Source apportionment and source-specific health risk assessment of heavy metals in size-fractionated road dust from a typical mining and smelting area, Gejiu, China

Accurate prediction of spatial distribution of soil potentially toxic elements using machine learning and associated key influencing factors identification: A case study in mining and smelting area in southwestern China

Accurate prediction of spatial distribution of potentially toxic elements (PTEs) is crucial for soil pollution prevention and risk control. Achieving accurate prediction of spatial distribution of soil PTEs at a large scale using conventional methods presents significant challenges. In this study, machine learning (ML) models, specially artificial neural network (ANN), random forest (RF), and extreme gradient boosting (XGB), were used to predict spatial distribution of soil PTEs and identify associated key factors in mining and smelting area located in Yunnan Province, China, under the three scenarios: (1) natural + socioeconomic + spatial datasets (NS), (2) NS + irrigation pollution index (IPI) datasets, (3) NS + IPI + deposition (DEPO) datasets. The results highlighted the combination of NS+IPI+DEPO yielded the highest predictive accuracy across ML models. Particularly, XGB exhibited the highest performance for As (R2 =0.7939), Cd (R2 =0.6679), Cu (R2 =0.8519), Pb (R2 =0.8317), and Zn (R2 =0.7669), whereas RF performed the best for Ni (R2 =0.7146). The feature importance and Shapley additive explanation (SHAP) analysis revealed that DEPO and IPI were the pivotal factors influencing the distribution of soil PTEs. Our findings highlighted the important role of DEPO in spatial distribution prediction of soil PTEs, which has often been ignored in previous studies.

Distinguishing the contributions of different smelting emissions to the spatial risk footprints of toxic elements in soil using PMF, Bayesian isotope mixing models, and distance-based regression

Toxic element pollution of soils emanating from smelting operations is an escalating global concern due to its severe impact on ecosystems and human health. In this study, soil samples were collected and analyzed to quantify the risk contributions and delineate the spatial risk footprints from smelting emissions for 8 toxic elements. A comprehensive health risk contribution and delineation framework was utilized, consisting of Positive matrix factorization (PMF), spatial interpolation, an advanced Bayesian isotope mixing model via Mixing Stable Isotope Analysis in R (MixSIAR), and distance-based regression. The results showed that the mean concentrations of As, Cd, Cu, Hg, Pb, and Zn exceeded the background levels, indicating substantial contamination. Three sources were identified using the PMF model and confirmed by spatial interpolation and MixSIAR, with contributions ranked as follows: industrial wastewater discharge and slag runoff from the smelter site (48.9 %) > natural geogenic inputs from soil parent materials (26.7 %) > atmospheric deposition of dust particles from smelting operations (24.5 %). Among the identified sources, smelter runoff posed the most significant risk, accounting for 97.9 % of the non-carcinogenic risk (NCR) and 59.9 % of the carcinogenic risk (CR). Runoff also drove NCR and CR exceedances at 7.8 % and 4.7 % of sites near the smelter, respectively. However, atmospheric deposition from smelting emissions affected soils across a larger 0.8 km radius. Although it posed lower risks, contributing just 1.1 % to NCR and 22.6 % to CR due to the limited elevation of toxic elements, deposition reached more distant soils. Spatial interpolation and distance-based regression delineated high NCR and CR exposure hotspots within 1.4 km for runoff and 0.8 km for deposition, with exponentially diminishing risks at further distances. These findings highlight the need for pathway-specific interventions that prioritize localized wastewater containment and drainage controls near the smelter while implementing broader regional air pollution mitigation measures.

Spatial distribution characteristics and source apportionment of heavy metal(loid)s in park dust in the Mianyang urban area, China

Park dust is a carrier of heavy metal pollutants and could potentially harm the health of urban residents. The concentrations of 10 heavy metal(loid)s (HMs) in park dust from the Mianyang urban area were analysed via X-ray fluorescence spectrometry. Based on ArcGIS spatial analysis, Spearman correlation analysis, spatial autocorrelation analysis, and the positive matrix factorization (PMF) model, the spatial distribution and sources of HMs in park dust were studied. The average contents of Zn, Co, Cu, Cr, Pb, and Ba in park dust were 185.0, 33.7, 38.7, 178.7, 51.0, and 662.1 mg/kg, respectively, which are higher than the reference values. The 10 HMs exhibited obvious spatial distribution and local spatial agglomeration patterns. High concentrations of As and Pb were primarily concentrated in the eastern part of the Mianyang urban area. High concentrations of Zn, Cr, and Cu were largely distributed in parks near the Changjiang River and Fujiang River. A high concentration of Co was concentrated in the northern region. The high-value areas of Mn, Ba, V, and Ni occurred far from the city centre and were located in the southwestern region. We found that Pb and As primarily originated from mixed traffic and natural sources; Zn, Cr, and Cu mainly originated from industrial activities; Co largely originated from building sources; and Ba, Ni, Mn, and V were mostly derived from natural sources. Mixed, industrial, building, and natural sources accounted for 24.5%, 24.8%, 24.7%, and 26.0%, respectively, of the HM sources. Co, Cu, Cr, and Zn in the Mianyang urban area were obviously influenced by human activities and should receive close attention.

