atmosphere Article A Multiscale Tiered Approach to Quantify Contributions: A Case Study of PM2.5 in South Korea During 2010–2017 Minah Bae 1 , Byeong-Uk Kim 2 , Hyun Cheol Kim 3,4 and Soontae Kim 1,* 1 Department of Environmental and Safety Engineering, Ajou University, Suwon 16499, Korea; [email protected] 2 Georgia Environmental Protection Division, Atlanta, GA 30354, USA; [email protected] 3 Air Resources Laboratory, National Oceanic and Atmospheric Administration, College Park, MD 20740, USA; [email protected] 4 Cooperative Institute for Satellite Earth System Studies, University of Maryland, College Park, MD 20740, USA * Correspondence: [email protected] Received: 17 January 2020; Accepted: 25 January 2020; Published: 27 January 2020 Abstract: We estimated long-term foreign contributions to the particulate matter of 2.5 µm or less in diameter (PM2.5) concentrations in South Korea with a set of air quality simulations. The Weather Research and Forecasting (WRF)-Sparse Matrix Operator Kernel Emissions (SMOKE)-Community Multiscale Air Quality (CMAQ) modeling system was used to simulate the base and sensitivity case after a 50% reduction of foreign emissions. The effects of horizontal modeling grid resolutions (27- and 9-km) was also investigated. For this study, we chose PM2.5 in South Korea during 2010–2017 for the case study and emissions from China as a representative foreign source. The 9-km simulation results show that the 8-year average contribution of the Chinese emissions in 17 provinces ranged from 40–65%, which is ~4% lower than that from the 27-km simulation for the high-tier government segments (particularly prominent in coastal areas). However, for the same comparison for low-tier government segments (i.e., 250 prefectures), the 9-km simulation presented lowered the foreign contribution by up to 10% compared to that from the 27-km simulation. Based on our study results, we recommend using high-resolution modeling results for regional contribution analyses to develop an air quality action plan as the receptor coverage decreases. Keywords: PM2.5; long-range transport; source-receptor relationship; receptor coverage; model horizontal grid resolution 1. Introduction Many cities in South Korea often experience high particulate matter of 2.5 µm or less in diameter (PM2.5) levels that exceed the PM2.5 National Ambient Air Quality Standard [1]. With the rise in public concern about PM2.5 concentration, the South Korean government has developed and implemented various emission reduction measures to lower the PM2.5 concentration in the country [2,3]. When developing an effective air quality management plan, it is essential to understand key factors affecting the PM2.5 concentration, such as the major contributors [4]. Previous studies have attempted to quantify the contributions of foreign and domestic sources to PM2.5 concentrations in Northeast Asian countries [5–7]. Itahashi et al. [8] reported that contributions from China, South Korea, and Japan to the ambient sulfate concentration over Oki Island were 46–50%, 5–15%, and approximately 1%, respectively, via a chemical transport model. Uno et al. [7] concluded that contributions to fine-mode nitrate in Japan from Northeast Asian sources outside of the Atmosphere 2020, 11, 141; doi:10.3390/atmos11020141 www.mdpi.com/journal/atmosphere Atmosphere 2020, 11, 141 2 of 16 country can reach 85%. Among the past studies, two specifically examined variability in domestic and foreign contributions for South Korea. Kim et al. [6] estimated that the foreign contribution to particulate matter of 10 µm or less in diameter (PM10) in the Seoul Metropolitan Area (SMA) is approximately 60% of the annual PM10 concentration. They also reported that contributions to the annual PM2.5 concentrations from domestic and foreign sources can vary by 20% or more depending on the meteorological and emission conditions. Choi et al. [5] reported that the Chinese contribution to South Korean PM2.5 concentrations was up to 68% during May and June 2016.While they exhibited one consensus that long-range transport of air pollutants is important to elucidate local air quality over the region, one potential major drawback of these studies is that they employed a single modeling domain with a coarse horizontal grid resolution ranging from 25–80 km while focusing on regional scale air pollution. Recently, the local governments in South Korea have been tasked with analyzing the cause of their PM2.5 problems and to prepare their corresponding air quality improvement plans. This means that they need to account for PM2.5 and its precursors due to local emissions with high horizontal resolution as well as the long-range transport of air pollutants. However, the area of a South Korean local prefecture is 382 km2 on average, which is too small to be evaluated at 25–80 km resolutions. At the same time, South Korea itself has very high population densities in many cities along with highly developed industrial complexes, which has resulted in significant local pollution emission contributions toward its own air quality degradation [9,10]. Therefore, it