Sources Of Heavy Metal Pollution Research Articles

Pollution of Heavy Metals in Topsoil of Remote Mountainous Areas in Western Yunnan-Guizhou Plateau

Heavy metals from anthropogenic emissions have had a negative impact on the ecological environment in remote regions. A total of 69 topsoil samples were collected from 13 remote mountainous areas in the western Yunnan-Guizhou Plateau at altitudes of 2 563-4 037 m, and the concentrations of ten heavy metals in the samples were determined. Enrichment characteristics and pollution sources of heavy metals in topsoil were discussed by referencing the enrichment factor （EF）, positive matrix factorization （PMF）, and Pb isotopes. The results showed that the average concentrations of Al, Fe, Cu, V, and Zn in the topsoil were lower than the soil background values in Yunnan Province； the average concentrations of Ni and Pb were similar to the background values； and the average concentrations of Cd, Cr, and Hg were 1.8-3.6 times higher than the background values. The average EF values of Pb, Cr, and Ni were 3.8, 3.4, and 2.3, respectively, showing moderate enrichment according to the EF classification criteria； the average EF values of Cd and Hg were 15.2 and 10.0, reflecting significant enrichment； and the average EF values of the other metals ranged from 1.1 to 1.9, displaying none-weak enrichment. Combining the comparisons of heavy metal concentrations and ratios in topsoil and bedrock and the EF and PMF results, Al, Fe, V, Cr, Cu, Ni, and Zn in topsoil were considered primarily from detrital sources, and the spatial concentration variations of the metals should have been mainly regulated by the parent material. Cadmium, Hg, and Pb were obviously polluted by anthropogenic emissions, and the main sources were non-ferrous metal smelting and coal combustion. The areas with relatively high Cd, Hg, and Pb pollution were mainly distributed in the Jiaozi snow mountain, Bitahai watershed, Luoji Mountain, and Laojun Mountain areas. Anthropogenic emissions contributed 23.8% of the accumulation of heavy metals in the topsoil.

Pollution Assessment and Source Apportionment of Heavy Metals in the Surrounding Soil of Typical Mining Areas in Tongling, Anhui Province

To study the level of heavy metal pollution and ecological risks in the soil around typical mining areas in Tongling, a total of 150 soil samples were collected from the study area. The content characteristics of 10 elements, namely, As, Cd, Cr, Cu, Hg, Mn, Ni, Pb, Fe, and Zn, in the soils were analyzed. Methods including enrichment factor, the geo-accumulation index, single-factor pollution index, Nemero comprehensive pollution index, and potential ecological risk index were used to evaluate the pollution status of heavy metals in the soil of the study area. The pollution sources of heavy metals in the soil were also analyzed using correlation analysis, cluster analysis, and principal component analysis. The results showed that except for Cr and Fe, the average contents of the other eight heavy metal elements were higher than the soil background values in the study area. Pb, Zn, As, Cu, and Cd had a high degree of variation and were significantly affected by external interference. The spatial distribution showed that both Cr and Ni showed a decreasing trend from the edge to the central region, whereas the other eight heavy metals showed a decreasing trend from the central region to the surrounding areas. The pollution level of Cd and Cu in the soil of the research area was relatively severe. The overall ecological risk was at a medium to low level. Cd and Hg were the main contributing factors. As, Cd, Cu, Fe, Mn, Pb, and Zn mainly came from agricultural, industrial, and transportation sources, whereas Cr and Ni were mainly from natural sources. However, the sources of Hg were relatively complex. The research results can provide a scientific basis for the prevention and control of soil heavy metal pollution in metal mining areas, as well as the remediation of mine pollution.

Sources Analysis and Health Risk Assessment of Heavy Metals in Street Dust from Urban Core of Zhengzhou, China

