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

Bioaccessibility, human health risks, and source apportionment of heavy metals in street dust from coal mining-influenced environments

Road dust samples were collected from 50 locations across four distinct categories (coal mine areas, thermal power plants, commercial zones, and residential neighbourhoods) in Singrauli, Madhya Pradesh, India. These samples were analyzed to evaluate heavy metal contamination, bioaccessibility, human health risks, and contamination sources. The region demonstrated metal(loid) contamination, with elevated concentrations of As (225.8 ± 26.2 mg/kg), Co (31.8 ± 12.7 mg/kg), Cr (206.2 ± 121.2 mg/kg), Cu (120.6 ± 86.4 mg/kg), Mo (9.6 ± 9.4 mg/kg), Ni (91.1 ± 59.1 mg/kg), V (130.1 ± 30.9 mg/kg), and Zn (277.5 ± 65.2 mg/kg) indicating potential environmental and health concerns. A physiologically based extraction test was utilized to assess the bioaccessibility of the metal(loid)s, simulating its potential uptake through the human digestive system. Results revealed high bioaccessibility for Zn, Mn, Co, and Cu in both gastric (< 10%) and intestinal phases (> 10%), highlighting the likelihood of human exposure through ingestion. Health risk assessment, incorporating both non-carcinogenic and carcinogenic risk evaluations, identified Mn, Cr, Fe, and As as posing non-carcinogenic risks, with hazard index values exceeding 1 for both children and adults. Additionally, As, Cr, and Ni were found to present carcinogenic risks, with risk values surpassing the accepted threshold of 10-4, highlighting serious long-term health implications. To identify contamination sources, Positive Matrix Factorization, a statistical model, was employed, which revealed that Factor 4 predominantly contributed to metal(loid) contamination, with Zn and Cu primarily originating from industrial activities such as coal mining, steel production, metal smelting, and transport-related emissions. The results of this study highlight the global relevance of integrating bioaccessibility testing, detailed health risk assessments, and source apportionment modelling to address heavy metal contamination in mining and industrial regions.

Unveiling sources, contamination, and eco-human health implications of potentially toxic metals from urban road dust

To investigate the pollution characteristics, ecological and health risks assessment, and source apportionment of potentially toxic metal(loid)s (PTMs) in urban road dust, 140 dust samples collected from eight renowned roads of city Lahore, Pakistan. The geo-accumulation index (Igeo) and enrichment factor (EF) were used for pollution characteristics, modified ecological risk index used for ecological risk assessment, the USEPA models used for health risk assessment, and multivariate statistical analyses were used for source apportionment of PTMs. The ranges of average concentrations (mgkg-1) in road dust Cd, Hg, Mo, and Pb were 1.91 (CR) to 3.35 (BR), 11.7 (JR) to 29.3 (MuR), 452 (JR) to 1115 (MuR), and 36.9 (MaR) to 110 (BR), respectively, which were several times higher than reference values. The Cd was moderate to highly polluted in road dust with highest Igeo=2.58 from MuR and EF = 26.9 from CR. Whereas Hg and Mo were categorized as extremely polluted PTMs with mean Igeo and EF values beyond the uppermost level (class 5). The road dust collected from mall road (MaR) was polluted with high levels of most of the PTMs among other roads. The adjacent areas of roads were at extreme ecological risks due to Cd, Hg, and Mo pollution. The employed statistical methods proved that PTMs pollution was induced severely by industrial, exhaustive, and non-exhaustive vehicular emissions in road dust. The Hg pollution in road dust was causing potential non-carcinogenic risks in children with HQing and HI higher than 1. No carcinogenic risk was found for both adults and children. The study helps to create awareness about PTMs' pollution and associated health concerns among public.

Environmental assessment of metals in road dust: what do geochemical indices really tell us about pollution?

