[ Research Article ]
Asian Journal of Atmospheric Environment - Vol. 12, No. 3, pp.255-273
ISSN: 1976-6912 (Print) 2287-1160 (Online)
Print publication date 30 Sep 2018
Received 31 Mar 2018 Revised 07 Aug 2018 Accepted 05 Sep 2018

# Numerical Study on the Impact of Power Plants on Primary PM10 Concentrations in South Korea

Il-Soo Park ; Chang-Keun Song1) ; Moon-Soo Park2) ; Byung-Gon Kim3) ; Yu-Woon Jang ; Sang-Sub Ha ; Su-Hwan Jang ; Kyung-Won Chung ; Hyo-Jung Lee5) ; Uh-Jeong Lee ; Sang-Kyun Kim4) ; Cheol-Hee Kim5), *
Korea-Latin America Green Convergence Center, Hankuk University of Foreign Studies, Seoul
1)School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan
2)Research Center for Atmospheric Environment, Hankuk University of Foreign Studies, Yongin
3)Department of Atmospheric Environmental Sciences, Gangneung-Wonju National University, Gangneung
4)Division of Global Environmental Research, National Institute of Environmental Research, Incheon
5)Department of Atmospheric Sciences, Pusan National University, Busan

Correspondence to: *Tel: +82-51-510-3687 E-mail: chkim2@pusan.ac.kr

Copyright ⓒ 2018 by Asian Journal of Atmospheric Environment
This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

## Abstract

To develop effective emission abatement strategies for eighteen coalfired power plants located throughout Korea, power plant emission data and TAPM (The Air Pollution Model) were used to quantify the impact of emission reductions on primary PM10 concentrations. TAPM was validated for two separate time periods: a high PM10 concentration period from April 7 to 12, 2016, and a low PM10 concentration period from June 1 to June 6 2016. The validated model was then used to analyze the impacts of five applicable power plant shut-down scenarios. The results showed that shut-down of four power plants located within the Seoul metropolitan area (SMA) would result in up to 18.9% reduction in maximum PM10 concentrations, depending on synoptic conditions. A scenario for the shutdown of a single low stack height with highest-emission power plant located nearest to Seoul showed a small impact on averaged PM10 concentrations (~1%) and 4.4% (0.54 μg/m3) decrease in maximum concentration. The scenario for four shutdowns for power plants aged more than 30 years within SMA also showed a highest improvement of 6.4% (0.26 μg/m3 in April) in averaged PM10 concentrations, and of 18.9% (2.33 μg/m3 in June) in maximum concentration, showing almost linear relationship in and around SMA. Reducing gaseous air pollutant emissions was also found to be significant in controlling high PM10 concentrations, indicating the effectiveness of coreduction of power plant emissions together with diesel vehicle emissions in the SMA. In addition, this study is implying that secondary production process generating PM10 pollution may be a significant process throughout most regions in Korea, and therefore concurrent abatement of both gas and particle emissions will result in more pronounced improvements in air quality over the urban cities in South Korea.

## Keywords:

Emission reduction, Power plants, TAPM model, Impact Assessment, South Korea

## 1. INTRODUCTION

High PM10 concentrations in a particular region can caused by the trapping of direct particle emissions and/or local secondary generation of particulate matter through photochemical reactions, and at same time it can come from other regions through the long-range transport process in the atmosphere. Recent high PM10 concentrations observed in Korea appear to partly result from transboundary pollution from China, but also from domestic sources originating from local emissions ( Jo and Kim, 2013; Kim et al., 2012a). In 2014, there were approximately 30 cases of high PM10 concentration episodes (i.e., >81 μg/m3) in the Seoul Metropolitan area (SMA). Among these episodes, 12 and 15 episodes occurred during the spring and winter, respectively. These episodes accounted for almost 90% of the episodes occurring in either spring or winter during that year (Park, 2016, 2014). Higher PM10 concentrations occur generally on warm days, implying the importance of high temperatures in the atmosphere (Park, 2014). For example, PM10 concentrations during 6 days from Feb. 23 to 28 ranged from 81 to 120 μg/m3, and on Feb. 25 ranged from 121 to 200 μg/m3. In 2016, an advisory by the Ministry of Environment was in effect over the SMA from April 8 to 12 in 2016 with a PM10 concentration on April 9 of 241 μg/m3.

Previous studies have investigated the occurrence of high PM10 concentration episodes in Korea. During most episodes, the Korean peninsula is influenced by an anticyclone pattern with warm and humid air with stagnant conditions, based on surface and upper atmosphere maps during high PM10 concentration episodes. The stable atmosphere, with an inversion layer and thick fog, are unfavorable conditions for transport and dispersion of particulate matter in the atmosphere (Park, 2016). Other reasons such as transboundary processes from regional sources also need to be considered for a comprehensive understanding of the occurrence of long-lasting high PM10 concentration episodes. Understanding impacts of both transboundary processes and local air pollutant emissions is still an open question for developing effective emission abatement strategies in Korea.

In Korea, power plants account for nearly 65% of total electric production and emit 3,455 tons/yr of PM10 (2,618 tons/yr of PM2.5) with much more amounts of the PM precursor emissions such as SO2 and NOx (KMOE, 2017). In this context, the Ministry of Environment in Korea (KMOE) has tentatively shut down eight power plant stacks in old power plants aged more than 30 years (since June 2017). About 496 tons of PM lower was expected to be emitted compared with other PM10 counter measures by a stack shut down. KMOE also plans to shut down old power plants regularly in spring beginning from 2018 (KMOE, 2017).

This studies was carried out to evaluate the impact of power plants on PM10 concentrations during the periods in Korea. We report the results for a numerical study on the impacts of several reductions in emissions from power plants on air quality in both the SMA and the areas around power plants in Korea. The main objective of this study is to assess the impacts of reductions in emissions from power plants on PM10 concentrations in the SMA and the areas around power plants using several emission reduction scenarios. In this study, we consider various scenarios; we investigate extremes in PM10 concentrations to demonstrate the impacts of each scenario. In this approach, the implications of each shutdown scenario can be investigated. The results of this study will contribute to our understanding of the relative importance of local emission sources and transboundary air pollutant sources.

To examine the impacts of reductions in power plants on air quality, a numerical simulation model can be used with different emission characteristics but the same meteorological conditions within the same simulation domain. The modeling results can be compared between simulations excluding emissions from area and mobile sources in the same domain, and including all of the other remaining power plant emissions. The difference between these two simulations can demonstrate the impacts of the shutdown of the targeted power plants. Therefore numerical air quality models are essential tools to quantitatively estimate the impact of emission reductions. In this study, we use The Air Pollution Model (TAPM). TAPM is an integrated online model developed by CSIRO (Commonwealth Science and Industrial Research Organization) which includes coupled prognostic meteorological and air pollutant concentration components (Hurley, 2008a). Here we examine the impacts on primary PM10 concentrations of various scenarios for shutdowns of 18 power plants in Korea using the modelling approach described above for two extreme synoptic conditions: April 7-12 and June 1-6, 2016.

