Inverse Modeling of Air Pollutant Emissions Using Drone-based Air Monitoring Data

Landfill gas, a major contributor to air pollution, results from anaerobic microbial decomposition of organic matter in waste. Classified as a greenhouse gas, it comprises over 99% methane and carbon dioxide, contributing to approximately 3–4% of annual anthropogenic methane emissions. Landfills also release particulate matter (PM), carbon dioxide, non-methane volatile organic compounds, nitrous oxide, nitrogen oxides, odorous substances, and carbon monoxide.

In Korea, landfill emissions calculations primarily measure surface emissions using a flux chamber directly on the landfill surface or employ the First Order Decay (FOD) method. However, the method of measuring surface emission cannot simultaneously measure emissions that occur irregularly over a large area, and the FOD method also has the problem of making it difficult to accurately calculate the landfill gas generation rate constant (k) that reflects landfill characteristics. To address these issues, a supplementary approach to emission estimation is being introduced. This involves measuring microclimatic conditions and air pollutant concentrations within and around landfills, coupled with the application of atmospheric dispersion modeling techniques. This method, known as inverse modeling, aims to estimate emissions by accounting for irregularly occurring emissions over extensive areas.

This study aims to employ drones to measure air pollutants concentrations occurring irregularly over extensive areas and subsequently perform inverse modeling using atmospheric dispersion modeling. In terms of measurement method, drones have the advantage of being able to obtain data on air pollutants in a short period of time at altitudes and wide ranges that other equipment cannot access. By using Drone-based Air Monitoring, particulate matter, carbon dioxide, methane, Nitrogen Dioxide, Various measurements were made, including volatile organic compound, ozone, and water vapor concentrations. Utilizing the data collected through these measurements, inverse modeling with the CALPUFF model is intended. The CALPUFF model can represent changes in the wind field over time and space through the movement of the puff, and can relatively accurately implement the same unsteady state as the real thing. By using this to calculate emissions by performing ineverse modeling, it is expected that the accuracy of calculating methane gas and fine dust emissions from landfills will be improved.

 

Acknowledgments

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)