Pollution of road dust has been studied for decades because of potential health and environmental risks resulting from elevated levels of metals in this medium. In many studies, environmental assessment of road dust pollution has been made with the use of indices that were originally introduced as a tool to assess the quality of air, soils and sediments. Examples of the most popular geochemical indices are the enrichment factor, the contamination factor, the index of geoaccumulation and the pollution index. We calculated these indices using the element concentrations (Ba, Ca, Cu, Fe, K, Mn, Pb, Si, Sr, Ti, Zn and Zr) in three grain fractions of road dust samples (< 2 mm, < 1 mm and < 0.063 mm) collected in the city of Kielce, Poland. The study showed that critical parameters to be considered in data interpretation are the selection of geochemical background value, the use of appropriate reference element and grain size fraction. Application of local geochemical background instead of the Earth's crust composition, grain fraction < 2 mm and appropriate reference elements are recommended for calculation of geochemical indices in road dust samples. This study shows that the use of geochemical indices for environmental assessment of road dust pollution should be carefully considered.

Systematic evaluations of receptor models in source apportionment of particulate solids in road deposited sediments: A practical application for tracking heavy metal sources on urban road surfaces

Receptor models have been widely used to identify pollution sources in the urban environment. However, evaluating the accuracy of source apportionment results for road deposited sediments (RDS) using these models has not been the focus of previous studies. This study compared canonical receptor models, i.e., positive matrix factorization (PMF), Unmix, chemical mass balance (CMB) and chemical mass-balance based stochastic approach (SCMD) using six synthetic datasets generated from real-world source profiles, and three error evaluation indicators (ie., relative error (RE), relative prediction error (RPE), and symmetric mean absolute percentage error (SMAPE)) were employed. The SCMD model showed more stable and accurate results, with ranges from 8.48 % - 30.76 %, 16.32-32.34 %, and 7.81-24.55 % of RE, RPE, and SMAPE, respectively. SCMD was then applied for tracking Pb, Zn, Cr, Cu, Ni, and Mn on urban road surfaces in Guangzhou, China. The results showed that vehicle exhaust, tire wear, roadside soil, and brake wear contributed 50.15 %, 41.15 %, 6.84 %, and 1.86 % of the mass of particulate solids, respectively; vehicle exhaust contributed more than half of these six heavy metals, particularly Cr and Ni. These findings provide scientific support for the effective selection of appropriate receptor models for source apportionment in RDS.

Sources and distribution of potentially toxic elements in urban road dust: A comparative insights and risk assessment of two polluted cities

This study provides a comprehensive comparative analysis of seven potentially toxic elements (PTEs) from thirty samples using instrumental neutron activation analysis on the roadside dust samples collected from a south-western city (Khulna) and a highly urban megacity (Dhaka), Bangladesh. The mean concentrations (μg.g-1) of Cr, Mn, Fe, Co, Zn, As, and Sb in the analyzed dust samples were 67.5±33.2, 386±136, 25648±5334, 6.86±1.79, 98±63, 3.02±1.08, and 1.37±1.10, respectively in Khulna city and 66.7±6.9, 547±110, 25150±1723, 8.39±0.65, 125±17, 3.63±0.56, and 0.75±0.28, respectively, in Dhaka city, showing uneven distribution in both cities. PMF modelling and multivariate statistical approaches demonstrated that 65.68% anthropogenic and 34.32% geogenic sources for Khulna city, whereas 64.93% mixed (anthropogenic and geogenic) and 35.07% anthropogenic sources were the main contributors of measured elements in Dhaka city. In both cities, anthropogenic contributions were primarily linked to traffic emissions and industrial activity. Various geo-environmental indicators, including element-specific (Igeo, EF, CF), site-specific (Cd, mCd, PLI, NIPI), and ecological indices (Eri, RI), were used to assess the contamination characteristics of PTEs and contamination levels in both cities were in the following decreasing order: Sb >Zn >Cr >Fe >As >Mn >Co, whereas individual ecological risks were in the following decreasing order: Sb(34.28) >As(6.28) >Co(1.98) >Cr(1.47) >Zn(1.46) >Mn(0.50) in Khulna, and Sb(18.64) >As(7.56) >Co(2.43) >Zn(1.86) >Cr(1.45) >Mn(0.71) in Dhaka. The study site demonstrated lower potential ecological risks, even though non-carcinogenic and carcinogenic risks from various exposure pathways appeared minimal. Notably, children in both urban cities exhibited heightened vulnerability compared to adults.

