Application of synthetic principal component analysis model to mine area farmland heavy metal pollution assessment

Referring to GB5618-1995 about heavy metal pollution,and using statistical analysis SPSS,the major pollutants of mine area farmland heavy metal pollution were identified by variable clustering analysis.Assessment and classification were done to the mine area farmland heavy metal pollution situation by synthetic principal components analysis (PCA).The results show that variable clustering analysis is efficient to identify the principal components of mine area farmland heavy metal pollution.Sort and clustering were done to the synthetic principal components scores of soil sample,which is given by synthetic principal components analysis.Data structure of soil heavy metal contaminations relationships and pollution level of different soil samples are discovered.The results of mine area farmland heavy metal pollution quality assessed and classified with synthetic component scores reflect the influence of both the major and compound heavy metal pol- lutants.Identification and assessment results of mine area farmland heavy metal pollution can provide reference and guide to propose control measures of mine area farmland heavy metal pollution and focus on the key treatment region.

Application of Principal Component Analysis, Cluster Analysis, Pollution Index and Geoaccumulation Index in Pollution Assessment with Heavy Metals from Gold Mining Operations, Tanzania

Gold mining is now widely acknowledged as one of the significant sources of soil pollution in developed countries. In developing countries, the sources and levels of soil contamination have not been thoroughly addressed. Thus, this study was intended to determine the source of soil pollution and the level of contamination in the active and closed gold mining areas. The research paper presents the pollution load of heavy metals (lead-Pb, chromium-Cr, cadmium-Cd, copper-Cu, arsenic-As, manganese-Mn, and nickel-Ni) in 90 soil samples collected from the studied sites. Multivariate statistical analysis, including Principal Component Analysis (PCA) and Cluster Analysis (CA), coupled with correlation coefficient analysis, was performed to determine the possible sources of pollution in the study areas. The results indicated that Pb, Cr, Cu and Mn come from different sources than Cd, As and Ni. The results obtained from the metal pollution assessment using the Pollution Index (PI) and the Geoaccumulation Index (Igeo) confirmed that soils in the mining areas were contaminated in the range from moderately through strongly to highly contaminated soils. This study verified that soil contamination in the gold mining areas results from natural and anthropogenic processes. The current study findings would enhance our knowledge regarding the soil contamination level in the mining areas and the source of contamination. It is recommended to use PCA, CA, PI and Igeo to assess and monitor the heavy metal contaminated soil in gold mining areas.

Application of chemometrics methods for the estimation of heavy metals contamination in river sediments

The concentration and speciation of six heavy metals in sediments of eight sampling sites of Haihe River were investigated. The metals, namely Cd, Cu, Co, Ni, Mn and Pb were considered. By using sequential extraction(SE), the total metals were divided into five fractions: exchangeable, carbonate bound, iron/manganese oxide bound, sulfides and organic matter fraction and residual fraction. A multivariate statistical approach(principal component analysis, PCA) was used to evaluate the contamination of heavy metals by the total levels and chemical forms, respectively. The results showed that the total metals concentration(TMC) could not provide sufficient and accurate information because the mobility, bioavailability and toxicity of metals depend not only on their total concentration but also on the physicochemical form in which they occur.

Source analysis and risk evaluation of heavy metal in the river sediment of polymetallic mining area:Taking the Tonglüshan skarn type Cu-Fe-Au deposit as an example,Hubei section of the Yangtze River Basin,China

In this paper,25 sampling points of overlying deposits in Tonglushan mining area,Daye City,Hubei Province,China were tested for heavy metal content to explore pollution characteristics,pollution sources and ecological risks of heavy metals in sediments.A geo-accumulation index method was used to evaluate the degree of heavy metal pollution in the sediment.The mean sediment quality guideline quotient was used for evaluating the ecological risk level of heavy metal in the sediment.And a method of correlation analysis,clustering analysis,and principal component analysis was used for preliminary analysis on the source of heavy metal in the sediment.It was indicated that there was extremely heavy metal pollution in the sediment,among which Cd was extremely polluted,Cu strongly contaminated,Zn,As,and Hg moderately contaminated,and Pb,Cr,and Ni were slightly contaminated.It was also indicated by the mean sediment quality guideline-quotient result that there was a high ecological risk of heavy metals in the sediment,and 64%of the sample sites had extremely high hidden biotoxic effects.For distribution,the contamination of branches was worse than that of the main channel of Daye Dagang,and the deposition of each heavy metal was mainly influenced by the distance from this sample site to the sewage draining exit of a tailings pond.The source analysis showed that the heavy metals in the sediment come from pollution discharging of mining and beneficiation companies,tailings ponds,smelting companies,and transport vehicles.In the study area,due to the influence of heavy metal discharging from these sources,the ecotoxicity of heavy metals in the sediment was extremely high,and Cd was the most toxic pollutant.The research figured out the key restoration area and elements for ecological restoration in the sediment of the Tonglüshan mining area,which could be referenced by monitoring and governance of heavy metal pollution in the sediment of the polymetallic mining area.