Fifty-one street dust samples were systematically collected from the urban core of Zhengzhou, China, and analyzed for potentially toxic metals. The concentrations of vanadium (V), manganese (Mn), copper (Cu), zinc (Zn), arsenic (As), lead (Pb), and nickel (Ni) in the samples surpassed the background values of the local soil, indicating a notable potential for contamination. Spatially, the traffic area was the most polluted with a total heavy metal concentration of Cu, Zn, As, Pb, and Ni, while the pollution levels were lower in the culture and education area and commercial area with total concentrations of V and Mn. Seasonal variations were discerned in the concentrations of heavy metals, with V, Cu, Zn, and As exhibiting heightened levels during the fall and winter, while Mn, Ni, and Pb reached peaks in the spring season. Zn exhibited the highest mean geo-accumulation index (Igeo) value at 2.247, followed by Cu at 2.019, Pb at 0.961, As at 0.590, Ni at 0.126, Mn at −0.178, and V at −0.359. The potential ecological risk index (RI) in the traffic-intensive area markedly exceeded other functional areas. Health risk assessments showed that children were more vulnerable to heavy metal exposure than adults, particularly through the ingestion pathway. Correlation analysis, principal component analysis (PCA), and cluster analysis (CA) were applied in conjunction with the spatial–temporal concentration patterns across various functional areas to ascertain the plausible sources of heavy metal pollutants. The results indicated that heavy metals in the urban street dust of Zhengzhou were multifaceted, stemming from natural processes and diverse anthropogenic activities such as coal burning, industrial emissions, traffic, and construction operations.

Heavy Metal Contamination of Surface Sediments-Soil Adjoining the Largest Copper Mine Waste Dump in Central India Using Multivariate Pattern Recognition Techniques and Geo-Statistical Mapping

This detailed study assessed heavy metal contamination of sediments/soil near central India’s largest copper mining area using 38 sampling sites within 10 km of the mine using atomic absorption spectroscopy. This study utilized multivariate pattern recognition methods, namely hierarchical clustering analysis (HCA) and principal component analysis (PCA), for source identification. Twelve parameters, i.e., copper (Cu), manganese (Mn), cobalt (Co), zinc (Zn), nickel (Ni), lead (Pb), organic matter (OM), cation exchange capacity (CEC), soil pH, distance (D), and elevation (E) were analyzed. The hierarchical cluster analysis (HCA) was used to analyze the sample sites with similar metal contamination and principal component analysis (PCA) was used to analyze the relationship between the parameters as well as to identify sources of heavy metal pollution. Three major pollution hotspots were detected by AHC and were classified as unpolluted/low pollution sites (UPS: mean concentration factor of 1.35 for Cu), highly polluted sites (HPS: mean concentration factor of 22 for Cu), and extremely polluted sites (EPS: mean concentration factor of 74 for Cu). PCA revealed three hidden factors/components, namely PC1 (explaining 38% of the variability), PC2 (18% of the variability), and PC3 (14% of the variability). Metals showed strong positive loading in PC1, explaining the highest variability. The mean content of Cu in soil/sediment samples was 502.526 mg/kg. The mean copper content was 10 times higher than the natural crustal value of 45mg/kg, indicating severe pollution in several sites around the study area. Mapping of copper contamination was conducted to reveal the spatial distribution of copper contamination using QGIS. This study exposes the heavy metal contamination level in surface sediments/soil and the effectiveness of pattern recognition techniques for the assessment of multivariate datasets in discerning spatial disparities and identifying the contamination causes.

Pollution area identification, receptor model-oriented sources and probabilistic health hazards to prioritize control measures for heavy metal management in soil

Identifying the primary source of heavy metals (HMs) pollution and the key pollutants is crucial for safeguarding eco-health and managing risks in industrial vicinity. For this purpose, this investigation was carried out to investigate the pollution area identification with soil static environmental capacity (QI), receptor model-oriented critical sources, and Monte Carlo simulation (MCS) based probabilistic environmental and human health hazards associated with HMs in agricultural soils of Narayanganj, Bangladesh. The average concentration of Cr, Ni, Cu, Cd, Pb, Co, Zn, and Mn were 98.67, 63.41, 37.39, 1.28, 23.93, 14.48, 125.08, and 467.45 mg/kg, respectively. The geoaccumulation index identified Cd as the dominant metal, indicating heavy to extreme contamination in soils. The QI revealed that over 99% of the areas were polluted for Ni and Cd with less uncertain regions whereas Cr showed a significant portion of areas with uncertain pollution status. The positive matrix factorization (PMF) model identified three major sources: agricultural (29%), vehicular emissions (25%), and industrial (46%). The probabilistic assessment of health hazards indicated that both carcinogenic and non-carcinogenic risks for adult male, adult female, and children were deemed unacceptable. Moreover, children faced a higher health hazard compared to adults. For adult male, adult female, and children, industrial operations contributed 48.4%, 42.7%, and 71.2% of the carcinogenic risks, respectively and these risks were associated with Ni and Cr as the main pollutants of concern. The study emphasizes valuable scientific insights for environmental managers to tackle soil pollution from HMs by effectively managing anthropogenic sources. It could aid in devising strategies for environmental remediation engineering and refining industry standards.