is necessary to perform air quality simulations with a fine-grid resolution to quantify the impact of local source impacts while a coarse-grid resolution is sufficient to estimate the contributions of the long-range transport of pollutants due to remote upwind sources such as Chinese emissions. For these reasons, a modeling approach needs to reconcile estimating the long-range transport of PM2.5 from Northeast Asian countries along with regional sources to understand the domestic and foreign contributions to the PM2.5 levels of specific regions as quantifying both of these contributions is crucial for pollution control policy-making. The focus of our study comprises two goals: (a) to quantify the contribution of foreign emission sources to the PM2.5 concentration in South Korea and (b) to explore the associated uncertainties in the selection of the analysis method. For this, we designed a multiscale tiered contribution analysis approach to assess local and regional PM2.5 contributions in space and time. Section2o ffers a description of the multiscale modeling configuration and approach used in this study, while Section3 presents the impact of foreign emissions on local PM 2.5 concentrations at different government tiers (i.e., province (high-tier) vs. prefecture (low-tier)) in South Korea. 2. Data and Methodology 2.1. Observations In this study, we used surface temperature and wind speed observations from the Meteorological Aerodrome Reports (METAR) data for the evaluation of the meteorological model simulation; 98 sites in China and South Korea were considered. PM2.5 composition ground observation data were provided by the National Institute of Environmental Research. Air quality observations at six supersites in South Korea were used for the long-term model performance evaluation. 2.2. The Modeling Domain and Study Period Considering the horizontal scale of transboundary transport (100 s of km) of air pollutants in Northeast Asia, a coarse horizontal grid resolution (e.g., 25–80 km) may be sufficient to investigate the foreign source apportionment or impact [5–8]. At the same time, air quality simulation with a fine horizontal grid resolution (e.g., 9- or 3-km) is desirable to examine detailed spatial distributions of local emissions, terrain height, and population so as to better represent the source-receptor relationships [11]. However, air quality simulation with a fine horizontal grid resolution requires heavy computational resources such as long computing time and large data storage for the input and output data [12]. Atmosphere 2020, 11, 141 3 of 16 Atmosphere 2020, 11, 141 3 of 16 heavy computational resources such as long computing time and large data storage for the input and output data [12]. To overcome the aforementioned limitations and to perform computationally efficient contribution To overcome the aforementioned limitations and to perform computationally efficient estimation, we used a nested modeling domain in this study (Figure1): an outer domain with a 27-km contribution estimation, we used a nested modeling domain in this study (Figure 1): an outer domain horizontal grid resolution to cover major emission regions in Northeast Asia and an inner domain with a 27-km horizontal grid resolution to cover major emission regions in Northeast Asia and an with a 9-km horizontal grid resolution over the Korean Peninsula. For the temporal scale, we note inner domain with a 9-km horizontal grid resolution over the Korean Peninsula. For the temporal that recent meteorological changes in Northeast Asia had affected air quality in the region [13,14]. scale, we note that recent meteorological changes in Northeast Asia had affected air quality in the Therefore, we attempted to account for the effect of interannual variability of meteorology on the region [13,14]. Therefore, we attempted to account for the effect of interannual variability of estimated contributions by conducting 8-year-long (2010–2017) simulations in this study. The long-term meteorology on the estimated contributions by conducting 8-year-long (2010–2017) simulations in simulation also gave us the opportunity to acquire more robust seasonal variations of contribution this study. The long-term simulation also gave us the opportunity to acquire more robust seasonal estimation than is possible via a single year simulation. variations of contribution estimation than is possible via a single year simulation. FigureFigure 1. 1. ModelingModeling domains, domains, meteorological meteorological stations, stations, and and air air quality quality monitoring monitoring sites. sites. The The 27- 27- and and 9- km9-km grid grid domains domains have have 174 174 × 128128 cells cells and and 67 67 × 8282 cells, cells, respectively. respectively. The The blue blue dots dots represent represent × × METeorologicalMETeorological Aerodrome ReportsReports (METAR)(METAR) monitors. monitors. The The green green and and red red circles circles depict depict the the urban urban air airquality quality monitoring monitoring sites sites (AMS) (AMS) and and six six supersites, supersites, respectively.