徐州城市表层土壤中重金属环境风险测度与源解析

研究了徐州城市表层土壤的21个样品中重金属元素富集特征,结果表明,与我国土壤元素的背景值(算术平均值)相比,表层土壤中Zn、Cd、As、Hg、Sb、Sn和Ag等元素富集程度高;Se、Sc、Ba、Bi、Pb、Cu、Ni、Cr、Mn、Mo、Be、Ga和Co等元素的富集程度较低。不同城市表层土壤中重金属元素有着不同的来源,统计分析结果表明,研究区表层土壤样品中重金属元素可分成4种类别:“自然因子”类别元素(Ti、Ga、Li、V、Co、Mn、Be和Pt);“交通因子”类别元素(Ag、Se、Sc、Pb、Cu、Zn、Cd、Br、S、Mo和Au);“燃煤因子”类别元素B(i、Cr、Hg、As、Sb和Pd)和混合源类别元素(Sn和Ba)。环境风险指数的计算结果表明,表层土壤中重金属污染具有较大的环境风险,其中属于中等环境风险级别以上的样品占近40%。     

Application of multivariate statistical approach to identify heavy metal sources in sediment and waters: a case study in Yangzhong,China

Multivariate statistical approach is used to identify the sources of heavy metals (Bi, Cd, Co, Cr, Mn, Pb, U, V, and Zn) in surface water and freshly deposited riverine sediment samples in Yangzhong city, China. The metal concentration data for the water and sediment samples are reported in terms of basic statistical parameters and metal-to-metal correlations. In both surface water and sediment samples, significant correlations are observed between some metals. Principal component analysis and cluster analysis distinguishes factors of lithogenic and anthropogenic origin. Bismuth, Cd, Co, and Pb (Co only for water samples) contents are controlled by the regional lithogenic high background factor; Co, Mn, U, and V (Co only for sediment samples) are interpreted to be mainly inherited from soil parent materials, while Cr, Zn, and Mn in the two kinds of samples are recognized as the tracer of industrial pollution. Obvious similarity between factor loadings of the two kinds of samples is observed, evidencing that metal variability in the two kinds of samples is controlled by the same sources. Statistical analysis agrees with discussion based on background value and field survey of point-source pollutant affected sediment, making this statistical discussion more convincing.     

Assessment of heavy metals contamination in urban topsoil from Changchun City,China

Due to the rapid urbanization and industrialization that has occurred in China over the last few decades, metals have been continuously emitted into the urban environment and now pose a serious threat to human health. Indeed, there is a growing concern over the potential for pollution of urban soils with heavy metals. Therefore, an extensive soil survey was conducted in urban areas of Changchun, China, to evaluate the current status of heavy metal contamination in soils and to evaluate its potential sources. A total of 352 samples of urban soils were collected from urban areas of Changchun using a systematic sampling strategy in which one sample per km² was taken (0 ~ 20 cm). The levels of Cu, Pb, Zn and the major elements (Mn, Al₂O₃, CaO, Fe₂O₃, MgO, SiO₂, K₂O and NaO) were then determined by X-Ray fluorescence spectrometry (XRF), while the level of Cd was determined by graphite furnace atomic absorption spectrometry (GF-AAS), and the Hg and As concentrations were determined by atomic fluorescence spectroscopy (AFS). The results indicated that, when compared with the background values of topsoil in the Changchun region, the topsoil in urban areas were enriched with metals, particularly Cu, Cd, Zn, Pb and Hg. The results of correlation coefficient analysis showed that Hg, As, Cd, Cu, Pb and Zn were significantly positive correlated with each other, while Cr and Mn formed another group. Moreover, significantly positive correlations were observed between pH and Zn, Pb, Cu, Cd, As and Hg, indicating that pH influences the distributions of these metals in urban soils in Changchun. Principal component analysis (PCA) was conducted to identify sources of heavy metals and the results revealed distinctly different associations among the trace metals and the major elements in the urban soils. The concentration of Cr appeared to be controlled by the parent material (natural sources), while Cu, Pb and Zn were mainly from vehicle emissions, with Zn primarily coming from vehicle tires. Additionally, Hg and As primarily originated from coal combustion, while Cd was mainly associated with industrial sources. According to the pollution index (PI) of each metal, the overall levels of metal pollution were not especially high, but there were clearly contaminated sites concentrated in the central and northeast portion of the studied region. The Nemerow integrated pollution index (NIPI) of the seven metals also indicated that urban soils in Changchun city were classified as having low level of pollution.     