Transcriptome analysis provides new insight into the mechanism of Bombyx mori under zinc exposure

Zinc is a significant source of heavy metal pollution that poses risks to both human health and biodiversity. Excessive concentrations of zinc can hinder the growth and development of insects and trigger cell death through oxidative damage. The midgut is the main organ affected by exposure to heavy metals. The silkworm, a prominent insect species belonging to the Lepidoptera class and widely used in China, serves as a model for studying the genetic response to heavy metal stress. In this study, high-throughput sequencing technology was employed to investigate detoxification-related genes in the midgut that are induced by zinc exposure. A total of 11,320 unigenes and 14,723 transcripts were identified, with 553 differentially expressed genes (DEGs) detected, among which 394 were up-regulated and 159 were down-regulated. The Gene Ontology (GO) analysis revealed that 452 DEGs were involved in 18 biological process subclasses, 14 cellular component subclasses and 8 molecular functional subclasses. Furthermore, the KEGG analysis demonstrated enrichment in pathways such as Protein digestion, absorption and Lysosome. Validation of the expression levels of 9 detoxification-related DEGs through qRT-PCR confirmed the accuracy of the RNA-seq results. This study not only contributes new insights into the detoxification mechanisms mechanism of silkworms against zinc contamination, but also serves as a foundation basis for understanding the molecular detoxification processes in lepidopteran insects.

Identification Priority Source of Heavy Metals in Urban Soils Based on Source-specific Ecological Risk Analysis in a Typical Tourist City

To understand the pollution status, distribution characteristics, and pollution sources of soil heavy metals in tourist cities in northwest China, the soil content of heavy metals As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn in the main areas of Dunhuang City was collected and analyzed. The soil heavy metal pollution level was quantitatively evaluated by the methods of the geo-accumulation index and improved Nemerow pollution index, and the sources of heavy metal pollution were quantitatively analyzed using cluster analysis and the positive matrix factorization （PMF） model. The contribution rate of each pollution source to ecological risk was determined by combining the PMF model and comprehensive ecological risk index. The results showed that except for the mean contents of As and Ni, the mean contents of Cd, Cr, Cu, Hg, Pb, and Zn were all higher than the background values in Gansu Province. However, the average content of the eight elements was lower than the screening value of construction land in the Standard for Soil Pollution Risk Control of Soil Environmental Quality Construction Land （trial） （GB 36600-2018）. Among them, the enrichment of Cd, Pb, and Hg was more serious, and the exceedance rate was higher than 90%. The results of the geo-accumulation index indicated that urban soils were mainly polluted by Hg in the study area, and Cd, Cr, Cu, Pb, and Zn pollution also existed in different degrees. The improved Nemerow pollution index illustrated that the comprehensive pollution degree of the soil was clean to moderate pollution, and the overall pollution was light pollution. Based on the PMF model, we could conclude that soil heavy metals in the study area were affected by natural sources, industrial deposition sources, industrial sources, traffic sources, and comprehensive sources, and the contribution rates were 29.28%, 25.86%, 20.13%, 16.5%, and 8.23%, respectively. The specific source-integrated ecological risk assessment model found that the industrial deposition source contributed the most to the ecological risk in the study area and could be regarded as the priority control pollution source, and Hg was considered to be the priority control pollution element for ecological risk.

