Airborne estimation of SO2 emissions rates from a coal-fired power plant using two top-down methods: A mass balance model and Gaussian footprint approach

In this study, two top-down methods—mass balance and Gaussian footprint—were used to determine SO₂ emissions rates via three airborne sampling studies over Korea's largest coal power plant in October 2019 and 2020. During the first two flights in October 2019, mass balance approaches significantly underestimated the SO₂ emissions rates by 75 % and 28 %, respectively, as obtained from the real-time stack monitoring system. Notably, this large discrepancy accounted for the insufficient number of transects altitudes and high levels of background SO₂ along the upwind side. Alternatively, the estimated SO₂ emissions rates of the third flight (October 2020) displayed a difference of <10 % from rea-time monitoring data (630 vs. 690 kg·hr⁻¹), owing to the enhanced vertical resolution with increased transects and lower background SO₂ levels. In contrast to the mass balance method, Gaussian footprints offered significantly improved accuracy (relative error: 41 %, 32 %, and 2 % for Flights 1, 2, and 3, respectively). This relatively good performance was attributed to prior emissions knowledge via the Clean Air Policy Support System (CAPSS) emissions inventory and its unique ability to accurately estimate stack-level SO₂ emissions rates. Theoretically, the Gaussian footprint was less prone to sparse transects and upwind background levels. However, it can be substantially influenced by atmospheric stability and consequently by effective stack heights and dispersion parameters; basically, all factors with minimal-to-no influence on the mass balance approach. Conversely, the mass balance method was the only plausible approach to estimate unidentified source emissions rates when well-defined prior emission information was unknown. Here, the footprint approach supplemented the mass balance method when the emission inventories were known, and employing both strategies approaches greatly enhanced the integrity of top-down emissions inventories from the power plant sources, thus, supporting their potential for ensuring operational compliance with SO₂ emissions regulation.