Sources Of Heavy Metal Pollution Research Articles

Study on the Distribution and Source of Heavy Metal Pollution in Surface Sediments of Huanghua Habor

In order to study the pollution characteristics of heavy metals in surface sediments of Huanghua Sea area, the concentrations of organic carbon, sulfide, petroleum, Hg, Cu, Pb, Zn, Cr, Cd and As in surface sediments of 15 stations in Huanghua Sea area were analyzed, and their sources and ecological risks were evaluated. The results show that all the elements in the sediments can meet the quality standard of a class of Marine sediments. The coefficient of variation of heavy metals ranged from 18.71% to 37.69%, indicating that the distribution of heavy metals among all stations was relatively uniform and the dispersion was small. The correlation of Pb, Cr and As in heavy metals is good, which indicates that these elements have homology. The results of ground accumulation index Igeo calculated by the average value of sediments in shallow sea of China show that Hg and Cd of surface sediments in Huanghua Sea are pollution-free to light-moderate pollution, and the pollution of Cd and Hg is more serious. The evaluation of potential ecological risk coefficient shows that the potential risk index RI of Huanghua Port area ranges from 112.78 to 449.89, all of which are greater than 110, belonging to moderate to strong ecological risk, in which the contribution values of Cd and Hg are greater than 90%, which also indicates that Cd and Hg are the main ecological risk factors in Huanghua Sea area.

Heavy metal pollution in the aquatic environment: efficient and low-cost removal approaches to eliminate their toxicity: a review.

Heavy metal contamination of water sources has emerged as a major global environmental concern, threatening both aquatic ecosystems and human health. Heavy metal pollution in the aquatic environment is on the rise due to industrialization, climate change, and urbanization. Sources of pollution include mining waste, landfill leachates, municipal and industrial wastewater, urban runoff, and natural phenomena such as volcanic eruptions, weathering, and rock abrasion. Heavy metal ions are toxic, potentially carcinogenic, and can bioaccumulate in biological systems. Heavy metals can cause harm to various organs, including the neurological system, liver, lungs, kidneys, stomach, skin, and reproductive systems, even at low exposure levels. Efforts to find efficient methods to remove heavy metals from wastewater have increased in recent years. Although some approaches can effectively remove heavy metal contaminants, their high preparation and usage costs may limit their practical applications. Many review articles have been published on the toxicity and treatment methods for removing heavy metals from wastewater. This review focuses on the main sources of heavy metal pollution, their biological and chemical transformation, toxicological impacts on the environment, and harmful effects on the ecosystem. It also examines recent advances in cost-effective and efficient techniques for removing heavy metals from wastewater, such as physicochemical adsorption using biochar and natural zeolite ion exchangers, as well as decomposition of heavy metal complexes through advanced oxidation processes (AOPs). Finally, the advantages, practical applications, and future potential of these techniques are discussed, along with any challenges and limitations that must be considered.

