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Asian Journal of Atmospheric Environment - Vol. 16 , No. 4

[ Technical Information ]
Asian Journal of Atmospheric Environment - Vol. 16, No. 4
Abbreviation: Asian J. Atmos. Environ
ISSN: 1976-6912 (Print) 2287-1160 (Online)
Print publication date 31 Dec 2022
Received 25 Aug 2022 Revised 19 Oct 2022 Accepted 20 Oct 2022
DOI: https://doi.org/10.5572/ajae.2022.084

Analysis of the National Air Pollutant Emissions Inventory (CAPSS 2018) Data and Assessment of Emissions Based on Air Quality Modeling in the Republic of Korea
Seong-woo Choi1) ; Hyeonjeong Cho1) ; Yumi Hong1) ; Hee-ji Jo1) ; Min Park1) ; Hyeon-ji Lee1) ; Ye-ji Choi1) ; Ho-hyun Shin1) ; Dongjae Lee1) ; Eunji Shin1) ; Wooseung Baek1) ; Sung-kyu Park1) ; Eunhye Kim1) ; Hyung-cheon Kim1) ; Seung-joo Song1) ; Yunseo Park1) ; Jinsik Kim2) ; Jihye Baek2) ; Jinsik Kim2) ; Chul Yoo1), *
1)Emission Inventory Management Team, National Air Emission Inventory and Research Center, Chungcheongbuk-do, Republic of Korea
2)Policy Support Team, National Air Emission Inventory and Research Center, Chungcheongbuk-do, Republic of Korea

Correspondence to : *Tel: +82-43-279-4550 E-mail: s7424yoo@korea.kr


Abstract

According to the 2018 National Air Pollutant Emissions Inventory (NEI), air pollutant emissions in the Republic of Korea comprised 808,801 tons of CO, 1,153,265 tons of NOX, 300,979 tons of SOX, 617,481 tons of TSP, 232,993 tons of PM10, 98,388 tons of PM2.5, 15,562 tons of black carbon (BC), 1,035,636 tons of VOCs, and 315,975 tons of NH3. As for national emission contributions to primary PM2.5 and PM precursors (NOX, SOX, VOCs, and NH3), major source categories were the road sector for NOX, the industry sector for SOX and PM2.5, and the everyday activities and others sector for VOCs and NH3. In the case of emissions by region, the largest amount of NOX was emitted from the Seoul Metropolitan Areas (SMA; Seoul, Incheon, and Gyeonggi-do, hereafter SMA) and the largest amounts of SOX, PM2.5, VOCs, and NH3 were from the Yeongnam region. A 3D chemical transport modeling system was used to examine the uncertainty of the national air pollutant emissions based on the National Emission and Air Quality Assessment System (NEAS). Air quality was simulated using CAPSS 2018, and the simulation data were compared with observed concentrations to examine the uncertainties of the current emissions. These data show that emissions from five si (cities) (Pohang, Yeosu, Gwangyang, Dangjin, and Ulsan) need to be improved. Most of all, it is necessary to examine the emissions from places of business that use anthracite, which is the major PM2.5 emission source, as fuel in these areas.


Keywords: NEI, CAPSS, CMAQ, NEAS, PM2.5

1. INTRODUCTION

The government of the Republic of Korea announced the Comprehensive Measures on Fine Dust Management that aims to reduce particulate matter (PM) emissions by 30% (MOE, 2017), and implemented the strengthened plan on fine dust management for emergency and on a regular basis, which includes emergency reduction measures conducted during the periods recording high PM concentrations (MOE, 2018). However, the annual mean atmospheric concentration of PM2.5 in 2018 was 23 μg/m3, which exceeded the criterion of Korea (15 μg/m3) (MOE, 2019). Evidently, despite these aggressive reduction efforts of the government, atmospheric PM2.5 concentrations were not significantly reduced and high PM concentrations were not mitigated while public awareness is low. Therefore, the Comprehensive Plans for Fine Dust Management and plan on air Environment Management by Region (SMA, Central area, Southern area, and Southeast area) were established and implemented (Kim et al., 2022; MOE, 2020; MOE, 2019).

Since the characteristics of pollutant emission differ by area, it is necessary to identify the major emission sources and analyze their emission contributions to effectively improve PM emissions (Bae et al., 2021). Air pollutant emissions have different characteristics in different regions depending on the topography and industrial structure. For example, SMA has the largest amounts of car-related pollutants in Korea as it has 50% of the country’s total population and cars; whereas, Gangwon-do’s annual air pollutant emissions are relatively low because of small population and underdevelopment of industrial complexes, which is the result of its mountainous topography (NAIR, 2021). In recent years, research has been conducted on the PM concentrations and emission status considering such regional characteristics (Gong et al., 2021; Hwang et al., 2021), and mutual impacts among neighboring areas, caused by PM emissions, have been analyzed (Kim et al., 2021a, b, c; You et al., 2020).

NAIR assesses and publishes the emissions of 9 air pollutants (CO, NOX, SOX, TSP, PM10, PM2.5, black carbon [BC], VOCs, and NH3) for 17 dos (provinces) and metropolitan cities and 250 si (city), gun (county), gu (district) every year (note: Emissions from the sea were managed separately) (Choi et al., 2021). Based on this, the central and local governments need to establish customized PM reduction measures to protect the health and property of local residents and minimize the economic loss of industries.

In this study, the 17 dos (provinces) and metropolitan cities were classified into 5 regions (SMA, Gangwon region, Chungcheong region, Honam region, and Yeongnam region) and changes in air pollutant emissions by region were analyzed using the 2018 national air pollutant emission estimation results. In addition, the uncertainty of domestic emissions was examined by region and pollutant through a comparison between the simulated concentrations using 3D chemical transport model with ground level observed concentrations.


2. NATIONAL AIR POLLUTANT EMISSION ESTIMATION METHOD AND IMPROVEMENTS

As for national air pollutant emissions, the measurement-based emission data of a tele-monitoring system (TMS) were utilized, as it was in previous studies (Choi et al., 2021; Choi et al., 2020; Yeo et al., 2019), or the emissions of 9 pollutants (e.g., PM2.5, NOX, and SOX) were estimated in 13 first-level categories, 56 second-level categories, and 240 third-level categories by applying approximately 30,000 emission factors using approximately 300 statistical data from approximately 150 related organizations (e.g., pollutant-emitting places of business, and those related to transportation and meteorology) as activity data (NAIR, 2022). There were improvements in emission estimation method compared to 2017; The way to collect the activity data from road transport, non-road transport, and agriculture were improved. The details are as follows (NAIR, 2021) (Table 1).

Table 1. 
Improvements in the emission estimation method.
Category Improvement
Activity data <Road transport>
- A change in the method of counting the number of registered cars aged 10‒15 years
(integrated model year counting → individual model year counting)
ㆍ(Before change) 10 to <15 years
ㆍ(After change)<15 years, <14 years, <13 years, <12 years, and <11 years
<Non-road transport>
- (Construction equipment) An increase in the maximum car age subjected to the deterioration rate from 20‒30 years
- (Marine ships) An improvement in fuel consumption for passenger ships and fishing boats as well as subdivided criteria for the application of the sulfur content by oil type
→ Detailed classification of the fuels used for each ship
<Agriculture>
- An improvement in the method of counting livestock population
ㆍ(Before change) Collect the latest information on the number of livestock population in as of fourth quarter
ㆍ(After change) Collect the latest information on the number of livestock population as of the first, second, third, and fourth quarters


3. 2018 NATIONAL AIR POLLUTANT EMISSION ESTIMATION RESULTS
3. 1 National Air Pollutant Emissions

In the 2018 NEI, the national emissions of air pollutants comprised 808,801 tons of CO; 1,153,265 tons of NOX; 300,979 tons of SOX; 617,481 tons of TSP; 232,993 tons of PM10; 98,388 tons of PM2.5; 15,562 tons of BC; 1,035,636 tons of VOCs; and 315,975 tons of NH₃ (Table 2).

Table 2. 
2018 air pollutant emissions and contributions by first-level category of emission sources. (Unit: metric tons/year)
Source category CO NOX SOX TSP PM10 PM2.5 BC VOCs NH3
Total 808,801 1,153,265 300,979 617,481 232,993 98,388 15,562 1,035,636 315,975
100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%
Energy production 69,972 104,420 65,868 4,305 3,975 3,308 405 9,161 1,626
8.7% 9.1% 21.9% 0.7% 1.7% 3.4% 2.6% 0.9% 0.5%
Non industry 58,172 87,599 16,566 1,439 1,269 890 172 2,936 1,414
7.2% 7.6% 5.5% 0.2% 0.5% 0.9% 1.1% 0.3% 0.4%
Manufacturing industry 20,060 168,967 78,867 117,150 68,315 35,099 753 3,579 737
2.5% 14.7% 26.2% 19.0% 29.3% 35.7% 4.8% 0.3% 0.2%
Industrial processes 27,866 57,020 107,353 11,975 6,758 5,189 15 188,247 45,981
3.4% 4.9% 35.7% 1.9% 2.9% 5.3% 0.1% 18.2% 14.6%
Energy transport and storage 30,770
3.0%
Solvent use 547,353
52.9%
Road transport 213,568 406,227 217 8,858 8,858 8,149 4,935 43,658 3,322
26.4% 35.2% 0.1% 1.4% 3.8% 8.3% 31.7% 4.2% 1.1%
Non-road transport 195,020 307,942 29,831 17,236 17,232 15,981 7,014 67,867 126
24.1% 26.7% 9.9% 2.8% 7.4% 16.2% 45.1% 6.6% 0.04%
Waste 1,954 12,492 2,202 338 245 209 3 57,735 22
0.2% 1.1% 0.7% 0.1% 0.1% 0.2% 0.02% 5.6% 0.01%
Agriculture 249,777
79.0%
Other 7,556 184 560 356 320 19 737 12,957
0.9% 0.02% 0.1% 0.2% 0.3% 0.1% 0.1% 4.1%
Fugitive dust 427,916 112,472 18,025 121
69.3% 48.3% 18.3% 0.8%
Biomass burning 214,632 8,413 76 27,703 13,514 11,217 2,125 83,592 14
26.5% 0.7% 0.03% 4.5% 5.8% 11.4% 13.7% 8.1% 0.00%

The emission contributions of different emission source categories by pollutant were as follows: biomass burning (26.5%), road transport (26.4%), and non-road transport (24.1%) for CO; road transport (35.2%), non-road transport (26.7%), manufacturing industry (14.7%) for NOX; industrial process (35.7%), manufacturing industry (26.2%), energy production (21.9%) for SOX; manufacturing industry (35.7%), fugitive dust (18.3%), non-road transport (16.2%) for PM2.5; solvent use (52.9%), industrial process (18.2%) for VOCs; agriculture (79.0%), industrial process (14.6%) for NH3 (Fig. 1).


Fig. 1. 
2018 emission contributions of different emission source categories by pollutant.

For primary PM2.5 and PM precursors (NOX, SOX, VOCs, and NH3), the 13 first-level source categories were classified into five sectors (energy, industry, road, non-road, and everyday activities and others), as presented in Table 3. The national air pollutant emissions in 2018 were compared with those in 2017, and major causes of changes in emissions were analyzed.

Table 3. 
Emission source classification.
Classification Source category
Energy (oil refinery not included) Energy production
(public power generation, private power generation, and district heating)
Industry (oil refinery included) Manufacturing industry
Industrial processes
Waste
Oil refinery
Road Road transport
(passenger cars, vans, buses, freight cars, special cars, RVs, and two-wheeled vehicles)
Non-road Non-road transport
(railroads, ships, agricultural machinery, and construction machinery)
Everyday activities and others Non-industry
Energy transport and storage
Solvent use
Agriculture
Others
Fugitive dust
Biomass burning

NOX emissions decreased by 3.1% compared to the previous year due to the replacement old cars with new cars in the road sector and the reinforcement of the emission control for power plants in the energy sector. SOX emissions decreased by 4.6% compared to the previous year due to the reduction in fuel consumption (including B-C oil) of power plants and strengthened emission control. PM2.5 emissions increased by 7.3% due to the increase in the number of ships and construction machinery registrations in the non-road sector. VOCs emissions decreased by 1.1% compared to the previous year due to the decline in paint supply in the everyday activities and others sector. NH3 emissions increased due to the increase in fertilizer consumption and the number of livestock population in the everyday activities and others sector (Fig. 2).


