Landfills are city infrastructures used for the treatment of municipal solid waste (MSW) in China. However, due to technical failure and/or management problem most of them are facing serious secondary pollution such as groundwater contamination and odor nuisance. The latter is the main reason causing a growing number of public complaints. Atmospheric dispersion models are routinely adopted for odor impact assessment, but these models provide deterministic predictions only. To determine the potential odorant paths and treat the uncertainty of odor pollution, Monte Carlo simulation coupled with an odor dispersion model was proposed and named Monte Carlo-dispersion simulation method (MCDSM). By introducing a series of random values of error components in the dispersion model, MCDSM can produce probabilistic odor impact results. Values of these variances were randomly selected according to their probability density functions (PDFs) due to the imprecise knowledge of the meteorological and emission conditions. After running the odor dispersion model for numerous times, the randomization produces a set of possible results that closely resembles the expected behavior of the odorants.

This study applied MCDSM to estimate the odor impact of methyl mercaptan (CH₃SH) on an MSW landfill in Beijing, China. The PDF of the CH₃SH emission rate was derived from the field data. The uncertainty of odor impact was analyzed statistically, and the results were summarized using the probability of odor exceedance (POE). A POE map of CH₃SH was plotted for a particular interest, in which the north downwind direction was the most polluted area. MCDSM provides a scientific approach for the assessment of odor pollution from individual odorant, which can benefit the formulation of standard for odor impact assessment in landfill sites.