Modeling and Optimization of Relocation of Some Heavy Metals and Micro-nutrients in the Argan Trees

This work constitutes a contribution to the study of the physical-chemical performance of some heavy metals and micro-nutrients in the Argan trees. The samples are harvested from small Argan trees, three samples （soil, wood, leaves） per tree. The content of various heavy metals and micro-nutrient in aerial parts （leaves, wood and soil） were analyzed in the Argan tree by the method of ICP-ES. The results of analysis of data for the chemical variables were measured for several trees at different measurement times, do not affect the dispersion of the data. The use of chemometrics has allowed us to interpret the results obtained especially those related to the characterization of heavy metals and micro-nutrient after calibration by variable regression of these results in order to get an overall estimate of the optimal content, and to follow changes in the methods of treatment. The results of the characteristics of Argan trees have been treated using the statistical method of Principal Component Analysis （MFA）, The MFA was used to analyze the three groups simultaneously. These groups are fairly similar.

Assessment of Heavy Metals in Deep Groundwater Resources of the Kathmandu Valley, Nepal

A study was carried out to address distribution of some heavy metals in deep groundwater resources of the Kathmandu Valley. Groundwater samples were analyzed for pH, ORP, EC, iron, manganese, zinc, and arsenic in 41 deep groundwater wells during pre monsoon and post monsoon seasons for two consecutive years. The study showed elevated concentrations of iron and manganese in the groundwater of the valley. The occurrence of elevated concentrations of arsenic was also exhibited and observed up to 0.160 mg/L. The spatial distribution patterns demonstrated elevated levels of EC, iron, manganese, zinc, and arsenic in central groundwater district (CGWD) of the valley. The monitored parameters except ORP are not significantly correlated with studied time series, inferring similar distribution of the metals. Correlation analysis and principal component analysis (PCA) were performed to find out relationships among examined parameters and metals. The ORP has strong negative correlations with iron, manganese, and arsenic, suggesting reductive mobilization mechanism of the metals in the groundwater. PCA results showed that iron and manganese with high positive loading factors were due to common natural source of origin of these metals in the groundwater, while negative loading factors of pH and ORP indicated that iron and manganese mobilization was favorable in low pH and reducing environment. Cluster analysis (CA) evidenced high mineralization in most of the wells in the CGWD.

Integrated approach of heavy metal pollution indices and complexity quantification using chemometric models in the Sirsa Basin, Nalagarh valley, Himachal Pradesh, India

Chemometric techniques and pollution assessment indices were applied to determine the source and intensity of pollution in the Sirsa River, Himachal Pradesh,India. Results show EC, Cr, Fe, Mn, and Ni were above the permissible limit as per the Bureau of Indian Standards.The heavy metal pollution index(HPI) and contamination index(Cd) provided contrasting outcome and poor correlation was observed. A heavy metal evaluation index(HEI)method was developed using a multiple of the mean and correlation coefficient values to provide an alternative pollution classification. The criteria of HEI adopted for reclassification of HPI and Cdproduced comparable results; 40 % samples were labeled as low contamination,50 % as medium contamination, and 10 % as high contamination for all indices. Principal component analysis along with cluster analysis was used to identify the main factors responsible for degradation of water quality,namely discharge of industrial effluent, river bed mining,agricultural runoff, and minor natural or geogenic input.The methods and chemometric study proposed here can be used as effective tools to gather information about water quality and water resource management.

