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.

Heavy metals and their source identification in particulate matter(PM_(2.5)) in Isfahan City, Iran

The presence of heavy metals（HMs） in particulate matters（PMs） particularly fine particles such as PM2.5 poses potential risk to the health of human being. The purpose of this study was to analyze the contents of HMs in PM2.5 in the atmospheric monitoring stations in Isfahan city,Iran, in different seasons between March 2014 and March 2015 and their source identification using principle component analysis（PCA）. The samples of PM2.5 were taken using a high volume sampler in 7 monitoring stations located throughout the city and industrial zones since March 2014 to March 2015. The HMs content of the samples was measured using ICP-MS.The results showed that the concentrations of As, Cd and Ni were in a range of 23–36, 1–12,and 5–76 ng/m3 at all the stations which exceeded the US-EPA standards. Furthermore,the concentrations of Cr and Cu reached to 153 and 167 ng/m3 in some stations which were also higher than the standard levels. Depending on the potential sources of HMs, their concentration in PM2.5 through the various seasons was different. PCA illustrated that the different potential sources of HMs in the atmosphere, showing that the most important sources of HMs originated from fossil fuel combustion, abrasion of vehicle tires, industrial activities（e.g., iron and steel industries） and dust storms. Management and control of air pollution of industrial plants and vehicles are suggested for decreasing the risk of the HMs in the region.

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.

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.

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.

球黏土对单及多组分重金属离子的吸附效果

重金属污染水的防范和治理一直是国际上的难点和热点研究课题。吸附法具有经济可行和环境友好等优点,被认为是去除水中重金属离子的最佳技术。球黏土具有潜在的高吸附性能,为进一步探索利用球黏土治理重金属污染水的可行性,该研究通过单组分与多组分重金属离子的批式吸附试验,重点考察球黏土对Pb^(2+)、Cu^(2+)、Zn^(2+)、Cd^(2+)、Cr^(6+)的吸附效果和竞争吸附关系。单组分离子吸附试验表明,球黏土对重金属离子的吸附量遵循先快速增加后缓慢增长,随pH值的增大而增大,随初始浓度增加而增加,直至吸附饱和的规律。最佳吸附平衡时间为90 min;最佳溶液pH值为5。pH值对球黏土吸附5种重金属离子的影响程度由大到小依次为Zn^(2+)、Cd^(2+)、Cr6+、Cu^(2+)、Pb^(2+),当pH值从2增到5时,Zn^(2+)吸附量提高了184%。球黏土对5种离子Pb^(2+)、Cr^(6+)、Zn^(2+)、Cd^(2+)、Cu^(2+)的饱和吸附量分别为311.847、301.437、263.213、195.435和179.635 mg/g。通过动力学模型和等温线模型拟合,吸附数据更加符合准二阶动力学模型和Langmuir等温吸附模型,说明吸附过程为单层吸附,吸附方式为化学吸附,最大吸附量依次为Pb^(2+)(497.593 mg/g)>Cr6+(442.868 mg/g)>Zn^(2+)(339.376 mg/g)>Cd^(2+)(290.039 mg/g)>Cu^(2+)(194.941 mg/g)。多组分离子吸附试验表明,球黏土对4种离子的吸附量同样呈先快速后缓慢增长,直至平稳的趋势。各离子在球黏土的活性位点上表现出的竞争能力由强到弱依次为Cr^(6+)、Zn^(2+)、Cd^(2+)、Cu^(2+)。通过等温线模型拟合,发现多组分重金属离子吸附数据更加符合Langmuir等温吸附模型,吸附方式和吸附过程与单组分一致,各组分的最大吸附量与单组分相比有所下降,但吸附总量却大大增加,说明球黏土对多组分重金属离子具有良好的吸附能力。与其他原生矿物材料相比,球黏土对重金属离子具有更大的吸附量,该研究成果可为球黏土作为吸附剂的利用和推广提供理论依据和数据支持,具有较大的现实意义。

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。

茄果类蔬菜矿质元素特征分布与膳食风险评估

为了探究广西市售茄果类蔬菜矿质元素含量特征和食用安全性,采用电感耦合等离子体质谱仪(ICP-MS)测定了150份茄果类蔬菜样品中7种矿质元素(Fe、Mn、Zn、Cu、Mo、Co、Ni)和5种常见危害重金属(Pb、Cd、As、Hg、Cr)含量并进行主成分分析及健康风险评估。结果表明,茄果类蔬菜中7种矿质元素平均含量排序为Fe>Mn>Zn>Cu>Ni>Mo>Co,变异系数介于44.7%(Fe)~247.8%(Ni),不同样品之间矿质元素含量差异较大;辣椒中6种矿质元素(Fe、Zn、Ni、Mn、Cu、Co)含量均最高。主成分分析结果显示,Fe、Mn、Zn、Cu、Co和Ni是广西市售茄果类蔬菜的特征无机元素,提取了2个主成分,累积方差贡献率为99.983%。茄果类蔬菜矿质元素综合得分排序为辣椒>番茄>茄子,表明辣椒中的有益矿质元素含量相对较高。茄果类蔬菜中5种重金属含量均未超过国家标准限量值,但存在Cd超标情况,辣椒、番茄和茄子超标率分别为10.4%、10.3%和8.0%。5种重金属对不同人群的目标危害系数(Target Hazard Quotient,THQ)和综合目标危害系数(Total Target Hazard Quotient,TTHQ)均远小于1。总体而言,儿童或成人食用广西市售茄果类蔬菜的膳食健康风险较低,处于安全级;但Cd是今后茄果类蔬菜需要重点监控的重金属污染因子。

基于电化学生物传感器的重金属离子检测研究进展

电化学生物传感器是基于抗原抗体、酶、核酸适配体、蛋白质、细胞等各种生物成分建立的电化学方法,由于这些生物成分具有强的特异性,决定了电化学生物传感器具有高选择性。同时该方法还具有便捷快速、灵敏、操作简单等优点,已经被广泛应用于污染物重金属离子的检测,并具有良好的应用前景。本文综述了近年来基于各种生物成分的电化学生物传感器在重金属离子检测中的应用,并对该领域未来的发展趋势做了展望。

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。

美味牛肝菌药理及安全使用研究进展

目的归纳与总结珍稀野生真菌美味牛肝菌的研究进展,并对其安全性进行评价,提出安全使用建议。方法从美味牛肝菌的形态特征、系统发育分析、生物活性成分、药理活性和重金属富集等多方面进行论述。结果美味牛肝菌子实体中蛋白质、氨基酸、碳水化合物、膳食纤维和矿物元素等含量丰富,所含多糖、酚类化合物、黄酮类化合物、新型多功能蛋白等活性成分为其发挥抗氧化、抗炎、抗肿瘤、保肝等药理作用的物质基础,具有优越的药理活性,成为候选药物,临床应用前景开阔。野生美味牛肝菌易富集重金属(Cd、Hg、As和Pb),具有一定的风险,避免采用土地污染地区的野生美味牛肝菌,选择合适的加工方式可有效减少重金属摄入风险。结论美味牛肝菌具有优越的药用价值,但也存在潜在的安全风险,需注意安全问题。

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。