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.

An integrated overview of metals contamination, source-specific risks investigation in coal mining vicinity soils

Heavy metals in soil are harmful to natural biodiversity and human health, and it is difficult to estimate the effects accurately. To reduce pollution and manage risk in coal-mining regions, it is essential to evaluate risks for heavy metals in soil. The present study reviews the levels of 21 metals (Nb, Zr, Ag, Ni, Na, K, Mg, Rb, Zn, Ca, Sr, As, Cr, Fe, Pb, Cd, Co, Hg, Cu, Mn and Ti) in soils around Barapukuria coal-mining vicinity, Bangladesh which were reported in literature. An integrated approach for risk assessments with the positive matrix factorization (PMF) model, source-oriented ecological and health hazards were applied for the study. The contents of Rb, Ca, Zn, Pb, As, Ti, Mn, Co, Ag, Zr, and Nb were 1.63, 1.10, 1.97, 14.12, 1.20, 3.13, 1.22, 3.05, 3.85, 5.48, and 7.21 times greater than shale value. About 37%, 67%, 12%, and 85% of sampling sites posed higher risks according to the modified contamination factor, Nemerow pollution index, Nemerow integrated risk index, and mean effect range median quotient, respectively. Five probable metal sources were computed, including industrial activities to coal mining (17%), agricultural activities (33%), atmospheric deposition (19%), traffic emission (16%), and natural sources (15%). Modified Nemerow integrated risk index reported that agricultural activities, industrial coal mining activities, and atmospheric deposition showed moderate risk. Health hazards revealed that cancer risk values computed by the PMF-HHR model with identified sources were higher than the standard value (1.0E-04) for children, adult male, and female. Agricultural activities showed higher cancer risks to adult male (39%) and children (32%) whereas traffic emission contributed to female (25%). These findings highlight the ecological and health issues connected to potential sources of metal contamination and provide useful information to policymakers on how to reduce such risks.

Heavy metals contamination, receptor model-based sources identification, sources-specific ecological and health risks in road dust of a highly developed city

The present study quantified Ni, Cu, Cr, Pb, Cd, As, Zn, and Fe levels in road dust collected from a variety of sites in Tangail, Bangladesh. The goal of this study was to use a matrix factorization model to identify the specific origin of these components and to evaluate the ecological and health hazards associated with each potential origin. The inductively coupled plasma mass spectrometry was used to determine the concentrations of Cu, Ni, Cr, Pb, As, Zn, Cd, and Fe. The average concentrations of these elements were found to be 30.77 ± 8.80, 25.17 ± 6.78, 39.49 ± 12.53, 28.74 ± 7.84, 1.90 ± 0.79, 158.30 ± 28.25, 2.42 ± 0.69, and 18,185.53 ± 4215.61 mg/kg, respectively. Compared to the top continental crust, the mean values of Cu, Pb, Zn, and Cd were 1.09, 1.69, 2.36, and 26.88 times higher, respectively. According to the Nemerow integrated pollution index (NIPI), pollution load index (PLI), Nemerow integrated risk index (NIRI), and potential ecological risk (PER), 84%, 42%, 30%, and 16% of sampling areas, respectively, which possessed severe contamination. PMF model revealed that Cu (43%), Fe (69.3%), and Cd (69.2%) were mainly released from mixed sources, natural sources, and traffic emission, respectively. Traffic emission posed high and moderate risks for modified NIRI and potential ecological risks. The calculated PMF model-based health hazards indicated that the cancer risk value for traffic emission, natural, and mixed sources had been greater than (1.0E-04), indicating probable cancer risks and that traffic emission posed 38% risk to adult males where 37% for both adult females and children.