Pollution characteristics and quantitative source apportionment of heavy metals within a zinc smelting site by GIS-based PMF and APCS-MLR models

The abandoned smelters present a substantial pollution threat to the nearby soil and groundwater.In this study,63 surface soil samples were collected from a zinc smelter to quantitatively describe the pollution characteristics,ecological risks,and source apportionment of heavy metal(loid)s(HMs).The results revealed that the average contents of Zn,Cd,Pb,As,and Hg were 0.4,12.2,3.3,5.3,and 12.7 times higher than the risk screening values of the construction sites,respectively.Notably,the smelter was accumulated heavily with Cd and Hg,and the contribution of Cd(0.38)and Hg(0.53)to ecological risk was 91.58%.ZZ3 and ZZ7 were the most polluted workshops,accounting for 25.7%and 35.0%of the pollution load and ecological risk,respectively.The influence of soil parent materials on pollution was minor compared to various workshops within the smelter.Combined with PMF,APCS-MLR and GIS analysis,four sources of HMs were identified:P1(25.5%)and A3(18.4%)were atmospheric deposition from the electric defogging workshop and surface runoff from the smelter;P2(32.7%)and A2(20.9%)were surface runoff of As-Pb foul acid;P3(14.5%)and A4(49.8%)were atmospheric deposition from the leach slag drying workshop;P4(27.3%)and A1(10.8%)were the smelting process of zinc products.This paper described the distribution characteristics and specific sources of HMs in different process workshops,providing a new perspective for the precise remediation of the smelter by determining the priority control factors.

Spatial distribution and source identification of heavy metal pollution in roadside surface soil:a study of Dhaka Aricha highway,Bangladesh

Introduction:In this study,metal pollution and their sources in surface soils were evaluated by pollution indices and multivariate statistical techniques in association with a geographical information system(GIS).Methods:Surface soil samples were collected in dry season from different locations of Dhaka Aricha highway and analyzed by energy dispersive X-ray fluorescence(EDXRF).Results:Thirteen different metals were found in the tested samples.Pollution indices are determined by enrichment factor in an order of Zr>Sn>P>Mn>Zn>Rb>Fe>Ba>Sr>Ti>K>Ca>Al.The resulting geoaccumulation index(Igeo)value shows the following order:Sn>Zr>P>Mn>Zn>Rb>Fe>Ba>Ti>Sr>K>Ca>Al.Contamination factors(CFs)of the metals range from 1.422 to 3.979(Fe);0.213 to 1.089(Al);0.489 to 3.484(Ca);1.496 to 2.372(K);1.287 to 3.870(Ti);2.200 to 14.588(Mn);5.938 to 56.750(Zr);0.980 to 3.500(Sr);2.321 to 4.857(Rb);2.737 to 6.526(Zn);16.667 to 27.333(Sn);3.157 to 16.286(P);and 0.741 to 3.328(Ba).Pollution load index calculated from the CFs indicates that soils are strongly contaminated by Zr and Sn.Principal component analysis(PCA)of parameters exhibits three major components.R-mode cluster analysis reveals three distinct groups in both site and metal basis clustering that shows a similar pattern with the PCA.Conclusions:These results might be helpful for future monitoring of further increase of heavy metal concentrations in surface soils along highways.

Source identification, contamination status and health risk assessment of heavy metals from road dusts in Dhaka, Bangladesh

In this study, concentrations of Cr, Mn, Ni, Cu, Zn, Cd and Pb were determined in road dusts collected from different locations in Dhaka to assess source, contamination status and health risk. Energy-dispersive X-ray fluorescence spectroscopy and energy-dispersive X-ray spectroscopy were used to determine Cr, Mn, Ni, Cu, Zn, Cd and Pb and their mean concentrations were 162.27 ± 29.46, 721.18 ± 180.14, 35.65 ± 12.55, 104.56 ± 128.33, 515.32 ± 321.90,BDL, and 342.82 ± 591.20 mg/kg, respectively. Among the heavy metals, highest concentrations of Cu, Zn and Pb were found at urban sites-7(municipal waste dumping) and 8(medical waste incineration). Highest concentration of Cr followed by Cu and Zn was found at site-5(Tejgaon, urban). Principal component analysis revealed that anthropogenic activities are the potential sources for Cr, Ni, Cu, Zn and Pb while earth crust for Mn. Pollution index and pollution load index results suggested that all the sites were contaminated and/or degraded by Cr, Cu, Zn and Pb except sites-9(urban), 10(sub-urban), 11(rural) while sites-7 and 8(urban) were extremely degraded. For noncarcinogenic health risk, hazard quotient values for dermal were higher compared to that of inhalation/ingestion. Though hazard index values were less than 1 at all the sites, these were at least one order of magnitude higher for children group than that of adult group, thus the children group may face more noncarcinogenic health risk at sites -7 and 8. Values of incremental lifetime cancer risk were from 10to 10showed no carcinogenic health risk by road dusts contaminated with the heavy metals.