Assessment and Source Analysis of Heavy Metal Pollution in Farmland Soil Around Tin Mine

Studying the status and source analysis of heavy metal pollution in farmland in typical mining and processing areas is an important prerequisite for promoting farmland soil ecological restoration and farmland protection in concentrated mining areas. In this study, the heavy metal content of farmland soil around a mining area in southwest China was detected, and the pollution status, distribution law, health risks, and sources of heavy metals were studied by using the land accumulation index method, comprehensive pollution index method, kriging interpolation method, health risk assessment method, and PMF receptor model on the sampling data. The results showed that the mean values of eight heavy metals in farmland soil except Ni exceeded the local soil background values, and the results of the ground accumulation index evaluation showed that Cd and Hg were extremely polluted； Pb and As showed medium pollution-heavy pollution； and Cr, Zn, Ni, and Cu were lightly polluted. In the health risk assessment, oral ingestion was the main exposure route posing a health risk to the human body； the main element that constituted non-carcinogenic health risks was As, and the carcinogenic risks were from As and Cd. PMF model analysis showed that the contribution rate of weathering natural sources of iron-bearing ore was 28.02%, and the main factors were Ca and Fe. The contribution rate of agricultural sources was 3.02%, and the main factors were Pb and As. The contribution rate of industrial and atmospheric deposition composite sources was 33.09%, and the main factor was Hg. The contribution rate of the parent material source was 17.27%, and the main factor was Ca. The contribution rate of mining activities such as mining and smelting was 18.60%, and the main factors were Zn and Cd.

Characterization and Source Analysis of Heavy Metal(loid)s Pollution in Soil of an Industrial Park in Kunming, China

This study investigated the characteristics and sources of heavy metal(loid) pollution in the soil of a key industrial park in Kunming, China. In total, 60 soil samples (40 from agricultural land and 20 from construction land) were collected from and around the park. The soil pH and contents of Arsenic (As), lead (Pb), copper (Cu), zinc (Zn), cadmium (Cd), mercury (Hg), nickel (Ni), and chromium (Cr) were measured. The contents of the eight heavy metal(loid)s were analyzed using the background values of heavy metal(loid)s in the Kunming soil. The pollution load, geoaccumulation, and Nemero Comprehensive Pollution Indices were used for environmental risk evaluation. Cluster and principal component analyses were used to resolve heavy metal(loid) sources. Cd was enriched in construction and agricultural soils. As, Hg, Cd, Pb, Cu, and Zn exhibited large spatial differentiation and were significantly affected by the external environment. A regional pollution load index of 3.02 indicated overall heavy pollution. The pollution load index for each heavy metal(loid) indicated light pollution. The geoaccumulation index indicated relatively severe As, Cd, Cu, Pb, and Zn pollution. The Nemero Composite Pollution Index value showed that the study area was heavily polluted, with construction land being mildly polluted by Cd, and agricultural land being moderately polluted. The results of the spatial distribution show that there were high levels of contamination in the center. Correlation and principal component analyses showed that the pollution sources of the eight heavy metal(loid)s varied. Hg, Cd, and Pb originate primarily from industrial and agricultural pollution. Traffic sources significantly impacted Cu, Pb, Cd, and Cr. Natural sources are the main sources of Cr, Ni, and Cd. Ni is also affected by industrial sources, whereas Zn and Cu are affected by agricultural pollution. The influences of As, Cd, and Pb on the surface soil in the study area were more serious. Cd is more widely polluted and should be a priority in controlling soil heavy metal(loid)s.

Contamination Characteristics and Source Apportionment of Heavy Metal in the Topsoil of a Small Watershed in South Taihang