工矿区不同类型生物结皮对大气降尘重金属的富集规律及其影响因子

PDF HTML阅读 XML下载 导出引用 引用提醒 工矿区不同类型生物结皮对大气降尘重金属的富集规律及其影响因子 DOI: 10.5846/stxb202203220700 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 宁夏自然科学基金项目(2021AAC03007); 国家自然科学基金项目(41961001, 31760144) Enrichment law of atmospheric dustfall heavy metal pollution by different types of biocrusts in mining areas and its influencing factors Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:大气降尘是矿区土壤重金属的主要来源,而生物结皮对大气降尘重金属有显著的富集作用。为探究不同类型生物结皮对大气降尘重金属的富集规律及其影响因子,选取宁东典型火电厂周边生物结皮广布区作为试验样地,3类生物结皮作为研究对象,并以临近裸土作为对照,对比分析了生物结皮富集大气降尘过程中土壤理化性质、酶活性及重金属含量的变化,采用相关分析、冗余分析和方差分解方法探讨了不同类型生物结皮的结皮层(A层)和层下土壤(B层)重金属含量与其土壤物理、化学性质及酶活性之间的关系。结果表明:燃煤烟尘是矿区大气降尘重金属污染的主要来源,涉及重金属元素包括Cd、Cr、Hg、Pb、Zn、As。生物结皮对源自大气降尘的重金属元素均具有显著的富集作用,且在不同演替阶段生物结皮间的富集规律完全一致:藓结皮>混生结皮>藻结皮;重金属综合污染指数评价结果显示:生物结皮对重金属具有表层富集效应,表现为A层污染程度高于B层。不同类型生物结皮A、B层综合污染指数存在显著差异,排序为:藓结皮>混生结皮>藻结皮;和对照相比,三类生物结皮均能通过富集大气降尘增加其A、B层养分和细颗粒物含量并改善土壤质地。方差分解结果表明,土壤化学性质是影响重金属含量解释度最高的单类环境变量,而相关性和冗余分析结果表明:有机质含量、电导率、酶活性、容重等是影响不同类型生物结皮A、B层土壤重金属含量的主要环境因子。 Abstract:Atmospheric dust fall is the main source of soil heavy metals in mining area. Biocrusts have a significant enrichment effect on heavy metal. In order to explore enrichment laws of how biocrust at different succession stages capture and concentrate heavy metals from atmospheric dust fall and its influencing factors, biocrusts wide-spread area around typically thermal power station in Ningdong Energy Industrial Base were taken as experimental site, three types of biocrusts were selected as study objects, and bare soils nearby were set as control. Based on the comparison analysis of changes of soil physicochemical properties, enzyme activities and heavy metal contents during the process of the enrichment of atmospheric dust fall by biocrusts, the relationship between heavy metal contents and soil physicochemical properties, enzyme activities of biocrustal layer (A horizon) and subsoil (B horizon) were analyzed by using the methods of correlation analysis, redundancy analysis and variance partitioning analysis. The results indicated that, flue gas was the main source of atmospheric dust fall heavy metal pollution, the involved heavy metal elements including Cd, Cr, Hg, Pb, Zn, As. Biocrusts had significant enrichment effect on heavy metal elements derived from atmospheric dust fall, and demonstrated a totally consistent law among biocrusts of different types as followed Moss crust >Mixed crust >Algal crust. The comprehensive pollution index indicated that biocrusts had a certain surface enrichment effect on heavy metals, which demonstrated as A horizon had a higher pollution level than that of B horizon. Compared with control, the three types of biocrusts all could enrich atmospheric dust fall and increase the nutrient, fine particles and improve its soil textures of A and B horizons. Variation partitioning results indicated that soil chemical properties were the single environmental variables of the highest degree of explanation influencing the heavy metals content. Redundancy and correlation analysis showed that soil organic matter, electrical conductivity, enzymatic activity, bulk density were the main environmental factors that influenced the heavy metals content in A, B horizons of different biocrusts. 参考文献 相似文献 引证文献

Assessment of heavy metal pollution associated with tailing dam in gold mining area, southern ethiopia

ABSTRACT Tailings dam is the main heavy metal pollution source in mining areas. In this study, the concentration of heavy metals in soil and water from upstream and downstream of the tailings dam was analyzed. The concentration of As, Cd, Cu, Ni, Pb, and W in tailings soil exceeded the standard value but Hg and Zn in the tailings soil were far below the standard. Average concentrations of As, Pb, and W in soil samples were above the upstream reference soil. The level of As and Pb decreases, downstream as the distance from the tailings dam increases. The mean concentrations of the heavy metals in water for Ni, Cu, Zn, Pb, and Cd were 106.3, 57.3, 21.8, 14.5, and 8.0 µg/L, respectively. The Ni contents in all studied water samples had concentrations higher than Awata River and WHO guidelines for drinking water. Both geo-accumulation and ecological risk indices have indicated significant heavy metal pollution in the study area. The overall results of this study showed that the soil and water downstream of the tailings dam were not safe and must be protected against access to humans and domestic animals.

Processing of the zinc-lead-bearing flotation middlings by sulfidizing roasting with pyrrhotites production by predicted properties

The accumulated amount of lead-zinc ore flotation tailings in the dumps of concentrating plants today can be considered as independent man-made deposits. In addition to their resource value as sources of lead and zinc, as well as associated gold, silver, cadmium, selenium and other metals, tailings are an environmentally hazardous source of heavy metal pollution of ground and surface waters. The environmental hazard of stale tailings is exacerbated by the fact that they occupy large areas that cannot be used for agricultural or other purposes of the national economy. Wastes of flotation enrichment of lead-zinc ores significantly differ from the source material not only in the content of minerals, but also in the degree of oxidation of their surface, fractional composition, and the presence of a significant amount of mineral intergrowths. In view of this, the use of existing flotation technologies is ineffective for obtaining standard lead and zinc concentrates from enrichment tailings. This paper describes the technology that has been developed for processing of zinc and lead-bearing enrichment wastes by sulfidizing roasting followed by magnetic and flotation concentration of cinders. It was found that, as a result of the sulfidizing- pyrrhotizing roasting process, the flotation ability increases for lead compounds and decreases for iron compounds, while the magnetic susceptibility of lower iron sulfides formed during roasting increases. It has been established that sulfidizing takes place with sufficient completeness, and during subsequent flotation, it is possible to extract up to 95% of zinc and up to 80% of lead into sulfide concentrate. These results have a technological advantage in contrast to the other methods that have been used. It was found that at roasting temperatures of 700–800 °C, pyrrhotites have a maximum magnetic susceptibility of 3.75, 5.43 and 2.18 SI units for Fe0.855S, Fe0.888S and Fe0.909S, respectively. Technological recommendations are acceptable for similar raw materials. The research was funded by the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan Grant №AP08052829.

