Optimizing an airborne mass-balance methodology for accurate emission rate quantification of industrial facilities: A case study of industrial facilities in South Korea.

Accurate estimation of emissions from industrial point sources is crucial in understanding the effectiveness of reduction efforts and establishing reliable emission inventories. In this study, we employ an airborne Chemical Ionization Mass Spectrometry (CIMS) instrument to quantify sulfur dioxide (SO2) emissions from prominent industrial facilities in South Korea, including power plants, a steel mill, and a petrochemical facility. Our analysis utilizes the box mass balance technique to derive SO2 emissions and associated uncertainty. We evaluate the interpolation methods between 2D kriging and 3D radial basis function. The results demonstrate that the total uncertainty of the box mass balance technique ranges from 5 % to 28 %, with an average of 20 %. Mixing ratio ground extrapolation from the lowest altitude of the airborne sampling to the ground emerges as the dominant source of uncertainty, followed by the determination of the boundary layer height. Adequate sampling at multiple altitudes is found to be essential in reducing the overall uncertainty by capturing the full extent of the plume. Furthermore, we assess the uncertainty of the single-height transect mass balance method commonly employed in previous studies. Our findings reveal an average precision of 47 % for this method, with the potential for overestimating emissions by up to 206 %. Samplings at fewer altitudes or with larger altitude gaps increase the risk of under-sampling and elevate method uncertainties. Therefore, this study provides a quantitative basis to evaluate previously airborne observational emission constraints.

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.