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Asian Journal of Atmospheric Environment - Vol. 15 , No. 4

[ Technical Information ]
Asian Journal of Atmospheric Environment - Vol. 15, No. 4
Abbreviation: Asian J. Atmos. Environ
ISSN: 1976-6912 (Print) 2287-1160 (Online)
Print publication date 31 Dec 2021
Received 20 May 2021 Revised 25 Aug 2021 Accepted 08 Nov 2021
DOI: https://doi.org/10.5572/ajae.2021.064

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
Seong-woo Choi ; Chang-han Bae ; Hyung-cheon Kim ; Taekyu Kim ; Hyang-kyeong Lee ; Seung-joo Song ; Jeong-pil Jang ; Kyoung-bin Lee ; Su-ah Choi ; Hyeon-ji Lee ; Yunseo Park1) ; Seo-yeon Park1) ; Young-min Kim1) ; Chul Yoo*
Emission Inventory Management Team, National Air Emission Inventory and Research Center, Chungcheongbuk-do, Republic of Korea
1)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


Copyright © 2021 by Asian Association for 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

According to the 2017 National Air Pollutant Emissions Inventory (NEI), air pollutant emissions in the Republic of Korea comprised 817,420 metric tons (hereafter tons) of CO, 1,189,800 tons of NOx, 315,530 tons of SOx, 592,582 tons of TSP, 218,476 tons of PM10, 91,731 tons of PM2.5, 15,555 tons of black carbon (BC), 1,047,585 tons of VOCs, and 308,298 tons of NH3. Emissions of the 13 first-level emission source categories, which constitute the NEI, were estimated and, based on their characteristics, the emission source categories were grouped into five sectors (energy, industry, road, non-road, and everyday activities and others). In addition, the contributions of primary PM2.5 and its four precursors (NOx, SOx, VOCs, and NH3) to the 2017 NEI were assessed in this study. The emission contributions of NOx to the NEI were 36.5% for the road sector, which was the highest of those of all the air pollutants for this sector; NOx emissions for this sector were 4.2% lower than those in the previous year. The emission contributions of SOx and PM2.5 to the NEI were higher than those of the other air pollutants for the industry sector; SOx and PM2.5 emissions for this sector decreased by 9.8% and 19.7%, respectively, compared with those in the previous year. The emission contributions of VOCs and NH3 to the NEI were 65.3% and 83.9% for the everyday activities and others sector, respectively, higher than those of the other air pollutants for this sector; VOCs and NH3 emissions for this sector increased by 0.8% and 2.9%, respectively, compared with those in the previous year. A three-dimensional (3D) chemical transport modeling system was used to validate the emission estimates. These data suggest that simulated SOx emissions were overestimated in areas with dense large-scale industrial complexes, such as Jeollanam-do, Gyeongsangbuk-do, and Ulsan, and that simulated NOx emissions were overestimated in Seoul, Incheon, and Jeollanam-do.


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

1. INTRODUCTION

In September 2017, the Government of the Republic of Korea announced the Comprehensive Plan on Fine Dust1 Management, aiming to reduce particulate matter (PM) emissions by 30% by 2022 in response to increasing public demand. Moreover, additional efforts were made to reduce PM emissions by announcing three more plans, namely, the Strengthened Measures to Manage Fine Dust at Normal and Emergency Levels in November 2018, designed to preemptively supplement the previous plan; the Comprehensive Plan on Fine Dust Management in November 2019; and the Special Measures to Respond to High Levels of Fine Dust, which is also called the Seasonal Fine Dust Management System.

Even though the national annual mean concentrations of PM2.5 in 2019 were 3 μg/m3 lower than those in 2015 (2015: 26 μg/m3 → 2019: 23 μg/m3), only 1.9% of the regions in Korea met the national standards for PM2.5 (NIER, 2020). In this regard, basic research is increasingly needed to implement effective plans that help reduce air pollutant emissions (Hwang, 2018; Koo et al., 2018). Primarily, more research is needed to clarify the relationship between PM concentrations and PM precursors. To this end, it is necessary to obtain accurate and reliable emission data and comprehensively analyze current emission trends (Choi et al., 2020).

In Korea, the National Air Emission Inventory and Research Center (NAIR) assesses and declares annual air pollutant emission data. Although efforts have been made to explore trends in, and thereby mitigate, air pollutant emissions, it is difficult to estimate emissions that fluctuate due to various factors, including socioeconomic factors, and to reflect all the variations in emissions. As only qualitative assessment of emission sources and pollutants was conducted when evaluating emission factors and activity data using the data attribute rating system (DARS), the accuracy of the emission data generated in these assessments has often been questioned (Kim and Jang, 2014). Normally, it takes 2 to 3 years to review and improve input data, including raw data (Zhang et al., 2009). For this reason, a bottom-up approach that uses observed data is continuously studied and currently used to rapidly review and improve the reliability of emission data generated (Wang et al., 2012; Martin et al., 2003). In recent research, observed concentrations of air pollutants (i.e. satellite-derived and ground-level observed concentrations) and concentrations simulated using 3D chemical transport models have been used together to evaluate the reliability of emission data (Bae et al., 2020a, b; Kim et al., 2020; Liu et al., 2017; Fioletov et al., 2011).

In line with two previous studies analyzing the 2015 National Air Pollutant Emissions Inventory (NEI) data (Yeo et al., 2019) and 2016 NEI data (Choi et al., 2020), this study analyzed the 2017 NEI data to determine the contributions of various emission source categories to the 2017 NEI and the major factors responsible for changes in emissions from 2016. We here present the current state of emissions. Moreover, we used the 2017 NEI data to simulate the concentrations of the pollutants using a 3D chemical transport model and compared the simulated concentrations with ground-level observed concentrations. Thus, emission uncertainties by region and pollutant were indirectly reviewed.


2. METHODS OF ESTIMATING NATIONAL AIR POLLUTANT EMISSIONS

Emissions of nine air pollutants by emission source category were estimated by using emission data from a telemonitoring system (hereafter TMS)2 operated in places of business or by applying emission factors to activity data. An emission source classification was established based on the CORINAIR classification of the European Environment Agency (EEA) and was adapted in consideration of domestic realities, including industrial structures. Emission sources were grouped into 13 first-level categories, which were further divided into 56 second-level categories, and further classified into 240 third-level categories.

Emission factors are presented as emissions per unit of activity data. Approximately 30,000 emission factors are currently applied to NEI data. Emission factors developed by domestic research institutes such as the National Institute of Environmental Research (NIER) were primarily used for estimating emissions, but for most source categories, emission factors developed by the U.S. Environmental Protection Agency (US EPA) and EEA were adopted, except in the case of a few emission source categories including vehicles, construction machinery, and combustion facilities (NAIR, 2020a).

Activity data were determined using 300 sets of statistical data obtained from approximately 150 institutes related to energy, industry, transport, meteorology, etc. In general, there are three ways to validate these data: comparing the totals of raw data with registered data from the database to detect errors that may have occurred during the data registration process; studying the previous year’s data and analyzing changes; and comparing those data with other similar data (NIER, 2019).

Emissions from power generation and large-scale businesses in the energy and industry sectors were estimated based on activity data (e.g. the amount of fuel·raw material consumed, produced, and incinerated), which were collected using a TMS and the stack emission management system (SEMS). SEMS is a computing system operated by NAIR, and under the law, places of business are legally required to maintain records of air emission control, facility operations, and fuel consumption, among other things.

For the road sector (comprising the passenger cars and freight cars categories), vehicle kilometers traveled (hereafter VKT) data, which reflect the domestic traffic situation, were estimated based on the number of vehicle registrations, daily average VKT, and observed traffic volume, and were used as activity data. To estimate emissions, NIER applied emission factors developed based on tests, including real driving emission-light-duty vehicles (RDE-LDV) tests, to activity data.

For the non-road sector (comprising the railways, ships, aircraft, agricultural machinery, and construction machinery categories), activity data were used to estimate emissions; and the categories related to transport modes were as follows: railways - fuel consumption by section; ships - oil supplies and arrival and departure information obtained from a port management information system (Port-MIS); aircraft - takeoff and landing information by airport; agricultural machinery - the number of registered machinery and working hours by agricultural machinery; and construction machinery - working hours by construction machinery.

For the everyday activities and others sector (comprising the energy transport and storage, fugitive dust, and biomass burning categories), related national statistical data were used as major activity data to estimate emissions. The data used were as follows: energy transport and storage - gasoline production by oil refineries; solvent use - paint production; fugitive dust - VKT and building construction area; and biomass burning - crop cultivation area.


3. 2017 EMISSION ESTIMATES
3. 1 Air Pollutant Emissions

In the 2017 NEI, the nationwide emissions of air pollutants comprised 817,420 tons of CO; 1,189,800 tons of NOx, 315,530 tons of SOx; 592,582 tons of TSP; 218,476 tons of PM10; 91,731 tons of PM2.5; 15,555 tons of BC; 1,047,585 tons of VOCs; and 308,298 tons of NH3, as shown in Table 1 (NAIR, 2020b).