CADMIUM, NICKEL, AND LEAD CONCENTRATION OF MUNICIPAL DUMPSITE IN WESTERN SAMAR, PHILIPPINES

Heavy metal is one of the major problems due to its accumulation from the soil to the food chain, wherein dumpsites are the primary sources of heavy metal pollution. This study aimed to determine the presence of heavy metals in the soil of Santa Rita, Western Samar dumpsite and to quantify them to obtain knowledge on the possible high contamination that may affect the surrounding areas. This study focused on the presence and concentrations of heavy metals Cd, Ni, and Pb. Eighteen (18) soil samples were acquired within the three sampling sites: shoulder slope, main dumpsite, and foot slope. Each sampling site has three sampling points with a depth of 0 - 30 cm and 30 - 60 cm. Analysis showed that all heavy metals are present in the dumpsite, and the concentrations ranged from 0 - 0.1 mg/kg, 0.09 - 3.7 mg/kg, and 0.09 - 3.7 mg/kg for cadmium, lead, and nickel, respectively. In comparing heavy metals within the sampling sites and depths, only cadmium has a significant difference, while Ni and Pb have no significant difference. Compared with WHO standards, all heavy metals tested still fall within the standard limit. Therefore, the dumpsite is still at a safe level. However, residents should take measures to maintain the soil quality since heavy metal contamination in dumpsites is likely to exacerbate

Identification of Soil Heavy Metal Sources Around a Copper-silver Mining Area in Ningxia Based on GIS

This study area was based on the catchment area of the Yaoxianzi ditch located in the arid region of western China. A total of 194 topsoil samples of 0-20 cm depth were collected using the mesh distribution method. The contents of nine heavy metals (Ni, Cu, Zn, As, Ag, Cr, Cd, Hg, and Pb) were determined using ICP-MS. The source and spatial distribution of heavy metals were analyzed using PMF and IDW. Spatial autocorrelation and clustering and outlier analysis were performed using the Spatial Statistical Analysis tool of ArcGIS. The main sources and distribution areas of heavy metals in the soil were obtained through comprehensive analysis. In the study area, the average values of Hg, Ag, Cd, and Pb were 20.48, 3.13, 2.23, and 1.12 times the background values, and the maximum values of Cd, Cu, Pb, and As were 10.92, 5.52, 2.03, and 1.39 times the filter values, respectively. The coefficients of variation of Cu, Cd, Pb, and Hg were ordered as Cu(283.23%)>Cd(224.77%)>Pb(144.40%)>Hg(67.12%) and were closely affected by human activities. The heavy metals in the soil around mining areas came from four main sources:natural parent material (32%), the mixed source of mining activities and transportation (17.1%), the mixed source of industrial activities and atmospheric sedimentation (40.3%), and the mixed source of agricultural activity and putting sandy gravel in farmland (10.6%). Cr and Ni, As and Cu, Hg, and Cd could represent these four sources of heavy metal pollution, respectively. The main sources of soil heavy metal pollution were mining activities and agricultural activities. The heavily contaminated areas were distributed in the mining areas in the south of the study area and in the planting areas in the eastern, central, and northwestern parts of the study area.

Characteristics of Heavy Metal Pollution in Farmland Soil of the Yangtze River Economic Belt Based on Bibliometric Analysis