Ecological pollution features and health risk exposure to heavy metals via street dust and topsoil from Nkpor and Onitsha in Anambra, Nigeria

The manuscript presents the investigation results on the pollution and risk of metal mines, and it is considered an important report on environmental pollution near mines in Nigeria, with archival value. The research involved soil sampling and heavy metal analysis for about 12 months in three metal mines. Based on these results, the paper provides information on pollution levels and hazards using well-known methods like pollution and ecological risk indexes. The increasing population in urban communities attracted by various industrial, economic and social activities causes contamination of atmospheric environment that can affect human health. We investigated heavy metal distributions, correlation coefficient among elements, ecological indices and probable health risk assessment in street dust and topsoil from Nkpor and Onitsha urban suburb, Nigeria. The mean concentration of heavy metals in car dust from Onitsha and Nkpor suburb follows thus: Fe > Mn > Cu > As > Pb > Ni > Cr. The decreasing trend of heavy metal in rooftop dust from both area: Fe > Mn > Cu > Pb > As > Ni > Cr whereas metal contents in topsoil were: Fe > Mn > Cu > Pb > Ni > Cr > As for both areas. The degree of pollution indices was characterized by contamination factor (CF), geo-accumulation factor (I-geo), pollution load index (PLI), Nemerow (PN), ecological and potential ecological risk index (ER and PERI) which indicated low pollution in the urban street environment. The results of Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) showed that the estimated heavy metals displayed sources from atmospheric deposition, natural origin and anthropogenic sources. Risk assessment revealed that ingestion of dust and soil was the significant route for heavy metals exposure to the populace followed by inhalation, then dermal contact. Considering all factors, non-cancer risk was more prominent in children than adults and no significant health hazard could be attributed to both aged groups as of the period of study except for As and Ni that needs constant monitoring to avoid exceeding organ damaging threshold limit of 1 × 10-4.

Road dust exposure and human corneal damage in a plateau high geological background provincial capital city: Spatial distribution, sources, bioaccessibility, and cytotoxicity of dust heavy metals

Ocular surface diseases are common in the plateau city, Kunming China, the continued daily exposure to heavy metals in dust may be an important inducement. In this study, the 150 road dust samples from five functional areas in Kunming were collected. The concentrations, distribution, possible sources, and bioaccessibility of heavy metals were analyzed. The adverse effects of dust extracts on human corneal epithelial cells and the underlying mechanisms were also assessed. The concentrations (mg·kg-1) of As (19.1), Cd (2.67), Cr (90.5), Cu (123), Pb (78.4), and Zn (389) in road dust were higher than the soil background, with commercial and residential areas showing the highest pollution. Their bioaccessibility in artificial tears was As (6.59 %) > Cu (5.11 %) > Ni (1.47 %) > Cr (1.17 %) > Mn (0.84 %) > Cd (0.76 %) > Zn (0.50 %) > Pb (0.31 %). The two main sources of heavy metals included tire and mechanical abrasion (24.5 %) and traffic exhaust (21.6 %). All dust extracts induced cytotoxicity, evidenced by stronger inhibition of cell viability, higher production of ROS, and altered mRNA expression of antioxidant enzymes and cell cycle-related genes, with commercial- areas-2 (CA2)-dust extract showing the greatest oxidative damage and cell cycle arrest. Our data may provide new evidence that dust exposure in high geological background cities could trigger human cornea damage.

The main strategies for soil pollution apportionment: A review of the numerical methods

Nowadays, a large number of compounds with different physical and chemical properties have been determined in soil. Environmental behaviors and source identification of pollutants in soil are the foundation of soil pollution control. Identification and quantitative analysis of potential pollution sources are the prerequisites for its prevention and control. Many efforts have made to develop methods for identifying the sources of soil pollutants. These efforts have involved the measurement of source and receptor parameters and the analysis of their relationships via numerical statistics methods. We have comprehensively reviewed the progress made in the development of source apportionment methodologies to date and present our synthesis. The numerical methods, such as spatial geostatistics analysis, receptor models, and machine learning methods are addressed in depth. In most cases, however, the effectiveness of any single approach for source apportionment remains limited. Combining multiple methods to address soil quality problems can reduce uncertainty about the sources of soil pollution. This review also constructively highlights the key strategies of combining mathematical models with the assessment of chemical profiles to provide more accurate source attribution. This review intends to provide a comprehensive summary of source apportionment methodologies to help promote further development.

Radiation exposure and health concerns associated with the environmental geochemistry of relatively higher radioactivity in a fresh water basin

This study was carried out on a negligible anthropogenically impacted Indo-Bangla transboundary river basin (Atrai, Bangladesh) to elicit radionuclides' and elemental distributions. Thirty sediment samples were collected from the Bangladesh portion of the river, and instrumental neutron activation analysis and HPGe γ-Spectrometry techniques were used to determine environmental radionuclides (e.g., 232Th, 226Ra, 40K) and associated elemental concentrations, respectively. Metal concentrations (Sc, V, Fe, Eu, Sm, La, Yb, Ce, Lu, Ta, Hf) were determined to comprehend the genesis of greater radioactivity. Recognizing the mean concentration of absorbed gamma dose rate (158.7 hGyh-1) is 2.88-times more than the recommended value (55 hGyh-1) that describes ionizing radiation concerns regarding potential health risks to the surrounding communities and the houses of native residents, which are constructed by Atrai river sediment. This work will assist relevant policymakers in exploring valuable heavy minerals and provide information regarding radiological health risks from a fluvial system.

