PM_2.5 Simulations for the Seoul Metropolitan Area: (II) Estimation of Self-Contributions and Emission-to-PM_2.5 Conversion Rates for Each Source Category

수도권 초미세먼지 농도모사 : (II) 오염원별, 배출물질별 자체 기여도 및 전환율 산정

A set of BFM (Brute Force Method) simulations with the CMAQ (Community Multiscale Air Quality) model were conducted in order to estimate self-contributions and conversion rates of PPM (Primary $PM_{2.5}$), $NO_x$, $SO_2$, $NH_3$, and VOC emissions to $PM_{2.5}$ concentrations over the SMA (Seoul Metropolitan Area). CAPSS (Clean Air Policy Support System) 2013 EI (emissions inventory) from the NIER (National Institute of Environmental Research) was used for the base and sensitivity simulations. SCCs (Source Classification Codes) in the EI were utilized to group the emissions into area, mobile, and point source categories. PPM and $PM_{2.5}$ precursor emissions from each source category were reduced by 50%. In turn, air quality was simulated with CMAQ during January, April, July, and October in 2014 for the BFM runs. In this study, seasonal variations of SMA $PM_{2.5}$ self-sensitivities to PPM, $SO_2$, and $NH_3$ emissions can be observed even when the seasonal emission rates are almost identical. For example, when the mobile PPM emissions from the SMA were 634 TPM (Tons Per Month) and 603 TPM in January and July, self-contributions of the emissions to monthly mean $PM_{2.5}$ were $2.7{\mu}g/m^3$ and $1.3{\mu}g/m^3$ for the months, respectively. Similarly, while $NH_3$ emissions from area sources were 4,169 TPM and 3,951 TPM in January and July, the self-contributions to monthly mean $PM_{2.5}$ for the months were $2.0{\mu}g/m^3$ and $4.4{\mu}g/m^3$, respectively. Meanwhile, emission-to-$PM_{2.5}$ conversion rates of precursors vary among source categories. For instance, the annual mean conversion rates of the SMA mobile, area, and point sources were 19.3, 10.8, and $6.6{\mu}g/m^3/10^6TPY$ for $SO_2$ emissions while those rates for PPM emissions were 268.6, 207.7, and 181.5 (${\mu}g/m^3/10^6TPY$), respectively, over the region. The results demonstrate that SMA $PM_{2.5}$ responses to the same amount of reduction in precursor emissions differ for source categories and in time (e.g. seasons), which is important when the cost-benefit analysis is conducted during air quality improvement planning. On the other hand, annual mean $PM_{2.5}$ sensitivities to the SMA $NO_x$ emissions remains still negative even after a 50% reduction in emission category which implies that more aggressive $NO_x$ reductions are required for the SMA to overcome '$NO_x$ disbenefit' under the base condition.