The Yangtze River Economic Belt is one of the major strategic development regions in China. It is of great significance to clarify the characteristics and sources of heavy metal pollution in farmland soil of the Yangtze River Economic Belt for the prevention and control of heavy metal pollution and to ensure safe agricultural production. After collecting extensive literature data, we analyzed the pollution characteristics, environmental risk, and potential sources of heavy metals (Cd, Cr, Hg, As, Pb, Cu, Zn, and Ni) in farmland soil of the Yangtze River Economic Belt through the integrated use of spatial and geo-accumulation index analyses. The results showed that:① the proportion of soil samples exceeding the risk screening values for soil contamination of agricultural land of Cd, Cu, Pb, Hg, Zn, and As were 39.8%, 18.5%, 8.3%, 6.9%, 6.9%, and 6.4%, respectively. Compared with the risk standard, soil Cd had the highest rate of exceeding the standard. ② The contents of Cr, Cu, Zn, and Ni in the upper reaches were higher than those in the middle and lower reaches, and the contents of Cd, As, and Pb in the middle reaches were higher than those in the upper and lower reaches. ③ The results of the geo-accumulation index analysis showed that the contamination degree of the eight heavy metals decreased in the order of Cd(0.42)>Hg(-0.28)>Pb(-0.32)>Zn(-0.39)>Cu(-0.42)>Cr(-0.7)>As(-0.81)>Ni(-0.73), where the accumulation risk of soil Cd and Hg was relatively higher. ④ Higher environmental background and mining activities were the main factors affecting the accumulation of heavy metals in soils in the upper and middle reaches. By contrast, rapid urbanization, industrial production, and intensive agricultural activities were the main factors affecting the heavy metal accumulation in soils in the middle and lower reaches. In view of the current status and control needs of heavy metal pollution in the Yangtze River Economic Belt, it is recommended to strengthen source prevention and control and to also carry out hierarchical control and regional classification management of heavy metal pollution in farmland soils according to the degree of heavy metal pollution, geological background, and quality of agricultural products. The ultimate objective of this research was to archive the farmland soil environmental quality safety and agricultural green sustainable production in the Yangtze River Economic Belt.

Source identification and spatial distribution of heavy metals in soil of central urban area of Chongqing, China

ABSTRACT To understand the characteristics of heavy metal pollution in soil and identify the source of heavy metals, 342 surface soil samples were collected in Chongqing, China. The contents of 11 heavy metals (i.e., arsenic (As), cadmium (Cd), chromium (Cr), lead (Pb), copper (Cu), zinc (Zn), nickel (Ni), mercury (Hg), antimony (Sb), manganese (Mn), and molybdenum (Mo)) were determined. The Principal Component analysis/absolute Principal component fraction (PCA/APCS) receptor model, the classified regression (CATREG) model, and the hot spot model were employed to analyze the data. The contents of As, Cd, Cr, Pb, Cu, Zn, Ni, Hg, Sb, Mn, and Mo in soil were 5.80, 0.13, 76.56, 25.55, 23.99, 75.58, 30.50, 0.05, 0.64, 573.32, and 0.56 mg·kg−1, respectively. The results of principal component analysis showed that the main heavy metal elements under the first principal component load were Cu, Ni, Zn, Mn, Cr, Pb, and Cd. The second principal component is mainly loaded with Mo, As, Hg, and Sb. The results of classified regression analysis showed that population density mainly affected Cu (0.54), soil mainly affected Ni (0.41), Sb (0.49), Zn (0.47), and Mn (0.21), and water quality mainly affected As (0.45) and Mo (0.37). Air quality mainly affected Cd (0.33) and Cr (0.37), traffic activity mainly affected Hg (0.31), and slope mainly affected Pb (0.31). The research results can be used to trace the environmental sources of soil heavy metals, fundamentally prevent and repair soil heavy metal pollution, and protect urban soil environmental quality.

Research on health risk assessment of heavy metals in soil based on multi-factor source apportionment: A case study in Guangdong Province, China

Environmental problems caused by heavy metal pollution in soil have attracted widespread attention worldwide. Identifying and quantifying the heavy metal pollution sources and risks is crucial for subsequent soil management. In this study, an integrated source-risk method for source apportionment and risk assessment based on the PMF model, the geodetector model and the health risk assessment model (HRA) was proposed and applied. Analysis of Hg, As, Pb, Cd, Cu, Ni, Cr, and Zn in 208 topsoils showed that the average contents of eight heavy metals were 1.87–5.86 times greater than corresponding background values, among which Cd and As were relatively high, which were higher than the specified soil risk screening values, high-value areas of heavy metals are mainly concentrated in the central part of the study area. The source apportionment showed that the accumulation of heavy metals was affected by five sources: atmospheric deposition (16.3 %), natural sources (33.1 %), industrial activities dominated by metal mining (15.1 %), industrial activities dominated by metal smelting (12.6 %) and traffic sources (22.9 %). The results of the health risk assessment showed that the carcinogenic risks (adult: 4.74E-05, children: 7.41E-05) of heavy metals in soil to the study population were both acceptable, the non-carcinogenic risk of adult (THI = 0.277) was within the limit, while the non-carcinogenic risk of children (THI = 1.70) was higher than the limit value. Ingestion (89.5 %–95.9 %) contributed the greatest health risk among all exposure routes. Source 3 (arsenic-related industrial activities dominated by metal mining) contributed the most to the HI and CRI of adults and children (all above 50 %), therefore, in the formulation stage of soil management strategy in this area, priority should be given to the control and management of this pollution source. These results can provide more detailed support for environmental protection departments to propose targeted soil pollution control measures.