A review on toxic metal pollution and source-oriented risk apportionment in road dust of a highly polluted megacity in Bangladesh

Heavy metal enrichment in road dust has resulted from intensive anthropogenic activity, particularly urbanization, industrial activities and traffic emission, posing a hazard to urban ecosystems and human health. To promote optimal road dust management in urban environments, it is necessary to assess the possible ecological and health impact of toxic elements in road dust. In a heavily populated megacity like Dhaka, Bangladesh, large-scale risk assessments of contamination in road dust with heavy metals are limited. The present study aims at presenting a concentration of twenty-five metals in road dust (Na, K, Cs, Rb, Mg, Ca, Sr, Ba, Al, Zn, Cd, Pb, As, Sb, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zr and W) in Dhaka megacity. We used a critical source-based positive matrix factorization model, source-oriented potential ecological risks and health risks. Out of the studied metals, Na, Ca, Zn, Cd, Cu, Zr and W exceeded the shale value. About 73%, 48%, 29% and 32% of sampling sites showed a higher level of pollution based on PLI, NIPI, PER and NIRI, respectively. PMF model identified that Cd (85.3%), Cr (62.4%), Ni (58.2%), Zn (81.8%) and Mn (65.9%) in road dust were primarily attributed to traffic emission, fuel combustion, metal processing, transport sources and natural sources, respectively. Fuel combustion and metal processing posed considerable and high risks based on modified potential ecological risk and NIRI. Based on health hazards, traffic emission posed a high cancer risk in adult males (29%), whereas transport sources contributed to females (21%) and children (23%).

Heavy metal content and health risk assessment of atmospheric particles in China: A meta-analysis

In recent decades, China has devoted significant attention to the heavy metals pollution in particulate matter. However, the majority of studies have only focused on the field monitoring in relatively remote areas, which may not be representative of air quality across the country. This study reevaluated the characteristics, temporal and spatial changes, and health concerns associated with heavy metal pollution in atmospheric particulates on a national scale by coupling Meta-analysis and Monte Carlo simulation analysis. In terms of spatial distribution, the heavy metals pollution levels in the northern coast and northeastern regions are relatively high, whereas it is low along the middle Yellow River, middle Yangtze River, as well as Southwest. With the exception of Cu, the distribution of all elements in PM2.5 steadily decreased over time Moreover, PM10 and PM2.5 performed similar where Cd and Ni both first increased followed by a decline while, Cr displayed a decrease before it showed an increment. And since the implementation of prevention and control policies about the atmospheric release, the focus of industrial emission has gradually shifted from energy production and processing to living products manufacturing. Moreover, the carcinogenic risk was shown to be Cr > As, Pb > Ni, Cd, while the non-carcinogenic risk was as follows: As, Ni > Cr, Cd. Among all contaminants, Cd, As, and Cr in PM2.5 and PM10 exceeded the WHO standard in the cities with worst air quality. It was observed that As posed the largest non-carcinogenic risk to adults while, Cr caused the most carcinogenic risk to adults and children, where the carcinogenic risk of children remains higher than that of adults. Therefore, the findings of this study may offer data support to the China's heavy metal pollution standards in airborne particles and offer theoretical data support for pollution management.

Contamination levels and source apportionment of potentially toxic elements in size-fractionated road dust of Moscow

The distributions of potentially toxic elements (PTEs) among PM₁, PM_1-10, PM_10-50, and PM_50-1000 fractions of the road dust were studied in the western and eastern parts of Moscow, impacted mainly by the road transport and the industrial sector, respectively. The partitioning of PTEs in road dust can provide more precise information on pollution sources and its further interpretation regarding human health risks. The concentrations of PTEs were analyzed by mass and atomic emission inductively coupled plasma spectrometry. Differences in the results between the western and eastern parts of the city were caused by the dissimilarity between traffic and industrial emissions. The source apportionment of the PTEs was carried out using absolute principal component analysis with multiple linear regressions (PCA/APCS-MLR). The contribution from anthropogenic sources was significant to PM₁ and PM_1-10 particles. In coarser fractions (PM_10-50, PM_50-1000), it decreased due to the input with the wind-induced resuspension of soil and rock particles. In the eastern part of the city, the accumulation of PTEs (especially Mo, Sb, Cd, Sn, Bi, Co, and As) is the most active in PM_1-10, while in the western part, it is most pronounced in PM₁ (especially Pb, Cu, Cr, and W) which is associated with differences in the size of particles coming from traffic and industrial sources. In the eastern part of Moscow, in comparison with the western part, the contribution from industrial sources to the accumulation of PTEs in all particle size fractions was higher by 10-30%. In the western part of Moscow, the finest particles PM₁ and PM_1-10 demonstrate the trend of rising pollution levels with the increase in road size, while in the eastern part of the city, only coarse particles PM_50-1000 show the same trend. In the fractions PM₁ and PM_1-10 of road dust, a significant contribution was made by anthropogenic sources; however, its role decreased in the coarse fractions-PM_10-50 and especially in PM_50-1000- due to the influence of roadside soils and their parent material.