## 2. MATERIALS AND METHODS

### 2. 1 Power Plants and Emissions

The locations of eighteen power plants that are considered by KMOE for temporary shutdowns or emission reduction measures are shown in Fig. 1. Detailed stack information such as stack height, stack radius, exit temperature and other data used in this study is listed in Table 1. As shown in Fig. 1, the power plants located in the western coastal area are distributed more densely (i.e., P03, P05, P09, P11, P08, and P12); two are located in eastern coastal area (i.e., P06 and P07), four are located in the southern coastal area (i.e., P01, P04, P14, P17, and P18); the others are in more inland areas of Korea (P13, P15, P16, and P02). Also note that Seoul, the capital of South Korea, is near the western coastal power plants, and Daejeon is located in the central inland area.

The location of power plants, meteorological stations, and air quality monitoring station used in this study.

Stack characteristics of power plants used in this study.

The stack heights are generally higher than 100 m, with the highest stack about 200 m height, P17 (Goseong), and P03 (Incheon), with a stack height of 199 m (Table 1). The average distance between power plant and meteorological observation site is 13.2 km, with the maximum distance of 31.8 km at P11 (Taean) among the eighteen power plants, and the shortest distance, 2.4 km, at P07 (Donghae). The average distance between the power plants and air quality station is 6.9 km, with the maximum distance of 25.6 km at P08 (Boryeong), and the minimum 0.7 km at P02 (Daegu), respectively.

Point source emission data available from the Korean National Institute of Environmental Research were used for this study (NIER, 2016). Table 2 shows the emission rates for each of the 18 power plants. PM10 emissions totaled 3,455 ton/year from 216 power plant stacks for the base year of 2013. As indicated in Table 2, P11 (Taean) showed the highest PM10 emissions (835.13 ton/yr), and the second and third highest emissions were at P17, and P08, respectively. Within the SMA, the highest PM10 emission rate was 560.94 ton/yr at P08 (Boryeong) (Table 2).

Emission rate (ton/yr) of power plants used in this study.

### 2. 2 TAPM Model and Input Data

TAPM, employed in this study, is a PC-based fast air quality model, driven by a user-friendly graphical interface to configure inputs, run the model, and analyze outputs. TAPM is composed of two prognostic subsections: meteorological and chemical modules. The overview of TAPM is described here. More details on TAPM equations and descriptions can be found in Hurley (2008a) and Hurley et al. (2008).

Meteorological module of TAPM predicts gridded three-dimensional meteorology and air pollution concentrations. The meteorological component of TAPM is an incompressible, non-hydrostatic, primitive equation model with a terrain-following vertical coordinate for three-dimensional simulations. The model solves the primitive meteorological equations: momentum, temperature, specific humidity of water vapor, and cloud/rain/snow components. It includes parameterizations for cloud/rain/snow micro-physical processes, turbulence closure, urban/vegetative canopy and soil, and radiative fluxes (Hurley, 2008a). Turbulence closure in the mean equations uses a gradient diffusion approach with diffusivity K; and includes a countergradient correction for temperature. An E-ε turbulence scheme is used to calculate K using prognostic equations for the turbulence kinetic energy (E) and its dissipation rate (ε). Plume buoyancy, momentum, and building wake effects are also included for point sources (Hurley et al., 2008).

The chemical component of TAPM consists of an Eulerian grid-based set of prognostic equations for both gas and particulate components. The TAPM model, an Eulerian grid-based model, includes gasphase photochemical reactions based on semi-empirical mechanism called the Generic Reaction Set (GRS) of Azzi et al. (1992) with the hydrogen peroxide modification of Venkatram et al. (1997), yielding the following 10 reactions for 13 species.

 ${\mathit{R}}_{\mathit{s}\mathit{m}\mathit{o}\mathit{g}}+\mathit{h}\mathit{v}\to RP+{\mathit{R}}_{\mathit{s}\mathit{m}\mathit{o}\mathit{g}}+\mathit{\eta }SNGOC$ (1)
 $RP+NO\to N{O}_{2}$ (2)
 ${NO}_{2}+\mathit{h}\mathit{v}\stackrel{{\mathit{O}}_{\mathit{2}}}{\to }NO+{O}_{3}$ (3)
 $NO+{O}_{3}\to N{O}_{2}$ (4)
 $RP+RP\to RP+\alpha {H}_{2}{O}_{2}$ (5)
 $RP+{NO}_{2}\to SGN$ (6)
 $RP+{NO}_{2}\to SNGN$ (7)
 $RP+{SO}_{2}\to SNGS$ (8)
 ${H}_{2}{O}_{2}+S{O}_{2}\to SNGS$ (9)
 ${O}_{3}+S{O}_{2}\to SNGS$ (10)

where hv denotes photo-synthetically active, and thirteen species used here are smog reactivity (Rsmog), radical pool (RP), hydrogen peroxide (H2O2), nitric oxide (NO), nitrogen dioxide (NO2), ozone (O3), Sulphur dioxide (SO2), stable non-gaseous organic carbon (SNGOC), stable gaseous nitrogen products (SGN), stable non-gaseous nitrogen products (SNGN), stable non-gaseous sulphur products (SNGS). The semiempirical GRS has been employed for photochemical analysis in numbers of previous studies (Kim et al., 2005; Venkatram et al., 1997). The concept of using Rsmog rather than volatile organic compounds (VOCs) in the reaction equations follows from the work of Johnson (1984). The concentration of Rsmog is defined as a reactivity coefficient multiplied by VOC concentration. For example, Johnson (1984) used [Rsmog]= 0.0067[VOC] for typical 1980s. Other detailed descriptions including reaction coefficients and yield coefficients such as α and η are found in Hurley (2008a) and Hurley et al. (2008).

The chemical component of TAPM has 3 modes in options: trace, gas and dust modes. The dust mode should be employed for the simulation of particulate matter. In dust mode, particle sizes are categorized in TAPM model for four size ranges: 2.5, 10, 20, and 30 μm, and the calculations of Particulate Matter concentrations are actually done for PM2.5, PM10, PM10-20 and PM20-30. Here Particulate Matter for PM10 and PM2.5 can be defined as sum of primary particulate matter plus secondary particulate concentrations consisting of (SNGOC), (SNGN), and (SNGS) as indicated in above equations of (1) and (7)-(10). Gas and aqueousphase chemical reactions for sulfur dioxide and particles with the aqueous-phase reactions based on Seinfeld and Pandis (1998) are implemented. The dry deposition formulation follows the approach of Physick (1994) for gaseous pollutants, and Seinfeld and Pandis (1998) for aerosols. Wet deposition processes were considered for highly soluble gases and aerosols such as SO2, and H2O2, PM2.5, PM10, PM20, and PM30 are considered in this model (Hurley, 2008a).

TAPM has been verified for a number of regions, industrial and urban scales in Australia, Seoul and overseas. TAPM had been used internationally for model verification studies against two US tracer experiments (Kincaid and Indianapolis), several annual US dispersion datasets (Bowline, Lovett and Westvaco), and annual meteorology and/or dispersion in various regions throughout Australia (Hurley et al., 2008; Park 2004).