Predicting non-carcinogenic hazard quotients of heavy metals in pepper (Capsicum annum L.) utilizing electromagnetic waves

Given the tendency of heavy metals to accumulate in soil and plants,the purpose of this study was to determine the contamination.levels of Cd,Ni,Pb,and Zn on peppers(leaves.and fruit),grown in contaminated soils in industral centers.For this purpose,we measured the uptake of the four heavy metals by peppers grown in the heavy metal contaminated soils throughout the four growth stages:two-leaf,growth,fiowering,and fruiting;and calculated various vegetation indices to evaluate the heavy metal contamination potentials.Electromagnetic waves were also applied for analyzing the responses of the target plants to various heavy metals.Based on the relevant spectral bands identified by principal component analysis(PCA)and random search methods,a regression method was then employed.to determine the most optimal.spectral bands for estimating,the target hazard quotient(THQ).The THQ was found to be the highest in the plants contaminated by Pb(THQ=62)and Zn(THQ=5.07).The results of PCA and random search indicated that the spectra at the bands of b570,b650,and b760 for Pb,b400 and b880 for Ni,b560 and b880 for Cd,and b560 b910,and b1050 for Zn were the most optimal spectra for assessing THQ.Therefore,in future studies,instead of examining the amount of heavy metals in plants by chemical analysis in the laboratory,the responses of the plants to the electromagnetic waves in the identified bands can be redily investigated in the field based on the established correlations.

芜湖内河水环境重金属分布特征及健康风险评价

为明确芜湖内河水体中重金属健康危险程度,以芜湖内河为研究对象,选取芜湖3条典型城市河道设置采样断面,对样品中Cr、Cu、Mn、Ni、Pb、Zn这6种重金属进行测试分析,使用健康风险模型评价重金属对人体健康风险。水样分析结果表明:研究区重金属Mn、Ni超过《地表水环境质量标准》(GB3838—2002)Ⅲ类水标准,Cr、Pb有部分点位超过Ⅲ类水标准。主成分分析结果表明:Cu、Ni、Mn和Zn由工业和生活源控制,Pb属于交通源因子,Cr是自然地质背景和人类活动因子。健康风险评价结果表明:6种重金属不存在非致癌风险,汇成河道和中山南路河道存在非致癌风险,直接摄入途径下,重金属Cr和Ni存在致癌风险。该研究结果可为芜湖城市内河水体重金属污染防治和居民生活安全用水提供措施和建议。

骆马湖流域表层沉积物重金属分布特征及风险评价

对骆马湖流域表层沉积物中的重金属分布特征及潜在生态风险进行评估,分析了枯水期、平水期和丰水期3个时段内10种重金属元素(铬、镍、铜、镉、铅、锑、砷、汞、铍、铊)的浓度水平、空间分布和来源。采用潜在生态风险指数法对沉积物中的重金属风险进行了评估。结果显示,镍、铜、镉、铅在部分水期平均浓度高于江苏土壤环境背景值,汞和铊无点位超过背景值,且与其他水体相比,骆马湖流域沉积物重金属污染整体处在中低水平。沉积物中重金属除镉外整体单项生态风险较低,各点位综合潜在风险水平属于中等及以下范畴。相关性分析和主成分分析揭示了重金属污染主要源自工业和农业。

江苏徐圩港区表层沉积物重金属污染评价及来源分析

为科学评估江苏徐圩港区建设开发对周边生态环境的影响,2022年9月测定了徐圩港区表层沉积物10种常见重金属含量,运用单因子污染指数法、潜在生态风险指数法、地质累积指数法、相关性分析和主成分分析,对该港区进行重金属污染评价及来源分析。调查区域沉积物中所有站位重金属含量均符合第一类海洋沉积物标准,单因子污染指数法显示有个别站位属于中等污染,综合污染指数结果显示,有4个站位属于轻微污染,5个站位属于中等污染。潜在生态风险指数显示沉积物重金属潜在生态风险由高到低为Cd>As>Cu>Ni>Pb>Co>Zn>V>Cr>Mn,综合生态风险指数结果显示仅有1个站位属于严重污染。地质累积指数显示沉积物重金属污染程度由高到低为Cd>As>Zn>Cu>Ni>Mn>Co>Pb>V>Cr,总体污染情况较轻。Cr,V,Co,Pb,Ni和Cu之间具有极显著相关性,Cd与其他重金属元素均无强相关性。PC1(V,Cr,Mn,Co,Ni,Cu,Pb)主要为水产养殖、工业生产、航运交通和自然源的混合源;PC2(Cd)主要为机械制造等工业活动来源;PC3(As,Zn)主要为农业和生活污染等人为来源。Cd是徐圩港区潜在污染重金属,生态风险指数较高,因此应加强对徐圩港区相关重金属的监测,查清重金属污染的主要来源,为生态环境保护和管理提供基础资料。