Fig. 2. 
2018 Air pollutant emissions by sector.

3. 2 Comparison of Air Pollutant Emissions by Region

To examine air pollutant emission characteristics and changes in emissions by region in Korea, the 17 dos (provinces) and metropolitan cities were grouped into the following five regions: SMA (Seoul, Incheon, and Gyeonggi-do), Gangwon region (Gangwon-do), Chungcheong region (Daejeon, Sejong, Chungcheongbuk-do, and Chungcheongnam-do), Honam region (Gwangju, Jeollabuk-do, and Jeollanam-do), and Yeongnam region (Busan, Daegu, Ulsan, Gyeongsangbuk-do, and Gyeongsangnam-do) (Table 4).

Table 4. 
Classification of administrative districts by region.
Region Administrative divisions Region Administrative divisions
SMA Seoul Honam region Gwangju
Incheon Jeollabuk- do
Jeollanam-do
Gyeonggi-do Yeongnam region Busan
Gangwon region Gangwon-do Daegu
Chungcheong region Daejeon Ulsan
Sejong Gyeongsanbuk-do
Gyeongsangnam-do
Chungcheongbuk-do Others Jeju Island
Chungcheongnam-do

To examine pollutant emission characteristics by region, the current status of factors related to major pollutant emission, such as population, economy, large-scale places of business, cars, and construction machinery, was analyzed. For the analysis of the current status of the economy by region, Gross Regional Domestic Product (GRDP) data published by the Korean Statistical Information Service (KOSIS) were utilized. GRDP is the sum of the market prices of all final goods and services produced in a fixed economic zone for a certain period of time. It is used to establish local financial and economic policies because it comprehensively represents the status of the local economy. As of 2018, SMA showed the highest values and proportions for both population (49.8%) and GRDP (52.2%), followed by the Yeongnam, Chungcheong, Honam, and Gangwon regions, and Jeju Island (Table 5).

Table 5. 
GRDP by region in 2018.
Population (thousands) GRDP (trillion)
Nationwide 51,826 100.0% 1,903 100.0%
SMA 25,797 49.8% 992 52.2%
Gangwon region 1,543 3.0% 47 2.5%
Chungcheong region 5,530 10.7% 238 12.5%
Honam region 5,179 10.0% 166 8.7%
Yeongnam region 13,110 25.3% 440 23.1%
Jeju Island 667 1.3% 20 1.1%
Source: KOSIS (Korean Statistical Information Service)

According to the analysis results, the Yeongnam region had the largest number of large-scale places of business (annual pollutant emissions>20 tons; 37.5%), followed by SMA (24.4%) and the Chungcheong region (19.1%). For the analysis of construction machinery, SMA had the largest number of registered vehicles (44.5%) and excavators (33.5%), followed by the Yeongnam region (26.8 and 27.3%, respectively) (Table 6).

Table 6. 
Current Status of places of business and the number of registered cars and construction machinery by region in 2018
Region Places of business1) Cars2) Construction machinery3)
Number of registrations Proportion (%) Number of registrations Proportion (%) Number of registrations Proportion (%)
SMA 1,000 24.4 10,319,869 44.5 168,093 33.5
Gangwon region 123 3.0 766,374 3.3 26,442 5.3
Chungcheong region 783 19.1 2,726,164 11.7 79,053 15.8
Honam region 644 15.7% 2,612,334 11.3% 82,348 16.4%
Yeongnam region 1,539 37.5% 6,224,236 26.8% 136,966 27.3%
Jeju Island 15 0.4% 553,578 2.4% 8,744 1.7%
Total 4,104 100.0% 23,202,555 100.0% 501,646 100.0%
*Sources: 1) Stack Emission Management System (SEMS), National Air Emission Inventory and Research Center, Ministry of Environment (Places of business represent large-scale places of business with annual NOX, SOX, and TSP emissions>20 tons)
2) Number of registered cars: KOSIS (Korean Statistical Information Service)
3) Number of registered construction machinery: Ministry of Land, Infrastructure and Transport

Table 7 and Fig. 3 show the emissions by administrative division and region in 2018. SMA exhibited the largest emissions of CO (238,525 tons; 29.5%), NOX (322,296 tons; 27.9%), and BC (5,215 tons; 33.5%). The Yeongnam region recorded the largest emissions of SOX (113,601 tons; 37.7%), TSP (189.829 tons; 30.7%), PM10 (72,160 tons; 31.0%), PM2.5 (32,945 tons; 33.5%), VOCs (344,649 tons; 33.3%), and NH3 (81,881 tons; 25.9%).

Table 7. 
Air pollutant emissions by administrative divisions in 2018. (Unit: metric tons/year)
Dos (provinces) and metropolitan cities CO NOX SOX TSP PM10 PM2.5 BC VOCs NH3
Total 808,801 1,153,265 300,979 617,481 232,993 98,388 15,562 1,035,636 315,975
100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%
SMA Seoul 59,091 88,319 1,095 31,069 15,130 3,973 1,498 72,393 3,469
7.3% 7.7% 0.4% 5.0% 6.5% 4.0% 9.6% 7.0% 1.1%
Incheon 42,473 54,996 12,165 22,496 7,601 2,701 607 55,061 7,166
5.3% 4.8% 4.0% 3.6% 3.3% 2.7% 3.9% 5.3% 2.3%
Gyeonggi-do 136,960 178,981 8,859 84,050 31,342 10,488 3,110 190,940 47,387
16.9% 15.5% 2.9% 13.6% 13.5% 10.7% 20.0% 18.4% 15.0%
Sub total 238,525 322,296 22,120 137,615 54,074 17,162 5,215 318,393 58,023
29.5% 27.9% 7.3% 22.3% 23.2% 17.4% 33.5% 30.7% 18.4%
Gangwon region Gangwon-do 50,996 79,834 13,802 36,165 9,772 4,109 749 30,263 14,848
6.3% 6.9% 4.6% 5.9% 4.2% 4.2% 4.8% 2.9% 4.7%
Chungcheong region Daejeon 10,660 16,051 492 5,271 1,908 653 219 16,758 764
1.3% 1.4% 0.2% 0.9% 0.8% 0.7% 1.4% 1.6% 0.2%
Sejong 4,956 5,260 80 2,500 1,024 345 123 5,962 2,760
0.6% 0.5% 0.0% 0.4% 0.4% 0.4% 0.8% 0.6% 0.9%
Chungcheongbuk-do 42,067 60,899 7,223 32,096 9,462 3,591 853 43,144 16,981
5.2% 5.3% 2.4% 5.2% 4.1% 3.7% 5.5% 4.2% 5.4%
Chungcheongnam-do 65,226 107,613 69,989 81,841 37,203 18,129 1,318 78,132 53,163
8.1% 9.3% 23.3% 13.3% 16.0% 18.4% 8.5% 7.5% 16.8%
Sub total 122,909 189,823 77,784 121,708 49,598 22,719 2,513 143,997 73,667
15.2% 16.5% 25.8% 19.7% 21.3% 23.1% 16.1% 13.9% 23.3%
Honam region Gwangju 7,956 12,270 173 5,225 1,710 546 153 15,722 968
1.0% 1.1% 0.1% 0.8% 0.7% 0.6% 1.0% 1.5% 0.3%
Jeollabuk-do 46,257 38,562 3,761 42,097 10,629 3,563 773 69,846 35,197
5.7% 3.3% 1.2% 6.8% 4.6% 3.6% 5.0% 6.7% 11.1%
Jeollanam-do 64,643 105,269 58,621 71,464 28,206 13,156 1,130 88,958 43,727
8.0% 9.1% 19.5% 11.6% 12.1% 13.4% 7.3% 8.6% 13.8%
Sub total 118,856 156,101 62,555 118,787 40,545 17,265 2,056 174,525 79,892
14.7% 13.5% 20.8% 19.2% 17.4% 17.5% 13.2% 16.9% 25.3%
Yeongnam region Busan 26,662 49,951 7,897 17,031 6,886 2,644 525 42,340 1,620
3.3% 4.3% 2.6% 2.8% 3.0% 2.7% 3.4% 4.1% 0.5%
Daegu 17,213 26,370 2,595 10,708 3,911 1,294 338 31,875 1,668
2.1% 2.3% 0.9% 1.7% 1.7% 1.3% 2.2% 3.1% 0.5%
Ulsan 31,400 48,719 42,794 8,932 4,080 2,274 298 91,961 15,129
3.9% 4.2% 14.2% 1.4% 1.8% 2.3% 1.9% 8.9% 4.8%
Gyeongsangbuk-do 96,585 104,098 37,718 107,358 45,300 22,007 2,055 89,304 36,544
11.9% 9.0% 12.5% 17.4% 19.4% 22.4% 13.2% 8.6% 11.6%
Gyeongsangnam-do 49,199 73,050 22,596 45,799 11,984 4,726 1,031 89,168 26,920
6.1% 6.3% 7.5% 7.4% 5.1% 4.8% 6.6% 8.6% 8.5%
Sub total 221,058 302,187 113,601 189,829 72,160 32,945 4,248 344,649 81,881
27.3% 26.2% 37.7% 30.7% 31.0% 33.5% 27.3% 33.3% 25.9%
Jeju-do 11,130 17,285 1,836 10,028 3,495 1,065 223 9,000 7,655
1.4% 1.5% 0.6% 1.6% 1.5% 1.1% 1.4% 0.9% 2.4%
Sea* 45,327 85,739 9,282 3,349 3,349 3,123 557 14,809 8
5.6% 7.4% 3.1% 0.5% 1.4% 3.2% 3.6% 1.4% 0.0%
*Sea: Air pollutant emissions from maritime transport such as ships and fishing boats


Fig. 3. 
2018 Air pollutant emissions by administrative division (Unit: metric/km2).

The analysis on the major cause of changes in emissions and the comparison of regional and sectoral emissions based on emissions by region and pollutant is presented in the next section.

3. 2. 1 Analysis of Changes in Emissions for SMA

Almost half of the national population of Korea is concentrated in SMA, which consists of Seoul Metropolitan City (the capital), Incheon, and Gyeonggi-do, as it is the center of politics, economy, society, and culture. To improve the air pollution of SMA caused by high population density, traffic congestion, and industrialization, a separate law (Special Act On The Improvement Of Air Quality In Seoul Metropolitan Area, 2003) was enacted. Based on this, the Air Quality Management Plan in Seoul Metropolitan Area (2005) has been established and implemented. The plan includes strengthening of vehicle emission standards, supply of eco-friendly vehicles and expansion of infrastructure, details regarding total air pollutant emissions limitations for places of business, mandatory installation of VRU at gas stations, reinforcing the management of fugitive dust from vacant lands and places of business.

The population and economy indicators showed that SMA had the largest population (approximately 49.8%) and recorded the highest GRDP (approximately 52.2%) in 2018. The electric, electronic, and precision instrument manufacturing sector constituted the highest proportion of GRDP.