The domain for TAPM model evaluation study was based on the grids of 80×80×25 at three domain (12 km, 4 km, 2 km, centered at each of the 18 power point location) to evaluate the model performance for each of the 18 power plants. Therefore 18 simulations should be carried out for this case. In this way, the boundary conditions will be effectively provided for finer nested domain to simulate precisely the impact of point sources. However, for the scenario study, the model domain over Korea was centered at the center of Daejeon city (latitude 36°21ʹ14.83ʺ N and longitude 127°23ʹ4.36ʺ E) with the grid of 120×120×25 grid points was employed. For the initial conditions of the meteorological variables, LAPS (Local Administrator Password Solution) data with a resolution of 75 km×100 km were used (ftp://ftp.csiro.au/TAPM). Surface and land use data (1 km×1 km) from the USGS (United States Geological Survey) and initial meteorological data (75 km×100 km) from the Local Administrator Password Solution from the Australia Meteorology Administration were used as inputs to the TAPM model (Hurley, 2008b).

### 2. 3 Methodology and Description of Scenarios

To simulate the impacts of a reduction in emissions from power plants, we ran the TAPM model in two steps. In the first step, we carried out model performance simulations for the model validation study, prior to the impact simulations. The average concentrations of PM10, PM2.5, SO2, NO2, and O3 in and around power plants during the study period were used for the initial background model conditions. The calculated initial background concentrations are listed in Table 3. Here it is noted that initial background concentrations are surface measurement only, and thus applied 4-day spin-up times to minimize the initial condition influences for both surface and non-surface concentrations. The resultant model outputs were evaluated by comparing the simulated meteorological variables and PM10 concentrations against measurements. The statistical evaluation parameter, the index of agreement (IOA), was used to evaluate model performance.

Measured mean concentrations for 18 power plants, used as an initial concentration for the validation of simulated concentrations

In the second modeling step, the emission reduction impact study was performed. We set the base case by forcing the initial background conditions to zero in this study and applied emissions of power plants only for scenario studies. Current study is a very simple approach without heavy computational costs base on semi-empirical chemical reactions of TAPM, and thus it is contrasted to the comprehensive sensitivity simulations for calculation of source-receptor relations employed by previous comprehensive studies (Baker et al., 2016; Zhou et al., 2012; Bergin et al., 2008).

We carried out five scenarios with varying reductions in power plant emissions in different areas. Table 4 illustrates the scenarios in this study: [Scenario I], complete shutdown for P08 (Boryeong); [Scenario II], complete shutdown for P03 (Incheon), P05 (Ansan), P09 (Dangjin) and P11 (Taean); [Scenario III], partial shutdown for P17 (Goseong) and P08 (Boryeong) and a complete shutdown for P06 (Gangneung), and P10 (Seocheon); [Scenario IV], halving the gas phase emissions with no reduction of PM emission; and finally [Scenario V], 20% emissions reductions of both PM and gas emissions in all power plants during October.

Emission reduction scenarios for impact assessments of power plants in this study.

[Scenario I] focuses on Seoul by shutting down P08 (Boryeong), with electricity power generation (EPG) of 30,778,882 MWh (14.6% reduction among total coal-fired EPG) and the highest emission rate (560.94 ton/yr for PM10) but lowest stack height among the power plants located within the SMA (Table 4). [Scenario II] also simulates a shutdown of the four power plants (49.7% reduction of EPG) located within the SMA. Scenarios [I] and [II] both compare Daejeon in contrast to Seoul. [Scenario III] examines the effects of the shutdown of plants more than 30 years old located over the whole south Korea (8.5% reduction of EPG), whose operation had been suspended by KMOE. [Scenario IV] estimates the contributions from secondary production, by reducing 50% of the NOx and SOx emissions of all 18 power plants. In Scenario V, the emissions of PM10, NOx, SO2 were assigned to 20% reduction amounts of all 18 power plants in the assumption that the generated electric power is proportion to the consumed fuel amount. The Scenario V is chosen to reflect the recent PM mitigation plans of Korean MOE, among which temporary shutdown of power plants during October is included (http://www.seoul.co.kr/news/newsView.php?id=20180629018012&wlog_ tag3=naver). Each of the above five scenarios are compared to quantify the impacts of the changes in each scenario. Additional details on each scenario are given in Table 4.

### 2. 4 Case Selection and its Synoptic

Two episodes are selected as being representative for high and low aerosol episodes, respectively. The episode cannot be a perfect but suitable one for this numerical study as estimating specific sources’ contribution to local and regional air quality.

First, a long-lasting haze episode such as April 8-10 in 2016 tends to be determined by unfavorable meteorological conditions like a stagnant High pressure system accompanied with the weak boundary layer wind, which could suppress vertical mixing and ventilation, largely driven by the synoptic conditions. This kind of synoptic setting, quite typical for severe haze events, is quite consistent with the previous studies (Seo et al., 2017; Oh et al., 2015; Wang et al., 2014). The moving Low pressure system before the haze event preceded very stagnant synoptic condition and weak pressure gradient, which helped to intensify the accumulation of aerosol loadings. Fig. 2 shows spatial distributions of 850 hPa geopotential heights and wind field for April 8 to 9. Boundary layer mean wind speed (850 hPa) increased specifically around Manchuria on April 8, which facilitated transport of anthropogenic and dust aerosols from the northeastern China to Korea with strong northwesterly up to 20 m/s. Later, wind speed gradually decreased until the end of haze (Fig. 2). The first period (April 8) of the haze event characterized aerosols external transport from China by strong northwesterly. After then, relatively calm and stagnant conditions contributed to build up of domestic haze in Korea.

Spatial distributions of 850 hPa geopotential heights and wind fields for (a) April 7 to 12, and (b) June 1 to 6, 2016.

Meanwhile the June period from 1 to 7 is selected as a low-aerosol event. The synoptic features as shown in Fig. 2 indicate even lighter wind speed with overall lower pressure gradient. It is interesting to note that the most domain of Korea was influenced by the easterly on June 2, which shifted to southeasterly specifically in the southern peninsula later. The most striking difference between April haze and June clean periods is the boundary layer wind direction with the others largely similar. For Scenario V, impact assessment was carried out for October 1 to 11, 2017, when the prevailed wind direction was dominantly observed northeasterlies.

## 3. RESULTS

### 3. 1 Model Evaluation: Site Measurements of PM10 Concentration around Power Plants

Measured background PM10 concentrations during the study period were analyzed to characterize the vicinity of 18 measurement sites routinely monitored by KMOE. PM10 concentrations at eighteen sites for both cases (April and June) are shown in the boxplots (Fig. 3). The median PM10 concentration ranged from 35 μg/m3 to 73 μg/m3 in April and from 23 μg/m3 to 68.5 μg/m3 in June, respectively. The mean PM10 concentration in April was 67% higher than the mean concentration in June. The mean PM10 concentration in Seoul was 58 μg/m3; in Shanghai the mean PM10 concentration was 149 μg/m3 (Wang et al., 2013).

Boxplots of PM10 concentration during 7-12 April and 1-6 June, 2016 at the monitoring sites around 18 power plants.