Table 1. 
2017 emissions and contributions of air pollutants by emission source category. (unit: metric tons/year)
Source category CO NOx SOx TSP PM10 PM2.5 BC VOC NH3
Total 817,420 1,189,800 315,530 592,582 218,476 91,731 15,555 1,047,585 308,398
100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Energy production 59,304 114,192 77,574 4,109 3,829 3,162 319 7,753 1,330
7.3% 9.6% 24.6% 0.7% 1.8% 3.4% 2.0% 0.7% 0.4%
Non-industry 62,716 86,803 20,714 1,572 1,374 935 167 2,830 1,429
7.7% 7.3% 6.6% 0.3% 0.6% 1.0% 1.1% 0.3% 0.5%
Manufacturing industry 18,263 169,790 72,327 95,815 55,872 28,501 620 3,199 688
2.2% 14.3% 22.9% 16.2% 25.6% 31.1% 4.0% 0.3% 0.2%
Industrial processes 27,750 53,618 106,730 12,096 6,759 5,186 17 188,324 42,977
3.4% 4.5% 33.8% 2.0% 3.1% 5.7% 0.1% 18.0% 13.9%
Energy transport and storage 30,695
2.9%
Solvent use 563,648
53,8%
Road transport 237,152 434,038 277 9,473 9,473 8,715 5,315 45,920 4,437
29.0% 36.5% 0.1% 1.6% 4.3% 9.5% 34.2% 4.4% 1.4%
Non-road transport 176,455 309,309 35,710 16,198 16,194 15,002 6,749 59,407 120
21.6% 26.0% 11.3% 2.7% 7.4% 16.4% 43.4% 5.7% 0.04%
Waste 2,051 12,994 2,120 377 274 234 4 58,405 22
0.3% 1.1% 0.7% 0.1% 0.1% 0.3% 0.02% 5.6% 0.01%
Agriculture 244,335
79.3%
Others 8,656 214 679 431 388 24 901 12,945
1.1% 0.02% 0.1% 0.2% 0.4% 0.2% 0.1% 4.2%
Fugitive dust 422,420 109,932 17,690 120
71.3% 50.3% 19.3% 0.8%
Biomass burning 225,073 8,841 77 29,843 14,338 11,919 2,221 86,500 15
27.5% 0.7% 0.02% 5.0% 6.6% 13.0% 14.3% 8.3% 0.00%
*BC: BC (Black Carbon) as EC (Elemental Carbon)

The emission contributions of different emission source categories by pollutant were as follows: road transport (29.0%), biomass burning (27.5%), and non-road transport (21.6%) for CO; road transport (36.5%), non-road transport (26.0%), and manufacturing industry (14.3%) for NOx; industrial process (33.8%), energy production (24.6%), and manufacturing industry (22.9%) for SOx; fugitive dust (71.3%) and manufacturing industry (16.2%) for TSP; fugitive dust (50.3%) and manufacturing industry (25.6%) for PM10; manufacturing industry (31.1%), fugitive dust (19.3%), and non-road transport (16.4%) for PM2.5; non-road transport (43.4%) and road transport (34.2%) for BC; solvent use (53.8%) and manufacturing industry (18.0%) for VOCs; and agriculture (79.3%) and industrial process (13.9%) for NH3 (Fig. 1).


Fig. 1. 
2017 emission contributions of different emission source categories by pollutant.

3. 2 Analysis on Changes in Emissions Compared with the Previous Year

This study analyzed the contributions of primary PM2.5 and four of its precursors (NOx, SOx, VOCs, and NH3) to the 2017 NEI based on an emission source classification, which is presented in Table 2. The table shows that thirteen first-level categories of emission sources were grouped into five sectors (energy, industry, road, non-road, everyday activities and others). In addition, the major causes that contribute to changes in emissions from 2016 to 2017 were analyzed and described. Further details on emissions of air pollutants by emission source category can be found in Appendix 1.

Table 2. 
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

3. 2. 1 Energy Sector Emissions

The contributions of the energy sector to the 2017 NEI, by pollutant, were as follows: NOx (8.9%), SOx (20.7%), PM2.5 (3.4%), VOCs (0.7%), and NH3 (0.4%). More specifically, NOx, SOx, PM2.5, VOCs, and NH3 emissions decreased by 23.3% (32,099 tons), 17.2% (13,513 tons), 2.9% (93 tons), 2.9% (221 tons), and 14.7% (204 tons), respectively, compared with the previous year (Fig. 2).


Fig. 2. 
Emissions from the energy sector by pollutant in 2016 and 2017.

The contributions of the public power generation category to the emissions from the energy sector, by pollutant, were as follows: NOx (73.2%), SOx (90.2%), PM2.5 (78.8%), VOCs (57.8%), and NH3 (37.3%). Specifically, NOx, SOx, PM2.5, VOCs, and NH3 emissions from public power generation decreased by 29.6% (32,426 tons), 17.6% (12,597 tons), 4.7% (123 tons), 10.5% (506 tons), and 37.8% (268 tons), respectively, compared with the previous year. Although there were increases in coal consumption (e.g bituminous coal) compared with the previous year, emissions of those pollutants decreased because consumption of oil (e.g. Bunker C oil) and LNG decreased by 66.9% (1.93 million kL) and 20.9% (1.676 billion m3), respectively, and environmental facilities operated by places of business were upgraded. NOx, PM2.5, VOCs, and NH3 emissions from private power generation increased by 0.2% (53 tons), 1.7% (9 tons), 6.3% (143 tons), and 5.8% (30 tons), respectively, compared with the previous year while SOx emissions decreased by 11.3% (662 tons).

3. 2. 2 Industry Sector Emissions

Emissions from the industry sector were estimated by totaling the emissions from the manufacturing industry, industrial processes, waste, and oil refinery categories. Overall, the contributions of the industry sector to the 2017 NEI, by pollutant, were as follows: NOx (20.6%), SOx (61.3%), PM2.5 (37.0%), VOCs (23.9%), and NH3 (14.2%). NOx, SOx, and PM2.5 emissions decreased by 3.0% (7,585 tons), 9.8% (20,921 tons), and 19.7% (8,305 tons), respectively, compared with the previous year, while VOCs and NH3 emissions increased by 0.6% (1,468 tons) and 1.1% (479 tons), respectively (Fig. 3).


Fig. 3. 
Emissions from the industry sector by pollutant in 2016 and 2017.

The contributions of the manufacturing industry category to the emissions from the industry sector, by pollutant, were as follows: NOx (69.3%), SOx (37.4%), PM2.5 (84.0%), VOCs (1.3%), and NH3 (1.6%). NOx, SOx, PM2.5, and VOCs emissions decreased by 3.2% (5,541 tons), 16.5% (14,266 tons), 22.5% (8,284 tons), and 4.3% (143 tons), respectively, compared with the previous year, while NH3 emissions increased by 2.4% (16 tons). Overall emissions decreased mainly because coal consumption (e.g. bituminous coal) decreased by 12.0% (1.707 million tons) compared with the previous year.

The contributions of the industrial processes category to the emissions from the industry sector, by pollutant, were as follows: NOx (21.9%), SOx (55.2%), PM2.5 (15.3%), VOCs (75.3%), and NH3 (98.0%). NOx, SOx, and PM2.5 emissions decreased by 4.1% (2,314 tons), 5.3% (6,004 tons), and 0.1% (6 tons), respectively, compared with the previous year, while VOCs and NH3 emissions increased by 1.2% (2,220 tons) and 1.1% (488 tons), respectively. The overall emissions in this sector declined mainly because emissions from some large oil refineries and steel producers, which are measured by a TMS, decreased despite a 1.9% increase (2.946 million kL) in crude oil consumption by the petroleum industry and a 0.1% increase (86,000 tons) in the output of sintered products in the iron and steel industry.

The contributions of the waste category to the emissions from the industry sector, by pollutant, were as follows: NOx (5.3%), SOx (1.1%), PM2.5 (0.7%), VOCs (23.3%), and NH3 (0.1%). NOx, SOx, PM2.5, and VOCs emissions decreased by 4.2% (575 tons), 1.9% (41 tons), 7.2% (18 tons), and 1.0% (583 tons), respectively, compared with the previous year, and there was a 0.3% increase in NH3 emissions (0.1 tons). The overall decline in the waste category was because the amounts of incinerated municipal solid waste and industrial waste slightly decreased, by 5.5% (296,000 tons) and 0.3% (20,000 tons), respectively, compared with the previous year.

3. 2. 3 Road Sector Emissions

The contributions of the road sector to the 2017 NEI, by pollutant, were as follows: NOx (36.5%), SOx (0.1%), PM2.5 (9.5%), VOCs (4.4%), and NH3 (1.4%). NOx, PM2.5, VOCs, and NH3 emissions decreased by 4.2% (18,956 tons), 10.6% (1,033 tons), 3.5% (1,641 tons), and 12.5% (634 tons), respectively, compared with the previous year, while there was a 19.8% increase in SOx emissions (46 tons). The contributions of the freight cars and RVs categories to the emissions from the road sector were larger than those of the other types of vehicles (Fig. 4).


Fig. 4. 
Emissions from the road sector by pollutant in 2016 and 2017.

The overall decline in emissions, despite an increase in the number of car registrations and VKT, was mainly due to the replacement of old cars that produce relatively large amounts of air pollutants with new cars, as shown in Table 3.

