Source-oriented probabilistic risk assessment of polycyclic aromatic hydrocarbons in coal-mining streams: Coupling...

Mineral exploitation and energy consumption contribute to significant environmental pollution, threatening ecological stability and human health, especially in large coal mining areas. To evaluate environmental risks in the Yulin coal mining area, a study was carried out on polycyclic aromatic hydrocarbons (PAHs) in a representative river across different seasons. Multivariate statistical analysis and a positive matrix factorization (PMF) model were employed for source identification and quantitative allocation. To assess ecological-health risks linked to PAH concentrations and source orientation, an integrated methodology was applied, combining Monte Carlo simulation (MCS), PMF, and risk assessment models. Among all PAHs, BaA, BaP, BbF, InP, and DahA exhibited elevated concentrations, surpassing USEPA threshold values at many sampling sites. Seasonal variations were observed, with ∑PAH concentrations averaging 185.11 and 128.22 ng∙L^-1 in the dry and wet seasons, respectively. PAHs in Kuye River were primarily influenced by coking/petroleum, coal combustion, traffic emissions, and fuel-wood burning, contributing 37.39%, 34.78%, 14.40%, and 13.44%, respectively, to total concentrations. Ecological risks associated with these source-specific PAHs were classified as moderate, mainly attributed to BaA derived from coal combustion sources. Non-carcinogenic risks for all population groups were within safe limits based on the USEPA hazard quotient threshold. In contrast, under conservative exposure assumptions, the average total carcinogenic risks for all six population groups associated with source-specific PAHs exceeded the USEPA acceptable risk level, with a relatively high probability of risk exceedance. Females aged 2 -15 years showed the highest estimated risks, and dermal exposure was estimated as the primary pathway. In addition, traffic emission sources and BaP were inferred as potential priorities for controlling carcinogenic risks. These results are screening-level estimates based on default parameters and require validation using local data. The findings highlight the importance of probabilistic risk assessment methods based on specific pollution sources and provide a scientific basis for comprehensive PAH risk assessment and prevention strategies in coal mining areas.