Physico-chemical Properties and Heavy metal Contamination Levels of Soils from Riruwai Mining Area, North-western Nigeria

Mining activities, despite their economic importance, are well documented as one of the most significant sources of heavy metal pollution in the soil. In the present study, the physico-chemical properties and heavy metal contamination levels of the soils around the Riruwai mining area, in north-western Nigeria, were investigated. A total of 60 surface soil samples from four (4) sampling sites, which include 18 from active mine sites, 12 from abandoned mine sites, 21 from farmlands and 9 from control sites, were collected during the dry and rainy seasons. The physico-chemical properties such as pH, electrical conductivity (EC), soil organic matter (SOM), and cation exchange capacity (CEC) were determined using various analytical techniques. The concentrations of heavy metals were analyzed using a Microwave Plasma Atomic Emission Spectrometer (MP-AES), and the measured concentrations were used to evaluate the pollution load index (PLI). The findings of the study revealed that mining activities had a significant impact on the physicochemical characteristics of the soils, with lower pH, CEC, and SOM values in particular in active and abandoned mining sites. The physico-chemical characteristics of soil vary seasonally, with higher values typically reported during the rainy season, except for pH. The concentrations of arsenic, cadmium, lead, and zinc in the soils of the Riruwai mining area exceeded the threshold value recommended by WHO/FAO (2001) in all the sampling locations and seasons, with the exception of the control sites. The PLI values indicated that the soils in active and abandoned mining sites are strongly polluted by heavy metals, while farmland soils are moderately polluted and control site soils are unpolluted. The soils in the study area are significantly contaminated by heavy metals, particularly in active and abandoned mining sites, and the contamination has spread to farmland, suggesting a significant need for proper containment of heavy metal pollution levels in the area, particularly arsenic, cadmium, lead, and zinc.

An interpolation method incorporating the pollution diffusion characteristics for soil heavy metals - taking a coke plant as an example

The existing spatial interpolation methods in the prediction of soil heavy metal distribution are generally based on spatial auto correlation theory, rarely considering the pollution patterns. By contrast, in polluted sites, heavy metals have a strong heterogeneity even within a very small area, which is not exactly in line with auto correlation theory. This contradiction may lead to inaccuracy in spatial prediction. Atmospheric diffusion and deposition are one of the main sources of soil heavy metal pollution caused by coal-related production activities. To improve the prediction accuracy, the diffusion patterns of pollutants were considered in this paper by integrating Geodetector, Co-Kriging (COK), and partition interpolation. Geodetector was used to identify the main driving factors of soil pollution, based on which, the main driving factors were used as covariates introduced into the interpolation method (COK). Specifically, the amount of particulate matter deposition obtained by a pollutant diffusion model (AERMOD) was used as a covariate. For comparison, the distances to quenching, coke oven, and ammonium sulfate section were also used as covariates. Compared with the Ordinary Kriging method, the method COK-AERMOD established here decreased the root mean square error values of As (2.05 reduced to 1.89), Cd (0.18 reduced to 0.16), Cr (19.07 reduced to 12.97), Cu (6.92 reduced to 4.72), Hg (0.32 reduced to 0.28), Ni (16.92 reduced to 16.10), Pb (18.29 reduced to 16.62), and Zn (159.68 reduced to 153.66). This method in this paper is informative for the interpolation of soil elements in contaminated areas with known pollution source and diffusion patterns.

Spatial analysis, ecological risk assessment, control factors, and sources of heavy metal pollution in the shelf surface sediments of the southwest Bay of Bengal, India

Heavy metals such as Mn, Zn, Ni, Cu, Pb, and Co besides Hg were determined to assess the distribution, contamination status, and possible sources in the shelf surface sediment of the southwest Bay of Bengal. Physico-chemical parameters significantly influenced the metals. Higher concentrations of all the metals except Hg were recorded in the Andhra Pradesh and Chennai waters than in waters south of these in Tamil Nadu and Puducherry. Significant differences between areas were found only for Co, Cu, Mn, and Ni. Generally, the pollution indices, namely Contamination factor (CF = <1), Geo-accumulation index (Igeo= <0), Potential contamination index (Cp=<1), and Potential ecological risk factor (Eri=<40) and Risk factor index (RI =<95) revealed the unpolluted nature of marine sediments by Mn, Zn, Ni, Cu, Pb, Co. However, the Cuddalore-SIPCOT transect was moderately and considerably contaminated with Hg (CF = 1.538, Igeo=>0.04, Cp=<1.6, Eri=<80- >40, RI = 95> - <190–190> - <380). The source of Hg was traced to the discharges from the nearby industries. Principal component analysis (factor analysis) suggested the natural input derived from terrigenous detritus materials (lithogenic factor) to influence the distribution of most of the considered metals in the southwest Bay of Bengal. The results are likely to be useful for the coastal zone regulation authorities to develop sustainable management strategies.