Table 3. 
Changes in the number of car registrations and VKT by vehicle type.
Type of vehicles Number of car registrations (1,000 units) VKT (million km)
2016 2017 Change 2016 2017 Change
Passenger cars 12,495 12,965 3.8% 153,686 155,439 1.1%
Taxis 247 246 -0.4% 10,805 10,965 1.5%
Vans 765 746 -2.5% 7,992 7,201 -9.9%
Buses 76 76 -0.6% 7,538 7,479 -0.8%
Freight cars 3,192 3,262 2.2% 63,578 63,924 0.5%
Special cars 24 23 -2.2% 640 572 -10.7%
RVs 5,088 5,297 4.1% 72,848 75,801 4.1%
Total 21,888 22,615 3.3% 317,086 321,380 1.4%

3. 2. 4 Non-Road Sector Emissions

The contributions of the non-road sector to the 2017 NEI, by pollutant, were as follows: NOx (26.0%), SOx (11.3%), PM2.5 (16.4%), VOCs (5.7%), and NH3 (0.04%). NOx and SOx emissions decreased by 0.2% (677 tons) and 13.8% (5,732 tons), respectively, compared with the previous year, whereas PM2.5, VOCs, and NH3 emissions increased by 4.5% (648 tons), 45.6% (18,592 tons), and 2.4% (3 tons), respectively (Fig. 5).


Fig. 5. 
Emissions from the non-road sector by pollutant in 2016 and 2017.

The contributions of the ship category to the emissions from the non-road sector, by pollutant, were as follows: NOx (52.5%), SOx (96.9%), PM2.5 (51.5%), VOCs (69.1%), and NH3 (12.3%). NOx, PM2.5, VOCs, and NH3 emissions increased by 0.4% (688 tons), 10.5% (735 tons), 85.1% (18,879 tons), and 2.2% (0.3 tons), respectively, compared with the previous year, while there was a 14.4% decline in SOx emissions (5,819 tons). Since the methodology of obtaining activity data was changed (i.e. the agency in charge of obtaining data on leisure boats was changed), the number of ship registrations increased, which, in turn, contributed to increases in emissions from the non-road sector, while the decrease in SOx emissions resulted from an 11.4% decrease (41,882 kL) from the previous year in the use of oil on which cargo ships operate (e.g. Bunker C oil) (Table 4).

Table 4. 
Changes in the number of leisure boat registrations.
2016 2017 Change (registrations) Change (%)
Motorboats 3,071 16,120 13,049 425 %
Inflatable boats 930 2,689 1,759 189 %
Power yachts 152 698 546 359 %
Personal watercrafts (PWCs) 593 5,464 4,871 821 %
Total 4,746 24,971 20,225 426 %

The contributions of the construction machinery category to the emissions from the non-road sector, by pollutant, were as follows: NOx (36.9%), SOx (0.2%), PM2.5 (37.3%), VOCs (24.7%), and NH3 (34.3%). NOx, PM2.5, and VOCs emissions decreased by 2.5% (2,881 tons), 1.4% (80 tons), and 2.3% (339 tons), respectively, compared with the previous year, while SOx and NH3 emissions increased by 28.2% (16 tons) and 5.3% (2.1 tons), respectively. Although the number of registered construction machinery increased by 4.9% (18,000 units), the retrofitting of old construction machinery mainly contributed to these decreases in emissions.

3. 2. 5 Everyday Activities and Others Sector Emissions

The everyday activities and others sector consisted of the non-industry, energy transport and storage, solvent use, agriculture, other (area sources), fugitive dust, and biomass burning categories. The contributions of this sector to the 2017 NEI, by pollutant, were as follows: NOx (8.1%), SOx (6.6%), PM2.5 (33.7%), VOCs (65.3%), and NH3 (83.9%). NOx, PM2.5, VOCs, and NH3 emissions increased by 0.9% (808 tons), 0.9% (268 tons), 0.8% (5,358 tons), and 2.9% (7,353 tons), respectively, compared with the previous year, while SOx decreased by 13.7% (3,301 tons), as shown in Fig. 6.


Fig. 6. 
Emissions from the everyday activities and others sector by pollutant in 2016 and 2017.

The non-industry category included fuel combustion for heating and other purposes in residential, commercial, institutional, agricultural, and livestock facilities. The contributions of the non-industry category to the emissions from the everyday activities and others sector, by pollutant, were as follows: NOx (90.6%), SOx (99.6%), PM2.5 (3.0%), VOCs (0.4%), and NH3 (0.6%). NOx, VOCs, and NH3 emissions increased by 1.1% (979 tons), 3.3% (90 tons), and 1.0% (14 tons), respectively, compared with the previous year, while SOx and PM2.5 emissions decreased by 13.7% (3,300 tons) and 4.4% (43 tons), respectively. Increased NOx emissions were caused by a 5.2% increase (672 million m3) in LNG consumption by residential, commercial, and institutional facilities, while SOx emissions decreased because the consumption of oil (e.g. Bunker C oil) and anthracite coal decreased by 2.2% (236,000 kL) and 14.0% (176,000 tons), respectively, compared with the previous year.

The solvent use category (e.g. painting facilities and other solvent use) was responsible for 82.3% of the VOCs emissions from the everyday activities and others sector. Emissions from this category increased by 1.0% (5,644 tons) compared with the previous year, mainly due to an increase in the paint thinner consumption by coating facilities.

The agriculture category (e.g. fertilizer use and livestock manure management) accounted for 94.4% of NH3 emissions in the everyday activities and others sector. There was a 3.1% increase in NH3 emissions (7,318 tons) from the previous year due to a 0.4% increase (790,000 animals) in the number of livestock (e.g. cattle and pigs) from the previous year.

The fugitive dust category included road dust from vehicles running on the road and dust from construction sites and vacant lands without dust outlets. Fugitive dust accounted for 57.2% of the PM2.5 emissions from the everyday activities and others sector, which was an increase of 2.3% (403 tons) from the previous year. The paved roads category (one of its second-level categories), which accounted for 42.5% of fugitive dust emissions, showed a 6.0% increase in PM2.5 emissions (428 tons) from the previous year. Although there were 12 fewer days with at least 0.254 mm of precipitation, one of the major factors that contribute to changes in the fugitive dust category (US EPA, 2011), than in the previous year, the overall fugitive dust emissions increased because total VKT in Korea increased by 1.4% (4.294 billion km) compared with the previous year.

The biomass burning category included burning in everyday life, such as agricultural residue incineration, and the contributions of this category to emissions from the everyday activities and others sector, by pollutant, were as follows: NOx (9.2%), PM2.5 (38.5%), and VOCs (12.6%). NOx, PM2.5, and VOCs emissions decreased by 2.4% (217 tons), 1.7% (205 tons), and 1.4% (1,188 tons), respectively, compared with the previous year. This was because the cultivation area for fruits (e.g. pears and apples) and specialty crops (e.g. sesame and peanuts) declined by 0.5% (742 ha), compared with the previous year, thereby decreasing the amount of incineration.

3. 3 Air Pollutant Emissions by Region

Data on the 2017 NEI from 17 regions (first-tier administrative divisions3) are shown in Table 5 and Fig. 7. Gyeonggi-do emitted the largest proportions of CO, NOx , and VOCs emissions in Korea, at 15.8% (128,925 tons), 15.5% (184,239 tons), and 18.3% (191,840 tons), respectively. NOx emissions in Gyeonggi-do decreased by 19,416 tons compared with the previous year, mainly due to decreased emissions by the construction machinery category, which, in turn, was attributed to reductions in the VKT of freight cars and in building construction areas. Chungcheongnam-do emitted the largest proportions of SOx and NH3, at 22.2% (69,905 tons) and 17.1% (52,578 tons), respectively. SOx emissions in Chungcheongnam-do declined by 14.6% (11,938 tons), mainly because of a reduction in emissions measured using a TMS as a result of strengthened regulations on air pollution for power generation facilities. Gyeongsangbuk-do generated the largest proportions of TSP, PM10, and PM2.5, at 16.5% (97,910 tons), 18.6% (40,586 tons), and 21.5% (19,738 tons), respectively. PM2.5 emissions in Gyeongsangbuk-do decreased by 12.9% (2,932 tons) compared with the previous year, due to a reduction in the consumption of anthracite coal used for steel production.