### 3. 2 Wind and Concentration Fields around Power Plant

Figs. 4 and 5 show the horizontal distribution of the PM10 with the wind vector averaged for two simulated periods centered at the selected four power plants: P01 (Busan), P02 (Daegu), P07 (Donghae), and P09 (Dangjin), under the initial concentrations (Table 3) and the emission from the power plants (Table 2). PM10 concentrations during the first simulation period are higher than that for the second simulation period because of differences in the initial concentration and meteorological conditions.

Predicted wind and concentration fields for an averaged values during 7-12 April, 2016 at four power plants (P01, P02, P07, and P09) area.

Predicted wind and concentration fields for an averaged values during 1-6 June, 2016 at four power plants (P01, P02, P07, and P09) area.

### 3. 3 Wind and Concentration Fields over the Country

Figs. 6 and 7 show the horizontal distribution of PM10 with the wind vector simulated on 10 April and 4 June 2016 with zero background PM10 concentration and the emissions from the power plants, respectively. The patterns are strongly governed by the wind: upwind regions have lower concentrations while downwind regions have higher concentrations; the area where there is a confluence of wind has high concentrations and areas where the wind direction diverges have lower concentrations. Especially the overall concentration in June showed much higher due to the relatively weaker wind speed rather than in April. On 10 April 2016, northerly winds are dominant in Korea, so the high PM10 concentration region moves southward with the maximum concentrations in the south such as P14 (Yeosu), P18 (Hadong), and P17 (Goseong). Meanwhile, on 4 June 2016, southerly and easterly winds are dominant in Korea, so the high PM10 concentration region moves northward with the maximum concentrations in the northwest.

Predicted wind and concentration fields on April 10, 2016 over the country at 00 LST, 04 LST, 08 LST, 12 LST, 16 LST, and 20 LST.

Predicted wind and concentration fields on June 4, 2016 over the country at 00 LST, 04 LST, 08 LST, 12 LST, 16 LST, and 20 LST.

Fig. 8 shows the horizontal distribution PM10 with the wind vector simulated during the two simulation period with zero background PM10 concentration and the emissions from the power plants. The second simulation period had higher concentrations than the first simulation period by approximately 3 times. During the first simulation period, PM10 concentrations are higher in southern Korea and lower in northern Korea. The power plant sites of P14 (Yeosu), P17 (Goseong), and P18 (Hadong) have higher PM10 concentrations, corresponding to the wind convergence. During the second simulation period, PM10 concentrations are higher in the west and lower in the southeast. Lower PM10 concentrations are governed by easterly winds, while higher concentrations are governed by northerly or westerly winds.

Predicted wind and concentration fields for an averaged values during (a) 7-12 April, and (b) 1-6 June, 2016.

### 3. 4 Model Evaluation

In order to validate the simulated meteorological variables and PM10 concentrations for each power plant, data for the nearest meteorological station and the nearest air quality station were used. The index of agreement (IOA) between the simulated value at the power plant and the observed value at the nearest meteorological or air quality monitoring station for each power plant was used to evaluate the simulated result. The IOA is a frequently used measure of how well the predicted variation about the observed mean are represented, with a value greater than about 0.50 considered to indicate a good prediction.

Table 5 shows the IOAs between modeled and observed PM10 concentrations and the meteorological variables such as wind speed, wind direction, and air temperature for the study period. Temperature in June showed the highest IOA with an average of 0.89 while PM10 concentration showed the lowest IOA with an average of 0.43. The IOA was similar to IOA found in other studies in urban areas (Hurley et al., 2008, Park et al., 2004). The IOA deviation (station IOA - station averaged IOA) for two different periods shows nearly the same patterns. At P01 (Busan) and P02 (Deagu), IOAs for all variables are higher than the station-averaged value. At P12 (Gunsan) and P13 (Iksan), IOAs for the meteorological variable are higher than the stationaveraged value, but those for PM10 concentration are lower than the station-averaged IOAs.

Index of agreement (IOA) between simulated and observations of meteorological variables and PM10 concentrations.

### 3. 5 Results of Scenario Studies

The results for simulated mean and maximum PM10 concentrations to assess the contribution of each power plant in the study region, the contribution to the maximum surface concentration in April and June ranged from 3.79 μg/m3 (P05: Ansan, April) to 17.00 μg/m3 (P11: Taean, June) (Table 6). At P09 (Dangjin), P11 (Taean), P17 (Goseong), and P18 (Hadong), PM10 emissions can be classified as a large power plant with regard to emissions, exceeding 300 ton/year, so that their high emissions are related to elevated maximum PM10 concentrations (ranging from 12.84 to 17.00 μg/m3 in June). At power plants such as P01 (Busan), P02 (Daegu), P04 (Ulsan), P05 (Ansan), P15 (Gimcheon), P05 (Ansan), and P16 (Gumi), the maximum concentrations are lower (ranging from 3.79 to 9.59 μg/m3 in April and June). In particular, the power plant at P08 (Boryeong) had the lowest stack height (42 m) (see Table 1), but the second highest PM10 emission (560.94 ton/yr) (see Table 2). Due to the lower stack height and larger emission amount, the maximum surface concentration at P08 (Boryeong) was 12.12 μg/m3, the highest in April. As considering that the locations showing the maximum concentrations were more than 100 km far from half of all stacks, it should be also mentioned the secondary production during long-range transport is also important in PM formation in addition to their contribution of the primary PM sources.

Simulated hourly maximum and averages of daily maximum PM10 concentrations of base case for impact assessment of power plants.

The maximum and average concentration and the location where those concentration occurred for each scenarios are shown in Table 7. The results of sensitivity experiment, such as increased/decreased concentrations, relative to BAU (Business As Usual) is shown for each scenario in Table 8. In Scenario I the maximum decrease in PM10 concentrations in the SMA due to a shutdown of the P08 (Boryeong) was less than 0.00 to 0.54 μg/m3 (0.0-4.4%). There is a small impact of the P08 (Boryeong) on the SMA because the stack height is low and the SMA is distant from the plant. In scenario II, simulating a shutdown of four power plants the SMA, reduced emissions (1,320 tons/year) comprise more than one-third of the total emissions from 18 power plants (3,455 tons/year). The impact of shutting down four power plants on the decrease in PM10 concentrations in the SMA was as high as 18.9% in June, and a decrease of 23.9% was also simulated in the Daejeon region, proportional to the reduced emissions in the SMA.

Position of PM10 hourly maximum and averages of daily maximum concentrations of base case, and its distance from Dajeon.

Results of reduced mean and maximum PM10 concentrations for five scenarios over the selected areas.

For scenarios III, there is a large decrease in PM10 concentrations around the power plants of P17 (Goseong), P08 (Boryong), P06 (Gangneung) from 6.4 to 31.8%, compared to the decrease in the SMA and Daejeon area from 0.9 to 7.7%, relatively far away from the power plants, even though the emission reductions of 60 to 190 tons/year in each power plant due a shutdown of aged plants are not significant compared to the total emission amount from all power plants considered in our study. The maximum PM10 concentrations in in Seoul, Daejeon, P17 (Goseong), P08 (Boryeong), P06 (Gangneung) and P10 (Seocheon) decreased from 0.3 to 19.7% in scenario IV when SO2, NOx emissions from all 18 power plants were reduced (Table 8). In Scenario V, the maximum and averaged concentrations were higher than those in April and lower than those in June for BAU. In compared with Scenario IV, the reduction rates were higher than those in April and lower than those in June except Seoul and Gangneung.

