temporal variations of the ¹⁵N _NO3 value suggested that in residential areas it probably resulted from denitrification.     

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.     

Relationship between aquatic insects and heavy metals in an urban stream using multivariate techniques

In the study, the relationship between some aquatic insect species (Ephemeroptera, Plecoptera, Trichoptera and Odonata) and some heavy metals (cadmium, lead, copper, zinc, nickel, iron and manganese) and boron were assessed using data obtained from the Ankara Stream, which flows through Ankara, the capital city of Turkey and receives high organic and industrial wastes. Sampling was carried out monthly along the Ankara Stream in 1991. environmental data were used to explain biological variation using multivariate techniques provided by the program canonical correspondence analysis ordination. The ordination method canonical correspondence analysis was applied to evaluate the relationships between environmental variables and distribution of aquatic insect larvae. Data sets were classified by two way indicator species analysis. In this study, aquatic insecta communities have been shown by canonical correspondence analysis ordination as related to total hardness, pH, cadmium, lead, copper, zinc, nickel, iron, manganese and boron. Cadmium, lead, copper and boron exceeded limits of the United States Environmental Protection Agency criteria for aquatic life. Trichopteran, Dinarthrum iranicum was an indicator of two way indicator species analysis and was placed close to the arrow representing copper. Odonate, Aeschna juncea was an indicator of two way indicator species analysis in site 10 and was placed close to the arrows representing manganese, lead, and nickel. Trichopteran, Cheumatopsyche lepida and odonate, Platycnemis pennipes were indicators of two way indicator species analysis for sites 6, 7, 11, 14, 15, 18 and were placed close to the arrows representing cadmium, boron, iron and total hardness.     

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.     

矿区地下水系统水质分类判别的多元统计分析

以某矿区地下水系统为例,对该矿区地下水水化学资料进行了多元统计分析方法耦合应用研究,主要包括利用因子分析对存在相关关系的离子变量进行空间降维处理,找出能够反映众多离子信息的基础变量(正交因子),以其作为系统聚类变量;运用系统聚类法获取能代表各地下水子系统水化学特征的典型水样;使用贝叶斯逐步线性判别分析建立地下水各子系统水化学判别模型(判别函数),并对随机检验样品进行判别归属和判别模型统计检验。结果表明:这是一种稳定性较好且切实有效的、适用于矿区地下水系统水化学分类及水源水化学判别的方法。     

Determination of lead, naphthalene, phenanthrene, anthracene and pyrene in street dust

Street dust is a potential source of lead exposure to humans, however scarce information about the pollution levels with lead and polyromatic hydrocarbons exists in Venezuela, limiting the appropriate evaluation of the levels of risk of the people. This work was aimed in the determination of the concentrations of lead, naphtalene, anthracene, phenanthrene and pyrene in the street dust of the most transited avenues and streets of Maracay city. Thirty street dust samples were collected at the streets and avenues, troll and bus main station. Lead was determined by atomic absorption spectrometry after acid digestion (Pb-total), also the fractions of lead soluble in 1 M MgCl₂ and 0.5 M ammonium acetate (pH = 7) were quantified. The polyaromatic hydrocarbons concentrations were determined by capillary gas chromatography equipped with a flame ionization detector. The Pb-total ranged between 734 and 11.439 μg/g with the higher values at the most transited streets and avenues. About 60 % of samples exhibited concentrations between 1.000 and 2.500 μg/g, similar to the values reported in the literature for soils of urban areas. The fraction of lead soluble in magnesium accounted for less than 3 % of Pb-Total, while the 0.5 M ammonium acetate solution represented more than the 75 % of the total loads of the pollutant. The most contaminated samples were those taken at the toll with concentrations of 695.5 and 252.1 μg/g phenanthrene and anthracene were the most abundant, while at the bus station all compounds were detected.     

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 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.     