Correlation between magnetic susceptibility and heavy metals in urban topsoil: a case study from the city of Xuzhou, China

Anthropogenic influence, mainly due to urban and industrial activities and traffic exhaust, may affect urban topsoil via atmospheric contamination and solid waste. Magnetic susceptibility measurements were conducted on 21 urban topsoil samples from the city of Xuzhou, China. High intensities of magnetic susceptibility were detected in the majority of the samples. SEM analysis shows that magnetic minerals are in the form of spherules and mainly due to anthropogenic inputs. The heavy metals Pb, Cu, Zn, Se, Sc, Mo, Fe, and Bi show strong correlations with magnetic susceptibility, and Ag, Ba, Cd, Ni, Cr, Sb, and Sn, on the other hand, show a weak correlation with magnetic susceptibility. Whereas, of these metals studied, only Hg has no significant correlation with the susceptibility. The Tomlinson pollution load index (PLI) also shows significant correlation with the susceptibility (χ). The present study shows that magnetic susceptibility is a fast, inexpensive, and non-destructive method for the detection and mapping of contaminated soils.     

河北平原表层土壤重金属环境质量及污染评价

土壤环境质量评价是了解土壤环境质量状况、采取保护措施的重要前提,本文用土壤环境质量标准(GB15618-1995),对河北平原表层土壤重金属环境质量进行了评价,结果表明:河北平原表层土壤环境质量优良,一、二级土壤分别占全区93.02%和6.62%。同时又根据重金属元素的环境地球化学行为,采用富集因子,有效地区分了重金属污染的自然影响和人为影响,结果显示,河北平原表层土壤人为污染的面积较大,但以轻微污染为主。     

山东省烟台地区土壤重金属的生态效应——以砷为例

烟台市是山东半岛蓝色经济区核心城市之一,通过对山东省烟台市生态地球化学资料的系统整理,研究土壤重金属污染现状与分布迁移规律,发现土壤主要污染因子是As、Cd、Hg、Cu、Pb、Zn等重金属元素污染,工矿三废排放是土壤重金属污染的主要原因;主要致污因子砷是环境中毒性最大的有害元素之一。土壤-苹果树体系中As迁移及形态转化规律为:植物根系吸收土壤中的砷,并在根部发生了AsⅤ→AsⅢ的还原作用,由植物的根部向上迁移过程中,AsⅢ的比例逐渐降低。砷在根系土-植物体系中的分布为:根系土>根>叶子>茎>苹果。     

Accumulation and sources of heavy metals in urban topsoils: a case study from the city of Xuzhou, China

The knowledge of the variability, the anthropogenic versus natural origin and corresponding environmental risk for potentially harmful elements in urban topsoils is of importance to assess human impact. The aims of the present study were: (1) to assess the distribution of heavy metals (Sn, Li, Ga, Ba, Fe, Mn, Co, Be, Ti, Al, Hg, Cr, Sb, As, Bi, Pd, Pt, Au, Ni, Cd, Zn, Cu, Pb, Se, Mo, Sc and Ag) in urban environment; (2) to discriminate natural and anthropogenic contributions; and (3) to identify possible sources of pollution. Multivariate statistic approaches (principal component analysis and cluster analysis) were adopted for data treatment, allowing the identification of three main factors controlling the heavy metal variability in Xuzhou urban topsoils. Results demonstrate that Hg, Cr, Sb, As, Bi, Pd, Pt, Au, Ni, Cd, Br, Zn, Cu, S, Pb, Se, Mo, Sc and Ag could be inferred to be tracers of anthropogenic pollution, whereas Al, Ti, Ga, Li, V, Co, Pt, Mn and Be were interpreted to be mainly inherited from parent materials. Iron, Ba, Sn, Pd and Br were interpreted to be affected by mixed sources.     

Spatial distribution of heavy metals in soils: a case study of Changxing,China

Six hundred and sixty-five soil samples were taken from Changxing County in Zhejiang Province, China, to characterize the spatial variability of Hg Cd, Pb, Cu, As and Cr. The geostatistics and geographic information system (GIS) techniques were applied, and the ordinary kriging and lognormal kriging were used to map the spatial patterns of the six heavy metals. Hg, Pb, Cu and As were fitted to the spherical model with a range of 85.75, 82.32, 86.10, and 23.17 km, respectively. Cr was fitted to the exponential model with a range of 6.27 km, and Cd was fitted to the linear model with a range of 37.66 km. Both Pb and Cu had strong spatial dependence due to the effects of natural factors including parent material, topography and soil type. Hg, Cd, Cr and As had, however, moderate spatial dependence, indicating an involvement of human factors. Meanwhile, based on the comparison between the original data and the guide values of the six metals, the disjunctive kriging technique was used to quantify their pollution risks. The results showed that only Cd and Hg exhibited pollution risks in the study area. The pollution source evaluated was closely corresponded with the real discharge of industrial production and the application of organomercury pesticides. The results of this study provide insight into risk assessment of environmental pollution and decision making for agricultural production and industrial adjustment of building materials.     

