Microplastic Pollution Characteristics and Ecological Risk Assessment in Soils of a Historical Non-Sanitary Landfill and Adjacent Farmland

ObjectiveHistorically non-sanitary landfills continuously release microplastics (MPs) into adjacent soils due to the absence of impermeable lining systems, thereby exacerbating ecological risks at the site and in surrounding environments. This study aims to investigate the distribution characteristics and ecological risks of MPs in a historically non-sanitary landfill and adjacent farmland soils in Jiashan County, Zhejiang Province, East China. The research aims to identify key pollution sources and evaluate the efficacy of multi-method risk assessment frameworks for informing targeted pollution control strategies.MethodFor landfill sites and surrounding farmland topsoil, the distribution characteristics and ecological risks of microplastic pollution were revealed through the combined application of micro-Fourier transform infrared spectroscopy (µ-FTIR), the Pollution Load Index (PLI), Risk Characterization Ratio(RCR), the Multi-characteristics Potential Ecological Risk Index (MPERI), and Monte Carlo simulation.Result(1) Landfill soils exhibited significantly higher MPs abundance (7, 425.5–21, 306.5 items/kg) than farmland soils (199.5–2, 868.5 items/kg). Polyethylene terephthalate (PET) and polyethylene (PE) were the main MP types in both sites (> 70% of total MPs). Small-sized MPs (ConclusionThe landfill acts as a critical source of MPs contamination in adjacent agricultural soils. Discrepancies in risk outcomes across models highlight the necessity of integrating multi-method assessments with probabilistic approaches (e.g., Monte Carlo) to quantify uncertainties. Polymer type is a decisive factor in ecological risk, necessitating prioritized controls for PAN-containing MPs. This empirical-modeling framework provides a scientific basis for hierarchical management of MPs pollution in non-sanitary landfills and surrounding environments, promoting the application of probabilistic risk models in contaminated sites.