Air pollutant emissions from SMA in 2018 were estimated to be 17,162 tons of PM2.5, 22,120 tons of SOX, 322,296 tons of NOX, 318,393 tons of VOCs, and 58,023 tons of NH3. In addition, the contributions of each pollutant to the national emissions were as follows: PM2.5 (17.4%), SOX (7.3%), NOX (27.9%), VOCs (30.7%), NH3 (18.4%). PM2.5 and VOCs emissions increased by 4.4% and 1.1% compared to the previous year, while SOX, NOX, and NH3 emissions decreased by 10.8%, 0.2%, and 0.2%, respectively. Meanwhile, the contributions of NOX and SOX to the emissions from the road and industry sector respectively were the largest compared to other pollutants. In addition, PM2.5, VOCs, and NH3 contributed the largest to the emissions from the everyday activities and others sector (Fig. 4).


Fig. 4. 
Air pollutant emissions from SMA in 2018.

SMA’s emissions from the road transport recorded the largest compared to other regions as it recorded the largest number of vehicles registered (44.5%), and VKT (40.5%) (PM2.5 36.1%, SOX 40.2%, NOX 38.9%, VOCs 46.9%, and NH3 40.9%). SOX, NOX, VOCs, and NH3 emissions decreased compared to those of the previous year. This is due to the replacement of old vehicles with new ones, which offset the effects of the increase in the number of vehicle registrations (3.1%, 307,000 units) and VKT (2.7%, 3,428 million km).

SMA’s emissions from the non-transport sector were also the largest compared to those of other regions. (NOX: 27.5%, PM2.5: 28.0%, VOCs: 27.5%, and NH3: 27.6%). The region’s PM2.5 and NOX emissions from the construction machinery increased by 12.3% (366 tons) and 11.3% (6,867 tons) compared to those in the previous year. This was because construction machinery registrations (including excavators) increased by 9.9% (20,671 units) and the swaths of construction sites increased by 7.0% (5,001 m2). VOCs emissions increased by 23.7% (3,568 tons) compared to that of the previous year. This was mainly because of the decrease in the number of registered leisure boat using gasoline in Incheon (by 26.4%, 880 units).

SOX emissions decreased by 10.8% (2,686 tons) compared to those of the previous year. In particular, SOX emissions from the industry sector decreased by a large margin (10.3%, 1,077 tons). This was due to the decreased consumption of industrial bituminous coal (66,000 tons, 19.5%) in Gyeonggi-do.

3. 2. 2 Analysis of Changes in Emissions for the Gangwon Region

Most of the Gangwon region, which is located in the northeastern part of Korea, is mountainous. Under the influence of such geographical conditions, industrial complexes are underdeveloped, which led to relatively low proportion of manufacturing-based industries.

The population and economy indicators showed that this region accounted for approximately 3.0% of the national population as of 2018. The GRDP of the region was approximately 2.5% of the national GRDP. More specifically, the public administration, defense and social security-related administration sector showed the highest proportion in GRDP. The manufacturing sector represented approximately 0.9% of the national GRDP.

Air pollutant emissions from the Gangwon region in 2018 were estimated to be 4,109 tons of PM2.5, 13,802 tons of SOX, 79,834 tons of NOX, 30,263 tons of VOCs, and 14,848 tons of NH3. In addition, the contributions of each pollutant to the national emissions were as follows: PM2.5 (4.2%), SOX (4.6%), NOX (6.9%), VOCs (2.9%), NH3 (4.7%). PM2.5, SOX, and NOX emissions decreased by 0.1%, 2.3%, and 7.2%, respectively, compared to those of the previous year, whereas VOCs and NH3 increased by 6.7% and 7.6%, respectively. Meanwhile, in the Gangwon region, the contributions of PM2.5, VOCs, and NH3 to the emissions from everyday activities and others sector respectively were the largest compared to other pollutants. In addition, NOX and SOX contributed the largest to the emissions from the industry sector (Fig. 5).


Fig. 5. 
Air pollutant emissions from the Gangwon region in 2018.

Emissions from the energy sector increased compared to those of the previous year (NOX: 10.9%, SOX: 29.6%, PM2.5: 66.8%, VOCs: 50.2%, and NH3: 58.0%). This was because the consumption of coal (including bituminous coal) and LNG increased by 43.7% (3,228,000 tons) and 62.2% (269 million m3), respectively, due to the operation of new thermal power plants (coal and LNG).

VOCs emissions from the non-road transport sector increased by 38.0% (1,449 tons) compared to those of the previous year. This was mainly because of the increase in the number of registered leisure boat (by 47.2%, 1,570 units).

On the other hand, NOX and SOX emissions from the industry sector decreased by 10.4% (4,992 tons) and 9.1% (764 tons), respectively, compared to those of the previous year. This was due to the reduction in the fuel (bituminous coal) consumption of cement production facilities. NH3 emissions increased by 67.1% compared to those in the previous year. This was mainly because emissions from DeNOX facilities in the industry sector increased by 67.7% (826 tons).

3. 2. 3 Analysis of Changes in Emissions for the Chungcheong region

The Chungcheong region, located in the center of Korea, consists of Daejeon Metropolitan City, Sejong Special Self-governing City, Chungcheongnam-do, and Chungcheongbuk-do. In the western part of the region, thermal power plants (coal and LNG), petrochemical complexes, iron and steel mills, and large manufacturing industries are located near trading ports. Meanwhile, in the eastern part of the region, high-value-added manufacturing industries (e.g., medicine and electronics) and food manufacturing industries are located.

The population and economy indicators showed that the region represented approximately 10.7% of the national population as of 2018. The GRDP of the region was approximately 12.5% of the national GRDP. The electric, electronic, and precision instrument manufacturing sector showed the highest proportion in GRDP, followed by the coal and petrochemical product manufacturing sector.

Air pollutant emissions from the Chungcheong region in 2018 were estimated to be 22,719 tons of PM2.5, 77,784 tons of SOX, 189,823 tons of NOX, 143,997 tons of VOCs, and 73,667 tons of NH3. In addition, the contributions of each pollutant to the national emissions were as follows: PM2.5 (23.1%), SOX (25.8%), NOX (16.5%), VOCs (13.9%), NH3 (23.3%). PM2.5 and NH3 emissions increased by 9.5% and 1.5%, respectively, compared to the previous year, whereas SOX, NOX, and VOCs emissions decreased by 1.8%, 5.9%, and 3.8%, respectively. Meanwhile, in the Chungcheong region, the contributions of PM2.5, NOX and SOX to the emissions from the industry sector respectively were the largest compared to other pollutants. In addition, VOCs and NH3 contributed the largest to the emissions from the everyday activities and others sector (Fig. 6).


Fig. 6. 
Air pollutant emissions from the Chungcheong region in 2018.

In the case of the Chungcheong region, pollutant emissions from the energy sector were large compared to other regions (PM2.5: 37.4%, SOX: 36.9%, NOX: 25.6%). NOX and SOX emissions from the energy sector decreased by 24.1% (7,838 tons) and 16.3% (4,081 tons), respectively, compared to those of the previous year. This was because of the reinforcement of the power plant emission management, which offset the effects of increased consumption of coal (including bituminous coal) in the coal-fired power plants of the region (1.0%, 444,000 tons) compared to the previous year.

PM2.5 and SOX emissions from the industry sector increased by 20.1% (2,242 tons) and 6.6% (3,295 tons) compared to those of the previous year. This was because of the increase in anthracite consumption in the primary metal industry (23.3%).

VOCs emissions decreased by 3.8% (5,737 tons) compared to those of the previous year. More specifically, VOCs emissions from the everyday activities and others sector decreased by 7.3% (7,246 tons) compared to those of the previous year. This was due to the emissions reductions (6,501 tons, 22.5%) caused by the decrease (21.8%) in the consumption of paint used for architecture and buildings in the region.

Meanwhile, NH3 emissions represented 23.3% of the national emissions, and increased by 1.5% (1,084 tons) compared to those of the previous year. This was mainly because the emissions from the agriculture-manure management sector increased by 1.3% (720 tons), which was caused by a 3.3% (1,606,000 units) increase in the number of livestock population, including cows, pigs, and chickens.

3. 2. 4 Analysis of Changes in Emissions for the Honam Region

The Honam region, which consists of Gwangju Metropolitan City, Jeollabuk-do, and Jeollanam-do, is located in the southwestern part of Korea. It is Korea’s representative breadbasket with wide plains, such as Honam and Naju plains. Thermal power plants (coal and LNG) and the nation’s largest petrochemical complex are located in Yeosu, a southern part of the region, in addition to nearby iron and steel mills in Gwangyang.

The population and economy indicators showed that the region accounted for approximately 10.0% of the national population as of 2018. The GRDP of the region is approximately 8.7% of the national GRDP. More specifically, the coal and petrochemical product manufacturing sector showed the highest proportion of GRDP.

Air pollutant emissions from the Honam region in 2018 were estimated to be 17,265 tons of PM2.5, 62,5554 tons of SOX, 156,101 tons of NOX, 174,525 tons of VOCs, and 79,892 tons of NH3. In addition, the contributions of each pollutant to the national emissions were as follows: PM2.5 (17.5%), SOX (20.8%), NOX (13.5%), VOCs (16.9%), NH3 (25.3%). PM2.5 and NH3 emissions increased by 11.7% and 6.3%, respectively, compared to those of the previous year, whereas SOX, NOX, and VOCs decreased by 0.1%, 0.8%, and 2.5%, respectively. Meanwhile, the contributions of PM2.5, and SOX to the emissions from the industry sector, the contributions of NOX to the emissions from the road sector, the contributions of VOCs and NH3 emissions from the everyday activities and others sector were the largest in the region (Fig. 7).


Fig. 7. 
Air pollutant emissions from the Honam region in 2018.

In the case of the industry sector, PM2.5, SOX, and NOX emissions increased by 30.9% (2,010 tons), 5.8% (2,671 tons), and 6.4% (2,666 tons), respectively, compared to those of the previous year. This was mainly because the increased consumption of coal, including anthracite, in the manufacturing sector (13.3%, 318,000 tons) in Jeollanam-do.

NOX emissions from the road transport and non-road transport sectors decreased by 5.1% (2,611 tons) and 5.9% (2,078 tons), respectively, compared to those of the previous year. For road transport, emissions from the sector decreased because of the decrease in the number of old cars registrations and the replacement of old cars with new ones, which offset the impacts of the increase in the number of car registrations (3.0%, 77,000 units) in the region. In the case of the non-road transport, NOX emissions decreased mainly because of the decrease in emissions from the non-road-construction machinery sector (13.6%, 1,674 tons) caused by the reduction in the number of registered construction machinery (14.6%, 6,162 units) in the region.

VOCs emissions decreased by 2.5% (4,561 tons) compared to those in the previous year. More specifically, this was because emissions by paint that is used for shipbuilding decreased by 16.9% (2,528 tons). For paint consumption, it decreased by 17.1% (4,606 kL) compared to those in the previous year.

NH3 emissions increased by 6.3% (4,722 tons) compared to those in the previous year. The Honam region exhibited the largest NH3 emissions in the country from the everyday activities and others sector. This was due to the 21.0% (1,335 tons) increase in emissions caused by a 20.8% (44,000 tons) increase in fertilizer consumption in farmlands, and the 5.0% (2,873 tons) increase in NH3 emissions from the manure sector caused by a 10.2% (5,902,000 units) increase in the number of livestock population.

3. 2. 5 Analysis of Changes in Emissions for the Yeongnam Region

The Yeongnam region, which consists of Busan Metropolitan City, Daegu Metropolitan City, Ulsan Metropolitan City, Gyeongsangbuk-do, and Gyeongsangnamdo, is located in the southeastern part of Korea. Iron and steel manufacturing, shipbuilding, automobile manufacturing, and petrochemical industries as well as the nation’s largest trading port (Busan Port) are located in the region.

The population and economy indicators showed that the region represented approximately 25.3% of the national population as of 2018. The GRDP of the region is approximately 23.1% of the national GRDP. More specifically, the machinery transport equipment, and other product manufacturing sector showed the highest proportion of GRDP, followed by electric, electronic, and precision instrument manufacturing and non-metallic mineral and metal product manufacturing sector.