Pollution characteristics and health risks of heavy metals in road dust in Ma'anshan, China

Road dust contains various heavy metals, which are re-suspension in the air under the action of wind and other external forces, threatening people's health all the time. Road dust was collected in the industrial heavy traffic area (IHT), non-industrial heavy traffic area (HT), urban area (UA), and study recreation area (SR) of Ma'anshan. The pollution degree of heavy metals in the four areas was calculated and demonstrated IHT > HT > UA > SR. In addition to the Ni (24.24 mg kg^-1)metals, the metals concentrations of Cr (74.14 mg kg^-1), Cu (91.8 mg kg^-1), Zn (393.03 mg kg^-1), Cd (9.93 mg kg^-1), and Pb (72.85 mg kg^-1) were all higher than the local soil background values. Cu comes from traffic emissions, Pb, Cd, and Zn mainly come from industrial emissions, as well as traffic emissions. While Cr and Ni mainly come from industrial emissions and local soil re-suspension. The non-carcinogenic risk of each heavy metal to children is 10 times higher than that of adults. Among them, the non-carcinogenic risk of Cr, Cd, and Pb to children is close to 1, so great attention should be paid to it. According to the study of enrichment factor (EF) and geo-accumulation index (I_geo), Cd is extremely polluted and it is imperative to reduce Cd pollution.

A comprehensive study of potentially toxic element contamination and source quantitative assessment by positive matrix factorization model: risk from the fine road dust of Chehe mining area, China

Extreme mining activities can risk human life and the environment via potentially toxic elements (PTEs) in road dust, thus making their quantification and assessment unavoidable. For this purpose, we collected 50 fine road dust samples from the Chehe mining area, China, to quantify the level of contamination and ecological and health risks of PTEs comprising As, Cd, Co, Cr, Cu, Mn, Ni, Pb, Sb, and Zn, and their quantitative source apportionment using the positive matrix factorization model (PMF). Results indicated that the average values of Cd, Sb, As, Zn, Pb, and Cu in road dust were 1555.21, 586.78, 429.68, 429.43, 72.88, and 26.61 times higher than their background values. Pollution indices of PTEs revealed a strong level of contamination by Cd, Sb, As, Zn, and Pb, which were extremely polluted in the study area. The average values of the Nemerow integrated risk index (NIRI) and potential ecological risk index (RI) were 104.09 and 86.49 times the highest risk limit, respectively, which are extremely high ecological risks. Based on PMF for quantitative source identification, mining activities and fuel combustion were the main sources of PTEs in road dust contributing 57.25% and 35.95%, respectively. Furthermore, the health risk assessment indicated that Sb, As, Cr, Cd, and Pb in the Chehe road dust could lead to significantly serious carcinogenic and non-carcinogenic risks to both children and adults. The results of this study could be used to opt for strategies to mitigate the ecological and human health risk in the mining area of Hechi, China.

Morphological characterization, hazardous metal contamination, source identification, and health risk assessment of the fine road dust from Dachang mining area, China

Dachang mining area in China is known as "paradise for mineralogists" due to its most reserves of Sn, Sb, Pb, and Zn non-ferrous metal resources; thus, its evaluation for heavy metal assessment and consequent health risk is unavoidable. Sixty road dust samples were collected from study area to explore pollution level, ecological, and health risks from heavy metals and were analyzed by an inductively coupled plasma optical emission spectrometer and atomic fluorescence spectrometer. The results showed that average concentration of all the heavy metals in road dust in both mining and residential areas were remarkable higher than its corresponding background values, the former being more severe, except for Cr and Co. The morphological investigation showed that most of the particles were much less than 100 μm illustrating fine part of the road dust samples. Based on integrated pollution indices, Cd, Sb, As, Zn, and Pb were extremely contaminated and exceeded hundred times of the maximum risk value. The health risk assessment revealed substantially higher carcinogenic and non-carcinogenic risks to children and adult. Highest non-carcinogenic risk resulted from arsenic in mining and residential area with HQ_ing of 644.56 and 267.94 respectively (standard HQ ≥ 1) while carcinogenic risk to children (1.94E + 00) which greatly exceeded from the threshold value of (1.0E-4). Sb, Cd, and Pb also posed carcinogenic and non-carcinogenic risk in road dust which is caused by excessive mining activities and heavy vehicle movement in the study area.

Contamination, Source Identification, Ecological and Human Health Risks Assessment of Potentially Toxic-Elements in Soils of Typical Rare-Earth Mining Areas

The mining and leaching processes of rare-earth mines can include the entry of potentially toxic elements (PTEs) into the environment, causing ecological risks and endangering human health. However, the identification of ecological risks and sources of PTEs in rare-earth mining areas is less comprehensive. Hence, we determine the PTE (Co, Cr, Cu, Mn, Ni, Pb, Zn, V) content in soils around rare-earth mining areas in the south and analyze the ecological health risks, distribution characteristics, and sources of PTEs in the study area using various indices and models. The results showed that the average concentrations of Co, Mn, Ni, Pb and Zn were higher than the soil background values, with a maximum of 1.62 times. The spatial distribution of PTEs was not homogeneous and the hot spots were mostly located near roads and mining areas. The ecological risk index and the non-carcinogenic index showed that the contribution was mainly from Co, Pb, and Cr, which accounted for more than 90%. Correlation analysis and PMF models indicated that eight PTEs were positively correlated, and rare-earth mining operations (concentration of 22.85%) may have caused Pb and Cu enrichment in soils in the area, while other anthropogenic sources of pollution were industrial emissions and agricultural pollution. The results of the study can provide a scientific basis for environmental-pollution assessment and prevention in rare-earth mining cities.

