Prediction and uncertainty analysis of pollution flux of typical chlorinated hydrocarbon contaminated sites

Accurate assessment of the pollution flux and its uncertainty in the plume boundary of NAPL dissolved phase pollution in organic contaminated sites is very important for site risk assessment and decision management. Due to the complexity of underground water flow field, the directions of dissolved pollution plume diffusion of multiple pollution sources are not completely consistent. Traditional numerical models require a lot of data, and usually, the site data is difficult to meet the needs, while current commonly used analytical models do not consider the complexity of groundwater flow field and the simultaneous existence of multiple pollution sources. To solve this issue, this study utilized soil and groundwater monitoring data from a typical chlorinated hydrocarbon polluted site in Changzhou, applying an upscale analytical model integrated with the flow function and coordinate transformation method. The maximum likelihood estimation inversion method was employed to identify pollution source areas, estimate structural parameters, groundwater flow rates, and equivalent low-permeability medium thickness, and predict pollution flux at the site boundary. The uncertainty is evaluated based on linearized uncertainty transfer method. The inversion results show that considering a complex flow field rather than assuming single-direction flow significantly reduces parameter uncertainty while improving agreement between simulated and observed values. The pollution situation at the site is still serious, and the pollution scope has exceeded the field limit. The pollution plume needs to be controlled and repaired in time. Under natural attenuation conditions, the simulation results show that After model correction, the uncertainty of predictions remains minimal, with the 95% confidence interval changed from (73.66±0.71) g/d in 2023 to (66.77±0.87) g/d in 2027. The results of site pollution flux prediction provide the scientific basis for risk assessment and restoration of the site.