Table 5. 
Air pollutant emissions by region. (unit: metric tons/year)
Region and Sea CO NOx SOx TSP PM10 PM2.5 BC VOC NH3
Total 817,420 1,189,800 315,530 592,582 218,476 91,731 15,555 1,047,585 308,298
100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%
Seoul 56,994 77,096 1,493 24,904 10,553 2,926 977 66,948 3,804
7.0% 6.5% 0.5% 4.2% 4.8% 3.2% 6.3% 6.4% 1.2%
Busan 26,433 51,870 9,526 16,667 6,958 2,617 584 42,945 1,686
3.2% 4.4% 3.0% 2.8% 3.2% 2.9% 3.8% 4.1% 0.5%
Daegu 18,335 27,198 3,489 10,056 3,696 1,267 300 31,490 1,714
2.2% 2.3% 1.1% 1.7% 1.7% 1.4% 1.9% 3.0% 0.6%
Incheon 47,228 61,522 13,302 25,501 9,676 3,131 765 56,110 7,461
5.8% 5.2% 4.2% 4.3% 4.4% 3.4% 4.9% 5.4% 2.4%
Gwangju 9,371 13,864 222 5,602 2,054 645 204 17,184 937
1.1% 1.2% 0.1% 0.9% 0.9% 0.7% 1.3% 1.6% 0.3%
Daejeon 11,307 15,694 702 4,897 1,702 619 204 24,861 796
1.4% 1.3% 0.2% 0.8% 0.8% 0.7% 1.3% 2.4% 0.3%
Ulsan 31,218 48,630 46,018 8,846 4,022 2,229 322 93,686 15,182
3.8% 4.1% 14.6% 1.5% 1.8% 2.4% 2.1% 8.9% 4.9%
Sejong 5,087 5,556 84 2,559 1,113 368 133 5,796 2,821
0.6% 0.5% 0.0% 0.4% 0.5% 0.4% 0.9% 0.6% 0.9%
Gyeonggi-do 128,925 184,239 10,011 83,410 31,409 10,386 3,121 191,840 46,879
15.8% 15.5% 3.2% 14.1% 14.4% 11.3% 20.1% 18.3% 15.2%
Gangwon-do 50,486 86,041 14,124 36,655 9,686 4,114 784 28,366 13,804
6.2% 7.2% 4.5% 6.2% 4.4% 4.5% 5.0% 2.7% 4.5%
Chungcheongbuk-do 45,204 67,614 8,532 31,892 9,588 3,733 896 41,714 16,388
5.5% 5.7% 2.7% 5.4% 4.4% 4.1% 5.8% 4.0% 5.3%
Chungcheongnam-do 67,215 112,876 69,905 76,527 33,243 16,021 1,306 77,362 52,578
8.2% 9.5% 22.2% 12.9% 15.2% 17.5% 8.4% 7.4% 17.1%
Jeollabuk-do 45,930 39,672 5,526 39,676 10,265 3,537 818 71,182 32,742
5.6% 3.3% 1.8% 6.7% 4.7% 3.9% 5.3% 6.8% 10.6%
Jeollanam-do 67,863 103,750 56,844 66,103 24,593 11,272 1,130 90,720 41,491
8.3% 8.7% 18.0% 11.2% 11.3% 12.3% 7.3% 8.7% 13.5%
Gyeongsangbuk-do 95,299 102,251 35,573 97,910 40,586 19,738 2,046 86,670 36,948
11.7% 8.6% 11.3% 16.5% 18.6% 21.5% 13.2% 8.3% 12.0%
Gyeongsangnam-do 52,202 84,338 26,860 48,163 12,503 4,937 1,161 96,277 25,370
6.4% 7.1% 8.5% 8.1% 5.7% 5.4% 7.5% 9.2% 8.2%
Jeju-do 11,710 17,037 2,044 9,856 3,469 1,054 234 9,190 7,688
1.4% 1.4% 0.6% 1.7% 1.6% 1.1% 1.5% 0.9% 2.5%
Sea* 46,610 90,554 11,275 3,361 3,361 3,138 568 15,245 8
5.7% 7.6% 3.6% 0.6% 1.5% 3.4% 3.6% 1.5% 0.0%
*Sea: air pollutant emissions from maritime transport such as ships and fishing boats


Fig. 7. 
Air pollutant emissions per area by pollutant and region (unit: ton/km2).

3. 4 Air Pollutant Emissions by Country

To quantitatively analyze air pollutant emissions in Korea, a comparison of emissions in Korea and those in 28 European countries and the United States was conducted. The ratios of emissions in South Korea to those of the 29 other countries, by pollutant, ranged between 1.2% and 12.4%. Moreover, emissions per 1 million people (Emission/Population [E/P]) in Korea and the 29 other countries were compared, and the ratio of each pollutant was found to be between 7.7% and 78.3%. It should be noted that simple comparison is not appropriate here because emissions data are partly determined by the economic size and population of a country. Nevertheless, since the comparison revealed that the emissions may have been under- or overestimated, it is necessary to continuously review and improve the accuracy of emission estimates by utilizing various methods, including measurement and observation (i.e. measured or observed data using environmental satellites) (Table 6).

Table 6. 
2017 Air pollutant emissions by country. (unit: 1,000 ton/year)
Nation Year CO NOx SOx PM2.5 VOC NH3
Republic of Korea 2016 795 1,248 359 100 1,024 301
2017 817 1,190 316 92 1,048 308
Change 2.8% -4.7% -12.1% -8.5% 2.3% 2.3%
E/P* 16 23 6 2 20 6
EU-28 2016 42,498 17,959 5,972 2,749 18,259 7,817
2017 43,979 17,685 5,897 2,796 18,574 7,864
Change 3.5% -1.5% -1.3% 1.7% 1.7% 0.6%
E/P* 86 35 12 5 36 15
France 2016 6,031 1,912 365 311 2,894 1,206
2017 5,971 1,876 372 301 2,966 1,198
Change -1.0% -1.9% 2.0% -3.1% 2.5% -0.6%
E/P* 89 28 6 4 44 18
Germany 2016 6,112 2,924 634 217 2,327 1,353
2017 6,166 2,822 615 209 2,336 1,333
Change 0.9% -3.5% -3.1% -4.1% 0.4% -1.4%
E/P* 75 34 7 3 28 16
United Kingdom 2016 3,168 2,279 434 228 1,669 562
2017 3,136 2,217 423 227 1,688 565
Change -1.0% -2.7% -2.5% -0.4% 1.1% 0.7%
E/P* 48 34 6 3 26 9
United States 2016 58,904 10,304 3,204 5,060 15,459 3,955
2017 66,805 9,907 2,550 5,699 17,218 4,297
Change 13.4% -3.8% -20.4% 12.6% 11.4% 8.7%
E/P* 206 30 8 18 53 13
*E/P (Emission/Population): air pollutant emission estimates per 1 million population as of 2017 to simply compare each country’s emissions
• Source: KOSIS (Republic of Korea), European Environment Agency and Eurostat (EU), US Environmental Protection Agency and Census.gov (United States)


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

It is difficult to directly assess the uncertainty in emissions of air pollutants since pollutants are emitted in various forms from different sources. In this regard, a 3D chemical transport model is useful for determining the relationship between emissions and concentrations because the meteorology, emissions, and chemical reactions in the atmosphere can be treated as variables in the model (Ju et al., 2018; Kim et al., 2018; Bae et al., 2017; Kim et al., 2017a; Kim et al., 2017b). Thus, studies are currently being conducted to indirectly review emission data via air quality modeling, which assists in converting emissions data into concentrations and comparing them with different kinds of observed data (Bae et al., 2020a, b; Kim et al., 2020).

In this study, air quality modeling was conducted using the National Emission and Air quality assessment System (NEAS) consisting of the Weather Research and Forecasting (WRF) model, the Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system, and the Community Multiscale Air Quality (CMAQ) model. The model simulation was performed using the Clean Air Policy Support System (CAPSS) 2017, and emission data were reviewed by comparing the air pollutant concentrations obtained during the simulation process with those obtained during the observation of urban air quality monitoring stations. To understand the effect of changes in emissions on air pollutant concentrations in the atmosphere, an air quality simulation was additionally performed using CAPSS 2016. The domains and horizontal resolutions used for the simulation were as follows: Northeast Asia (27 km), the Korean Peninsula (9 km), and South Korea (3 km). The simulated year was 2017, and the simulation configurations are presented in the Supplementary Materials.

4. 2 Assessment of Uncertainty in Criteria Pollutant Emissions

Simulated concentrations based on CAPSS 2017 were compared with observed concentrations from urban air quality monitoring stations (Fig. 8). The target pollutants for the comparison were SO2 and NOx, primary pollutants whose concentrations are directly affected by emission changes. CO, one of primary pollutants, was excluded from this study because its high long-range transport potential makes it unsuitable for assessing local emissions. Considering the spatiotemporal resolution of CAPSS data (by si (city), gun (county), and gu (district) and by month), a comparison was performed using the monthly mean values of each region. The national mean observed SO2 concentrations were 4.4 ppb and the simulated concentrations were 4.3 ppb, showing a 0.1 ppb (2%) error between the observation and simulation data. When classified by region and month, the concentration data of most regions showed an error of ≤2 ppb. As their overall biases were negative, it can be seen that the simulated concentrations were underestimated. In contrast, Jeollanam-do, Geongsangbuk-do, and Ulsan showed the opposite results, with notable errors. In particular, Ulsan had a monthly maximum error of >20 ppb. Since biases and errors of those three regions were positive in a similar range, their simulated concentrations were seen to be consistently overestimated. For simulated SO2 emissions, there was a stark contrast between regions. Since large-scale industrial complexes were responsible for most of the SO2 emissions in the three regions, the reliability of SOx emissions from the industrial sector should be primarily reviewed.


Fig. 8. 
Monthly mean error and bias range of simulated concentrations of (a and b) SO2 and (c and d) NO2 by region using CAPSS 2017.

The national mean observed concentrations of NOx were 34.3 ppb, and the simulated concentrations were 28.6 ppb, presenting an underestimation of 5.7 ppb (17%). Errors in yearly NOx concentration data in the different regions ranged from 26% to 55%. Sejong recorded the highest error of 17.1 ppb (55%), followed by Gyeongsangnam-do (16.8 ppb; 50%) and Incheon (15.5 ppb; 40%). The simulated concentrations of NOx in Seoul, Incheon, and Jeollanam-do were deemed to be overestimated, while those in the other regions were deemed to be underestimated. Over- or underestimations of the emissions were found even within the same regions depending on the month, with overestimations generally occurring in the summer months and underestimations occurring in the winter months. These data may have been affected by the uncertainty of the temporal profile of NOx, in addition to the uncertainty in its annual total emissions.

The emissions and concentrations of primary pollutants generally have a linear relationship; thus, the overestimation/underestimation ratios of their emissions were indirectly estimated using the relative ratios of their simulated and observed concentrations (Bae et al., 2020a; Kim et al., 2020). SO2 emissions in Ulsan that showed notable overestimations were overestimated by 60%, while those in Gyeongsangbuk-do and Jeollanam-do were overestimated by 30% each. NOx emissions in Seoul, Incheon, and Jeollanam-do were overestimated by 13%, 23%, and 28%, respectively. However, since the difference between the observed and simulated concentrations may have arisen due to the uncertainty of the air quality models and the meteorological models, the emission estimates presented in this study should be interpreted keeping in mind the possibility of the current emissions being over- or underestimated.

