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.