Spatial Distribution and Sources of Heavy Metals in Soil of a Typical Lead-Zinc Mining Area, Yangshuo

Taking a typical lead-zinc mining area in Yangshuo county, Guangxi as the research object, the contents of 10 metal elements (Cr, Mn, Ni, Cu, Zn, As, Cd, Sb, Hg, and Pb) in the surface soil of Sidihe River basin in Yangshuo were analyzed and determined. Pearson correlation analysis, principal component analysis (PCA), positive definite matrix factorization (PMF), and other methods were comprehensively used to quantitatively analyze their contributions and identify pollution sources. In total, 168 surface soil samples were collected across the study area. The mean concentrations of Zn, Cd, Hg, and Pb in the soils were higher than the National Environmental Quality Standards for Soils in China. The mean contents of Sb, Cd, Cu, Pb, and Zn were higher than their corresponding local background values by approximately 1.01, 5.50, 3.29, 9.11, and 10.67 times, respectively, indicating that heavy metals have been enriched in topsoil. The Igeo showed that the major pollutant element in the soils was Hg, followed by Pb, Zn, and Mn. Correlation analysis and principal component analysis showed that the sources of metal pollution in surface soil in the study area were complex and mainly from human activities. Cu, Zn, Cd, Sb, As, and Pb were mainly derived from mining activities; Hg, Cr, and Ni were controlled by soil parent material sources; and Mn and Cd were mainly derived from mining activities and agricultural activities. PMF model analysis results showed that the metal pollution sources in the surface soil were jointly affected by these three sources. Mining activities, natural sources, and a mixed source of mining activities and agricultural activities were the main sources of heavy metal pollution in the soils, accounting for 58.0%, 13.5%, and 28.6% of the total heavy metal accumulation, respectively. Ni, Cu, Zn, As, Sb, Hg, and Pb were derived mainly from mining activities. Cr, Ni, and Hg were mainly attributed to natural sources, such as soil parent materials and rainfall erosion (44.6%, 23.2%, and 21.0%, respectively), and Mn and Cd were associated with a mixed source of mining activities and agricultural activities (75.4% and 70.4%).

Estimating the heavy metal contents in farmland soil from hyperspectral images based on Stacked AdaBoost ensemble learning

• Assessment of heavy metal pollution in soil base on hyperspectral image. • Estimating the soil heavy metal content by constructing ensemble learning model. • The estimation accuracy of heavy metal content in soil is improved. • Mapping the distribution trend of heavy metals in farmland soil. • The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Heavy metal pollution poses a huge challenge to the soil environment. With the increasing pollution level, the traditional monitoring methods cannot quickly obtain information on large-area pollution. Therefore, a large-scale mapping method with high precision is urgently needed to effectively control heavy metal pollution. This study explored a method for mapping soil heavy metal concentrations through hyperspectral images. On this basis, a new Stacked AdaBoost ensemble learning algorithm was constructed to construct the inversion model of soil heavy metal contents. The characteristic spectral bands of heavy metals were extracted as model input variables using Pearson’s correlation coefficient and successive projections algorithm. With three sets of heavy metal content data, the prediction accuracy and mapping outcomes of various machine learning methods were compared. Furthermore, the potential sources of heavy metal pollution in the study area were analyzed based on the Moran’s index. The results showed that the Stacked AdaBoost model was relatively stable with higher accuracy than traditional machine learning models. For Cr, Cu, and As, the determination coefficients ( R 2 ) of the verification set were 0.66, 0.61, and 0.74, respectively. Afterward, the results of this model were used to map the heavy metal concentration over the study area. The mapping results suggested that the heavy metal conditions of soils in the Ganhetan area were caused by nature and human activities. The As pollution in agricultural soils was the most serious, with an exceedance rate of 38.66%. Industrial areas were potential sources of soil heavy metal pollution in the study area. In summary, the Stacked AdaBoost ensemble learning model provides detailed and reliable data for agricultural ecological protection and industrial pollution control, allowing the effective management of heavy metal pollution sources.