Identification of groundwater contamination sources in Dindugal district of Tamil Nadu,India using GIS and multivariate statistical analyses

The purpose of this research is to evaluate the groundwater quality in Dindugal district of Tamil Nadu based on the water quality index by geographic information system (GIS) and statistical analysis. This area consists of 80 functional tanneries around Dindigul town with a capacity to process about 200 Mt of hides and skins as leather. In 13 villages, as many as 1090 houses were damaged by tannery contamination. A total of 66 groundwater samples were collected to identify the geochemical sources and contamination. The order of major cations is Na > Ca > Mg > K, while that of anions is Cl > SO₄ > HCO₃ > F > PO₄. CaCl₂, MgCl₂, and (CaHCO₃)₂ types suggested that the mixing of high-salinity water was caused by irrigation return flow, domestic wastewater, and septic tank effluents, with existing water followed by ion exchange reactions. Moreover, Gibbs plots indicated that groundwater contamination was derived from the weathering of granitic gneisses as well as the leaching of evaporated and crystallized ions from agricultural and industrial effluents. The water quality index (WQI) exhibited 8 % of the groundwater samples were not suitable for drinking purpose. The GIS maps showed that the poor water quality decreased toward the southern part of the study area. WQI of TDS, fluoride, sodium, potassium, and bicarbonate were high in groundwater. Multivariate statistical analyses (principal component analysis (PCA), factor analysis (FA)) suggested that the groundwater chemistry was changed by the weathering of source rocks ion exchange and leaching of inorganic components and addition from anthropogenic effluents. Finally, it is thought that the monitoring and assessment works are very useful to understand the degree and sources of groundwater contamination.     

中国某些金属矿床矿石铅来源的铅同位素诠释

在矿质来源研究中,铅同位素组成作为一种有效手段,已应用于几乎所有的金属甚至非金属矿床,起到了重要的作用。随着矿床地球化学研究所的不断深入,一个矿床的矿质来源已不能简单地说来自地球的某个圈层,需要把矿质来源定位到矿区某个具体的岩体或层位,这样才具有实际意义,这样才具有实际意义。本文采取矿石、岩浆岩、地层及基底铅同位素对比的方法研究了４种矿床类型中９个金属矿床铅的来源。结果表明,与岩浆活动有关的夕卡岩     

Assessment of groundwater quality using multivariate statistical techniques in the Azmak Spring Zone,Mugla, Turkey

About 150 coastal spring outlets discharging from a karstified carbonate rock aquifer constitute the Azmak streamflow which is slightly brackish with 3000 mg/l of total dissolved solids. In this study, multivariate statistical methods were applied including the use of factor analysis, correlation analysis and cluster analysis to evaluate groundwater quality of Azmak Spring Zone using eight variables (Ca, Mg, Na, K, Cl, SO₄, EC₂₅ and B) at 19 water points sampled in the dry and wet seasons. Hydrochemical analysis results revealed that for majority of the sampling points, the abundance of cations and anions were ordered as Na?+?K?>?Mg?>?Ca and Cl?>?SO₄?>?HCO₃?+?CO₃, respectively. Factor analysis results indicated that three factors explain 98% and 91% of the total variance in the dry and wet seasons, respectively. Factor 1 was found to be associated with the seawater, factor 2 indicated the effect of fresh water and factor 3 was defined to reflect the effect of seasonal fresh surface water contribution. Cluster analysis results indicated that two main groups and four subgroups could be defined with respect to the ratio of the seawater contribution. Cluster A (A1 and A2) represents the waters affected by seawater while waters less affected by the seawater intrusion are grouped in cluster B (B1 and B2).     

Lead isotopic ratios in source apportionment of heavy metals in the street dust of Kolkata,India

Street dust from 29 locations, in some of the busiest parts of north and south Kolkata, was analysed for heavy metal composition. The decreasing order of average metal concentrations (mg kg^?1) found was Mn (390) > Pb (380) > Zn (300) > As (96) > Cu (61) > Cr (40) > Co (13) > Ag (2.1). The heavy metal composition of the Kolkata dust was compared with reported data for other cities. Enrichment factors of Pb and As were high. Multivariate statistical analysis of the heavy metals and analysis of lead isotopic ratios of the dust revealed a predominant anthropogenic influence in the contamination. The range of lead isotopic ratios found in the dust was between 0.8789 and 0.8998 with a mean Pb concentration of 383 mg kg^?1. The three Pb isotope plots of street dust, diesel and rainwater clustered linearly, while coal did not fit into this trend. The highest 207/206 lead isotopic ratio obtained was from diesel with a mean value of 0.9015, followed by the rainwater sample. The application of the binary mixing model showed that about 66.86% of lead contamination in the street dust was sourced from the atmosphere. The two components extracted by the principal component analysis explained 64.34% of the total variance. Vehicular and industrial emissions appeared to be an important contributor to the accumulation of heavy metals in the dust. The health risk assessment study of the dust indicated carcinogenic risk associated with As and Cr.