박스모델 기반 자료동화법을 활용한 오존 생성 전구물질 유입량의 시간해상도 분석

Accurate representation of precursor emissions and photochemical processes is essential for improving ozone (O₃) simulations in air quality models. In this study, we incorporated a wide range of observational constraints and photolysis rates derived from model calculations that reflect real atmospheric conditions to enhance O3 simulations using a box model. However, despite the inclusion of these comprehensive observational inputs, the model exhibited limitations in reproducing observed O3 concentrations accurately. To address this issue, a nudging-based data assimilation technique was applied to optimize precursor influx - including both transport and emissions - while maintaining the physical and chemical consistency of the model for subsequent applications, such as sensitivity analysis. When using the original emission inventory, nitrogen oxides (NOx), toluene, formaldehyde (HCHO), and other precursors were overestimated, whereas the carbon monoxide (CO), sulfur dioxide (SO2), and isoprene were underestimated. However, the assimilated influxes reduced these discrepancies. Additionally, diurnal variation analysis revealed that the assimilated influxes better captured time-dependent emission characteristics and statistical analysis indicated that the correlation coefficient between the observed and modeled results significantly improved, both demonstrating the enhanced capability of the assimilated influxes to capture observed trends. These improvements in precursor representation ultimately led to a more accurate simulation of O3, both in terms of absolute concentrations and temporal variations. The results emphasize the importance of integrating reliable observational constraints and data assimilation techniques in air quality modeling to achieve more reliable predictions of O3 and its precursors, which are crucial for the development of effective air pollution control strategies.