Reconciling the geogenic and non-crustal origins of elements in an Indo-Bangla transboundary river, Atrai: Pollution status, sediment quality, and preliminary risk assessment

This study has been conducted on an anthropogenically less influenced transboundary river (Atrai: Indo-Bangladesh) to comprehend the inherent geochemistry and identify potential elemental sources. In doing so, across the Bangladeshi portion, 30 river-bed samples were culled and studied by neutron activation analysis to quantify the abundances of 15 geochemically and toxicologically significant elementals (Na, Al, K, Ti, Cr, Mn, Co, Zn, As, Rb, Sb, Cs, Ba, Th, and U). The results revealed that the mean concentrations (μg/g) of Rb (154.6), Cs (7.53), Th (20.90), and U (4.88) were 1.5-2.0 times higher than crustal values. Besides, geo-environmental indices revealed 'uncontaminated to moderately contaminated' pollution status with minor enrichment or contamination for Rb, Th, Sb, U, and Cs, relatively concentrated in the mid-to-downstream zone possessed geogenic and non-crustal origins. The positive matrix factorization and other statistical approaches revealed predominant geogenic enrichment of Na, K, Al, Ti, Zn, Cs, Rb, As, Th, and U from differential mineralogical compositions via weathering, elemental fractionations, and biogeochemical mobilization. Contrariwise, several anthropogenic sources (for Cr, Sb, Co, Mn, Th) were also ascertained in the vicinity of Atari River. However, sediment characterization based on SQG threshold values manifested that Cr and Mn possess rare biological effects on local aquatic organisms. Nevertheless, SQGs-based and ecological risk indices invoked minor to no potential ecotoxicological intimidations for the considered metal(oid)s (Cr, Mn, Co, Zn, As, and Sb). Hence, this study manifested the usefulness of a less anthropogenically affected river to reckon geogenic and non-crustal elemental origins in the compounded riverine sediment.

Bioaccessibility, source and human health risk of Pb, Cd, Cu and Zn in windowsill dusts from an area affected by long-term Pb smelting

Non-ferrous metal smelting results in heterogenous spatial distribution of potentially toxic metals (PTM) near smelters. In this work, windowsill dusts were collected from smelting (SA) and urban (UJ) sub-areas of Jiyuan (a city affected by >70 years of Pb smelting) to investigate PTM source and bioaccessibility. The <10 μm fraction of dusts were analyzed for total and bioaccessible Pb, Cd, Cu and Zn concentrations; bioaccessibility was analyzed by a three-stage assay (i.e., lung phase, gastric phase and gastrointestinal phase) using artificial lysosomal fluid (ALF, L phase) followed by simulated gastric and gastrointestinal fluids (G and GI phases). This assay mimicked the movement of particles phagocytosed by alveolar macrophages in the respiratory system, then transported up the oropharynx and subsequently swallowed and transported into the digestive system. Zinc had greater bioaccessible concentrations in L and GI phases than other metals, and the mean L phase bioaccessible PTM concentrations in SA were greater than in UJ. The mean L + GI phase bioaccessible concentrations of Pb, Cd, Cu and Zn in SA were 280, 79, 124 and 1458 mg kg^-1, while those in UJ were 215, 54, 116 and 598 mg kg^-1, respectively. The L phase extracted 87.7 to 98.8 % of PTM within the L + GI assay. Lead had a lower L + GI bioaccessibility than Cd, Cu and Zn (70-76 % vs. 82-92 %). Higher tolerable Cd carcinogenic risks based on bioaccessibility were found in SA sub-area than in UJ while no carcinogenic or non-carcinogenic risk was found for other metals. Lead isotopic ratios indicated that both Pb ore and smelting bottom ash contributed to dust Pb accumulation in SA, while coal burning, lead ore, Pb smelting bottom ash and diesel engine exhaust contributed to dust Pb accumulation in UJ. Overall, results indicated heterogenous distribution of PTM source and bioaccessibility in the vicinity of Pb smelters.

Copper isotope ratios allowed for quantifying the contribution of coal mining and combustion to total soil copper concentrations in China

The most prominent source of Cu contamination in soils is metal mining and processing, partly since the Middle Age. However, coal mining and combustion can also cause (some) Cu contamination. We studied the distribution of Cu concentrations and isotope ratios in soils of the Huaibei coal mining area. The contribution of the coal mining and combustion to total Cu concentrations in soil was determined with a two-end-member mixing model based on the distinct δ⁶⁵Cu values of the Cu emitted from coal mining and combustion and in native soil. The mean Cu concentration of 75 mg kg^-1 exceeded the local soil background value (round to 22.13 mg kg^-1). The similar δ⁶⁵Cu value of grass near the coal mining and combustion operation as in gangue and flying ash indicated a superficial Cu contamination. Mining input was the dominant source of Cu in the contaminated soils, contributing up to 95% and on average 72% of the total Cu in the topsoils. The mining-derived Cu was leached to a depth of 65 cm, where still 29% of the Cu could be attributed to the mining emissions. Grasses showed lower δ⁶⁵Cu values than the topsoils, because of the preferential uptake of light Cu isotopes. However, the Δ⁶⁵Cu_grass-soil was lower in the contaminated than the uncontaminated area because of superficial adsorption of isotopically heavy Cu from the mining emissions. Overall, in this study the distinct δ⁶⁵Cu values of the mining-derived Cu emissions and the native soil allowed for the quantification of the mining-derived Cu and had already reached the subsoil and contaminated the grass by superficial adsorption in only 60 years of mining operation.