Air pollutant emissions from the Yeongnam region in 2018 were estimated to be 32,945 tons of PM2.5, 113,601 tons of SOX, 302,187 tons of NOX, 344,649 tons of VOCs, and 81,881 tons of NH3. In addition, the contributions of each pollutant to the national emissions were as follows: PM2.5 (33.5%), SOX (37.7%), NOX (26.2%), VOCs (33.3%), NH3 (25.9%). PM2.5 and NH3 emissions increased by 7.0 and 1.2%, respectively, compared to those in the previous year, whereas SOX, NOX, and VOCs emissions decreased by 6.5%, 3.8%, and 1.8%, respectively. Meanwhile, the contributions of PM2.5 and SOX to the emissions from the industry sector were the largest in the region. In addition, VOCs and NH3 contributed the largest to the emissions from the everyday activities and others sector (Fig. 8).


Fig. 8. 
Air pollutant emissions from the Yeongnam region in 2018.

In the case of the Yeongnam region, air pollutant emissions from the industry sector were found to be the largest in Korea. In this region, emissions from the industry sector were 17,459 tons of PM2.5, 79,097 tons of SOX, 76,780 tons of NOX, 95,344 tons of VOCs, and 17,153 tons of NH3. Each pollutant represented 43.1% (PM2.5), 40.1% (SOX), 31.2% (NOX), 38.2% (VOCs), and 36.6% (NH3) of national emissions from the industry sector, respectively. PM2.5 and NOX emissions from the sector increased by 15.8% (2,380 tons) and 7.7% (5,459 tons), respectively, compared to those in the previous year. This was mainly because the consumption of coal, including anthracite, in the manufacturing sector increased by 15.1% (504 tons) compared to that in the previous year.

PM2.5 emissions increased by 7.0% (2,156 tons) compared to those of the previous year. This was because of the increase in the consumption of anthracite in the industry sector. Meanwhile, NH3 emissions also increased by 1.2% (982 tons) compared to the previous year. This was mainly because emissions from DeNOX facilities in the industry sector increased by 55.0% (1,282 tons).

NOX emissions decreased by 3.8% (12,100 tons) compared to those in the previous year. These emissions decreased by 18.7% (5,854 tons) and 7.9% (9,581 tons) in the energy production and road transport sectors, respectively. For the energy production sector, this was mainly because of the reduction (4.1%) in the bituminous coal consumption by public power generation facilities and the reduction (19.6%, 5,292 tons) in emissions caused by the reinforcement of environmental facilities for power generation facilities. In the case of the road sector, the main cause of such reduction was the decrease in emissions caused by the reduction in the number of old vehicles, which offset the impacts of the increase in vehicles registrations and VKT increased by 2.0% (124,000 units) and 0.8% (750 million km), respectively, compared to those of the previous year.

SOX emissions decreased by 6.5% (7,866 tons) compared to those of the previous year. This was mainly due to the emissions reductions in the energy sector (15.5%, 3,697 tons), the non-road sector (17.1%, 1,966 tons), and the everyday activities and others sector (29.7%, 2,000 tons). More specifically, for the energy sector, the main cause of such reduction was the decrease in emissions from public power generation facilities as it was for NOX. In the case of the non-road sector, the emissions reductions caused by the decrease in the number of cargo ships entering the ports (6.9%, 6,640 units) and the decrease in the sulfur content in fuel (B-C oil). For the everyday activities and others sector, such reductions were due to the emissions reductions (49.3%, 1,424 tons) caused by the reduction in the consumption of fuel oil for cooling and heating (9.4%, 174,000 kL) in commercial and public facilities.

VOCs emissions from the everyday activities and others sector decreased by 1.8% (6,417 tons) compared to those of the previous year. This was mainly due to the decrease in emissions (6.7%, 8,609 tons) caused by the reduction in the consumption of paint at coating facilities (6.5%, 17,719 kL). VOCs emissions from the non-road sector, on the other hand, increased by 23.6% (2,662 tons). This was due to the emissions increase (47.0%, 2,844 tons) caused by an increase in the number of registered leisure boat (47.2%, 785 units).


4. ASSESSMENT OF UNCERTAINTY IN EMISSIONS USING AIR QUALITY MODELING
4. 1 Methodology

The latest activity data and the best available emission factors were applied to the emissions data estimated above. Nevertheless, there are uncertainties in some emission sources. Old emission factors, activity data with low reliability, and missing emission sources are mentioned as the causes of such uncertainties (Kim et al., 2020a; Lee et al., 2019; Kim and Jang, 2014). Therefore, it is necessary to examine the uncertainty of the estimated emission data. Since air pollutants have different emission characteristics depending on the emission sources, it is difficult to verify emissions in a consistent way and present the results in a quantitative manner. To overcome such difficulties, a method of indirectly examining the accuracy of emissions has been used. This methodology is about comparing the concentrations data from monitoring stations with the results of air quality modeling, a process of converting air pollutants emissions into atmospheric concentrations using 3D chemical transport model (Bae et al., 2020a, b; Kim et al., 2020a).

As such, this study uses a method of utilizing 3D chemical transport model to examine the uncertainty of national air pollutant emissions. This study was conducted based on the National Emission and Air quality assessment System (NEAS). NEAS consists of the Weather Research and Forecasting (WRF) model, the Sparse Matrix Operator Kernel Emissions (SMOKE) model, and the Community Multiscale Air Quality (CMAQ) model. The detailed physico-chemical options used in the WRF and CMAQ models are presented in Supplementary Materials (Table S1). CAPSS 2018 was used for domestic emissions and the KORUSv5 data was used for overseas emissions. The domains and horizontal resolutions were for the simulation were as follows: Northeast Asia (27 km), the Korean Peninsula (9 km), and South Korea (3 km). And 2018 was selected as the target year for simulation (Supplementary Materials Fig. S1). NO2 and SO2 were selected as the target pollutants for which the uncertainty of emissions was to be examined by considering the following three aspects: 1) The two pollutants themselves are harmful to people’s health. It is important to identify the emissions of their uncertainty as they are major precursors transformed into PM through secondary formation in the atmosphere; 2) It is easy to intuitively interpret the overestimation/underestimation as emissions and concentrations of NO2 and SO2 have a relatively linear relationship, nature of primary pollutants; 3) Since are NO2 and SO2 less affected by long-range transport, it is easy to assess the emissions of each region. However, this study suggests the results of comparison between simulated and observed concentrations of PM2.5 as well because of the importance PM2.5 has.

An error between the simulated and observed concentrations may occur due to various factors. Representative factors are the uncertainties of meteorological input data, emissions input data, and various physical and chemical equations included in atmospheric chemical transport models. This study assumes that the systematic bias found at a similar level in most regions drives from the errors between meteorological input data and atmospheric chemical transport models. This is to examine the model’s errors occurring from the perspectives of emissions. And the study presents and analyzes the regions with large errors between observed and simulated concentrations while comparing their annual mean concentrations to examine the uncertainty of emissions from the perspectives of total emissions.

4. 2 Comparison between Simulated and Observed Concentrations

Based on the locations of the urban air pollution monitoring network, errors between the simulated and observed annual mean concentrations across Korea were found to be 0.6 ppb (3%) for NO2, 0.1 ppb (4%) for SO2, and 5.6 μg/m3 (24%) for PM2.5. The simulated concentrations of gaseous pollutants were similar to the observed concentrations relative to PM2.5. And PM2.5 concentrations were underestimated compared to the observed concentrations. In addition, this study compared the simulated and observed concentrations at a provincial and metropolitan city level. The bias of the simulated NO2 concentrations was found to range from -7.5 ppb (-35%, Chungcheongbuk-do) to 5.6 ppb (41%, Jeollanam-do). And high reproducibility was observed in Daejeon, Daegu, and Jeju with the error<1 ppb (<5%) (Fig. 9[a]). The bias of the simulated SO2 concentrations ranged from -2.9 ppb (-66%, Seoul) to 7.4 ppb (144%, Jeollanam-do). Overestimation occurred in 5 out of 17 dos (provinces) and metropolitan cities. And it was particularly notable in Jeollanam-do, Gyeongsangbukdo, and Ulsan (Fig. 9[b]). The bias of the simulated PM2.5 concentrations ranged from -9.5 μg/m3 (-35%, Jeollabuk-do) to 6.4 μg/m3 (26%, Gyeongsangbuk-do), and underestimation occurred in 15 out of the 17 dos (provinces) and metropolitan cities (Fig. 9[c]).


Fig. 9. 
Observed and simulated annual mean air pollutant concentrations. (a) NO2, (b) SO2, and (c) particulate matter with an aerodynamic diameter≤2.5 μm (PM2.5) concentrations by region.

For dos (provinces) and metropolitan cities that exhibited an underestimation, a tendency towards underestimation was generally observed in most of the municipalities as well. On the other hand, for dos (provinces) and metropolitan cities that showed an overestimation, high simulated concentrations intensively occurred in some of the municipalities. Such municipalities include Gyeongsangbuk-do (Pohang), Jeollanam-do (Yeosu), Jeollanamdo (Gwangyang), and Chungcheongnam-do (Dangjin), and the simulated NO2, SO2, and PM2.5 concentrations in those municipalities were 2-3 times higher than the observed concentrations of the same pollutants. However, for Ulsan, overestimation of SO2 concentrations occurred at most of the air quality monitoring stations (11 stations, 73%), which was an exceptional case (Fig. 10).


Fig. 10. 
Spatial distributions of observed (circle) and simulated (tile) annual mean air pollutant concentrations. (a) NO2, (b) SO2, and (c) PM2.5 concentrations in 2018 and the bias between them by measurement point.

This study assumes that the uncertainty of emissions would to be high for regions where the errors between the simulated and observed concentrations were large. The PM2.5 concentrations in the atmosphere, however, are known to be affected in a complex manner by direct emissions from emission sources, secondary formation in the atmosphere by the chemical reactions of precursors, and long-range transport (Kim et al., 2021d; Kim et al., 2017a; Kim et al., 2017b). The possibility that long-range transport affected the errors between simulated and observed concentrations was determined to be low because it affects the entire country rather than specific regions (Bae et al., 2021). The components generated by the secondary formation caused by precursors accounted for 50-60% of the domestic PM2.5 concentrations (Kim et al., 2020b). This explains why it is necessary to analyze the uncertainty of precursor emissions in addition to the uncertainty of the air pollutants directly emitted as PM2.5.

Comparing observed and simulated concentrations on the basis of the concentrations of PM’s detailed components would be the most direct way to distinguish the impacts of secondary formation from those of direct emission in the process of analysis. However, since the number of monitoring stations measuring the concentrations of PM2.5 components is extremely limited, simulated concentrations were analyzed based on the following two assumptions: 1) If the uncertainty of primary PM emissions is large, the error will be relatively large in regions adjacent to emission sources due to their direct impacts from emission sources, and the proportion of primary PM components will be relatively high in the simulated PM2.5 concentrations; 2) If the uncertainty of precursor emissions is higher, on the other hand, it takes some time for PM2.5 to be generated after precursors come from emission sources. Therefore, the errors are likely to be larger in the downwind region relatively far from emission sources, and the proportion of secondary components (such as NOX and SOX), will be high in the simulated concentrations.

Figure 11 shows the spatial distribution of the relative proportion of primary PM components in the simulated PM2.5 concentrations. For the Pohang, Yeosu, Gwangyang, and Dangjin regions mentioned above, the proportion of primary PM was>70%, which was relatively high compared to that in other regions. Based on this, the main cause of the error in PM2.5 simulation for Pohang, Yeosu, Gwangyang, and Dangjin was determined to be the uncertainty of emissions (primary PM). And the major emission sources for the areas were analyzed reflecting this conclusion.