## 4. DISCUSSION

There has been much debate in Korea concerning identifying sources of PM10 pollution. Obviously, local emissions play a significant role and are often sufficient to cause PM10 concentrations which exceed national standard levels, and transboundary sources must be considered as another important source. The National Institute of Environmental Researches (NIER) has carried out monitoring activities and has studied sourcereceptor relationships to estimate the long-term contributions of both local emissions and transboundary processes (Kim et al., 2012a, 2012b; Park et al., 2005). The most recent project of NIER, Korea-United States Air Quality (KORUS-AQ) campaign conducted over the Korean Peninsula (NIER, 2017), showed that more than half of the PM10 sources are generated in Korea, with vehicles, power plants and petrochemical and chemical factories located on the west coast identified as major sources of PM10 pollution.

The simulation results showed that high emissions and low stack height are related to higher maximum PM10 concentrations. The location of a plant (coastal or inland) is also critical for determining how distant from the power plant the maximum PM10 concentration will occur. In the SMA and Daejeon areas, it is apparent that directly reducing emissions is essential to decrease PM10 concentrations. In coastal areas, the surface concentration of PM10 and most types of air pollutants tends to rapidly increase near emission sources because PM10 are transported into the thermal internal boundary layer (TIBL) and suddenly sink down to the surface under stable atmospheric conditions (Levitin, 2000). We found that the maximum impact on PM10 concentrations occurred within 50 km from some of power plants located in coastal areas, i. e., P06 (Gangneung), P07 (Donghae), P08 (Boryeong), P09 (Dangjin), P11 (Taean), P12 (Gunsan), P14 (Yeosu), P17 (Goseong), and P18 (Hadong), while at P02 (Daegu), P15 (Gimcheon), P16 (Gumi) and P13 (Iksan), located in inland areas, the maximum impact on PM10 concentrations occurred at distances more than 200 km from each power plant.

Therefore it is critical to curtail emissions from industries in Korea, especially those located in the western coastal area because sources of secondary air pollutants originate from this region. Studies on quantitative impact assessment are prerequisite for developing effective emissions abatement strategies to improve air quality. In this context, this study offers a reliable preliminary analysis to evaluate the effects of shutting down power plants with a focus on power plants in the western coastal area.

Scenario I examines a shutdown of the power plant of P08 (Boryeong) with the highest emission but lower stack height showed a negligible impact on the SMA with a maximum PM10 concentration reduction rate of <1% in April but a reduction of 4.4% in June, respectively (Table 8). As indicated in scenario II, a shutdown of four power plants located within the SMA is found to be more effective at reducing PM10 concentrations with a maximum reduction rate of 6.4% in April and 18.9% in June, respectively (Table 8). The reduction in PM10 concentration for scenario II is approximately proportional to the reduced emission fractions of power plants in the SMA.

Scenario III evaluated the effects of shutting down the power plants aged 30 years or older. During June 2017, this approach was tested by KMOE. Our results indicate that this scenario has a relatively insignificant effect on air quality in the SMA with a maximum concentration reduction rate of <-3% for both high and low PM10 cases, only showing an improvement in air quality near power plants with a maximum reduction in PM10 concentration more than 31.8-21.2% for P08 (Boryeong) and 10.5-19.3% for P17 (Goseong), respectively, which are the highest and second highest reductions in this study, The results for scenario III therefore suggest that ceasing operations of old power plants in Korea has a small but detectable impact on the air quality in the SMA. It is clear that quantifying co-reductions of power plant PM10 emissions together with reductions in exhaust from diesel vehicles will be needed to identify the major local sources in the SMA.

For scenario IV, where SO2 and NOx emissions from the 18 power plants were reduced by 50%, our simulation results showed that the maximum PM10 concentrations in Seoul, Daejeon, P17 (Goseong), P08 (Boryeong), P06 (Gangneung) and P10 (Seocheon) decreased from 0.3 to 19.7%. This implies that controlling emissions of gaseous air pollutant could have a nearly equally significant on reducing PM10 concentrations, it also suggests that secondary production processes of PM10 pollution might be a highly significant process generating the high PM10 concentrations in most regions in Korea. Scenario V showed the maximum reduction rates of 3.4% and 7.0% for in October for Seoul and Daejeon, respectively. The averaged concentration reduction rates are 1.5% and 3.6% for Seoul and Deajeon, respectively, in October, indicating the intermediate values between April and June in BAU, higher than those in April and lower than those in June.

In five scenarios, the reduction is more effective in June with the higher mixing height (Fig. 9). The simulation results of curbing PM10 emissions from industrial sectors indicate that PM10 emissions from power plants located in the western coastal area has a small effect on the SMA but this step seems to only affect air quality around power plants. The concurrent reduction of gas phase precursors such as SO2 and NOx showed a more pronounced effect. Halving emissions of gas phase pollutants only showed a nominal equivalent decrease with the shutdown of old power plants, as indicated for scenarios III and IV. As mentioned previously, this suggests that another important mechanism leading to elevated PM10 concentrations in Korea are processes which convert gases to the particle phase. These processes are likely more complicated with non-linear effects compared to directly decreasing PM10 emission from sources such as power plants. In this regard, further studies on the concurrent control of old power plants and restrictions on the use of older diesel vehicles will be important for effective air quality improvement in the SMA.

Daily variation of predicted mixing height in April and June in Seoul Metropolitan area.

The current study is limited to investigating the impacts on primary PM10 concentrations by reducing emissions from power plants in Korea, but the most effective emission abatement strategies will take into account the secondarily formed PM10 concentrations.

## 5. CONCLUSION

The high PM10 concentrations observed in Korea are partly explained by transboundary transport from China, but frequently mixed with domestic pollution originating from local emissions. Recent studies showed that the cases of high PM10 concentrations observed in Korea are frequently influenced by the stagnant synoptic patterns. Therefore it is worthwhile to quantify the effects of curbing emissions from local sources, and might provide valuable insights towards developing more effective air quality policies at the national level.

In this study, we carried out an impact of reduced emissions from power plants in Korea. The modeling results from this study can contribute to strategies of the power plants located South Korea. The TAPM model simulations for a high PM10 period of April 4 to 7, and a low PM10 period of June 1 to 6, and then carried out emission reduction simulation based on welldesigned power plant shut-down scenarios.

The results yielded that all shut-down of four power plants located within the SMA (Scenario II) showed marginal effects on the air quality of Seoul with the reduction rate of 6.4 (0.26 μg/m3) ~18.9% (2.33 μg/m3) of maximum PM10 concentrations, while the effect of a shut-down case of one highest-emission power plant, P08 (Boryeong) with low stack height on air quality of Seoul (Scenario I) was found to be rather lower reduction rate of only ~4.4% (0.54 μg/m3) of maximum concentration. Another shutdown of the power plants aged more than 30 years, showed also insignificant effects on the air quality of Seoul. This is suggesting the effectiveness of concurrent reduction of emissions of power plants together with diesel vehicle in Seoul metropolitan area for the improvement of air quality in Seoul. Finally the scenario of 50% reduction in gaseous air pollutant emissions are found to be relatively more significant in regulating the high PM10 concentrations then others, suggesting that the SIA (secondary inorganic aerosol) of PM10 might be also one of the important factors in regulating the PM10 concentrations over the nation-wide areas in Korea. In the current five scenarios, the reduction effectiveness in June under unstable condition is relatively much higher than one in April under stagnant synoptic condition.