4. 3 Changes in PM2.5 Concentrations Affected by Changes in Emissions

To understand the effect of annual changes in emissions on air pollutant concentrations, an additional experiment using air quality modeling was conducted. The experiment compared simulated concentrations of air pollutants based on CAPSS 2016 and CAPSS 2017, assuming the meteorological conditions to be equal. Changes in concentrations described below mean changes in simulated concentrations for each pollutant caused by changes in emissions. The national mean SO2 concentrations in the CAPSS 2017 simulation showed a 1.0 ppb (19%) decrease, compared with those in the CAPSS 2016 simulation. The emission changes in individual regions also varied, ranging from -11.1 ppb (-51%) to 0.7 ppb (17%). The Seoul Metropolitan Areas (SMA; Seoul, Incheon, and Gyeonggi-do), Chungcheong-do, and the Southeastern areas showed a decline in the simulated SO2 concentrations; as presented in Section 3. 2, SOx emissions in the CAPSS 2017 simulation decreased by approximately 12% across the country, compared with those in the previous year, mainly because both the emissions from public power generation facilities, measured by a TMS, and the use of anthracite coal for steelmaking in the manufacturing industry declined.

On the other hand, some sis (cities) such as Incheon, Pohang, Busan, and Yeosu saw an increase of 1-2 ppb in simulated SO2 concentrations. This may have been because SOx emissions in Incheon and Yeosu from the ship category in the non-road transport sector increased by 48.7% (749 tons) and 25.4% (713 tons), respectively, compared with those in the previous year, and SOx emissions in Busan and Pohang from the iron and steel industry category in the industrial process sector increased by 24.1% (161 tons) and 11.3% (919 tons), respectively.

The national mean NOx concentrations in the CAPSS 2017 simulation decreased by 0.1 ppb (<1%) annually compared with those in the CAPSS 2016 simulation. Further, noticeable changes occurred in the emissions from individual regions, ranging from -3.5 ppb (-11%) to 8.2 ppb (20%). NOx concentrations declined in most of the southern areas of Gyeonggi-do, Chungchung-do, and near highways. That decline occurred mainly due to a decline in the emissions from the road transport sector, which, in turn, happened as a result of effective emission reduction policies, such as the replacement of old vehicles with newer ones being instituted. In contrast, NOx concentrations in the other regions, such as Incheon and Gangwon-do, increased by more than 4 ppb per year. This increase was estimated to be distributed as follows: in Incheon, NOx emissions from the ship and construction machinery categories in the non-road transport sector increased by 74.9% (2,963 tons) and 156.6% (6,281 tons), respectively, from the last year; and in Gangwondo, NOx emissions increased by 114% (709 tons) from the last year as LNG power generation facilities were newly built in this region.

Concentrations of PM2.5, including primary emission and secondary formation, declined by 0.5 μg/m3 (3%) per year, on average. PM2.5 concentrations tended to decrease nationwide; however, these changes varied, ranging from -2.4 μg/m3 (-8%) to 0.7 μg/m3 (4%). Most notably, the PM2.5 concentrations in the CAPSS 2017 simulation in Seoul and Incheon were higher than those in the CAPSS 2016 simulation. This increase was attributed to PM2.5 emissions from the construction machinery category in the non-road transport sector in Seoul and Incheon increasing by 40.6% (241 tons) and 159.4% (310 tons), respectively, compared with the previous year. The decline in simulated PM2.5 concentrations that occurred across the country was brought about by two major factors: 1) reductions in SO2 and NOx emissions which are precursors to PM2.5; 2) reductions in primary PM2.5 emissions. It should be noted that further studies on this need to be conducted since ambient PM2.5 concentrations are affected by various chemical reactions.


Fig. 9. 
Annual mean simulated concentrations of (a) SO2, (b) NOx, and (c) PM2.5 using CAPSS 2017 and their changes compared with those using CAPSS 2016.


5. CONCLUSION

According to the 2017 NEI, air pollutant emissions in the Republic of Korea, estimated using CAPSS, comprised 817,420 tons of CO; 1,189,800 tons of NOx; 315,530 tons of SOx; 592,582 tons of TSP; 218,476 tons of PM10; 91,731 tons of PM2.5; 15,555 tons of BC; 1,047,585 tons of VOCs; and 308,298 tons of NH3. NOx, SOx, TSP, PM10, and BC emissions decreased by 4.7%, 12.1%, 3.1%, 6.3%, 8.5%, and 5.2%, respectively, compared with those in the previous year, while CO, VOCs, and NH3 emissions increased by 2.8%, 2.3%, and 2.3%, respectively.

Emissions of NOx, SOx, VOCs, and NH3, which contribute to the formation of primary PM2.5, by emission source category and their contributions to the NEI were also assessed in this study. Emissions from the energy sector comprised 105,646 tons of NOx, 65,266 tons of SOx, 3,137 tons of PM2.5, 7,484 tons of VOCs, and 1,179 tons of NH3, with their contributions to the 2017 NEI being 8.9%, 20.7%, 3.4%, 0.7%, and 0.4%, respectively. Emissions from the industry sector comprised 244,949 tons of NOx, 193,485 tons of SOx, 33,945 tons of PM2.5, 250,198 tons of VOCs, 43,838 tons of NH3, with their contributions to the 2017 NEI being 20.6%, 61.3%, 37.0%, 23.9%, and 14.2%, respectively. Emissions from the road transport sector comprised 434,038 tons of NOx, 277 tons of SOx, 8,715 tons of PM2.5, 45,920 tons of VOCs, and 4,437 tons of NH3, with their contributions to the 2017 NEI being 36.5%, 0.1%, 9.5%, 4.4%, and 1.4%, respectively. Emissions from the non-road transport sector comprised 309,309 tons of NOx, 35,710 tons of SOx, 15,002 tons of PM2.5, 59,407 tons of VOCs, and 120 tons of NH3, with their contributions to the 2017 NEI being 26.0%, 11.3%, 16.4%, 5.7%, and 0.04%, respectively. Emissions from the everyday activities and others sector comprised 95,858 tons of NOx, 20,791 tons of SOx, 30,932 tons of PM2.5, 684,575 tons of VOCs, and 258,724 tons of NH3, with their contributions to the 2017 NEI being 8.1%, 6.6%, 33.7%, 65.3%, and 83.9%, respectively.

It should be noted that SO2 emissions in the vicinity of industrial complexes and NOx emissions in Seoul, Incheon, and Jeollanam-do may have been overestimated; thus, it is necessary to secure reliable data on monthly activity data. Since the method of estimating air pollutant emissions assumes the errors in simulation to be the uncertainty of input data for emission estimation in each region, it did not consider the transport of ambient air pollutants and was difficult to figure out uncertainty in emissions. In addition, this method may produce different outcomes depending on the model configurations and can be affected by input data that are used for modeling emissions. Going forward, a new methodology that can tackle these limitations needs to be developed to assess uncertainty in a more sophisticated manner. The result produced in simulation included different values of uncertainty, but it was presented in this study because it was seen as reference data that allowed us to promptly assess uncertainty to provide information on regions whose uncertainty is likely to be high. Thus, the result of emission estimation is more useful for understanding characteristics of input data than as quantitative values.

The NEI is being used for establishing, implementing, and assessing national atmospheric environmental policies, as well as for conducting studies designed to understand and interpret atmospheric conditions. To improve the accuracy and reliability of the NEI, it is crucial to continuously conduct various studies on emission estimation, such as identifying missing emission sources, including the defense sector, upgrading and developing antiquated emission factors, and advancing methodologies for estimating emissions.


Notes
1The term “particulate matter” (PM) is commonly used to describe a type of fine air pollutants in academic writing; however, the term “fine dust” has been used here since Korean law in English, provided by the Korean Legislation Research Institute, uses “fine dust”, instead of “PM”.
2TMS refers to a monitoring system that is meant to manage emissions by measuring air pollutants emitted from smoke stacks in real time.
3South Korea is made up of 17 first-tier administrative divisions: Six gwangyeok-sis (metropolitan cities) consisting of Busan, Daegu, Incheon, Gwangju, Daejeon, and Ulsan; one teukbyeol-si (metropolitan city) of Seoul; one teukbyeol-jachi-si (self-governing city) of Sejong; and nine dos (provinces), including one teukbyeol jachi-do (self-governing province), comprising Gyeonggi-do, Gangwon-do, Chungcheongbuk-do, Chungcheongnam-do, Jeollabuk-do, Jeollanam-do, Gyeongsangbuk-do, Gyeongsangnam-do, and Jeju-teukbyeol jachi-do.