Human health risk ​and receptor model-oriented sources of heavy metal pollution in commonly consume vegetable and fish species of high Ganges river floodplain agro-ecological area, Bangladesh

This study was intended to assess heavy metal contents and sources in commonly consumed vegetables and fish collected from the Jashore district of Bangladesh and to evaluate the probable human health risks via the ingesting of those vegetables and fish species. A total of 130 vegetable and fish samples were analyzed for As, Mn, Cu, Cr, Ni, and Pb concentration by an atomic absorption spectrophotometer. Metals and metalloids like As, Pb, and Cr in vegetable species were greater than the maximum allowable concentration (MAC), while Pb and cu in fish species exceeded the MAC. Pollution evaluation index values were ranges from 0.40-10.35 and 1.53–2.78 for vegetable and fish species, respectively, indicating light to serious pollution. Lactuca sativa followed by Cucurbita moschata, Amaranthus gangeticus for vegetables and Channa punctate, Oreochromis mossambicus, followed by Dendrobranchiata for fish are the most contaminated food items. The positive matrix factorization model showed that As (81.9%), Ni (48%), Cr (49.6%), Mn (46%), Pb (44.3%), and Cu (44.4%) for vegetable species and As (86.9%), Ni (90.5%), Mn (67.6%), Pb (65.3%), Cr (57%) and Cu (46.2%) for fish species were resulting from agrochemical, atmospheric emission, irrigation, contaminated feed, and mixed sources. The self-organizing map and principle component analysis indicates three spatial patterns e.g., As–Mn–Cu, Pb–Cr, and Ni in vegetables and As–Mn–Cr, Cu–Ni, and Pb in fish samples. The THQ values for single elements were less than 1 (except As for vegetables and Pb for fish species) for all food items but the HI values for all of the vegetables (2.18E+00 to 2.04E+01) and fish (1.07E+00 to 9.39E+00) samples were exceeded the USEPA acceptable risk level (HI > 1E+00). While the cancer risks only induced by As for all vegetables and fish species, which exceeded the USEPA safe level (TCR>1E-04). Sensitivity analysis indicates that metal concentration was the most responsible factor for carcinogenic risk.

Traffic patterns, more than adjacent land use, influence element content of roadside forbs for insect pollinators

Abstract Roadsides are targeted for restoration of pollinator‐friendly plants. Yet, roads are sources of macronutrient, micronutrient and heavy metal pollution that may contaminate roadside plants. Adjacent landscape features such as railroads and agriculture provide additional macronutrient and heavy metal pollution that may exacerbate traffic effects. However, we lack perspective on how roads combine with rural landscape features to influence nutrition of roadside plants, which could have implications for pollinator health. We surveyed roadsides across Minnesota, USA and measured foliar levels of dietary macronutrients (nitrogen, phosphorous and potassium), a micronutrient (sodium) and metals (iron, zinc, copper, chromium, nickel, lead, aluminium and cadmium) in six abundant roadside forb species used by insect pollinators: Asclepias syriaca, Dalea purpurea, Monarda fistulosa, Ratibida pinnata, Solidago spp. and Trifolium pratense. We aimed to determine (1) how road variables (traffic volume and distance from road) combine with adjacent land use (railroad and agriculture) to influence element content of roadside forbs and (2) whether some forb species show consistent differences in their accumulation of potentially toxic heavy metals, which could inform selection of species to plant along roadsides. We found that foliar concentrations of nine elements increased with greater traffic volume (nitrogen, phosphorous, iron, zinc, copper, chromium, nickel, lead and aluminium), and concentrations of six elements declined with distance from the road (nitrogen, phosphorous, potassium, iron, zinc and copper). Leaves collected adjacent to railroad had less phosphorous, potassium, iron, nickel and aluminium than leaves collected from sites not adjacent to railroad. Additionally, leaves collected from sites adjacent to agriculture had lower copper levels than leaves from sites without adjacent agriculture. We found no evidence that particular ford species along roadsides consistently rank higher than other species in their accumulation of heavy metals. Our results show that traffic alters more elements in roadside plants than does adjacent landscape context, alleviating concerns that landscape features exacerbate pollutant levels in roadside pollinator habitat. However, nutrient contamination of most roadside plants is unlikely to reach toxic levels for insect pollinators. This work is consistent with the positive conservation potential of low to moderate traffic roadsides for pollinators.

Linking Measure of the Tropical Stingless Bee (Apidae, Meliponini, and Heterotrigona itama) Honey Quality with Hives Distance to the Source of Heavy Metal Pollution in Urban and Industrial Areas in Sabah, Borneo.