This study mainly pertains to the results primary PM10 concentrations from the reduction of the power plant emission. However, PM10 as well as PM2.5 which are emitted directly, and at the same time they are secondarily formed when emissions of gaseous precursors create particle formation. Therefore, in order to inform policies to reduce PM10 and PM2.5 concentrations, it is important to fully understand the particulate formation processes: both primary and secondary formations. Also long term and comprehensive monitoring would be important to analyze the assessment.

Finally, aside from the local anthropogenic emission sources, source-receptor relationship study is critical to making improvements on assessing other transboundary air pollutants. Therefore more detailed model sensitivity tests based on modeling and monitoring study will be conducted, plus the source-receptor relationship study will be carried out as a future task.

## Acknowledgments

We thank NIER (National Institute of Environmental Research) for providing the emissions of power plants for this study. This work was supported by the Ministry of Education of the Republic of Korea and grants from the National Research Foundation of Korea (NRF-2015S1A5B8036201) and partially supported by the National Strategic Project-Fine Particle of the National Research Foundation of Korea (NRF-2017M3D8A1092021).

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### Fig. 1.

The location of power plants, meteorological stations, and air quality monitoring station used in this study.

### Fig. 2.

Spatial distributions of 850 hPa geopotential heights and wind fields for (a) April 7 to 12, and (b) June 1 to 6, 2016.

### Fig. 3.

Boxplots of PM10 concentration during 7-12 April and 1-6 June, 2016 at the monitoring sites around 18 power plants.

### Fig. 4.

Predicted wind and concentration fields for an averaged values during 7-12 April, 2016 at four power plants (P01, P02, P07, and P09) area.

### Fig. 5.

Predicted wind and concentration fields for an averaged values during 1-6 June, 2016 at four power plants (P01, P02, P07, and P09) area.

### Fig. 6.

Predicted wind and concentration fields on April 10, 2016 over the country at 00 LST, 04 LST, 08 LST, 12 LST, 16 LST, and 20 LST.

### Fig. 7.

Predicted wind and concentration fields on June 4, 2016 over the country at 00 LST, 04 LST, 08 LST, 12 LST, 16 LST, and 20 LST.

### Fig. 8.

Predicted wind and concentration fields for an averaged values during (a) 7-12 April, and (b) 1-6 June, 2016.

### Fig. 9.

Daily variation of predicted mixing height in April and June in Seoul Metropolitan area.

### Table 1.

Stack characteristics of power plants used in this study.

Power plants Latitude Longitude Gross generation
(MWh)
(%)
temp.
(°K)
Exiting
speed
(m/s)
# of
stacks
(ea)*
Distance (km) from
the nearest met.
station
Distance (km) from
the nearest air
quality station
P01 Busan 35°05ʹ 129°0.5ʹ 166,440 (0.1%) 100 1.1 409.5 8.2 1 8.2 1.1
P02 Daegu 35°53ʹ 128°32.50ʹ 638,604 (0.3%) 100 1.4 381.2 5.5 3 11.6 0.7
P03 Incheon 37°14.5ʹ 126°26.57ʹ 38,610,719 (18.3%) 199 3.2 360.1 33.7 8 25.3 15.0
P04 Ulsan 35°30.5ʹ 129°22.5ʹ 343,864 (0.2%) 154 1.3 371.5 17.8 8 4.9 1.6
P05 Ansan 37°17.5ʹ 126°48ʹ 674,126 (0.3%) 123 1.3 363.7 4.2 18 20.3 1.7
P06 Gangneung 37°44ʹ 128°58.5ʹ 2,102,002 (1.0%) 80 2.5 384.7 12.6 4 7.0 6.0
P07 Donghae 37°29ʹ 129°8.5ʹ 2,924,854 (1.4%) 150 2.0 416.8 24.3 5 2.4 4.1
P08 Boryeong 36°21ʹ 126°35ʹ 30,778,882 (14.6%) 42 2.9 362.1 20.2 10 6.7 25.6
P09 Dangjin 37°2.5ʹ 126°31ʹ 32,432,355 (15.4%) 150 3.0 362.8 24.7 32 18.2 5.3
P10 Seocheon 36°8.5ʹ 126°29.5ʹ 3,370,249 (1.6%) 150 2.1 362.3 24.2 10 12.9 19.9
P11 Taean 36°45.5ʹ 126°19ʹ 32,984,153 (15.7%) 150 3.0 359.0 21.8 14 31.8 18.3
P12 Gunsan 35°58.5ʹ 126°38ʹ 810,536 (0.4%) 100 1.3 411.4 8.6 16 7.8 1.7
P13 Iksan 35°57ʹ 126°59ʹ 183,960 (0.1%) 80 0.8 428.5 14.3 8 14.6 1.0
P14 Yeosu 34°50ʹ 127°41ʹ 7,037,424 (3.3%) 77 1.8 398.9 15.3 25 10.0 1.4
P15 Gimcheon 36°9ʹ 128°7ʹ 516,840 (0.2%) 100 1.7 403.7 10.8 4 15.2 1.4
P16 Gumi 36°5.5ʹ 128°22ʹ 748,980 (0.4%) 153 1.1 366.9 15.6 18 7.3 1.1
P17 Goseong 34°54.5ʹ 128°6ʹ 24,880,364 (11.8%) 200 2.6 378.7 19.5 16 19.7 12.2
P18 Hadong 34°57ʹ 127°49ʹ 31,494,250 (14.9%) 150 3.3 357.9 15.1 16 13.1 6.4

### Table 2.

Emission rate (ton/yr) of power plants used in this study.

Power plants Emission Rate (ton/yr)
PM10 PM2.5 NOx SOx
P01 Busan 12.06 9.73 455.09 488.25
P02 Daegu 26.55 21.41 1042.42 1181.48
P03 Incheon 207.76 167.58 3862.97 5517.57
P04 Ulsan 14.04 11.32 905.46 847.82
P05 Ansan 14.11 11.38 686.03 324.74
P06 Gangneung 29.16 19.40 2692.47 910.16
P07 Donghae 35.59 25.21 1086.87 2406.16
P08 Boryeong 560.94 452.46 17454.32 11656.09
P09 Dangjin 319.95 111.50 17148.32 7223.25
P10 Seocheon 60.57 41.58 3613.69 1317.73
P11 Taean 835.13 673.63 22168.03 12792.28
P12 Gunsan 33.30 26.86 891.38 776.70
P13 Iksan 9.97 7.21 396.49 500.14
P14 Yeosu 229.13 178.85 6453.28 4074.51
P15 Gimcheon 2.48 2.0 83.68 69.84
P16 Gumi 29.0 23.39 2059.47 1860.64
P17 Goseong 564.47 455.32 23267.45 14531.64
P18 Hadong 470.91 379.84 13524.62 11478.44