With the rapid development of industrialization and urbanization, the issue of soil environmental pollution is becoming more and more prominent, especially concerning heavy metal contamination, which has garnered significant scholarly attention. The surface watershed formed by waterline is influenced by various factors such as topography, industrial emissions, and agricultural runoff, resulting in a complex process of migration and accumulation of heavy metal elements from multiple sources. In this study, the pollution characteristics and sources of heavy metal elements Hg, As, Pb, Ni, Cd, Cr, Cu and Zn in 165 surface soil samples from the Manghe River watershed in Jiyuan City were comprehensively analyzed using a variety of methods, including statistics, geostatistics, enriched factor analysis and the Positive Matrix Factorization Model (PMF). The results showed that the concentrations of Hg, Cd, As, Cu, Pb and Zn exceeded their corresponding background values with varying degrees of enrichment. Notably, the average contents of Cd, Hg and Pb were 26.70 times, 3.69 times and 4.49 times higher than those in Chinese soils on average, respectively, showing obvious enrichment characteristics. Moreover, there were distinct spatial distribution patterns for each heavy metal element; Ni and Cr exhibited similar trends mainly controlled by the parent material, while human activities significantly affect the other six elements forming high-value areas around mining and related industries. It is noteworthy that Cu, Hg and Zn were influenced by dominant wind direction in autumn and winter, forming sub-high-value zones in southern forested areas; meanwhile, Cu and Zn were also influenced by agricultural fertilizer application as well as surface runoff, leading to secondary high-value areas in the dryland areas. Further analysis revealed a significant positive correlation among these heavy metal elements, suggesting that they may share common sources. Through the PMF Model, four main factors were identified, with factor 2 (36.25%), factor 1 (23.00%), factor 3 (21.20%) and factor 4 (19.55%) ranked in descending order of contribution rate. The heavy metal pollution in the study area was attributed to anthropogenic activities and natural factors, accounting for 63.75% and 36.25%, respectively. Coal mining, chemical industry smelting, vehicle emissions and excessive use of agrochemicals were identified as the main sources of heavy metal pollution. These pollutants entered the soil through direct emissions, atmospheric deposition, transportation and agricultural activities, exerting a significant impact on the soil environment. Therefore, delving into the spatial distribution pattern of soil heavy metal pollution and precise analysis of its sources are of great importance for effective treatment and remediation of soil heavy metal pollution in small watersheds, maintaining healthy soil ecology and safeguarding human health.

Distribution Characteristics, Risk Assessment, and Source Analysis of Heavy Metals in Farmland Soil of a Karst Area in Southwest China

Soil environmental quality related to the residents’ life, health, and safety, has been the hotspot issues in science of ecological environment protection. Evaluating the distribution characteristics, ecological risk, and source of heavy metals in farmland is important for protecting soil resources. The agricultural area of Lianhua town, Gongcheng County, Guilin is a typical karst landform. In response to the problem of heavy metal pollution and complex sources in the soil of this area, the characteristics and sources of heavy metal pollution in the soil profiles from farmland, abandoned land, and forest were studied using the single-factor index method, the geoaccumulation index (Igeo) principal component analysis (PCA) and positive matrix factorization (PMF) model. The results showed that: (1) that the contents of cadmium (Cd), chromium (Cr), copper (Cu), nickel (Ni), lead (Pb), and zinc (Zn) in the soil profile of the study area were higher than that of the soil elements background values in Guangxi. The total and available forms contents of all heavy metal elements exhibited the characteristics of accumulation in the surface profile; (2) among the six heavy elements, the contents of Cd were in a moderately to heavily polluted state. The contents of Cd in some soil profiles exceeded the control standard for agricultural land soil pollution. The contents of Zn and Ni were from slightly to moderately polluted in areas with frequent agricultural activities; (3) according to the PCA and PMF model, there were three main sources of heavy metals in the study area. Among them, Cd, Cu, Ni, and Zn are related to agricultural activities; the elements As, Cd, Cr, and Hg are closely related to geological background; Pb and Zn are mainly affected by atmospheric sedimentation of transportation. Agricultural activities and natural geological background are the main contribution sources of heavy metals in soil. Human activities are the main factors that cause the accumulation of heavy metals in soil. This research has theoretical guidance and practical significance for the prevention and control of soil heavy metal pollution and the protection of farmland environmental quality in the region.

Spatial–temporal variation and source analysis of heavy metals in different land use types in Beilun District (2015 and 2022)

The soil environment plays an important role in urban ecosystems. To study the heavy metal contamination of soil in Beilun District, Ningbo, we collected soil samples from 60 points in urban and peri-urban areas of Beilun District and analyzed the spatiotemporal variation and sources of heavy metal pollution in various land-use types. The results shown that the heavy metal contents in 2015 and 2022 were higher than the background soil values of Ningbo city, and there was an accumulation of heavy metals over these 7 years. The contents of heavy metals in green belts and woodland in 2022 were higher than those in 2015, while there was no significant change in agricultural land. The heavy metal contents in both years were mainly in the order green belts > agricultural land > woodland. The spatiotemporal distribution of heavy metal content showed that heavy metal pollution in Beilun District was concentrated in five industrial areas, and there was a trend toward the disappearance of highly polluted points. But the single-factor pollution index, pollution load index (PLI), and geoaccumulation index (Igeo) indicated that there was no significant heavy metal pollution in Beilun District, and individual elements at specific points showed slight pollution. The source analysis results showed that the main source of Hg is chemical, As is mainly derived from agricultural, Cr, Ni and Cu are mainly derived from natural, the main sources of Zn and Cd are electroplating and machinery activities, and the main source of Pb is traffic. These results specify a reference for future investigation on urban soil heavy metals, and the source apportionment results provide a scientific foundation for subsequent soil heavy metal pollution treatment.