Fig. 11. 
(a) Simulated annual mean concentrations of PM2.5 in 2018 and (b) the spatial distribution of primary PM2.5 components’ relative proportion.

For the five regions where the simulated concentration was distinctively higher than the observed concentrations (Pohang, Yeosu, Gwangyang, Dangjin, and Ulsan), the manufacturing (first-level category)-others (second-level category), industrial process (first-level category)-iron and steel making (second-level category), industrial process (first-level category)-petroleum industry (second-level category), and non-road transport (first-level category)-ships (second-level category) sectors were major air pollutant emission sources. Among them, four emission sources at the second-level category level accounted for 57% (NOX), 78% (SO2), and 88% (PM2.5) of the total air pollutant emissions in the five regions. Major emission sources were slightly different by region. In Pohang, Gwangyang, and Dangjin, manufacturing (first-level category)-others (second-level category) and industrial process (first-level category)-iron and steel making (second-level category) were major emission sources. Meanwhile, in Yeosu and Ulsan, industrial process (first-level category)-petroleum industry (second-level category) and non-road transport (first-level category)-cars (second-level category) were major emission sources. In particular, manufacturing (first-level category)-others (second-level category) emission sources produces the large amounts of emissions of all the target air pollutants of this study (NOX, SO2, and PM2.5). In detail, the emission source of manufacturing (first-level category)-others (second-level category)-primary metal industry (third-level category), in which non-public anthracite is used as fuel, represented>99% of the emissions from manufacturing (first-level category)-others (second-level category). Thus, to improve the accuracy of emissions, it is necessary to first examine the uncertainty that may occur in the process of estimating emissions from corresponding emission sources. The uncertainty ahead, however, does not mean the uncertainty of emissions from point sources. When it comes to point sources of large-scale places of business, errors in emissions are not likely to occur because their emissions are estimated on the basis of TMS data. NAIR estimates national air pollutant emissions and has identified the problems with the activity data and the process of estimating emissions from corresponding emission sources. Accordingly, NAIR is conducting research on the improvement of the emission estimation method and the results of improvement to address these problems. The details will be presented in a follow-up paper.

In summary, in this study, air quality modeling was conducted using CAPSS 2018 emissions, and the uncertainty of the current emissions was examined through comparison between observed and simulated concentrations. It was determined that emissions from five regions (Pohang, Yeosu, Gwangyang, Dangjin, and Ulsan) need to be improved. Most of all, it is necessary to examine the emissions form point sources using non-public anthracite as a fuel in manufacturing (first-level category)-others (second-level category)-primary metal industry (third-level category).


5. CONCLUSIONS

According to the 2018 NEI, air pollutant emissions in the Republic of Korea, estimated using CAPSS, comprised 808,801 tons of CO; 1,153,265 tons of NOX; 300,979 tons of SOX; 617,481 tons of TSP; 232,993 tons of PM10; 98,388 tons of PM2.5; 15,562 tons of BC; 1,035,636 tons of VOCs; and 315,975 tons of NH3, and CO, NOX, SOX, VOCs emissions decreased by 1.1%, 3.1%, 4.6%, and 1.1% respectively, while TSP, PM10, PM2.5, BC, NH3 emissions increased by 4.2%, 6.6%, 7.3%, 0.04% and 2.5% respectively.

Emissions of primary PM2.5 as well as PM2.5, SOX, VOCs, and NH3, which contribute to the formation of secondary PM2.5 were assessed in this study. For PM2.5, SOX, VOCs, and NH3, Yeongnam region (33.5, 37.7, 33.3, and 25.9%, respectively) produced the largest amounts of emissions compared to other regions. Meanwhile, for NOX, the largest amounts of emissions occurred in SMA (27.9%). In SMA, the large amounts of PM2.5, VOCs, and NH3 emissions were observed in the everyday activities and others sector (49.1, 74.2, and 85.7%, respectively), and the large amounts of SOX emissions were observed in the industry sector (42.6%), and the large amounts of NOX emissions were observed in the road sector (49.1%). In the Gangwon region, the large amounts of PM2.5, VOCs, and NH3 emissions occurred in the everyday activities and others sector (55.2, 74.7, and 84.9%, respectively) and the large amounts of SOX and NOX emissions occurred in the industry sector (55.0 and 54.1%, respectively). In the Chungcheong region, the large amounts of PM2.5, SOX, and NOX emissions occurred in the industry sector (59.0, 68.0, and 34.7%, respectively) and the large amounts of VOCs and NH3 emissions occurred in the everyday activities and others sector (64.2 and 83.5%, respectively). In the Honam region, the large amounts of PM2.5 and SOX emissions occurred from the industry sector (49.3 and 77.3%, respectively), and the large amounts of NOX emissions occurred from the road sector (31.0%), and the large amounts of VOCs and NH3 emissions occurred from the everyday activities and others sector (48.3 and 86.8%, respectively). In the Yeongnam region, large amounts of PM2.5 and SOX emissions occurred from the industry sector (53.0 and 69.6%, respectively), and the large amounts of NOX emissions occurred from the road sector (37.1%), and the large amounts of VOCs and NH3 emissions occurred from the everyday activities and others sector (64.5 and 77.6%, respectively).

The method of utilizing 3D chemical transport modeling was used to examine the uncertainty of national air pollutant emissions based on the NEAS. In this study, air quality was simulated using CAPSS 2018, and the uncertainty of the current emissions was examined through comparison between the simulated and observed concentrations. The results indicate that the proportion of primary PM in the simulated PM2.5 concentrations was >70% for Pohang, Yeosu, Gwangyang, and Dangjin, which was relatively high compared to that for other areas. Based on this, the main cause of the errors in PM2.5 simulation for Pohang, Yeosu, Gwangyang, and Dangjin was determined to be the uncertainty of emissions (primary PM) In addition, it is necessary to examine the emissions from places of business that use anthracite, a major emission source of PM2.5, as fuel in these si (cities).

To improve the uncertainty of air pollutant emissions, NAIR of Republic of Korea has been conducting research as follows: development of emission factors for facility using SRF (Solid Refuse Fuel), asphalt concrete manufacturing facility, SRU (Sulfur Recovery Unit), latest car models; improvement of activity data on anthracite consumption, traffic volumes of cars, vacant land, and barbecue grilling; identification of missing emission sources such as CHE (Cargo Handling Equipment), military equipment, GSE (Ground Support Equipment). Based on these research efforts, NAIR aims to establish and implement air quality improvement policy, including highly effective PM reduction policies whose impacts can be felt by people, so that it can contribute to improve air quality and promote public health.


References
1. Bae, C., Kim, H.C., Kim, B.U., Kim, Y., Woo, J.H., Kim, S. (2020a) Updating Chinese SO2 emissions with surface observations for regional air-quality modeling over East Asia. Atmospheric Environment, 228, 117416.
2. Bae, C., Kim, H.C., Kim, B.U., Kim, S. (2020b) Surface ozone response to satellite-constrained NOX emission adjustments and its implications. Environmental Pollution, 258, 113469.
3. Bae, C., Kim, E., Yoo, C., Kim, H.-C., Kim, Y.-M., Kim, S. (2021) Prioritizing Local Authorities Effective to Lower the Nationwide PM2.5 Concentrations and the Personal Exposure Based on the Source Apportionment with the CAPSS 2016 Emissions Inventory. Journal of Korean Society for Atmospheric Environment, 37(4), 410-428.
4. Choi, S.-W., Kim, T., Lee, H.-K., Kim, H.-C., Han, J., Lee, K.-B., Lim, E.-H., Shin, S.-H., Jin, H.-A., Choi, E., Kim, Y.-M., Yoo, C. (2020) Analysis of the National Air Pollutant Emission Inventory (CAPSS 2016) and the Major Cause of Change in Republic of Korea. Asian Journal of Atmospheric Environment, 14(4), 422-445.
5. Choi, S.-W., Bae, C.-H., Kim, H.-C., Kim, T., Lee, H.-K., Song, S.-J., Jang, J.-P., Lee, K.-B., Choi, S.-A., Lee, H.-J., Park, Y., Park, S.-Y., Kim, Y.-M., Yoo, C. (2021) Analysis of the National Air Pollutant Emissions Inventory (CAPSS 2017) Data and Assessment of Emissions based on Air Quality Modeling in the Republic of Korea. Asian Journal of Atmospheric Environment, 15(4), 2021064.
6. Gong, J., Shim, C., Choi, K.-C., Gong, S. (2021) The Characteristics of PM2.5 Pollution and Policy Implications in Chungcheong Region. Korean Society of Environmental Engineers, 43(6), 407-418, (in Korean with English abstract).
7. Hwang, I., Lee, T.-J., Kim, T., Bae, G.-N. (2021) Characteristics of Air Pollutant Emissions and Distribution for Particulate Matter Concentration of Air Pollution Networks in Gyeongsangbuk-do. Korean Society for Atmospheric Environment, 37(3), 536-551.
8. Kim, J., Jang, Y.-K. (2014) Uncertainty Assessment for CAPSS Emission Inventory by DARS. Journal of Korean Society for Atmospheric Environment, 30(1), 26-36.
9. Kim, H.C., Kim, S., Kim, B.U., Jin, C.S., Hong, S., Park, R., Son, S.-W., Bae. C., Bae. M., Song. C.-K., Stein, A. (2017a) Recent increase of surface particulate matter concentrations in the Seoul Metropolitan Area, Korea. Scientific Reports, 7(1), 1-7.
10. Kim, S., Bae, C., Yoo, C., Kim, B.U., Kim, H.C., Moon, N. (2017b). PM2.5 simulations for the Seoul Metropolitan Area: (II) estimation of self-contributions and emission-to-PM2.5 conversion rates for each source category. Journal of Korean Society for Atmospheric Environment, 33(4), 377-392.
11. Kim, O., Bae, M., Kim, S. (2020a) Evaluation on Provincial NOX and SO2 Emissions in CAPSS 2016 Based on Photochemical Model Simulation. Journal of Korean Society for Atmospheric Environment, 36(1), 64-83.
12. Kim, J.A., Lim, S., Shang, X., Lee, M., Kang, K.S., Ghim, Y.S. (2020b). Characteristics of PM2.5 chemical composition and high-concentration Episodes observed in Jeju from 2013 to 2016. Journal of Korean Society for Atmospheric Environment, 36(3), 388-403.
13. Kim, S., You, S., Kang, Y.-H., Kim, E., Bae, M., Son, K., Kim, Y., Kim, B.-U., Kim, H. (2021a) Municipality-Level Source Apportionment of PM2.5 Concentrations based on the CAPSS 2016: (II) Incheon, Journal of Korean Society for Atmospheric Environment, 37(1), 144-168.
14. Kim, S., You, S., Kim, E., Kang, Y.-H., Bae, M., Son, K. (2021b) Municipality-Level Source Apportionment of PM2.5 Concentrations based on the CAPSS 2016: (III) Jeollanamdo. Journal of Korean Society for Atmospheric Environment, 37(2), 206-230.
15. Kim, E., You, S., Bae, M., Kang, Y.-H., Son, K., Kim, S. (2021c) Municipality-Level Source Apportionment of PM2.5 Concentrations based on the CAPSS 2016: (IV) Jeollabuk-do. Journal of Korean Society for Atmospheric Environment, 37(2), 292-309.
16. Kim, E., Kim, B.U., Kim, H.C., Kim, S. (2021d) Sensitivity of fine particulate matter concentrations in South Korea to regional ammonia emissions in Northeast Asia. Environmental Pollution, 273, 116428.
17. Kim, M., Kim, J., Lee, Y., Park, S., Oh, B., Cha, J.-D., Kim, J.-B. (2022) Analysis of Emission Characteristics and Estimation of Air Pollutants Emitted from Small Ship. Journal of Korean Society for Atmospheric Environment, 38(2), 258-268.
18. Lee, H.J., Song, M.G., Kim, D.K. (2019) Estimation of emissions and emission factor of volatile organic compounds from small-scale dry cleaning operations using organic solvents. Journal of Korean Society for Atmospheric Environment, 35(4), 413-422.
19. NAIR (National Air Emission Inventory and Research) (2021) 2018 national air pollutant emissions, https://www.air.go.kr/jbmd/sub90_detail.do?tabPage=1&detailKey=61027P06&inputSchTxt=&typeSchOption=titleNm&menuId=POT027
20. NAIR (National Air Emission Inventory and Research) (2022) Handbook of estimation methods for national air pollutant emissions (V), https://www.air.go.kr/jbmd/sub90_detail.do?tabPage=2&detailKey=62017P06&inputSchTxt=&typeSchOption=titleNm&menuId=POT027
21. Yeo, S.-Y., Lee, H.-K., Choi, S.-W., Seol, S.-H., Jin, H.-A., Yoo, C., Kim, J.-H., Kim, J.-S. (2019) Analysis of the National Air Pollutant Emission Inventory (CAPSS 2015) and the Major Cause of Change in Republic of Korea, Asian Journal of Atmospheric Environment, 13(3), 212-231.
22. You, S., Bae, C., Kim, C., Yoo, C., Kim, S. (2020) Municipality-Level Source Apportionment of PM2.5 Concentrations based on the CAPSS 2016: (I) Gyeonggi Province. Journal of Korean Society for Atmospheric Environment, 36(6), 785-805.