### Table 3.

Measured mean concentrations for 18 power plants, used as an initial concentration for the validation of simulated concentrations

7-12 April 2016 1-6 June 2016
SO2
(ppb)
CO
(ppm)
O3
(ppb)
NO2
(ppb)
PM10
(μg/m3)
SO2
(ppb)
CO
(ppm)
O3
(ppb)
NO2
(ppb)
PM10
(μg/m3)
P01 Busan 7.3 0.5 34.5 13.0 70.7 6.6 0.4 36.0 23.9 29.0
P02 Daegu 4.2 0.4 27.6 21.1 68.8 3.7 0.3 40.9 12.8 33.3
P03 Incheon 5.0 0.5 41.9 17.5 70.6 5.1 0.3 50.0 14.5 52.5
P04 Ulsan 13.2 0.7 31.2 32.9 68.6 9.8 0.4 39.9 24.3 36.6
P05 Ansan 5.2 0.5 27.2 31.1 68.7 4.4 0.5 30.2 29.8 40.8
P06 Gangneung 3.1 0.3 44.8 13.3 76.9 2.0 0.3 45.1 13.4 42.1
P07 Donghae 2.3 0.3 37.2 13.9 71.4 2.6 0.3 39.9 18.7 38.2
P08 Boryeong 3.1 0.5 46.6 15.4 62.0 3.9 0.4 57.9 14.9 49.0
P09 Dangjin 3.3 0.3 43.1 14.7 58.4 6.6 0.3 54.8 17.2 58.4
P10 Seocheon 1.7 0.4 47.1 8.0 62.8 1.7 0.3 53.7 7.2 31.8
P11 Taean 5.5 0.5 42.9 42.8 51.7 7.8 0.4 42.9 22.0 50.9
P12 Gunsan 5.1 0.5 42.1 18.4 71.1 5.2 0.4 52.0 14.0 50.9
P13 Iksan 6.1 0.5 44.8 16.9 90.5 6.6 0.5 54.7 13.2 67.7
P14 Yeosu 9.4 0.3 35.2 20.5 77.9 14.0 0.5 37.8 40.8 33.8
P15 Gimcheon 1.6 0.6 45.3 14.4 71.9 1.4 0.4 44.7 11.1 26.6
P16 Gumi 3.7 0.5 48.6 27.2 54.7 3.1 0.2 49.5 13.2 22.5
P17 Goseong 5.3 0.3 36.6 28.6 61.2 5.2 0.3 45.3 16.6 37.8
P18 Hadong 12.1 0.5 32.5 23.8 60.4 10.6 0.4 33.8 16.0 32.9

### Table 4.

Emission reduction scenarios for impact assessments of power plants in this study.

Scenarios Shut-down efficiencies Reduced emission (ton) Target areas
of impact
assessments
Power plants Operation
rate(%)
Reduced generation
(MWh)
PM10 PM2.5 NOx SOx
Scenario I P08 Boryeong 0.0% 30,778,882 (14.6%) 560.94 452.46 17,454.32 11,656.09 Seoul, Daejeon
Subtotal 30,778,882 (MWh) (14.6%) for Scenario I
Scenario II P03 Incheon 0.0% 38,610,719 (18.3%) 207.76 167.58 3,862.97 5,517.57 Seoul, Daejeon
P05 Ansan 0.0% 674,125.8 ( 0.3%) 14.11 11.38 686.03 324.74
P09 Dangjin 0.0% 32,432,355 (15.4%) 319.95 111.50 17,148.32 7,223.25
P11 Taean 0.0% 32,984,153 (15.7%) 835.13 673.63 22,168.03 12,792.28
Subtotal 104,701,353 (MWh) (49.7%) for Scenario II
Scenario III P17 Goseong 81.3% 4,652,628 (2.2%) 105.73 85.28 9,497.16 3,275.97 Seoul, Daejeon,
P06, P17, P08,
P10
P08 Boryeong 74.6% 7,817,836 (3.7%) 142.37 114.84 4,935.66 3,549.51
P06 Gangneung 0.0% 2,102,002 (1.0%) 188.28 151.87 2,741.27 3,394.57
P10 Seocheon 0.0% 3,370,249 (1.6%) 60.57 41.58 3,613.69 1,317.73
Subtotal 17942715 (MWh) (8.5%) for Scenario III
Scenario IV P01~P18 0.00 0.00 58,896.02 38,978.72 Seoul, Daejeon,
P06, P17, P08,
P10
Halving the emissions of NOx and SO2 only with no reduction of particulate emissions for all 18 power plants
Scenario V P01~P18 690.95 552.76 23,558.33 15,591.71 Seoul, Daejeon,
P06, P17, P08,
P10
20% reduction of PM, NOx, and SO2 emissions for all of 18 power plants

### Table 5.

Index of agreement (IOA) between simulated and observations of meteorological variables and PM10 concentrations.

Power plant 7-12 April 2016 1-6 June 2016
PM10 WS WD Temp. PM10 WS WD Temp.
P01 0.55 0.61 0.91 0.79 0.56 0.60 0.69 0.86
P02 0.25 0.70 0.73 0.92 0.54 0.80 0.68 0.88
P03 0.39 0.60 0.68 0.87 0.51 0.65 0.73 0.89
P04 0.48 0.58 0.65 0.82 0.48 0.65 0.24 0.90
P05 0.33 0.44 0.80 0.88 0.60 0.61 0.77 0.93
P06 0.25 0.47 0.34 0.89 0.26 0.33 0.43 0.92
P07 0.21 0.38 0.45 0.84 0.35 0.54 0.50 0.93
P08 0.34 0.37 0.48 0.89 0.36 0.34 0.62 0.90
P09 0.42 0.45 0.67 0.88 0.47 0.55 0.63 0.93
P10 0.26 0.26 0.70 0.80 0.42 0.32 0.56 0.84
P11 0.37 0.55 0.59 0.61 0.34 0.57 0.79 0.95
P12 0.23 0.72 0.62 0.89 0.28 0.67 0.65 0.90
P13 0.23 0.77 0.58 0.93 0.28 0.76 0.45 0.88
P14 0.53 0.54 0.87 0.78 0.38 0.61 0.53 0.88
P15 0.43 0.60 0.72 0.92 0.45 0.74 0.42 0.82
P16 0.39 0.28 0.59 0.93 0.49 0.23 0.64 0.91
P17 0.60 0.51 0.49 0.90 0.46 0.25 0.17 0.87
P18 0.55 0.63 0.87 0.80 0.43 0.68 0.74 0.89

### Table 6.

Simulated hourly maximum and averages of daily maximum PM10 concentrations of base case for impact assessment of power plants.