Contamination and source-specific risk analysis of soil heavy metals in a typical coal industrial city, central China

Source-specific risk apportionment for soil heavy metals (HMs) is crucial for pollution mitigation and risk control in coal-mining areas. The ecological and human health risks resulting from different sources were evaluated through an integrated method that combines risk assessments with positive matrix factorization (PMF) model. Thirty soil samples were collected from a typical coal-mining city in central China and analyzed for six HMs (Cu, Ni, Pb, Cd, As, and Hg). The results indicate that surface soil in the study area suffered from moderate HMs pollution, especially pollution by Cd and Hg. Four potential sources of soil HMs were identified and quantified in the study area, including natural source (27.7%), traffic emissions (33.4%), agricultural practices (16.2%), and industrial activities (22.7%). The ecological risk of the study area was at moderate level, and the leading contributions in urban and suburban areas were from industrial activities and agricultural practices, respectively. The non-carcinogenic risks for adults and children were lower than the risk threshold, while the carcinogenic risks ranged between 1E-06 and 1E-04, suggesting that carcinogenic risks and hazards to human health should not be neglected. Traffic emissions and natural sources mainly contributed to the non-carcinogenic and carcinogenic risks, due to the strong non-carcinogenicity and carcinogenicity of As and Ni. These findings highlight the ecological and health risks linked to potential sources of soil HMs contamination and provide valuable information on the reduction of corresponding risks for local environmental managers.

Quantitative source apportionment and associated driving factor identification for soil potential toxicity elements via combining receptor models, SOM, and geo-detector method

Quantitative source apportionment of soil potential toxicity elements (PTEs) and associated driving factor identification are critical for prevention and control of soil PTEs. In this study, 421 soil samples from a typical area in southeastern Yunnan Province of China were collected to evaluate the pollution level of soil PTE using pollution factors, pollution load index, and enrichment factors. Positive matrix factorization (PMF), absolute principal component score/multiple line regression (APCS/MLR), edge analysis (UNMIX) and self-organizing map (SOM) were applied for source apportionment of soil PTEs. The geo-detector method (GDM) was used to identify the driving factor to PTE pollution sources, which assisted in source interpretation derived from receptor models. The results showed that the geometric mean of As, Cd, Cu, Cr, Ni, Pb, and Zn were 94.94, 1.02, 108.6, 75.40, 57.14, 160.2, and 200.3 mg/kg, which were significantly higher than their corresponding background values (P < 0.00). Particularly, As and Cd were 8.71 and 12.75 times higher than their corresponding background values, respectively. SOM yielded four clusters of soil PTEs: AsCd, PbZn, CrNi, and Cu. APCS/MLR was regarded as the preferred receptor model for source apportionment of soil PTEs due to its optimal performance. The results of ACPS/MLR revealed that 36.64% of Pb and 38.30% of Zn were related to traffic emissions, Cr (92.64%) and Ni (82.51%) to natural sources, As (85.83%) and Cd (87.04%) to industrial discharge, and Cu (42.78%) to agricultural activities. Distance to road, lithology, distance to industries, and land utilization were the respective major driving factor influencing these four sources, with the q values of 0.1213, 0.1032, 0.2295 and 0.1137, respectively. Additionally, GDM revealed that nonlinear interactions between anthropogenic and natural factors influencing PTEs sources. Based on these results, comprehensive prevention and control strategies should be considered for pollution prevention and risk controlling.

Zinc in soil reflecting the intensive coal mining activities: Evidence from stable zinc isotopes analysis

In the mining area affected by coal mining activities for a long time, heavy metal Zn pollution poses a serious threat to soil quality and human health, and direct evidence showing the relationship between Zn accumulation mechanism in soils and mining activities is lacking. In this study, the Zn content and isotopes composition (δ⁶⁶Zn) from soil and environmental samples around mining area were determined and analyzed to clarify the Zn characteristics in soil. Moreover, the distribution and source of Zn content in soil of mining area were analyzed by mathematical statistics, correlation analysis and isotope mass mixing model. The results showed that: (1) the Zn content in soil ranged from 95 to 327 mg·kg^-1 (mean: 233 mg·kg^-1), exceeding the control point and the soil background value of Anhui Province; (2) the results of Zn isotope analysis showed that Zn in soil mainly derived from the wind dispersion input of fine particles in gangue and fly ash, followed by the natural weathering of parent material; (3) isotopic mass mixing model can be used to distinguish the contribution of anthropogenic and natural Zn sources. Mining input was the main contribution source of Zn in soil (mean: 67%), followed by natural background (mean: 33%). The employment of Zn isotopes can effectively evaluate the impact of anthropogenic and natural long-term processes on Zn in the soil of the mining area, and provide important information for the formulation of soil metal pollution control measures.

Spatial distribution and source apportionment of heavy metal(loid)s in urban topsoil in Mianyang, Southwest China

Spatial distributions and sources of some commonly concerned heavy metal(loid)s (HMs, As, Ba, Cr, Co, Cu, Ni, Pb, Mn, Zn, and V) in topsoil of Mianyang city, a typical medium-sized emerging industrial city in Southwest China, were determined to explore the influences of anthropogenic activities on the urban environment. The contents of the 10 HMs in 101 topsoil samples were analyzed using an X-ray fluorescence spectrometer, and their sources were analyzed by positive matrix factorization and statistical analysis. The spatial distributions of the HMs and the source contributions were mapped using GIS technology. The results showed that the mean contents of Ba, Cr, Cu, and Zn in the topsoil were significantly higher than their background values. Industrial activities resulted in high contents of Ba, Zn, Cu, and Cr. As, Co, Ni, and V that primarily came from natural sources; Pb, Cr, Cu, and Zn were chiefly derived from a mixed source of industry and traffic; and Ba and Mn primarily originated from industrial sources. Natural sources, mixed sources, and industrial sources contributed 32.6%, 34.4%, and 33.0% of the total HM contents, respectively. Industrial sources and mixed sources of industry and traffic were the main anthropogenic sources of HMs in the urban topsoil and should be the focus of pollution control.