APPENDIX

Appendix 1. 
National Air Pollutant Emission. (Unit: metric tons/year)
(a) Trends in CO emissions
Emission source category 2014 2015 2016 2017 2018 Change (%) (2018-2017)
First-level Second-level
Energy production Public power generation 41,534 33,425 35,515 33,924 36,979 9.0%
District heating 3,675 3,365 4,242 5,306 7,271 37.0%
Oil refining 2,320 2,136 1,605 1,862 1,788 -4.0%
Private power generation 10,327 16,212 17,217 18,212 23,934 31.4%
Subtotal 57,856 55,138 58,579 59,304 69,972 18.0%
Non-industry Commercial and public facilities 16,227 16,956 18,896 19,320 19,742 2.2%
Residential facilities 59,341 54,445 47,997 42,612 37,687 -11.6%
Agricultural·livestock·fishery facilities 1,026 898 842 784 743 -5.2%
Subtotal 76,594 72,299 67,735 62,716 58,172 -7.2%
Manufacturing industry Combustion facilities 1,389 1,608 3,265 3,129 3,505 12.0%
Process furnaces 6,587 6,607 7,138 7,043 7,070 0.4%
Others 10,740 8,639 7,767 8,092 9,484 17.2%
Subtotal 18,716 16,854 18,170 18,263 20,060 9.8%
Industrial process Petroleum industry 11,545 12,069 12,643 12,879 12,962 0.6%
Iron and steel industry 5,638 5,761 5,760 5,745 5,834 1.5%
Inorganic chemical industry 485 487 510 605 520 -14.1%
Organic chemical industry 5,316 5,011 5,661 5,889 6,000 1.9%
Pulp and paper industry 2,604 2,469 2,495 2,426 2,351 -3.1%
Others 267 272 271 205 200 -2.6%
Subtotal 25,855 26,069 27,340 27,750 27,866 0.4%
Road transport Passenger cars 136,451 123,534 118,777 114,450 92,483 -19.2%
Taxis 1,757 1,151 740 639 571 -10.7%
Vans 3,730 3,203 4,430 3,966 3,724 -6.1%
Buses 9,451 6,805 6,964 6,825 6,764 -0.9%
Freight cars 49,976 48,379 49,643 48,360 48,631 0.6%
Special cars 1,035 830 1,057 968 1,032 6.6%
RVs 26,634 21,349 22,342 21,104 19,342 -8.3%
Two-wheeled vehicles 52,190 40,265 40,604 40,840 41,021 0.4%
Subtotal 281,225 245,516 244,556 237,152 213,568 -9.9%
Non-road transport Railroads 3,057 2,734 2,426 2,360 2,379 0.8%
Ships 54,535 60,491 62,632 102,179 118,043 15.5%
Aircraft 7,117 7,838 8,865 10,370 10,454 0.8%
Agricultural machinery 7,165 7,097 7,076 7,090 7,038 -0.7%
Construction machinery 54,229 57,540 55,614 54,456 57,105 4.9%
Subtotal 126,103 135,700 136,612 176,455 195,020 10.5%
Waste Waste incineration 1,645 1,548 2,008 2,051 1,954 -4.7%
Others Forest fires and other fires 6,459 7,197 6,977 8,656 7,556 -12.7%
Biomass burning Open burning 4,498 4,200 4,080 3,959 3,784 -4.4%
Crop residue incineration 155,437 157,616 159,196 152,427 143,048 -6.2%
Grilled meat and fish 12 13 9 11 10 -15.0%
Wood stoves and boilers 58,938 57,772 57,029 56,066 55,298 -1.4%
Traditional fireplaces 6,031 5,856 5,750 5,609 5,493 -2.1%
Charcoal kilns 7,000 7,000 7,000 7,000 7,000 0.0%
Subtotal 231,917 232,455 233,066 225,073 214,632 -4.6%
Total 826,370 792,776 795,044 817,420 808,801 -1.1%

Appendix 1. 
Continued. (Unit: metric tons/year)
(b) Trends in NOX emissions
Emission source category 2014 2015 2016 2017 2018 Change (%) (2018-2017)
First-level Second-level
Energy production Public power generation 127,456 116,250 109,721 77,296 64,830 -16.1%
District heating 4,651 4,116 4,075 4,349 4,979 14.5%
Oil refining 8,066 7,818 7,701 8,547 7,881 -7.8%
Private power generation 22,644 22,634 23,948 24,001 26,731 11.4%
Subtotal 162,818 150,818 145,445 114,192 104,420 -8.6%
Non-industry Commercial and public facilities 29,871 32,630 34,249 34,610 34,120 -1.4%
Residential facilities 47,055 46,605 48,101 48,983 50,447 3.0%
Agricultural·livestock·fishery facilities 4,216 3,712 3,474 3,210 3,032 -5.5%
Subtotal 81,143 82,948 85,824 86,803 87,599 0.9%
Manufacturing industry Combustion facilities 13,612 13,955 17,137 16,201 17,294 6.7%
Process furnaces 95,197 94,326 98,494 99,775 89,771 -10.0%
Others 64,852 60,858 59,702 53,814 61,902 15.0%
Subtotal 173,660 169,139 175,332 169,790 168,967 -0.5%
Industrial process Petroleum industry 4,478 4,799 4,932 4,322 4,690 8.5%
Iron and steel industry 38,485 43,671 43,352 42,849 46,077 7.5%
Inorganic chemical industry 4,284 4,882 2,752 3,353 3,050 -9.0%
Organic chemical industry 23 16 19 24 29 19.8%
Others 6,042 6,462 4,877 3,070 3,175 3.4%
Subtotal 53,311 59,830 55,932 53,618 57,020 6.3%
Road transport Passenger cars 34,036 36,193 41,190 41,023 36,431 -11.2%
Taxis 487 363 249 238 221 -7.0%
Vans 15,346 13,121 17,350 15,451 14,428 -6.6%
Buses 31,365 34,097 32,011 28,981 25,013 -13.7%
Freight cars 204,086 206,915 239,450 226,640 210,361 -7.2%
Special cars 2,482 2,479 2,833 2,494 2,618 4.9%
RVs 70,509 73,506 116,938 116,175 114,061 -1.8%
Two-wheeled vehicles 2,919 2,911 2,974 3,037 3,094 1.9%
Subtotal 361,230 369,585 452,995 434,038 406,227 -6.4%
Non-road transport Railroads 7,476 6,688 5,932 5,771 5,819 0.8%
Ships 144,030 151,735 161,826 162,514 155,381 -4.4%
Aircraft 7,323 8,058 9,104 10,621 10,713 0.9%
Agricultural machinery 16,288 16,209 16,190 16,351 16,249 -0.6%
Construction machinery 116,053 121,686 116,934 114,053 119,780 5.0%
Subtotal 291,171 304,376 309,986 309,309 307,942 -0.4%
Waste Waste incineration 12,257 11,977 13,570 12,994 12,492 -3.9%
Others Forest fires and other fires 153 172 167 214 184 -14.0%
Biomass burning Open burning 590 550 535 519 496 -4.4%
Crop residue incineration 5,423 5,606 5,816 5,634 5,247 -6.9%
Grilled meat and fish 9 9 7 8 7 -15.3%
Wood stoves and boilers 2,205 2,195 2,188 2,179 2,172 -0.3%
Traditional fireplaces 528 513 504 491 481 -2.1%
Charcoal kilns 10 10 10 10 10 0.0%
Subtotal 8,765 8,883 9,059 8,841 8,413 -4.8%
Total 1,144,508 1,157,728 1,248,309 1,189,800 1,153,265 -3.1%

Appendix 1. 
Continued. (Unit: metric tons/year)
(c) Trends in SOX emissions
Emission source category 2014 2015 2016 2017 2018 Change (%) (2018-2017)
First-level Second-level
Energy production Public power generation 73,506 71,515 71,497 58,900 51,555 -12.5%
District heating 1,920 1,531 1,425 1,173 669 -42.9%
Oil refining 13,071 12,405 12,917 12,308 8,984 -27.0%
Private power generation 6,065 5,791 5,856 5,194 4,659 -10.3%
Subtotal 94,562 91,243 91,696 77,574 65,868 -15.1%
Non-industry Commercial and public facilities 6,328 12,015 9,744 8,202 6,045 -26.3%
Residential facilities 17,111 15,471 13,204 11,500 9,694 -15.7%
Agricultural·livestock·fishery facilities 1,229 1,249 1,067 1,012 827 -18.3%
Subtotal 24,668 28,736 24,015 20,714 16,566 -20.0%
Manufacturing industry Combustion facilities 3,232 2,441 2,727 2,223 2,066 -7.1%
Process furnaces 19,456 18,811 18,505 16,878 15,955 -5.5%
Others 60,294 63,847 65,362 53,226 60,845 14.3%
Subtotal 82,982 85,098 86,593 72,327 78,867 9.0%
Industrial process Petroleum industry 57,572 57,789 61,756 57,958 58,732 1.3%
Iron and steel industry 29,600 35,538 39,451 39,024 39,757 1.9%
Inorganic chemical industry 1,915 1,706 1,178 1,266 1,440 13.8%
Organic chemical industry 375 448 463 449 455 1.4%
Pulp and paper industry 129 122 123 120 116 -3.1%
Others 9,337 9,781 9,762 7,914 6,853 -13.4%
Subtotal 98,927 105,385 112,734 106,730 107,353 0.6%
Road transport Passenger cars 63 67 82 97 78 -19.3%
Taxis 5 7 4 4 4 -4.3%
Vans 5 5 5 6 4 -24.0%
Buses 9 11 12 15 11 -26.0%
Freight cars 69 82 85 101 76 -25.0%
Special cars 2 2 2 2 2 -1.0%
RVs 23 27 31 40 32 -19.8%
Two-wheeled vehicles 8 8 10 12 9 -22.5%
Subtotal 183 209 231 277 217 -21.7%
Non-road transport Railroads 191 171 151 147 149 0.9%
Ships 39,074 38,467 40,429 34,610 28,711 -17.0%
Aircraft 678 729 802 876 905 3.2%
Agricultural machinery 4 4 4 6 4 -24.3%
Construction machinery 45 53 56 71 62 -12.5%
Subtotal 39,991 39,424 41,443 35,710 29,831 -16.5%
Waste Waste incineration 1,846 2,119 2,161 2,120 2,202 3.9%
Biomass burning Grilled meat and fish 2 2 1 2 1 -15.2%
Wood stoves and boilers 62 60 60 59 58 -1.2%
Traditional fireplaces 9 9 9 9 8 -2.1%
Charcoal kilns 8 8 8 8 8 0.0%
Subtotal 80 79 78 77 76 -1.5%
Total 343,241 352,292 358,951 315,530 300,979 -4.6%