河南荥阳市耕地土壤重金属分布特征及来源解析

耕地质量关系着人民生活,而重金属是影响耕地质量的重要因素之一。根据全国土壤污染状况调查显示,中国耕地环境状况不容乐观,对耕地的重金属调查分析迫在眉睫。但仅简单地对重金属含量水平及来源类型进行判断已不足以为区域土壤重金属污染治理提供支持,而通过对各类污染源贡献率的定量计算,不仅可以明确农田土壤重金属分布特征,同时可判别污染源类别及来源,从而识别优先控制的污染元素,为重金属污染精准管控提供关键信息。本文采集河南荥阳市耕地表层土壤样品(0~20cm),应用电感耦合等离子体质谱和发射光谱法(ICP-MS/OES)、原子荧光光谱法(AFS)及离子选择电极法(IES)对As、Cd、Cr、Cu、Hg、Ni、Pb、Zn等8种重金属进行测试和p H分析;利用多元统计、绝对因子分析-多元线性回归(APCS-MLR)受体模型探讨研究区8种重金属污染含量空间分布特征及来源,利用富集因子和地累积指数开展土壤污染评价。结果表明:(1)耕地土壤中重金属含量整体偏高。除Cr外,其他元素为郑州市土壤背景值的1.04~1.40倍,其中Cd的累积效应较明显。(2)研究区重金属高值区主要分布于荥阳市城区周边。(3)基于富集因子法、相关性分析、主成分分析及APCS-MLR源解析结果显示,研究区重金属主要有三个来源:自然源对Ni、As、Cu、Cr的贡献率分别为98%、94%、80%及63%;工业源对Cd的贡献率为78%;其他源则主要是农业化肥源、燃煤源的混合源,对Cr、Pb、Hg的贡献率分别为37%、35%及33%。(4)地累积指数表明,研究区各重金属以无污染为主,而Cd超标率最高,其中度、中-重度污染、重度污染样点数分别为19个、5个及3个,并存在1个极重度污染样点。综上,Cd在研究区耕地中富集较明显,为潜在的主要污染元素;工业源、自然源、农业化肥源及燃煤源是重金属的主要来源,表明人类活动已对研究区耕地产生影响,需采取措施避免该影响进一步加剧。

阳新县浮屠镇农田土壤重金属含量评价

阳新县浮屠镇矿产资源丰富,近年来当地快速发展工矿业,矿产资源的开采势必会增加农田土壤重金属污染的风险,因此了解当地农田土壤的重金属含量对于污染防治具有重大意义。根据《土壤环境质量农用地土壤污染风险管控标准(试行)》(GB 15618—2018),利用国家监控的10个农田点位,采集土壤表层样品,测定土壤pH值和8项重金属(Hg、Cr、Ni、Cd、Cu、As、Pb、Zn)全量,综合利用地积累指数法、潜在生态风险指数法和空间插值法,评价农田土壤重金属含量和潜在生态风险。结果表明:1)阳新县浮屠镇农田土壤中除Cu以外,其他重金属含量均未超过农用地土壤分析筛选值;2)从地积累指数评价结果来看,Cu和Cd属于轻微污染水平,其他重金属均属于无污染水平;3)潜在生态风险指数为159.41,整体处于中等风险水平,主要贡献元素为Hg和Cd;4)依据主成分分析结合空间分析,Cd、Pb、As、Cu、Zn、Ni主要来源于工矿业的开采影响。Cr和Hg受到人为影响很小。综上,阳新县农用地土壤污染风险低,适合农业生产,但Cu和Cd的潜在风险需关注。

西藏羊卓雍措水体重金属空间分布特征与健康风险评估

为探究羊卓雍措水体重金属污染特征及其对人体的危害程度,于2018,2019年内分5次采集26个采样点的130个水样,对水样中的Cr,Co,Cu,Fe,Cd,Sb,Ba,Pb,As等9种重金属进行检测分析,并采用美国环境保护署推荐的健康风险评价模型评价羊卓雍措的潜在健康风险。研究结果可以为当地水资源管理部门今后在水资源有效利用与饮用水安全管理方面提供参考。