Source-specific ecological risk assessment and quantitative source apportionment of heavy metals in surface sediments of Pearl River Estuary, China

In this study, surface sediments of the Pearl River Estuary were collected from 29 stations and investigated the spatial distribution, pollution level, quantitative source apportionment, and source-specific ecological risk of 10 heavy metals. The mean concentrations followed the order of Mn > Zn > Cr > Cu > Ni > Pb > As > Co > Cd > Hg. In terms of spatial distribution, it showed that the heavy metals were enriched in the inner Pearl River Estuary with 'extremely high' level of Hg, whereas, Cd and Zn posed 'moderate to high' contamination potential. We apportioned four main sources using positive matrix factorization model, in which natural geogenic and industrial manufacturing sources accounted for 36.84% and 27.11% of the total, respectively. However, the source-specific risk assessment suggested that mixed anthropogenic sources were the main contributors, and ecological risks were strongly affected by anthropogenic imports from the surrounding cities.

Source apportionment, ecological, and human health risks of toxic metals in road dust of densely populated capital and connected major highway of Bangladesh

This study investigates pollution levels, source apportionment, ecological, and human health risks associated with toxic metals (Pb, As, Hg, Cr, and Cd) in road dust from the most populated Dhaka city and a connected major highway in Bangladesh. The mean concentration of Pb, Hg, and Cd were 1.3, 29.3, and 13.2 times higher than their corresponding background values with spatially uneven distribution all over the study area. Metal pollution indices, the geo-accumulation index (Igeo), NIPI, and PI, indicated extreme contamination at many sites depending on local environmental factors. The potential ecological risk ([Formula: see text] revealed that 84% and 54% of samples showed the extreme ecological risk for Hg and Cd pollution, respectively. On the other hand, the potential ecological risk index (PERI) and Nemerow integrated risk index (NIRI) showed that most sampling sites suffered high to extreme ecological risk. Source apportionment using positive matrix factorization (PMF) identified coal combustion, and gasoline (50.14%), traffic exhaust (35.26%), and industrial and agriculture activity (14.60%) were the main source of toxic metals of the study area. Non-carcinogenic health risk indicated that adults are more vulnerable than children, and hazard index (HI) of Hg for both age groups and Cd for adults were significantly higher than the safe level. The carcinogenic risk (CR) levels of toxic metals were acceptable (10-6 to 10-4), although the maximum limit of Cr for children and As for adults was close to the unacceptable limit (10-4). Continual exposure to toxic metals through road dust might develop lifetime cancer risk in local inhabitants.

The Contents of Potentially Toxic Elements and Emission Characteristics of PM2.5 in Soil Fugitive Dust around Six Cities of the Yunnan-Guizhou Plateau in China

The contents of potentially toxic elements (V, Cr, Mn, Co, Ni, Cu, Zn, As, Cd, and Pb) and emission characteristics of PM2.5 in soil fugitive dust (SFD) in six Yunnan cities (Baoshan, Kunming, Wenshan, Honghe, Yuxi, and Zhaotong) were investigated in this research. The results showed that the contents of Zn and Pb in PM2.5 of SFD were the highest around Honghe and Yuxi, respectively, while the contents of Mn were the highest in PM2.5 of SFD around the other four cities. The enrichment factor and correlation indicated that the potentially toxic elements’ pollution degrees of PM2.5 of SFD around Kunming, Yuxi, and Honghe were higher than those around the other three cities and that potentially toxic elements were generally affected by metal smelting activities, and in Zhaotong, were affected by coal burning activities, while in Wenshan and Baoshan were less affected by human activities. The total emission of PM2.5 of SFD in the six cities was 7705.49 t in 2018. The total emission factor of PM2.5 of SFD reached the highest level from January to May and the lowest level from July to October. The health risk assessment showed that the potentially toxic elements in PM2.5 of SFD for children in the six cities and for adults in Baoshan, Kunming, Honghe, and Zhaotong had non-carcinogenic risk (non-carcinogenic risk thresholds were greater than 1), and As contributes most to non-carcinogenic risk. The carcinogenic risk value of Cr in PM2.5 of SFD in Kunming and Zhaotong was between 1 × 10−6 and 1 × 10−4, which had a certain carcinogenic risk. More attention should be paid to alleviate health risks posed by particle-bound potentially toxic elements through SFD.