Honey is a natural product of bees, and its chemical composition depends on the nectar sources of the surrounding flora as well as environmental factors. However, keeping hives in areas polluted with heavy metals can affect the quality of bee products such as honey. To date, there have been very few studies on the health risks of consuming honey at various locations in the Malaysian state of Sabah, Borneo, in relation to food standards and heavy metal contamination of honey from the stingless bee, Heterotrigona itama in association with pollutant sources. A total of 63 samples of raw and unprocessed honey were collected directly from beekeepers producing honey at five sites in the industrial areas. All selected heavy metals were measured using an inductively coupled plasma optical emission spectrophotometer (ICP-OES). Overall, the most frequently detected element was Zn (0.090 mg/kg), followed by Pb (0.012 mg/kg), As (0.004 mg/kg), and Cr (0.003 mg/kg), while Cd (0.001 mg/kg) was the lowest element in honey from all areas. With the exception of Cr and Zn, a significant correlation was found between PCA factor score 1 and heavy metal concentration in honey for Pb, Cd, and As, suggesting that the source of pollution for these metal elements was from hives closer to major roads, cities/town, petrochemical hub, and power plants. Although the heavy metal concentrations in the honey samples did not exceed the food standard limits and therefore do not pose a health risk, the observed increase in heavy metal concentrations in honey in industrial areas could pose a potential risk in the future due to the growing interest in rearing of stingless bees for honey production in these areas of Sabah.

Distribution characteristics and ecological risk assessment of heavy metals in sediments of Shahe reservoir

Shahe Reservoir of Northern Canal Basin was selected as the study area. Nineteen surface (0~20 cm) sediment samples and three sediment core samples were collected to analyze the spatial distribution characteristics of As, Cd, Cu, Mn, Pb and Zn in the sediments. The geo-accumulation index, potential ecological risk index and risk assessment code were used to evaluate heavy metal pollution, as well as its potential risk was analyzed according to the speciation of heavy metals. Results showed that the average enrichment factors of heavy metals compared to the background value in soil of Beijing were ranked at the order as the point source pollution area > the central area of the reservoir > the downstream area of the reservoir > the Nansha River > the upstream area of the reservoir > the Beisha River, namely, 2.57 times, 2.06 times, 1.97 times, 1.95 times, 1.87 times and 1.85 times, respectively. The sediment core samples in the central area of the reservoir and the inlet of the Beisha River showed a trend of increasing firstly and then decreasing with the change of depth. Pollution assessment results showed that sediment was moderately contaminated or moderately to strongly contaminated by As, but the other heavy metals were not polluted or lightly polluted. The potential ecological risk index of all sampling sites was less than 150, showing a low ecological risk. As Cr and Cu were mainly in the speciation of residues, with low bioavailability. Although the content of Mn and Zn were low, they showed high bioavailability. Based on correlation analysis and principal component analysis, it was speculated that the sources of various heavy metal pollution in the sediment were similar, which were possibly input from the external wastewater. The heavy metals in sediment were positively related to nutrients and organic matter, indicating that all of them were mostly from the same point polluted sources.

Source apportionment of soil heavy metals: A new quantitative framework coupling receptor model and stable isotopic ratios

Tracing the source of heavy metals in soils is crucial for reversing the worrisome situation of heavy metal contamination. In this study, the origins of heavy metal pollution in soil were examined in a primary electronic waste treatment and disposal hub in China, using a synergistic source apportionment framework consisting of the positive matrix factorization (PMF) model and the Bayesian stable-isotope analysis mixing model (MixSIAR). Industrial activity is significant to heavy metal contamination in both industrial park and farmland soils, however, the contribution varied through PMF model (industrial park, 64.2%; farmland, 35.6%). In the industrial park, Pb was identified as the major pollutant in the soils, and the local children suffered from noncarcinogenic risks. Moreover, the contribution of Pb contamination sources were allocated more accurately (electronic waste dismantling, 25.1%; industrial production, 23.7%; vehicle exhaust from leaded gasoline, 9.1%; vehicle exhaust from unleaded gasoline, 20.2%; natural process, 21.9%) using MixSIAR for the first time. The main soil contaminants in surrounding farmland were Cd, Cu, and Zn. The variations in heavy metal pollution sources in soils were found to be associated with local policies and regulations, such as the phasing out of leaded gasoline and the conversion of industrial park from electronic waste demolition switched to production and storage. The identification of the source of heavy metals in soil will support targeted reduction of the associated emissions, which can immediately help alleviating soil contamination and control human health risks.