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APPENDIX

Appendix 1. 
National Air Pollutant Emission. (a) Trends in CO emissions. (unit: metric tons/year)
Emission source category 2013 2014 2015 2016 2017 Change (%) (2017-2016)
First-level Second-level
Energy production Public power generation 47,807 41,534 33,425 35,515 33,924 -4.5%
District heating 3,168 3,675 3,365 4,242 5,306 25.1%
Oil refining 1,842 2,320 2,136 1,605 1,862 16.0%
Private power generation 10,641 10,327 16,212 17,217 18,212 5.8%
Subtotal 63,457 57,856 55,138 58,579 59,304 1.2%
Non-industry Commercial and public facilities 16,989 16,227 16,956 18,896 19,320 2.2%
Residential facilities 69,180 59,341 54,445 47,997 42,612 -11.2%
Agricultural·livestock·fishery facilities 1,363 1,026 898 842 784 -6.9%
Subtotal 87,532 76,594 72,299 67,735 62,716 -7.4%
Manufacturing industry Combustion facilities 2,016 1,389 1,608 3,265 3,129 -4.2%
Process furnaces 8,082 6,587 6,607 7,138 7,043 -1.3%
Others 10,028 10,740 8,639 7,767 8,092 4.2%
Subtotal 20,125 18,716 16,854 18,170 18,263 0.5%
Industrial process Petroleum industry 11,322 11,545 12,069 12,643 12,879 1.9%
Iron and steel industry 5,103 5,638 5,761 5,760 5,745 -0.3%
Inorganic chemical industry 474 485 487 510 605 18.8%
Organic chemical industry 5,248 5,316 5,011 5,661 5,889 4.0%
Pulp and paper industry 2,521 2,604 2,469 2,495 2,426 -2.8%
Others 244 267 272 271 205 -24.5%
Subtotal 24,912 25,855 26,069 27,340 27,750 1.5%
Road transport Passenger cars 123,022 136,451 123,534 118,777 114,450 -3.6%
Taxis 27,101 1,757 1,151 740 639 -13.6%
Vans 5,082 3,730 3,203 4,430 3,966 -10.5%
Buses 18,494 9,451 6,805 6,964 6,825 -2.0%
Freight cars 64,108 49,976 48,379 49,643 48,360 -2.6%
Special cars 1,208 1,035 830 1,057 968 -8.5%
RVs 31,932 26,634 21,349 22,342 21,104 -5.5%
Two-wheeled vehicles 138,271 52,190 40,265 40,604 40,840 0.6%
Subtotal 409,218 281,225 245,516 244,556 237,152 -3.0%
Non-road transport Railroads 3,657 3,057 2,734 2,426 2,360 -2.7%
Ships 7,646 54,535 60,491 62,632 102,179 63.1%
Aircraft 7,228 7,117 7,838 8,865 10,370 17.0%
Agricultural machinery 7,244 7,165 7,097 7,076 7,090 0.2%
Construction machinery 56,841 54,229 57,540 55,614 54,456 -2.1%
Subtotal 82,615 126,103 135,700 136,612 176,455 29.2%
Waste Waste incineration 1,957 1,645 1,548 2,008 2,051 2.1%
Others Forest fires and other fires 6,865 6,459 7,197 6,977 8,656 24.1%
Biomass burning Open burning 8,565 4,498 4,200 4,080 3,959 -3.0%
Crop residue incineration 132,679 155,437 157,616 159,196 152,427 -4.3%
Grilled meat and fish 8 12 13 9 11 24.2%
Wood stoves and boilers 94,745 58,938 57,772 57,029 56,066 -1.7%
Traditional firplaces 10,894 6,031 5,856 5,750 5,609 -2.5%
Charcoal kilns 8,315 7,000 7,000 7,000 7,000 0.0%
Subtotal 255,206 231,917 232,455 233,066 225,073 -3.4%
Total 951,888 826,370 792,776 795,044 817,420 2.8%

Appendix 1. 
Continued. (b) Trends in NOx emissions. (unit: metric tons/year)
Emission source category 2013 2014 2015 2016 2017 Change (%) (2017-2016)
First-level Second-level
Energy production Public power generation 140,096 127,456 116,250 109,721 77,296 -29.6%
District heating 4,162 4,651 4,116 4,075 4,349 6.7%
Oil refining 9,176 8,066 7,818 7,701 8,547 11.0%
Private power generation 23,784 22,644 22,634 23,948 24,001 0.2%
Subtotal 177,219 162,818 150,818 145,445 114,192 -21.5%
Non-industry Commercial and public facilities 31,303 29,871 32,630 34,249 34,610 1.1%
Residential facilities 51,882 47,055 46,605 48,101 48,983 1.8%
Agricultural·livestock·fishery facilities 5,584 4,216 3,712 3,474 3,210 -7.6%
Subtotal 88,769 81,143 82,948 85,824 86,803 1.1%
Manufacturing industry Combustion facilities 13,706 13,612 13,955 17,137 16,201 -5.5%
Processe furnaces 94,292 95,197 94,326 98,494 99,775 1.3%
Others 70,036 64,852 60,858 59,702 53,814 -9.9%
Subtotal 178,034 173,660 169,139 175,332 169,790 -3.2%
Industrial process Petroleum industry 4,632 4,478 4,799 4,932 4,322 -12.4%
Iron and steel industry 38,622 38,485 43,671 43,352 42,849 -1.2%
Inorganic chemical industry 5,408 4,284 4,882 2,752 3,353 21.8%
Organic chemical industry 32 23 16 19 24 26.5%
Others 6,457 6,042 6,462 4,877 3,070 -37.1%
Subtotal 55,151 53,311 59,830 55,932 53,618 -4.1%
Road transport Passenger cars 21,697 34,036 36,193 41,190 41,023 -0.4%
Taxis 6,722 487 363 249 238 -4.5%
Vans 11,187 15,346 13,121 17,350 15,451 -10.9%
Buses 35,774 31,365 34,097 32,011 28,981 -9.5%
Freight cars 224,980 204,086 206,915 239,450 226,640 -5.3%
Special cars 2,550 2,482 2,479 2,833 2,494 -12.0%
RVs 29,353 70,509 73,506 116,938 116,175 -0.7%
Two-wheeled vehicles 3,458 2,919 2,911 2,974 3,037 2.1%
Subtotal 335,721 361,230 369,585 452,995 434,038 -4.2%
Non-road transport Railroads 8,943 7,476 6,688 5,932 5,771 -2.7%
Ships 89,887 144,030 151,735 161,826 162,514 0.4%
Aircraft 7,423 7,323 8,058 9,104 10,621 16.7%
Agricultural machinery 16,447 16,288 16,209 16,190 16,351 1.0%
Construction machinery 123,327 116,053 121,686 116,934 114,053 -2.5%
Subtotal 246,027 291,171 304,376 309,986 309,309 -0.2%
Waste Waste incineration 9,529 12,257 11,977 13,570 12,994 -4.2%
Others Forest fires and other fires 165 153 172 167 214 28.2%
Biomass burning Open burning 609 590 550 535 519 -3.0%
Crop residue incineration 4,954 5,423 5,606 5,816 5,634 -3.1%
Grilled meat and fish 6 9 9 7 8 23.8%
Wood stoves and boilers 2,540 2,205 2,195 2,188 2,179 -0.4%
Traditional fireplaces 954 528 513 504 491 -2.5%
Charcoal kilns 46 10 10 10 10 0.0%
Subtotal 9,110 8,765 8,883 9,059 8,841 -2.4%
Total 1,099,723 1,144,508 1,157,728 1,248,309 1,189,800 -4.7%

Appendix 1. 
Continued. (c) Trends in SOx emissions. (unit: metric tons/year)
Emission source category 2013 2014 2015 2016 2017 Change (%) (2017-2016)
First-level Second-level
Energy production Public power generation 78,786 73,506 71,515 71,497 58,900 -17.6%
District heating 1,337 1,920 1,531 1,425 1,173 -17.7%
Oil refining 6,749 13,071 12,405 12,917 12,308 -4.7%
Private power generation 10,692 6,065 5,791 5,856 5,194 -11.3%
Subtotal 97,565 94,562 91,243 91,696 77,574 -15.4%
Non-industry Commercial and public facilities 9,296 6,328 12,015 9,744 8,202 -15.8%
Residential facilities 20,253 17,111 15,471 13,204 11,500 -12.9%
Agricultural·livestock·fishery facilities 1,551 1,229 1,249 1,067 1,012 -5.1%
Subtotal 31,101 24,668 28,736 24,015 20,714 -13.7%
Manufacturing industry Combustion facilities 3,655 3,232 2,441 2,727 2,223 -18.5%
Process furnaces 19,954 19,456 18,811 18,505 16,878 -8.8%
Others 72,227 60,294 63,847 65,362 53,226 -18.6%
Subtotal 95,836 82,982 85,098 86,593 72,327 -16.5%
Industrial process Petroleum industry 59,858 57,572 57,789 61,756 57,958 -6.2%
Iron and steel industry 36,723 29,600 35,538 39,451 39,024 -1.1%
Inorganic chemical industry 1,533 1,915 1,706 1,178 1,266 7.4%
Organic chemical industry 360 375 448 463 449 -3.1%
Pulp and paper industry 125 129 122 123 120 -2.8%
Others 9,735 9,337 9,781 9,762 7,914 -18.9%
Subtotal 108,333 98,927 105,385 112,734 106,730 -5.3%
Road transport Passenger cars 55 63 67 82 97 18.4%
Taxis 6 5 7 4 4 1.5%
Vans 4 5 5 5 6 11.0%
Buses 9 9 11 12 15 24.4%
Freight cars 82 69 82 85 101 18.9%
Special cars 1 2 2 2 2 5.6%
RVs 24 23 27 31 40 28.8%
Two-wheeled vehicles 8 8 8 10 12 19.1%
Subtotal 189 183 209 231 277 19.8%
Non-road transport Railroads 228 191 171 151 147 -2.7%
Ships 64,186 39,074 38,467 40,429 34,610 -14.4%
Aircraft 650 678 729 802 876 9.2%
Agricultural machinery 4 4 4 4 6 24.2%
Construction machinery 51 45 53 56 71 28.2%
Subtotal 65,119 39,991 39,424 41,443 35,710 -13.8%
Waste Waste incineration 6,517 1,846 2,119 2,161 2,120 -1.9%
Biomass burning Grilled meat and fish 1 2 2 1 2 24.6%
Wood stoves and boilers 121 62 60 60 59 -1.4%
Traditional fireplaces 17 9 9 9 9 -2.5%
Charcoal kilns 9 8 8 8 8 0.0%
Subtotal 148 80 79 78 77 -0.9%
Total 404,808 343,241 352,292 358,951 315,530 -12.1%

