Atmospheric Dispersion Assessment of Fine Particulate Matter Precursors from Landfills Using the Gaussian Plume Model

Landfills emit a variety of pollutants in both gaseous and liquid phases during the final disposal of waste. Among the gaseous pollutants, hydrogen sulfide (H₂S), primarily generated during anaerobic decomposition, oxidizes in the atmosphere to form sulfur oxides (SO_x) and contributes as a precursor to particulate matter (PM_2.5) formation. While hydrogen sulfide (H₂S) can affect local air quality during its atmospheric transport and oxidation, there is a lack of research on the quantitative evaluation of the atmospheric movement and oxidation processes of hydrogen sulfide (H₂S) emitted from landfills.

Thus, this study aims to predict the emission of hydrogen sulfide (H₂S) through elemental analysis of landfill waste, calculate the conversion to sulfur oxides (SO_x), and then assess the impact on local air quality by modeling the diffusion of sulfur oxides (SO_x) using the Gaussian plume model.

The sulfur (S) content in waste samples was measured using an elemental analyzer (vario-MARCO), and the potential for hydrogen sulfide (H₂S) generation was calculated based on these measurements. Using chemical formulas, the amount of sulfur converted into sulfur oxides (SO_x) was estimated. The horizontal and vertical diffusion coefficients (σy, σz) of the converted sulfur oxides (SO_x) were determined using the Pasquill-Gifford empirical formula. The diffusion of sulfur oxides (SO_x) was then modeled using the Gaussian plume model in Python, up to a distance of 1 km from the emission source.

By utilizing the Gaussian plume model, this study evaluates the conversion and diffusion of hydrogen sulfide from landfills into sulfur oxides and their impact on local air quality. The findings can provide a basis for landfill emission management and the formulation of air pollution reduction policies. Future research should verify the accuracy of the model by comparing the results with real-time air concentration data.

 

Acknowledgment

"This research was supported by Particulate Matter Management Specialized Graduate Program through the Korea Environmental Industry & Technology Institute(KEITI) funded by the Ministry of Environment(MOE)"