Power
plant
April June
max.
(μg/m3)
Direction/Distance
(km) from PP
avg.
(μg/m3)
Direction/Distance
(km) from PP
max.
(μg/m3)
Direction/Distance
(km) from PP
avg.
(μg/m3)
Direction/Distance
(km) from PP
P01 4.3 SE 223.5 1.65 SSE 166.5 6.68 WNW 181.1 3.39 WNW 181.1
P02 4.15 ESE 210.0 1.63 SSE 175.3 7.93 NW 217.8 4.07 NW 223.5
P03 4.09 SSE 215.1 1.55 SSE 167.0 10.8 ESE 31.6 4.86 NW 130.9
P04 4.03 SE 220.6 1.6 SSE 192.5 5.93 NNW 157.2 2.69 N 79.0
P05 3.79 SSE 217.9 1.46 SSE 129.5 9.59 WNW 137.1 4.73 WNW 144.1
P06 5.94 WNW 12.6 1.38 SE 220.6 7.48 WSW 158.5 3.52 WSW 156.8
P07 5.89 WSW 5.7 1.42 SW 220.6 7.38 WSW 156.1 3.49 WNW 156.1
P08 12.1 NE 2.8 3.08 NE 2.8 15.32 NE 2.8 5.9 NE 2.8
P09 4.93 ESE 45.6 1.68 SSE 132.5 12.84 ESE 21.5 5.06 NW 181.0
P10 6.19 ESE 155.8 1.91 SSE 172.6 9.17 SSW 121.5 4.94 WNW 190.9
P11 8.3 NE 11.7 2.23 NE 2.8 17 NE 2.8 5.46 NE 2.8
P12 4.2 ENE 8.6 1.77 ESE 43.6 9.03 SSW 67.7 4.88 NW 190.3
P13 7.43 ESE 108.3 1.89 SW 203.7 8.73 SW 203.7 4.72 WNW 197.2
P14 5.41 WSW 50.1 2.07 ENE 5.7 8.82 WNW 101.0 4.26 NW 192.0
P15 4.14 SE 220.6 1.64 SE 212.4 8.33 NNW 204.1 4.26 WNW 162.6
P16 4.15 SSE 217.9 1.66 SSE 217.9 8.01 NW 212.1 4.16 WNW 177.0
P17 6.03 NE 8.5 2.05 NE 2.8 15.18 NE 2.8 4.24 WNW 180.4
P18 6.09 NE 2.8 2.03 SE 212.1 12.98 WNW 106.6 4.30 WNW 154.0

### Table 7.

Position of PM10 hourly maximum and averages of daily maximum concentrations of base case, and its distance from Dajeon.

Scenarios Case Max. Conc. Direction
from
Daejeon
Distance from
Daejeon (km)
Avg.
Conc.
Direction
from
Daejeon
Distance from
Daejeon (km)
BAU April 16.98 WNW 78.2 2.87 WNW 78.2
June 25.64 WNW 78.2 7.61 WNW 74.2
Scenario I April 11.81 SSE 158.6 2.62 SSE 157.7
June 22.34 SW 127.9 6.77 WSW 72.5
Scenario II April 17.03 WNW 78.2 2.75 WNW 78.2
June 23.82 WNW 78.2 6.73 WNW 74.2
Scenario III April 11.59 WNW 78.2 2.54 SSE 158.6
June 21.57 SSE 127.9 7.21 W 78.2
Scenario IV April 14.94 WNW 78.2 2.69 WNW 78.2
June 20.58 WNW 78.2 6.95 WNW 78.2
Scenario V October 17.99 NNE 119.3 3.21 NNE 119.3

### Table 8.

Results of reduced mean and maximum PM10 concentrations for five scenarios over the selected areas.

Scenarios Reduced
Conc.
(μg/m3)
Seoul Daejeon P06 P17 P08 P10
max.(Δfraction)
avg.(Δfraction)
max.(Δfraction)
avg.(Δfraction)
max.(Δfraction)
avg.(Δfraction)
max.(Δfraction)
avg.(Δfraction)
max.(Δfraction)
avg.(Δfraction)
max.(Δfraction)
avg.(Δfraction)
BAU April max. 4.05 max. 4.41 max. 4.50 max. 6.95 max. 16.99 max. 3.57
avg. 1.52 avg. 1.86 avg. 1.46 avg. 2.45 avg. 2.88 avg. 1.87
June max. 12.33 max. 12.99 max. 9.76 max. 16.96 max. 25.64 max. 16.17
avg. 5.44 avg. 4.97 avg. 3.88 avg. 3.78 avg. 7.55 avg. 6.99
October max. 6.51 max. 9.98 max. 4.92 max. 12.05 max. 20.10 max. 8.45
avg. 1.96 avg. 2.50 avg. 1.24 avg. 2.00 avg. 3.12 avg. 2.70
Scenario I April 4.05 ( - 0.0%) 4.41 ( - 0.0%) - - - -
1.51 ( - 0.7%) 1.82 ( - 2.2%)
June 11.79 ( - 4.4%) 12.24 ( - 5.8%) - - - -
5.39 ( - 0.9%) 4.78 ( - 3.8%)
Scenario II April 3.79 ( - 6.4%) 4.40 ( - 0.2%) - - - -
1.46 ( - 4.0%) 1.74 ( - 6.5%)
June 10.00 ( - 18.9%) 9.89 ( - 23.9%) - - - -
5.26 ( - 3.3%) 4.59 ( - 7.6%)
Scenario III April 4.00 ( - 1.2%) 4.07 ( - 7.7%) 4.21 ( - 6.4%) 6.22 ( - 10.5%) 11.59 ( - 31.8%) 3.56 ( - 0.3%)
1.51 ( - 0.7%) 1.82 ( - 2.2%) 1.44 ( - 1.4%) 2.35 ( - 4.7%) 2.48 ( - 13.9%) 1.82 ( - 2.7%)
June 11.97 ( - 2.9%) 12.57 ( - 3.2%) 9.39 ( - 3.8%) 13.68 ( - 19.3%) 20.21 ( - 21.2%) 15.39 ( - 4.8%)
5.39 ( - 0.9%) 4.85 ( - 2.4%) 3.85 ( - 0.8%) 3.47 ( - 8.2%) 7.15 ( - 5.3%) 6.81 ( - 2.6%)
Scenario IV April 3.79 ( - 6.4%) 4.35( - 1.4%) 4.05 ( - 10.0%) 6.22 ( - 10.5%) 14.94 ( - 12.1%) 3.65( - 0.3%)
1.49 ( - 2.0%) 1.81 ( - 2.7%) 1.44 ( - 1.4%) 2.35 ( - 4.1%) 2.69 ( - 6.6%) 1.81 ( - 3.2%)
June 11.06 ( - 10.3%) 11.70 ( - 9.9%) 8.45 ( - 13.4%) 15.12 ( - 10.7%) 20.58 ( - 19.7%) 14.33 ( - 11.4%)
5.27 ( - 3.1%) 4.63 ( - 6.8%) 3.75 ( - 3.4%) 3.55 ( - 6.1%) 6.81 ( - 9.8%) 6.32 ( - 9.6%)
Scenario V October 6.29 ( - 3.4%) 9.28 ( - 7.0%) 4.62 ( - 6.1%) 10.10 ( - 16.2%) 16.80 ( - 16.4%) 7.82 ( - 7.5%)
1.93 ( - 1.5%) 2.41 ( - 3.6%) 1.22 ( - 1.6%) 1.88 ( - 6.0%) 2.94 ( - 5.8%) 2.57 ( - 4.8%)