Soil Heavy Metal(loid) Pollution Evaluation, Risk Assessment, and Source Analysis of a Mineral Processing Plant

Yunnan Province is rich in mineral resources. Early mining, processing, metallurgy, and other mining activities produce three industrial wastes (waste water, waste gas, and waste residue) causing environmental pollution. Considering the legacy site of a mineral processing plant in Yunnan as the research object, 21 sampling points in the study area and 12 control sampling points in the periphery were set up to determine the contents of the heavy metal(loid)s As, Hg, Cd, Cu, Ni, Pb, and Cr in the soil. The spatial distribution of heavy metal(loid)s was interpolated and analyzed using Arcmap10.8, and combined with the single-factor index, Nemero Comprehensive Pollution Index, and the health risk assessment method for the heavy metal(loid) pollution status and health risk of the soil were evaluated. The soil in the study area was acidic, with the largest average value of elemental As and the largest percentages of control and screening values. The results of the single-factor and Nemero composite pollution index showed the following trend: As > Pb > Cd > Cu > Ni > Hg. Cd, Cu, and Pb mainly originate from mining and metallurgy and Hg from the combustion of fossil fuels, while soil-forming substrates are the main sources of Ni. Pollution by As was the most prominent element, whereas pollution by Cd, Cu, and Pb in some areas also cannot be ignored to prevent negative impacts on residents. It is recommended to remediate and treat the soil on site for public events; therefore, this study fills the gap in studying potential ecological risks, human health risk assessments, and sources of exposure (oral ingestion, respiratory ingestion, dermal contact).

Impact of Green Manures in Mitigating Heavy Metal Toxicity: A Review

The contamination of soil with heavy metals poses a serious threat to both ecology and public health. Human activity, such as mining, excessive fertilizer application, and industrial processes, are among the various sources of heavy metal pollution. There are various green manure plant species used as potential for mitigating heavy metal toxicity, certain legume plants like Albizia leibbeck L., Bauhinia purpurea L., Dalbergia sissoo, Millettia peguensis Ali, and Pongamia pinnata are being looked for applications in phytoremediation. Various techniques such as bioremediation, phytoextraction, phytofiltration, phytovolatilization, rhizofiltration, phytodegradation, and phyto desalinization are employed to address environmental pollution. Phytoremediation, in particular, aims to collect pollutants from the environment and convert them into forms that can be easily extracted from plant tissues. Contamination by heavy metals is a severe environmental matter. However, there are various contemporary methods that offer potential solutions to address this issue. One such approach involves the use of biochar to decrease the bioavailability of heavy metals in soil. By binding to them reducing their availability for plant absorption and entry into the food chain. Genetic engineering permits researchers to insert genes into plants that improve their acceptance of heavy metals or increase their ability to accumulate them (phytoextraction). Consequently, the utilization of these conventional methods in phytoremediation has demonstrated the most favourable and sustainable outcomes, ensuring an eco-friendly approach for the ecosystem.

Removal of Lead Using Novel Composite Synthesized from Rice Husk and Used Tea Leaves Extract

Untreated industrial effluents, particularly from tanneries and heavy industries, are the main source of heavy metal pollution, which poses serious environmental risks. Lead is one of the most toxic heavy metals, adversely affects human functioning and accumulates in the environment. This study presents ACUTLaqE (Activated Carbon-Used Tea Leaves Aqueous Extract) as an effective adsorbent for Pb2+ ion removal from their aqueous solution. The composite was synthesized by blending activated carbon from rice husks with a tea extract solution and characterized using SEM, XRD, and FTIR spectroscopy. Batch tests demonstrated that ACUTLaqE is highly efficient, removing about 99% of Pb2+ ions at an adsorbent dose of 2 g/L within 30 minutes. The adsorption data conformed well to the Langmuir isotherm model, suggesting a homogeneous monolayer formation on the composite surface. The qm was 38.61 mg/g, with an RL value close to 0, indicating highly favourable adsorption. Thermodynamic parameters showed negative Gibbs free energy (ΔG⁰) values, reflecting a spontaneous adsorption process. The enthalpy (ΔH⁰) and entropy (ΔS⁰) changes were 66.86 KJmol-1 and 257.12 JK-1mol-1, respectively. The adsorption mechanism involved electrostatic interactions and surface complexation with the composite's hydroxyl and carboxyl groups. These findings suggest that ACUTLaqE is a promising adsorbent material for Pb2+ and potentially other heavy metals removal from industrial effluents.