Appendix 1. 
Continued. (Unit: metric tons/year)
(d) Trends in TSP emissions
Emission source category 2014 2015 2016 2017 2018 Change (%) (2018-2017)
First-level Second-level
Energy production Public power generation 3,976 3,812 3,337 3,147 3,106 -1.3%
District heating 108 132 149 168 181 7.9%
Oil refining 169 182 157 148 215 45.0%
Private power generation 481 565 630 646 803 24.3%
Subtotal 4,733 4,692 4,273 4,109 4,305 4.8%
Non-industry Commercial and public facilities 121 184 165 154 128 -17.4%
Residential facilities 1,447 1,349 1,238 1,152 1,068 -7.4%
Agricultural·livestock·fishery facilities 340 308 291 265 244 -7.9%
Subtotal 1,908 1,841 1,694 1,572 1,439 -8.4%
Manufacturing industry Combustion facilities 449 445 408 237 239 1.2%
Process furnaces 3,771 3,825 3,196 3,044 3,463 13.7%
Others 98,518 117,399 119,533 92,535 113,448 22.6%
Subtotal 102,738 121,668 123,138 95,815 117,150 22.3%
Industrial process Petroleum industry 466 459 502 511 482 -5.6%
Iron and steel industry 7,617 7,740 7,797 7,801 7,990 2.4%
Inorganic chemical industry 635 620 634 771 705 -8.5%
Organic chemical industry 1,558 1,844 1,911 1,859 1,884 1.4%
Pulp and paper industry 44 44 44 43 41 -6.0%
Others 1,847 1,168 1,168 1,111 872 -21.5%
Subtotal 12,167 11,876 12,056 12,096 11,975 -1.0%
Road transport Passenger cars 81 88 158 169 137 -19.4%
Taxis 2 2 2 -4.5%
Vans 435 328 437 394 377 -4.3%
Buses 223 234 222 195 181 -7.2%
Freight cars 6,839 6,694 7,296 6,483 6,178 -4.7%
Special cars 74 58 97 65 63 -2.9%
RVs 2,367 2,182 2,307 2,085 1,840 -11.8%
Motorcycles 78 79 80 1.3%
Subtotal 10,019 9,583 10,596 9,473 8,858 -6.5%
Non-road transport Railroads 484 433 384 374 377 0.9%
Ships 6,983 7,091 7,589 8,290 8,973 8.2%
Aircraft 89 94 103 109 109 0.4%
Agricultural machinery 1,364 1,348 1,342 1,340 1,330 -0.8%
Construction machinery 5,945 6,354 6,173 6,086 6,448 5.9%
Subtotal 14,865 15,320 15,592 16,198 17,236 6.4%
Waste Waste incineration 335 340 406 377 338 -10.5%
Others Forest fires and other fires 428 498 481 679 560 -17.4%
Fugitive Dust Paved roads 140,840 143,644 152,599 161,824 163,640 1.1%
Construction 40,356 55,714 51,005 53,284 55,488 4.1%
Vacant lands 27,519 27,403 24,712 20,979 21,645 3.2%
Loading and unloading 25 26 26 27 25 -5.0%
Agricultural production 29,553 29,072 28,549 27,845 27,778 -0.2%
Livestock production 29,745 30,524 31,898 32,734 33,898 3.6%
Waste disposal 12,655 14,414 15,498 15,902 16,585 4.3%
Unpaved roads 115,250 107,445 108,400 109,825 108,856 -0.9%
Subtotal 395,944 408,242 412,686 422,420 427,916 1.3%
Biomass burning Open burning 1,438 1,342 1,304 1,265 1,209 -4.4%
Crop residue incineration 22,085 22,126 22,832 22,079 20,139 -8.8%
Grilled meat and fish 606 626 461 565 491 -13.2%
Wood stoves and boilers 4,173 4,072 4,008 3,924 3,857 -1.7%
Traditional fireplaces 173 168 165 161 157 -2.1%
Charcoal kilns 1,849 1,849 1,849 1,849 1,849 0.0%
Subtotal 30,323 30,183 30,618 29,843 27,703 -7.2%
Total 573,460 604,243 611,539 592,582 617,481 4.2%

Appendix 1. 
Continued. (Unit: metric tons/year)
(e) Trends in PM10 emissions
Emission source category 2014 2015 2016 2017 2018 Change (%) (2018-2017)
First-level Second-level
Energy production Public power generation 3,854 3,681 3,194 3,041 3,011 -1.0%
District heating 85 113 133 152 171 12.2%
Oil refining 104 57 53 53 69 30.5%
Private power generation 465 544 571 583 724 24.2%
Subtotal 4,508 4,394 3,951 3,829 3,975 3.8%
Non-industry Commercial and public facilities 112 170 152 144 119 -17.2%
Residential facilities 1,206 1,129 1,048 987 926 -6.2%
Agricultural·livestock·fishery facilities 312 283 267 243 224 -7.9%
Subtotal 1,629 1,582 1,468 1,374 1,269 -7.7%
Manufacturing industry Combustion facilities 323 249 240 132 134 2.0%
Process furnaces 2,282 2,290 1,955 1,855 2,122 14.4%
Others 57,370 68,354 69,599 53,886 66,058 22.6%
Subtotal 59,975 70,893 71,794 55,872 68,315 22.3%
Industrial process Petroleum industry 135 133 145 148 139 -5.6%
Iron and steel industry 4,755 4,833 4,856 4,856 4,981 2.6%
Inorganic chemical industry 359 348 356 435 399 -8.3%
Organic chemical industry 795 940 975 948 961 1.4%
Pulp and paper industry 27 27 26 26 25 -6.0%
Others 337 377 373 346 253 -26.7%
Subtotal 6,407 6,658 6,731 6,759 6,758 0.0%
Road transport Passenger cars 81 88 158 169 137 -19.4%
Taxis 2 2 2 -4.5%
Vans 435 328 437 394 377 -4.3%
Buses 223 234 222 195 181 -7.2%
Freight cars 6,839 6,694 7,296 6,483 6,178 -4.7%
Special cars 74 58 97 65 63 -2.9%
RVs 2,367 2,182 2,307 2,085 1,840 -11.8%
Motorcycles 78 79 80 1.3%
Subtotal 10,019 9,583 10,596 9,473 8,858 -6.5%
Non-road transport Railroads 484 433 384 374 377 0.9%
Ships 6,983 7,091 7,589 8,290 8,973 8.2%
Aircraft 85 90 99 104 105 0.4%
Agricultural machinery 1,364 1,348 1,342 1,340 1,330 -0.8%
Construction machinery 5,945 6,354 6,173 6,086 6,448 5.9%
Subtotal 14,861 15,317 15,588 16,194 17,232 6.4%
Waste Waste incineration 247 246 295 274 245 -10.6%
Others Forest fires and other fires 272 317 306 431 356 -17.4%
Fugitive Dust Paved roads 27,034 27,573 29,291 31,062 31,411 1.1%
Construction 27,685 38,221 34,990 36,553 38,065 4.1%
Vacant lands 10,733 10,687 9,638 8,182 8,442 3.2%
Loading and unloading 9 9 9 9 9 -5.0%
Agricultural production 10,141 9,961 9,791 9,596 9,572 -0.2%
Livestock production 9,939 10,200 10,658 10,938 11,325 3.5%
Waste disposal 3,416 3,926 4,220 4,335 4,473 3.2%
Unpaved roads 9,715 9,057 9,137 9,257 9,176 -0.9%
Subtotal 98,671 109,633 107,735 109,932 112,472 2.3%
Biomass burning Open burning 984 919 893 866 828 -4.4%
Crop residue incineration 9,121 9,183 9,474 9,150 8,471 -7.4%
Grilled meat and fish 606 626 461 565 491 -13.2%
Wood stoves and boilers 2,002 1,958 1,930 1,893 1,864 -1.5%
Traditional fireplaces 114 111 109 106 104 -2.1%
Charcoal kilns 1,757 1,757 1,757 1,757 1,757 0.0%
Subtotal 14,583 14,552 14,623 14,338 13,514 -5.7%
Total 211,172 233,177 233,085 218,476 232,993 6.6%

Appendix 1. 
Continued. (Unit: metric tons/year)
(f) Trends in PM2.5 emissions
Emission source category 2014 2015 2016 2017 2018 Change (%) (2018-2017)
First-level Second-level
Energy production Public power generation 3,162 2,989 2,593 2,470 2,454 -0.7%
District heating 63 99 120 140 166 18.4%
Oil refining 46 23 23 25 29 16.6%
Private power generation 407 496 517 526 659 25.2%
Subtotal 3,679 3,607 3,253 3,162 3,308 4.6%
Non-industry Commercial and public facilities 72 109 98 96 81 -15.5%
Residential facilities 782 745 721 694 673 -3.1%
Agricultural·livestock·fishery facilities 191 171 159 144 135 -6.4%
Subtotal 1,045 1,025 978 935 890 -4.9%
Manufacturing industry Combustion facilities 165 121 148 101 102 0.3%
Process furnaces 1,245 1,226 1,059 1,006 1,155 14.8%
Others 28,912 34,971 35,577 27,393 33,842 23.5%
Subtotal 30,322 36,317 36,785 28,501 35,099 23.2%
Industrial process Petroleum industry 30 29 32 32 31 -5.6%
Iron and steel industry 3,636 3,705 3,730 3,729 3,825 2.6%
Inorganic chemical industry 202 194 199 244 224 -8.2%
Organic chemical industry 715 846 877 853 865 1.4%
Pulp and paper industry 17 18 17 17 16 -8.0%
Others 303 340 336 311 229 -26.4%
Subtotal 4,903 5,132 5,191 5,186 5,189 0.1%
Road transport Passenger cars 75 81 145 156 126 -19.4%
Taxis 2 2 2 -4.5%
Vans 400 302 402 363 347 -4.3%
Buses 205 215 204 179 166 -7.2%
Freight cars 6,292 6,159 6,712 5,964 5,683 -4.7%
Special cars 68 53 89 60 58 -2.9%
RVs 2,178 2,008 2,123 1,918 1,693 -11.8%
Motorcycles 72 73 74 1.3%
Subtotal 9,218 8,817 9,748 8,715 8,149 -6.5%
Non-road transport Railroads 446 399 354 344 347 0.9%
Ships 6,423 6,539 6,995 7,731 8,383 8.4%
Aircraft 78 83 91 96 96 0.4%
Agricultural machinery 1,255 1,240 1,235 1,233 1,223 -0.8%
Construction machinery 5,469 5,846 5,679 5,599 5,932 5.9%
Subtotal 13,671 14,106 14,354 15,002 15,981 6.5%
Waste Waste incineration 204 209 252 234 209 -10.5%
Others Forest fires and other fires 245 285 275 388 320 -17.4%
Fugitive dust Paved roads 6,541 6,671 7,087 7,515 7,599 1.1%
Construction 2,769 3,822 3,499 3,655 3,807 4.1%
Vacant lands 1,610 1,603 1,446 1,227 1,266 3.2%
Loading and unloading 1 1 1 1 1 -5.0%
Agricultural production 2,028 1,992 1,958 1,919 1,914 -0.2%
Livestock production 1,840 1,861 1,960 2,013 2,073 3.0%
Waste disposal 342 393 422 433 447 3.2%
Unpaved roads 971 906 914 926 918 -0.9%
Subtotal 16,101 17,248 17,286 17,690 18,025 1.9%
Biomass burning Open burning 873 815 792 768 734 -4.4%
Crop residue incineration 7,563 7,621 7,878 7,627 7,046 -7.6%
Grilled meat and fish 556 574 423 518 451 -13.0%
Wood stoves and boilers 1,326 1,298 1,280 1,257 1,238 -1.5%
Traditional fireplaces 92 89 87 85 83 -2.1%
Charcoal kilns 1,664 1,664 1,664 1,664 1,664 0.0%
Subtotal 12,073 12,060 12,124 11,919 11,217 -5.9%
Total 91,460 98,806 100,247 91,731 98,388 7.3%