Source-specific risk apportionment and critical risk source identification of antibiotic resistance in Fenhe River basin, China

A comprehensive understanding of the sources and distribution of antibiotic resistance risk is essential for controlling antibiotic pollution and resistance. Based on surface water samples collected from the Fenhe River basin in the flood season, using the positive matrix factorization (PMF) model, the risk quotient (RQ) method and the multiple attribute decision making (MADM) method, the resistance risk and source-specific resistance risk of antibiotics were analyzed in this study. The results showed that sulfonamides (SAs) were the dominant antibiotics with a mean concentration of 118.30 ng/L, whereas tetracyclines (TCs) and macrolides (MLs) had the highest detection frequencies (100%). The significant resistance risk rate of antibiotics in the entire river basin was 48%, but no high risk occurred. The significant resistance risk rate of quinolones (QNs) was the highest (100%), followed by that of MLs and TCs. Owing to human activities, the most serious resistance risk occurred in the midstream of the river basin. The resistance risk was the lowest upstream. The antibiotics were mainly contributed by six sources. Pharmaceutical wastewater was the main source, accounting for 30%, followed by livestock discharge (22%). The resistance risk from the six sources showed clear differences, but none of the sources caused a high risk of antibiotic resistance. Pharmaceutical wastewater poses the greatest risk of antibiotic resistance in the Fenhe River basin and is widely distributed. The second greatest source was livestock discharge, which was mainly concentrated in the upstream and midstream areas. The critical sources upstream, midstream, and downstream were all pharmaceutical wastewater, whereas the sequences of other sources were different because different areas were affected by different human activities. The proposed method might provide an important reference for the identification the key source of antibiotics and management of antibiotic pollution, as well as help for the management of antibiotics in Fenhe and Shanxi Province.

Determination of Heavy Metal Contamination and Pollution Indices of Roadside Dust in Dhaka City, Bangladesh

Urban roadside dust samples from Dhaka City in Bangladesh were collected from a planned residential area (PRA), spontaneous residential area (SRA), commercial area (CA), and urban green area (UGA) in winter and summer to study how season and different urban land-use categories influence the concentrations of heavy metals (Cr, Mn, Co, Ni, Cu, Zn, As, and Pb) and different pollution indices. The dust samples were fractionated into <32 μm particles, extracted by acid digestion followed by estimation of heavy metals, using ICP-MS. Pollution indices were calculated from the metal concentrations, using standard protocols. The concentrations of heavy metals in roadside dust varied significantly (all p < 0.05), due to sampling seasons and the land-use category. Higher concentrations of heavy metals (Cr, Mn, Ni, Cu, Zn, and Pb) were found in the dust sampled during the winter season than in the summer season, except for As and Co. The geo-accumulation index (Igeo) indicated that the commercial area was heavily contaminated with Cu and Zn during the winter season. The contamination factor (CF) was higher for Cu and Zn in the CA, PRA, and SRA of Dhaka City in winter than in the summer season. The enrichment factor (EF) suggested that Mn and Co were the least enriched metals, and significant enrichment was seen for Cu and Zn for all land-use categories, both in summer and winter. A moderate potential ecological risk for Cu was estimated in CA and PRA in the winter season.

A Review of Metal Levels in Urban Dust, Their Methods of Determination, and Risk Assessment

This review gives insights into the levels of metals in urban dust, their determination methods, and risk assessment. Urban dust harbors a number of pollutants, including heavy metals. There are various methods used for the sampling of urban dust for heavy-metal analysis and source-apportionment purposes, with the predominant one being the use of plastic sampling materials to avoid prior contamination. There are also various methods for the determination of metals, which include: atomic absorption spectroscopy (AAS) and inductively coupled plasma-mass spectrometry (ICP-MS), among others. Studies have shown that pollutants in urban dust are mainly derived from industrial activities and coal combustion, whereas traffic emissions are also an important, but not a predominant source of pollution. The varying particle-size distribution of urban dust and its large surface area makes it easier for the deposition and transport of heavy metals. Risk-assessment studies have shown that metals in urban dust could cause such problems as human pulmonary toxicity and reduction of invertebrate populations. The risk levels seem to be higher in children than adults, as some studies have shown. It is therefore important that studies on metals in urban dust should always incorporate risk assessment as one of the main issues.

Predicting Bioaccumulation of Potentially Toxic Element in Soil–Rice Systems Using Multi-Source Data and Machine Learning Methods: A Case Study of an Industrial City in Southeast China

Potentially toxic element (PTE) pollution in farmland soils and crops is a serious cause of concern in China. To analyze the bioaccumulation characteristics of chromium (Cr), zinc (Zn), copper (Cu), and nickel (Ni) in soil-rice systems, 911 pairs of top soil (0–0.2 m) and rice samples were collected from an industrial city in Southeast China. Multiple linear regression (MLR), support vector machines (SVM), random forest (RF), and Cubist were employed to construct models to predict the bioaccumulation coefficient (BAC) of PTEs in soil–rice systems and determine the potential dominators for PTE transfer from soil to rice grains. Cr, Cu, Zn, and Ni contents in soil of the survey region were higher than corresponding background contents in China. The mean Ni content of rice grains exceeded the national permissible limit, whereas the concentrations of Cr, Cu, and Zn were lower than their thresholds. The BAC of PTEs kept the sequence of Zn (0.219) > Cu (0.093) > Ni (0.032) > Cr (0.018). Of the four algorithms employed to estimate the bioaccumulation of Cr, Cu, Zn, and Ni in soil–rice systems, RF exhibited the best performance, with coefficient of determination (R2) ranging from 0.58 to 0.79 and root mean square error (RMSE) ranging from 0.03 to 0.04 mg kg−1. Total PTE concentration in soil, cation exchange capacity (CEC), and annual average precipitation were identified as top 3 dominators influencing PTE transfer from soil to rice grains. This study confirmed the feasibility and advantages of machine learning methods especially RF for estimating PTE accumulation in soil–rice systems, when compared with traditional statistical methods, such as MLR. Our study provides new tools for analyzing the transfer of PTEs from soil to rice, and can help decision-makers in developing more efficient policies for regulating PTE pollution in soil and crops, and reducing the corresponding health risks.