A remote sensing analysis method for soil heavy metal pollution sources at site scale considering source-sink relationships

Conventional methods for identifying soil heavy metal (HM) pollution sources are limited to area scale, failing to accurately pinpoint sources at specific sites due to the spatial heterogeneity of HMs in mining areas. Furthermore, these methods primarily focus on existing solid waste polluted dumps, defined as “direct pollution sources”, while neglecting existing HM pollution hotspots generated by historical anthropogenic activities (e.g., mineral development, industrial discharges), defined as “potential pollution sources”. Addressing this gap, a novel remote sensing analysis method is proposed to identify both direct and potential pollution sources at site scale, considering source-sink relationships. Direct pollution sources are extracted using a supervised classification algorithm on high-resolution multispectral imagery. Potential pollution sources depend on the spatial distribution of HM pollution, mapped using a machine learning model with hyperspectral imagery. Additionally, a source identification algorithm is developed for gridded pollution source analysis. Validated through a case study in a cadmium (Cd)-polluted mine area, the proposed method successfully extracted 21 solid waste polluted dumps with an overall accuracy approaching 90 % and a Kappa coefficient of 0.80. Simultaneously, 4167 HM pollution hotspots were identified, achieving optimal inversion accuracy for Cd (Rv2 = 0.91, RMSEv = 4.27, and RPDv = 3.02). Notably, the spatial distribution patterns of these identified sources exhibited a high degree of similarity. Further analysis employing the identification algorithm indicated that 3 polluted dumps and 258 pollution hotspots were pollution sources for a selected high-value point of Cd content. This innovative method provides a valuable methodological reference for precise prevention and control of soil HM pollution.

Distribution Characteristics and Assessment of Heavy Metal Pollution in Sediments of Fengyuan River, Guangdong

Mining activities and industrial production discharges often generate heavy metal pollution and pose serious environmental impacts. It is of great significance to study the pollution status and sources of heavy metals in polluted rivers for environmental pollution control. This paper determines the concentration of 34 heavy metals, including As, Cd, Cr, Cu, Ni, Pb, and Zn, in the Fengyuan River and tailing pond in Yunfu City, Guangdong Province. The potential ecological risk index (RI) and integrated pollution index (IPI) were used to evaluate the spatial distribution and pollution levels of heavy metals, and the possible source and enrichment of heavy metal were identified and assessed using enrichment factor (EF). The results showed that Cd and Tl in the slag of the tailings pond were the most serious; The potential ecological risk index indicates that Cd in the sediment of the Fengyuan River is a very severe ecological risk, As and Pb caused heavy and moderate ecological risk, respectively, while, Co, Cr, Cu, Mn, Ni, V, and Zn constituted mild ecological risks; The integrated pollution index shows that all the sampling sites are above high pollution. The average enrichment factor of Cd in surface sediments reached 28.11. The EF values of As, Cu, Mn, Pb, and Zn were all greater than 1. The RI, IPI, and EF produced similar levels of heavy metal pollution in sediments, thereby confirming each other’s results, indicating that the Fengyuan River is heavily contaminated, and As, Cd, Mn, Pb, and Zn were likely to have an effect on the aquatic ecosystem. According to principal component and hierarchical cluster analyses, the Fengyuan River may contain heavy metals from a variety of sources, such as tailings ponds, industrial parks, chemical effluent, and more. In addition, correlation analysis indicates that total organic carbon is significantly correlated with the concentrations of As and Cr, respectively, and combining EF suggests that cluster 1 containing As, Mn, and Pb and cluster 2 containing Cd and Zn may be derived from similar anthropogenic sources. This study can provide meaningful basic information for environmental development planning and decision-making associated with human health in the region.