Appendix 1. 
Continued. (Unit: metric tons/year)
(g) Trends in Black Carbon emissions
Emission source category 2014 2015 2016 2017 2018 Change (%) (2018-2017)
First-level Second-level
Energy production Public power generation 219 146 151 128 148 15.9%
District heating 17 28 36 45 63 40.0%
Oil refining 5 1 2 3 2 -36.5%
Private power generation 83 132 141 143 192 34.6%
Subtotal 324 307 330 319 405 27.2%
Non-industry Commercial and public facilities 9 13 13 15 13 -9.6%
Residential facilities 130 128 136 140 147 4.8%
Agricultural·livestock·fishery facilities 16 14 13 12 11 -5.9%
Subtotal 156 155 161 167 172 2.8%
Manufacturing industry Combustion facilities 20 14 35 30 29 -2.0%
Process furnaces 74 60 62 64 75 16.3%
Others 554 666 679 526 649 23.4%
Subtotal 648 741 776 620 753 21.4%
Industrial process Petroleum industry 0.02 0.02 0 0.02 0.02 -5.6%
Iron and steel industry 11 11 11 11 11 2.5%
Pulp and paper industry 0.1 0.1 0.1 0.1 0.04 -33.3%
Others 4 5 6 6 4 -32.0%
Subtotal 15 16 17 17 15 -9.7%
Road transport Passenger cars 33 39 60 66 48 -26.2%
Vans 240 183 237 214 204 -4.6%
Buses 158 166 157 138 128 -7.2%
Freight cars 3,939 3,873 4,187 3,749 3,538 -5.6%
Special cars 52 41 69 46 45 -2.9%
RVs 1,252 1,154 1,219 1,102 971 -11.8%
Subtotal 5,674 5,456 5,930 5,315 4,935 -7.1%
Non-road transport Railroads 344 308 273 265 267 0.9%
Ships 1,004 1,042 1,105 1,141 1,154 1.1%
Aircraft 61 64 70 74 74 0.4%
Agricultural machinery 968 956 953 951 943 -0.8%
Construction machinery 4,218 4,509 4,380 4,318 4,575 5.9%
Subtotal 6,594 6,879 6,781 6,749 7,014 3.9%
Waste Waste incineration 3 3 4 4 3 -10.5%
Others Forest fires and others 11 15 14 24 19 -24.1%
Fugitive Dust Paved roads 68 70 74 79 79 1.1%
Vacant lands 0.3 0.3 0.3 0.2 0.3 3.2%
Loading and unloading 0.04 0.04 0.04 0.04 0.04 -5.0%
Agricultural production 0.4 0.4 0.4 0.4 0.4 -0.2%
Livestock production 28 27 30 30 31 1.8%
Unpaved roads 11 10 10 10 10 -0.9%
Subtotal 108 108 115 120 121 1.1%
Biomass burning Open burning 37 34 33 32 31 -4.4%
Crop residue incineration 1,707 1,709 1,738 1,687 1,599 -5.2%
Grilled meat and fish 23 23 17 21 18 -13.0%
Wood stoves and boilers 219 213 210 206 202 -1.7%
Traditional fireplaces 13 13 13 12 12 -2.1%
Charcoal kilns 263 263 263 263 263 0.0%
Subtotal 2,261 2,255 2,274 2,221 2,125 -4.3%
Total 15,795 15,934 16,401 15,555 15,562 0.04%

Appendix 1. 
Continued. (Unit: metric tons/year)
(h) Trends in VOCs emissions
Emission source category 2014 2015 2016 2017 2018 Change (%) (2018-2017)
First-level Second-level
Energy production Public power generation 5,486 4,497 4,832 4,327 4,774 10.3%
District heating 509 472 591 732 995 35.9%
Oil refining 318 327 296 269 208 -22.8%
Private power generation 1,384 2,169 2,282 2,425 3,185 31.3%
Subtotal 7,697 7,464 8,001 7,753 9,161 18.2%
Non-industry Commercial and public facilities 722 795 810 820 817 -0.4%
Residential facilities 1,777 1,773 1,879 1,963 2,075 5.7%
Agricultural·livestock·fishery facilities 59 53 51 47 44 -6.3%
Subtotal 2,558 2,622 2,740 2,830 2,936 3.7%
Manufacturing industry Combustion facilities 193 222 447 428 476 11.2%
Process furnaces 1,134 1,079 1,176 1,166 1,182 1.3%
Others 1,953 1,800 1,719 1,606 1,922 19.7%
Subtotal 3,280 3,101 3,342 3,199 3,579 11.9%
Industrial process Petroleum industry 53,588 56,021 58,686 59,780 60,165 0.6%
Iron and steel industry 19,325 19,408 19,546 19,756 20,117 1.8%
Inorganic chemical industry 579 564 613 741 679 -8.4%
Organic chemical industry 44,050 44,417 45,508 45,856 45,457 -0.9%
Pulp and paper industry 1 1 1 1 1 0.0%
Food and beverage industry 62,275 61,943 61,206 61,780 61,429 -0.6%
Others 534 544 543 410 399 -2.6%
Subtotal 180,351 182,899 186,104 188,324 188,247 0.0%
Energy transport and storage Gasoline supply 27,645 29,137 30,160 30,695 30,770 0.2%
Solvents use Painting facilities 339,582 344,671 347,608 348,822 334,364 -4.1%
Cleaning facilities 27,701 28,144 27,740 27,442 27,074 -1.3%
Laundry facilities 21,304 20,407 20,390 20,250 20,464 1.1%
Other solvent use 160,731 162,137 162,266 167,134 165,451 -1.0%
Subtotal 549,318 555,359 558,004 563,648 547,353 -2.9%
Road transport Passenger cars 18,045 16,071 15,877 15,315 13,984 -8.7%
Taxis 89 61 38 33 28 -13.7%
Vans 632 531 669 629 576 -8.5%
Buses 12,134 12,366 11,936 11,447 10,833 -5.4%
Freight cars 11,436 11,514 12,700 12,149 11,899 -2.1%
Special cars 266 246 317 285 278 -2.5%
RVs 2,610 2,384 3,017 3,027 2,999 -0.9%
Two-wheeled vehicles 4,255 2,973 3,008 3,036 3,061 0.8%
Subtotal 49,468 46,145 47,561 45,920 43,658 -4.9%
Non-road transport Railroads 1,225 1,095 973 948 954 0.7%
Ships 18,340 20,970 22,185 41,064 48,961 19.2%
Aircraft 672 700 749 834 789 -5.4%
Agricultural machinery 1,955 1,933 1,925 1,917 1,902 -0.8%
Construction machinery 14,681 15,613 14,984 14,645 15,261 4.2%
Subtotal 36,873 40,311 40,816 59,407 67,867 14.2%
Waste Waste incineration 44,612 53,173 55,520 55,366 54,770 -1.1%
Others 3,449 3,901 3,468 3,039 2,965 -2.4%
Subtotal 48,061 57,074 58,988 58,405 57,735 -1.1%
Others Forest fires and other fires 551 648 624 901 737 -18.2%
Biomass burning Open burning 4,807 4,488 4,361 4,231 4,044 -4.4%
Crop residue incineration 61,154 61,408 63,497 62,729 60,279 -3.9%
Grilled meat and fish 147 154 110 137 119 -13.3%
Wood stoves and boilers 17,406 17,071 16,858 16,581 16,361 -1.3%
Traditional fireplaces 1,687 1,638 1,608 1,569 1,536 -2.1%
Charcoal kilns 1,254 1,254 1,254 1,254 1,254 0.0%
Subtotal 86,454 86,012 87,687 86,500 83,592 -3.4%
Total 992,256 1,010,771 1,024,029 1,047,585 1,035,636 -1.1%

Appendix 1. 
Continued. (Unit: metric tons/year)
(i) Trends in NH3 emissions
Emission source category 2014 2015 2016 2017 2018 Change (%) (2018-2017)
First-level Second-level
Energy production Public power generation 798 557 708 440 534 21.4%
District heating 145 128 158 192 263 37.1%
Oil refining 174 198 177 151 106 -30.1%
Private power generation 308 496 516 547 723 32.2%
Subtotal 1,425 1,379 1,559 1,330 1,626 22.3%
Non-industry Commercial and public facilities 498 567 582 580 538 -7.3%
Residential facilities 618 641 698 723 757 4.6%
Agricultural·livestock·fishery facilities 164 143 134 125 119 -5.2%
Subtotal 1,280 1,351 1,415 1,429 1,414 -1.1%
Manufacturing industry Combustion facilities 57 67 130 122 134 9.6%
Process furnaces 229 233 254 245 250 1.9%
Others 431 327 288 320 353 10.2%
Subtotal 717 627 672 688 737 7.1%
Industrial process Petroleum industry 22,368 23,384 24,496 24,953 25,113 0.6%
Iron and steel industry 1,691 1,728 1,728 1,724 1,750 1.5%
Ammonia consumption 13,984 14,320 16,265 16,301 19,117 17.3%
Subtotal 38,043 39,432 42,489 42,977 45,981 7.0%
Road transport Passenger cars 9,906 9,863 4,554 3,914 2,800 -28.5%
Taxis 102 104 99 -4.3%
Vans 8 7 18 16 14 -13.6%
Buses 12 14 27 29 29 2.8%
Freight cars 83 88 162 160 157 -2.0%
Special cars 2 2 3 3 4 58.7%
RVs 52 56 154 160 166 3.8%
Two-wheeled vehicles 49 50 51 52 52 1.4%
Subtotal 10,113 10,078 5,071 4,437 3,322 -25.1%
Non-road transport Railroads 14 12 11 10 11 1.5%
Ships 13 14 14 15 14 -3.8%
Agricultural machinery 53 53 53 54 53 -0.6%
Construction machinery 36 38 39 41 47 15.0%
Subtotal 116 117 117 120 126 4.5%
Waste Others 23 22 22 22 22 -1.9%
Agriculture Fertilizer use 20,172 19,901 19,553 17,754 19,566 10.2%
Livestock manure management 207,781 211,362 217,464 226,582 230,211 1.6%
Subtotal 227,953 231,263 237,017 244,335 249,777 2.2%
Others Animals 12,832 12,882 12,924 12,945 12,957 0.1%
Biomass burning Open burning 2 2 2 2 2 -4.4%
Crop residue incineration 5 5 5 5 5 -6.0%
Wood stoves and boilers 6 6 6 6 6 -1.2%
Traditional fireplaces 2 2 2 2 2 -2.1%
Subtotal 16 15 15 15 14 -3.4%
Total 292,517 297,167 301,301 308,298 315,975 2.5%

SUPPLEMENTARY MATERIALS

Table S1. 
Configurations of (A)WRF and (B)CMAQ models in this study1
(A) WRF Description
Version WRF v3.4.1
Microphysics WSM6
Short wave radiation Dudhia
Land-Surface Model NOAH
PBL scheme YSU
(B) CMAQ Description
Version Version 4.7.1
Chemical Mechanism SAPRC99
Chemical Solver EBI
Aerosol Module AERO5
Boundary Condition Default profile
Advection Scheme YAMO
Horizontal Diffusion Multiscale
Vertical Diffusion Eddy


Fig. S1. 
Horizontal resolutions for the simulation by domain were as follows: 27 km (Domain 1), 9 km (Domain 2), and 3 km (Domain 3).