Abstract
After a landfill leaks, pollutants gradually accumulate in the strata surrounding the landfill to form a pollution source area, which provides a continuous stream of pollutants to the spreading pollution plume. The identification of the source area is of great significance to assess the scope of the pollution plume and to take corresponding remedial measures. The focus of this study was to construct a numerical model by integrating electrical resistivity tomography (ERT) data, hydrochemical data, and stratigraphic data, which can be used to assess the extent of the contamination from a leaky landfill and to suggest remediation designs. The concentration distribution of the leachate in the source area was obtained based on the underground resistivity distribution and the petrophysical relationship, which was used as the initial concentration in the numerical model. Borehole data and stratigraphic data provided stratigraphic information for the model construction. Hydrochemical data were used to calibrate the numerical model. The numerical model constructed by integrating multi-source data represented the spatial heterogeneity of the stratigraphic information and the geochemical information obtained based on borehole data, and the quantitative evaluation results such as the spatial distribution of pollutants were more accurate. The results showed that the leaking pollutants were collected in the fractured zones around the landfill. The important priority channel for pollutant migration was the densely distributed fault zone in the north and south of the landfill. Affected by hydraulic gradients and fractures, the spread area on the north side of the landfill was much larger than that on the south side. Pollutants moved from southwest to northeast, consistent with groundwater flow. After 20 years of leakage, the maximum diffusion range of the pollutants was estimated to be 1620.36 m. The vertical barrier walls designed based on ERT data and numerical simulation results were expected to block further migration of the pollutants. Combining multi-source data to build a numerical model can provide a valuable basis for environmental impacts and remediation recommendations.
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