基于主成分分析和宽度学习系统的土壤铅镉重金属元素定量分析

在土壤重金属元素定量分析研究中,X射线荧光分析(XRF)是一种有效的无损分析技术。由于受到矩阵效应以及元素干扰的影响,已有的机器学习方法在利用土壤XRF光谱预测铅(Pb)元素、镉(Cd)元素浓度时存在性能不足和不稳定的问题。该工作提出了基于主成分分析(PCA)结合宽度学习系统(BLS)的XRF土壤重金属元素定量分析方法(PCA-BLS),用于精确、高效、稳定测定土壤中Pb元素和Cd元素的浓度。使用PCA对56个标准土壤数据进行特征降维,并选取Pb和Cd的前3个主成分作为特征。将最优主成分特征输入宽度学习系统进行校正和测试,并使用网格搜索算法确定最佳网络结构。其中Pb元素和Cd元素对应的BLS的三个最优参数值分别为2,11,11和3,19,15。使用支持向量回归(SVR)、BP神经网络和原始BLS三种模型与PCA-BLS方法进行对比。PCA-BLS在测定Pb对应的决定系数R^(2)、均方根误差RMSE和平均绝对百分比误差MAPE三个指标上取得了0.954、1.433、1.014的结果,在定量Cd实验中取得R^(2)为0.982、RMSE为1.215和MAPE为1.059的精度。网格搜索可视化表明PCA-BLS在预测两种重金属元素时具有稳定的性能。实验结果表明,PCA-BLS可以有效校正土壤XRF光谱中的矩阵效应和干扰,在准确预测Pb和Cd元素浓度的同时保持模型稳定性,是一种具有潜力的XRF光谱定量分析方法。

天津市农田土壤重金属来源及健康风险评价

为揭示城市周边蔬菜粮食种植区土壤重金属风险,同时针对目前研究中缺少经手-口摄入、呼吸途径和皮肤接触暴露途径引起的人体健康风险及估算不同来源下量化的成人和儿童的健康风险情况,以天津市西青区农田表层土壤为研究对象,分析了Cr、Cu、Ni、Pb、Zn、As、Hg、Cd共8种重金属的浓度,采用主成分分析(PCA)、正定矩阵分解(PMF)、健康风险评价模型(PMF-HRA)分别评价土壤重金属污染程度、来源及健康风险,厘定主要的重金属污染物及其暴露途径。结果表明:天津市农用地土壤8种重金属中Cd、Hg污染最为严重,均值分别超出背景值151.9%、324.1%,约有15%的点位处于中度至重度污染等级。研究区土壤重金属污染的来源主要包括自然成土过程、农业生产和交通运输、燃煤、农药使用。Cd(农药源)和Hg(燃煤源)是研究区农用地土壤污染的重要元素。在摄食农作物、手-口摄入和皮肤接触3种暴露途径下,均不存在显著的非致癌健康风险。自然源的总致癌风险指数(TCR)最高,Cr元素是源贡献中儿童非致癌风险总指数(HI)和儿童TCR的主要贡献者;燃煤源中Hg对HI的贡献最大,Cu和Zn对HI的贡献来自于农药和交通混合源。以上重金属元素的污染来源均与周围居民的工业、农业及交通活动存在密切的联系,应引起重视。

玛曲高寒退化草甸土壤重金属污染风险评价

为了分析玛曲高寒草甸土壤重金属含量分布及重金属污染风险,采集玛曲高寒退化草甸土壤样品,分析研究区表层土壤(0—20 cm)中Cr、Ni、Cu、Zn、As、Cd、Hg、Pb等八种重金属含量,描述玛曲高寒草甸土壤重金属的空间分布特征,采用单因子污染指数、内梅罗污染指数、地累积指数评价重金属污染程度并分析重金属污染潜在生态风险,以相关性分析、主成分分析和聚类分析表征同源性重金属。结果表明:(1)研究区内As、Cd的质量分数高于背景值,Ni的质量分数偏低,变异系数大小为Cr>Hg>Cd>Cu>As>Ni>Zn>Pb;(2)Cd单因子污染指数为轻微污染,地累积指数反映As属于未受污染与轻微污染之间,各重金属元素均无潜在生态风险;以国标和土壤背景值为标准,内梅罗综合污染指数表明玛曲未受到重金属污染;(3)主成分和相关性分析表明玛曲土壤中Ni、Cu、Zn、As、Cd、Pb等元素具有相同或相似的来源,Ni和Pb的来源优先顺序最大,Cr和Hg含量呈现弱相关。