Appendix 1. 
Continued. (d) Trends in TSP emissions. (unit: metric tons/year)
Emission source category 2013 2014 2015 2016 2017 Change (%) (2017-2016)
First-level Second-level
Energy production Public power generation 4,181 3,976 3,812 3,337 3,147 -5.7%
District heating 114 108 132 149 168 12.7%
Oil refining 140 169 182 157 148 -5.6%
Private power generation 527 481 565 630 646 2.6%
Subtotal 4,961 4,733 4,692 4,273 4,109 -3.8%
Non-industry Commercial and public facilities 145 121 184 165 154 -6.1%
Residential facilities 1,703 1,447 1,349 1,238 1,152 -6.9%
Agricultural·livestock·fishery facilities 440 340 308 291 265 -9.0%
Subtotal 2,289 1,908 1,841 1,694 1,572 -7.2%
Manufacturing industry Combustion facilities 220 449 445 408 237 -42.0%
Process furnaces 4,763 3,771 3,825 3,196 3,044 -4.8%
Others 133,843 98,518 117,399 119,533 92,535 -22.6%
Subtotal 138,826 102,738 121,668 123,138 95,815 -22.2%
Industrial process Petroleum industry 447 466 459 502 511 1.8%
Iron and steel industry 7,372 7,617 7,740 7,797 7,801 0.0%
Inorganic chemical industry 644 635 620 634 771 21.6%
Organic chemical industry 1,497 1,558 1,844 1,911 1,859 -2.8%
Pulp and paper industry 48 44 44 44 43 -0.5%
Others 1,811 1,847 1,168 1,168 1,111 -4.9%
Subtotal 11,819 12,167 11,876 12,056 12,096 0.3%
Road transport Passenger cars 62 81 88 158 169 7.2%
Taxis 2 2 0.5%
Vans 599 435 328 437 394 -9.7%
Buses 274 223 234 222 195 -12.1%
Freight cars 8,409 6,839 6,694 7,296 6,483 -11.1%
Special cars 84 74 58 97 65 -32.6%
RVs 2,675 2,367 2,182 2,307 2,085 -9.6%
Motorcycles 78 79 1.4%
Subtotal 12,103 10,019 9,583 10,596 9,473 -10.6%
Non-road transport Railroads 579 484 433 384 374 -2.8%
Ships 6,922 6,983 7,091 7,589 8,290 9.2%
Aircraft 93 89 94 103 109 5.3%
Agricultural machinery 1,380 1,364 1,348 1,342 1,340 -0.2%
Construction machinery 6,196 5,945 6,354 6,173 6,086 -1.4%
Subtotal 15,170 14,865 15,320 15,592 16,198 3.9%
Waste Waste incineration 330 335 340 406 377 -7.1%
Others Forest fires and other fires 488 428 498 481 679 41.1%
Fugitive dust Paved roads 136,717 140,840 143,644 152,599 161,824 6.0%
Construction 34,243 40,356 55,714 51,005 53,284 4.5%
Vacant lands 32,534 27,519 27,403 24,712 20,979 -15.1%
Loading and unloading 25 25 26 26 27 3.7%
Agricultural production 29,657 29,553 29,072 28,549 27,845 -2.5%
Livestock production 29,263 29,745 30,524 31,898 32,734 2.6%
Waste disposal 13,112 12,655 14,414 15,498 15,902 2.6%
Unpaved roads 273,654 115,250 107,445 108,400 109,825 1.3%
Subtotal 549,207 395,944 408,242 412,686 422,420 2.4%
Biomass burning Open burning 1,485 1,438 1,342 1,304 1,265 -3.0%
Crop residue incineration 21,174 22,085 22,126 22,832 22,079 -3.3%
Grilled meat and fish 420 606 626 461 565 22.7%
Wood stoves and boilers 7,225 4,173 4,072 4,008 3,924 -2.1%
Traditional fireplaces 314 173 168 165 161 -2.5%
Charcoal kilns 1,932 1,849 1,849 1,849 1,849 0.0%
Subtotal 32,550 30,323 30,183 30,618 29,843 -2.5%
Total 767,743 573,460 604,243 611,539 592,582 -3.1%

Appendix 1. 
Continued. (e) Trends in PM10 emissions. (unit: metric tons/year)
Emission source category 2013 2014 2015 2016 2017 Change (%) (2017-2016)
First-level Second-level
Energy production Public power generation 3,831 3,854 3,681 3,194 3,041 -4.8%
District heating 95 85 113 133 152 14.1%
Oil refining 89 104 57 53 53 0.2%
Private power generation 509 465 544 571 583 2.2%
Subtotal 4,524 4,508 4,394 3,951 3,829 -3.1%
Non-industry Commercial and public facilities 135 112 170 152 144 -5.3%
Residential facilities 1,416 1,206 1,129 1,048 987 -5.9%
Agricultural·livestock·fishery facilities 404 312 283 267 243 -9.0%
Subtotal 1,955 1,629 1,582 1,468 1,374 -6.4%
Manufacturing industry Combustion facilities 180 323 249 240 132 -45.2%
Process furnaces 2,900 2,282 2,290 1,955 1,855 -5.1%
Others 77,933 57,370 68,354 69,599 53,886 -22.6%
Subtotal 81,014 59,975 70,893 71,794 55,872 -22.2%
Industrial process Petroleum industry 129 135 133 145 148 1.8%
Iron and steel industry 4,645 4,755 4,833 4,856 4,856 0.0%
Inorganic chemical industry 367 359 348 356 435 22.1%
Organic chemical industry 764 795 940 975 948 -2.8%
Pulp and paper industry 29 27 27 26 26 -0.5%
Others 314 337 377 373 346 -7.2%
Subtotal 6,249 6,407 6,658 6,731 6,759 0.4%
Road transport Passenger cars 62 81 88 158 169 7.2%
Taxis 2 2 0.5%
Vans 599 435 328 437 394 -9.7%
Buses 274 223 234 222 195 -12.1%
Freight cars 8,409 6,839 6,694 7,296 6,483 -11.1%
Special cars 84 74 58 97 65 -32.6%
RVs 2,675 2,367 2,182 2,307 2,085 -9.6%
Motorcycles 78 79 1.4%
Subtotal 12,103 10,019 9,583 10,596 9,473 -10.6%
Non-road transport Railroads 579 484 433 384 374 -2.8%
Ships 6,922 6,983 7,091 7,589 8,290 9.2%
Aircraft 90 85 90 99 104 5.3%
Agricultural machinery 1,380 1,364 1,348 1,342 1,340 -0.2%
Construction machinery 6,196 5,945 6,354 6,173 6,086 -1.4%
Subtotal 15,167 14,861 15,317 15,588 16,194 3.9%
Waste Waste incineration 243 247 246 295 274 -7.1%
Others Forest fires and other fires 310 272 317 306 431 41.0%
Fugitive dust Paved roads 26,243 27,034 27,573 29,291 31,062 6.0%
Construction 23,491 27,685 38,221 34,990 36,553 4.5%
Vacant lands 12,688 10,733 10,687 9,638 8,182 -15.1%
Loading and unloading 9 9 9 9 9 3.7%
Agricultural production 10,142 10,141 9,961 9,791 9,596 -2.0%
Livestock production 9,778 9,939 10,200 10,658 10,938 2.6%
Waste disposal 3,525 3,416 3,926 4,220 4,335 2.7%
Unpaved roads 23,067 9,715 9,057 9,137 9,257 1.3%
Subtotal 108,942 98,671 109,633 107,735 109,932 2.0%
Biomass burning Open burning 1,015 984 919 893 866 -3.0%
Crop residue incineration 8,820 9,121 9,183 9,474 9,150 -3.4%
Grilled meat and fish 420 606 626 461 565 22.7%
Wood stoves and boilers 3,365 2,002 1,958 1,930 1,893 -1.9%
Traditional fireplaces 206 114 111 109 106 -2.5%
Charcoal kilns 1,836 1,757 1,757 1,757 1,757 0.0%
Subtotal 15,663 14,583 14,552 14,623 14,338 -1.9%
Total 246,168 211,172 233,177 233,085 218,476 -6.3%