山东省莱芜市幅1:50 000水文地质图数据集

本数据集选取山东莱芜市幅1：50 000地形图空间数据库、莱芜市幅1：50 000地质图部分数据作为底图,采用本次水文地质调查取得的最新数据编制而成。除水文地质图外,数据集还包括机（民）井、泉、钻孔、水质分析及水量计算等数据表,主要分类及数据量为机（民）井209个、泉14个、水库13个、长期监测点13处（监测数据936点次）、地下水水位及水量统测100点次、环境地质点6个、样品采集（全分析、同位素及岩石）90组等。本数据集主要使用MapGIS 6.7平台辅助制图,数据量约169 MB,坐标系为CGCS 2000国家大地坐标系,投影方式为高斯-克吕格投影（6度带）。数据集不仅用二维方式标注了关键水文地质信息,还在主图及部分镶图引入三维地势表达方式。     

基于多变量统计分析和水化学特征的海水入侵特征研究——以青岛市崂山区为例

沿海地区地下水环境问题日益突出,进行地下水水化学特征及演化规律的研究,能够更有效地开展地下水环境的监测和保护。以青岛市崂山区地下水为研究对象,综合运用统计分析、主成分分析、Piper图解法、HFE-D图解法、Chadha’s矩形图法等方法,对研究区海水入侵特征与地下水化学特征演化进行分析,探究地下水水化学特征及演化规律,并进一步评价了海水入侵现状。结果表明,研究区地下水以Na+、Ca2+、Cl−、${\rm{SO}}_4^{2-}$为主要优势离子,地下水化学类型多为Cl·SO4-Na型和SO4·Cl-Ca·Mg型。地下水中Cl−浓度变化幅度较大,且其均值超出了有无海水入侵的分界值（250 mg·L−1）,地下水可能发生一定程度的海水入侵;青岛市崂山区地下水呈中性至弱碱性（pH均值=7.0~8.0）,是沿海地区长期的水文地球化学过程的影响;地下水化学变化主要受自然因素（岩石与水的相互作用）或人为因素（农业和家庭活动）的控制;采用反距离加权（IDW）方法,结合地理信息系统（GIS）,进行海水入侵位置的空间映射,研究结果表明崂山区海水入侵主要分布于江家土寨东−浦里社区北入侵段,王哥庄−港西−港东入侵段、仰口湾入侵段、登瀛村−栲栳岛入侵段。     

Risk assessment of heavy metals in topsoil along the banks of theYangtze River in Huangshi,China

High concentrations of heavy metal in farmland threatens food production and human health. Our study assesses soil quality and the distribution of Cd, Cr, Cu, Zn, Pb and Ni, to identify possible sources of heavy metals along the banks of the Yangtze River in Huangshi. Heavy metal concentrations of 22 topsoil samples were measured using the inductively coupled plasma mass spectrometry instrument and data was analyzed by multivariate statistic approaches. Heavy metal contamination risk assessment was performed using a combination of the Nemerow multi-factor index method, the Hakanson potential ecological risk index method and the Muller index of geo-accumulation method. These methods determined similar results indicating that cadmium (Cd) poses a health risk to residents at the study site while Cr, Cu, Zn, Pb and Ni do not. The Nemerow multi-factor index method demonstrated that 18 samples were heavily polluted, three moderately polluted and one lightly polluted. The Muller index of geo-accumulation method found ten samples were moderately to strongly polluted by Cd, five were moderately polluted, six were lightly to moderately polluted and one was lightly polluted. The Hakanson potential ecological risk index method proved six samples were strongly polluted, seven were moderately polluted and nine were lightly polluted. Since our sampling sites were all in agricultural lands, we recommend the potential ecological risk index method as the most effective given it not only considers the range of pollutants contributing to soil pollution but factors in heavy metals toxicity. We are apt that the source of the high concentrations of Cd found in topsoil is derived from alluvial sediments upstream of the Yangtze River with a high percentage of residual speciation and a low percentage of exchangeable speciation distribution of Cd. This in turns indicates that a high concentration of Cd in soil had little impact on the natural environment. However, 31.9% of the iron-manganese oxides bound speciation indicating that such levels of Cd in soil would be potentially hazardous to the crops, particularly if exposed to a reductive condition.     