A Monte Carlo simulation-based health risk assessment of heavy metals in soils of the tropical region in southern China.

The disturbance of ecological stability may take place in tropical regions due to the elevated biomass density resulting from heavy metal and other contaminant pollution. In this study, 62 valid soil samples were collected from Sanya. Source analysis of heavy metals in the area was carried out using absolute principal component-multiple linear regression receptor modelling (APCS-MLR); the comprehensive ecological risk of the study area was assessed based on pollution sources; the Monte-Carlo model was used to accurately predict the health risk of pollution sources in the study area. The results showed that: The average contents of soil heavy metals Cu, Ni and Cd in Sanya were 5.53, 6.56 and 11.66 times higher than the background values of heavy metals. The results of soil geo-accumulation index (Igeo) showed that Cr, Mo, Mn and Zn were unpolluted to moderately polluted, Cu and Ni were moderately polluted, and Cd was moderately polluted to strongly polluted. The main sources of heavy metal pollution were natural sources (57.99%), agricultural sources (38.44%) and traffic sources (3.57%). Natural and agricultural sources were jointly identified as priority control pollution sources and Cd was the priority control pollution element for soil ecological risk. Heavy metal content in Sanya did not pose a non-carcinogenic risk to the population, but there was a carcinogenic risk to children. The element Zn had a high carcinogenic risk to children, and was a priority controlling pollutant element for the risk of human health, with agricultural sources as the priority controlling pollutant source.

Snow dumping station – A considerable source of tyre wear, microplastics, and heavy metal pollution

Snow dumping stations can be a hotspots for pollutants to water resources. However, little is known about the amount of microplastics including tyre wear particles transported this way. This study investigated microplastics and metals in snow from four snow dumping stations in Riga, Latvia, a remote site (Gauja National Park), and a roof top in Riga. Microplastics other than tyre wear particles were identified with Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) (>500 µm) and focal plane array based micro-Fourier Transform Infrared (FPA-µFTIR) imaging (10–500 µm), tyre wear particles by Pyrolysis Gas Chromatography-Mass Spectroscopy (Py-GC–MS), and total metals by Inductively Coupled Plasma with Optical Emission Spectroscopy (ICP-OES). Microplastics detected by FTIR were quantified by particle counts and their mass estimated, while tyre wear particles were quantified by mass. The concentrations varied substantially, with the highest levels in the urban areas. Microplastic concentrations measured by FTIR ranged between 26 and 2549 counts L−1 of melted snow with a corresponding estimated mass of 19–573 µg/L. Tyre wear particles were not detected at the two reference sites, while other sites held 44–3026 µg/L. Metal concentrations varied several orders of magnitude with for example sodium in the range 0.45–819.54 mg/L and cadmium in the range 0.05–0.94 µg/L. Correlating microplastic measured by FTIR to metal content showed a weak to moderate correlation. Tyre wear particles, however, correlated strongly to many of the metals. The study showed that snow can hold considerable amounts of these pollutants, which upon melting and release of the meltwater to the aquatic environment could impact receiving waters.

Quantitative assessment of ecological risk from pollution source based on geostatistical analysis and APCS-MLR model.

The safety of human health and agricultural production depends on the quality of farmland soil. Risk assessment of heavy metal pollution sources could effectively reduce the hazard of soil pollution from various sources. This study has identified and quantitatively analyzed pollution sources with geostatistical analysis and the APCS-MLR model. The potential ecological risk index was combined with the APCS-MLR model which has quantitatively calculated the source contribution. The results revealed that As, Cr, Cd, Pb, Zn, and Cu were enriched in soil. Geostatistical analysis and the APCS-MLR model have apportioned four pollution sources. The Mn and Ni were attributed to natural sources; As and Cr were from agricultural activities; Cu and Zn were originated from natural sources; Cd and Pb were derived from atmospheric deposition. Atmospheric deposition and agricultural activities were the largest contributors to ecological risk of heavy metals in soil, which accounted for 56.21% and 36.01% respectively. Atmospheric deposition and agricultural activities are classified as priority sources of pollution. The combination of source analysis receptor model and risk assessment is an effective method to quantify source contribution. This study has quantified the ecological risks of soil heavy metals from different sources, which will provide a reliable method for the identification of primary harmfulness sources of pollution for future studies.