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.     

福建省龙海市南部表层土壤重金属元素累积特征研究

基于2004年和2015年福建省龙海市南部表层土壤获得的重金属元素和pH值测试数据,采用数理统计方法分析该区土壤Cd、Hg、Pb、As、Cu、Zn及pH值等指标的累积变化特征及影响因素.龙海市南部表层土壤Cd、Cu、As等重金属元素呈一定程度的正累积趋势,其中As和Cu的平均相对累积率分别为4.943％和5.486％;...     

Assessment of physico-chemical and microbiological surface water quality using multivariate statistical techniques: a case study of the Wadi El-Bey River,Tunisia

Located in the northeastern part of Tunisia, Wadi El Bey drains the watershed through farmland, industrial, and urban areas of the region. It serves to discharge treated wastewater of different types. In this work, the variations of the water quality of Wadi El Bey were studied and evaluated, during 2 years (2012–2013), using multivariate statistical techniques such as principal component analysis (PCA) and cluster analysis (CA). In addition, the similarities or dissimilarities among the sampling points were as well analyzed to identify spatial and temporal variations. The results obtained based on the cluster analysis, led to identify three similar water quality zones: relatively polluted (LP), moderately polluted (MP), and highly polluted (HP). The inorganic and organic parameters, temperature, conductivity, dissolved oxygen, chemical oxygen demand, salmonella, and enterococcus, seemed to be the most significant parameters of water quality. Three factors were identified as responsible for the data structure, explaining 60.95% of the total variance. The first factor is the physical and non-organic chemical parameters explaining 23.48% of the total variance. The second and third factors are, respectively, the microbiological (21.26%) and organic-nutrient (16.2%).This study shows that multivariate statistical methods can help the water managers to understand the factors affecting the water quality.     

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.     

Magnetic properties of urban topsoils and correlation with heavy metals: a case study from the city of Xuzhou, China

Heavy metals in urban topsoils have been shown to be very useful tracers of environmental pollution. Thus, their detailed studies are of great importance. Apart from expensive and time-consuming chemical methods, several simple, rapid and cheap proxy methods have been developed recently, one of them being based on rock-magnetic parameters. This examines the use of rock-magnetic methods designed to assess the degree of heavy-metal pollution of urban topsoils from the city of Xuzhou (China). The aim was to identify the magnetic properties and to link the “magnetic pollution” to the concentrations of the heavy metals. Since a strong correlation has been found between saturation isothermal remanent magnetization (SIRM) and the heavy metals, namely, Fe, Se, Ti, Sc, Ba, Bi, Pb, Cu, Zn, Cr and Mo, an anthropogenic contamination origin is thought to be the cause. The present study shows that SIRM is a fast, inexpensive and non-destructive method for the detection and mapping of heavy-metal-contaminated urban topsoils.     

Assessment of the topsoil heavy metals pollution in the Sarno River basin,south Italy

The Sarno River basin area is one of the most polluted in Europe and it is due to the waste products of the tomato industry, the leather tanneries and the pharmaceutical industry. This area also has been densely populated and urbanized since the Middle Bronze Age, as testified by the presence of numerous archeological sites, including the ancient Pompeii town, and environmental degradation that characterizes the area is absolutely unacceptable. This paper represents a detailed study to assess the potentially harmful element content of topsoils. In total, 283 soil samples were collected and analyzed, after an aqua regia extraction, by a combination of inductively coupled plasma atomic emission and inductively coupled plasma mass spectrometry for 53 elements. Univariate and multivariate analyses were carried out to show the single-element geochemical distribution and the distribution of factor scores of the elemental associations resulting from R-mode factor analysis. Maps showing elements and the association factor score distributions have been obtained using GeoDAS and ArcGIS software. The assessment of the soil contamination was also carried out using the contamination factor and degree of contamination. The results obtained indicate that soil pollution has different anthropogenic sources. Specifically, Cr pollution derives from tanneries discharging wastewaters in the main water bodies of the basin while Cu contamination seems to depend on widespread agricultural practices. Tin, Pb, Hg, Zn, Cd, Sb anomalies are found mainly in urban and industrial areas, sometimes close to roads with high traffic levels while there is a substantial coincidence with background values for other elements (Co, Ni, Se, Tl and V).