Appendix 1. 
Continued. (f) Trends in PM2.5 emissions. (unit: metric tons/year)
Emission source category 2013 2014 2015 2016 2017 Change (%) (2017-2016)
First-level Second-level
Energy production Public power generation 3,006 3,162 2,989 2,593 2,470 -4.7%
District heating 78 63 99 120 140 17.3%
Oil refining 43 46 23 23 25 9.5%
Private power generation 446 407 496 517 526 1.7%
Subtotal 3,573 3,679 3,607 3,253 3,162 -2.8%
Non-industry Commercial and public facilities 87 72 109 98 96 -2.1%
Residential facilities 889 782 745 721 694 -3.8%
Agricultural·livestock·fishery facilities 250 191 171 159 144 -8.9%
Subtotal 1,226 1,045 1,025 978 935 -4.4%
Manufacturing industry Combustion facilities 101 165 121 148 101 -31.6%
Process furnaces 1,525 1,245 1,226 1,059 1,006 -5.0%
Others 39,980 28,912 34,971 35,577 27,393 -23.0%
Subtotal 41,606 30,322 36,317 36,785 28,501 -22.5%
Industrial process Petroleum industry 28 30 29 32 32 1.8%
Iron and steel industry 3,603 3,636 3,705 3,730 3,729 0.0%
Inorganic chemical industry 209 202 194 199 244 22.7%
Organic chemical industry 687 715 846 877 853 -2.8%
Pulp and paper industry 19 17 18 17 17 -0.6%
Others 283 303 340 336 311 -7.6%
Subtotal 4,829 4,903 5,132 5,191 5,186 -0.1%
Road transport Passenger cars 57 75 81 145 156 7.2%
Taxis 2 2 0.5%
Vans 551 400 302 402 363 -9.7%
Buses 252 205 215 204 179 -12.1%
Freight cars 7,736 6,292 6,159 6,712 5,964 -11.1%
Special cars 77 68 53 89 60 -32.6%
RVs 2,461 2,178 2,008 2,123 1,918 -9.6%
Motorcycles 72 73 1.4%
Subtotal 11,135 9,218 8,817 9,748 8,715 -10.6%
Non-road transport Railroads 533 446 399 354 344 -2.8%
Ships 6,369 6,423 6,539 6,995 7,731 10.5%
Aircraft 82 78 83 91 96 5.3%
Agricultural machinery 1,269 1,255 1,240 1,235 1,233 -0.2%
Construction machinery 5,700 5,469 5,846 5,679 5,599 -1.4%
Subtotal 13,953 13,671 14,106 14,354 15,002 4.5%
Waste Waste incineration 202 204 209 252 234 -7.2%
Others Forest fires and other fires 279 245 285 275 388 41.0%
Fugitive dust Paved roads 6,349 6,541 6,671 7,087 7,515 6.0%
Construction 2,349 2,769 3,822 3,499 3,655 4.5%
Vacant lands 1,903 1,610 1,603 1,446 1,227 -15.1%
Loading and unloading 1 1 1 1 1 3.7%
Agricultural production 2,028 2,028 1,992 1,958 1,919 -2.0%
Livestock production 1,837 1,840 1,861 1,960 2,013 2.7%
Waste disposal 352 342 393 422 433 2.7%
Unpaved roads 2,307 971 906 914 926 1.3%
Subtotal 17,127 16,101 17,248 17,286 17,690 2.3%
Biomass burning Open burning 901 873 815 792 768 -3.0%
Crop residue incineration 7,290 7,563 7,621 7,878 7,627 -3.2%
Grilled meat and fish 389 556 574 423 518 22.4%
Wood stoves and boilers 2,197 1,326 1,298 1,280 1,257 -1.8%
Traditional fireplaces 164 92 89 87 85 -2.5%
Charcoal kilns 1,740 1,664 1,664 1,664 1,664 0.0%
Subtotal 12,681 12,073 12,060 12,124 11,919 -1.7%
Total 106,610 91,460 98,806 100,247 91,731 -8.5%

Appendix 1. 
Continued. (g) Trends in Black Carbon emissions. (unit: metric tons/year)
Emission source category 2014 2015 2016 2017 Change (%) (2017-2016)
First-level Second-level
Energy production Public power generation 219 146 151 128 -15.3%
District heating 17 28 36 45 25.7%
Oil refining 5 1 2 3 67.5%
Private power generation 83 132 141 143 1.1%
Subtotal 324 307 330 319 -3.3%
Non-industry Commercial and public facilities 9 13 13 15 14.3%
Residential facilities 130 128 136 140 3.6%
Agricultural·livestock·fishery facilities 16 14 13 12 -7.1%
Subtotal 156 155 161 167 3.6%
Manufacturing industry Combustion facilities 20 14 35 30 -13.4%
Process furnaces 74 60 62 64 4.3%
Others 554 666 679 526 -22.6%
Subtotal 648 741 776 620 -20.0%
Industrial process Petroleum industry 0.02 0.02 0 .023 1.8%
Iron and steel industry 11 11 11 11 -0.3%
Pulp and paper industry 0.1 0.1 0.1 0.1 -2.5%
Others 4 5 6 6 1.9%
Subtotal 15 16 17 17 0.5%
Road transport Passenger cars 33 39 60 66 8.6%
Vans 240 183 237 214 -9.9%
Buses 158 166 157 138 -12.1%
Freight cars 3,939 3,873 4,187 3,749 -10.5%
Special cars 52 41 69 46 -32.6%
RVs 1,252 1,154 1,219 1,102 -9.6%
Subtotal 5,674 5,456 5,930 5,315 -10.4%
Non-road transport Railroads 344 308 273 265 -2.8%
Ships 1,004 1,042 1,105 1,141 3.2%
Aircraft 61 64 70 74 5.3%
Agricultural machinery 968 956 953 951 -0.2%
Construction machinery 4,218 4,509 4,380 4,318 -1.4%
Subtotal 6,594 6,879 6,781 6,749 -0.5%
Waste Waste incineration 3 3 4 4 -7.2%
Others Forest fires and others 11 15 14 24 74.7%
Fugitive dust Paved roads 68 70 74 79 6.0%
Vacant lands 0.3 0.3 0.3 0.2 -15.1%
Loading and unloading 0.04 0.04 0.04 0.04 3.7%
Agricultural production 0.4 0.4 0.4 0.4 -2.0%
Livestock production 28 27 30 30 1.2%
Unpaved roads 11 10 10 10 1.3%
Subtotal 108 108 115 120 4.3%
Biomass burning Open burning 37 34 33 32 -3.0%
Crop residue incineration 1,707 1,709 1,738 1,687 -3.0%
Grilled meat and fish 23 23 17 21 22.4%
Wood stoves and boilers 219 213 210 206 -2.1%
Traditional fireplaces 13 13 13 12 -2.5%
Charcoal kilns 263 263 263 263 0.0%
Subtotal 2,261 2,255 2,274 2,221 -2.4%
Total 15,795 15,934 16,401 15,555 -5.2%

Appendix 1. 
Continued. (h) Trends in VOCs emissions. (unit: metric tons/year)
Emission source category 2013 2014 2015 2016 2017 Change (%) (2017-2016)
First-level Second-level
Energy production Public power generation 6,331 5,486 4,497 4,832 4,327 -10.5%
District heating 432 509 472 591 732 23.8%
Oil refining 352 318 327 296 269 -9.0%
Private power generation 1,430 1,384 2,169 2,282 2,425 6.3%
Subtotal 8,545 7,697 7,464 8,001 7,753 -3.1%
Non-industry Commercial and public facilities 774 722 795 810 820 1.2%
Residential facilities 1,932 1,777 1,773 1,879 1,963 4.5%
Agricultural·livestock·fishery facilities 79 59 53 51 47 -7.9%
Subtotal 2,784 2,558 2,622 2,740 2,830 3.3%
Manufacturing industry Combustion facilities 282 193 222 447 428 -4.4%
Process furnaces 1,169 1,134 1,079 1,176 1,166 -0.8%
Others 2,086 1,953 1,800 1,719 1,606 -6.6%
Subtotal 3,537 3,280 3,101 3,342 3,199 -4.3%
Industrial process Petroleum industry 52,553 53,588 56,021 58,686 59,780 1.9%
Iron and steel industry 17,847 19,325 19,408 19,546 19,756 1.1%
Inorganic chemical industry 606 579 564 613 741 20.9%
Organic chemical industry 43,790 44,050 44,417 45,508 45,856 0.8%
Pulp and paper industry 1 1 1 1 1 0.0%
Food and beverage industry 58,871 62,275 61,943 61,206 61,780 0.9%
Others 487 534 544 543 410 -24.5%
Subtotal 174,156 180,351 182,899 186,104 188,324 1.2%
Energy transport and storage Gasoline supply 27,241 27,645 29,137 30,160 30,695 1.8%
Solvents use Painting facilities 354,465 339,582 344,671 347,608 348,822 0.3%
Cleaning facilities 26,835 27,701 28,144 27,740 27,442 -1.1%
Laundry facilities 21,836 21,304 20,407 20,390 20,250 -0.7%
Other solvent use 158,934 160,731 162,137 162,266 167,134 3.0%
Subtotal 562,070 549,318 555,359 558,004 563,648 1.0%
Road transport Passenger cars 17,536 18,045 16,071 15,877 15,315 -3.5%
Taxis 1,017 89 61 38 33 -14.4%
Vans 1,164 632 531 669 629 -5.9%
Buses 12,850 12,134 12,366 11,936 11,447 -4.1%
Freight cars 14,848 11,436 11,514 12,700 12,149 -4.3%
Special cars 311 266 246 317 285 -10.1%
RVs 2,824 2,610 2,384 3,017 3,027 0.3%
Two-wheeled vehicles 15,258 4,255 2,973 3,008 3,036 1.0%
Subtotal 65,807 49,468 46,145 47,561 45,920 -3.5%
Non-road transport Railroads 1,466 1,225 1,095 973 948 -2.6%
Ships 2,480 18,340 20,970 22,185 41,064 85.1%
Aircraft 578 672 700 749 834 11.3%
Agricultural machinery 1,978 1,955 1,933 1,925 1,917 -0.4%
Construction machinery 15,785 14,681 15,613 14,984 14,645 -2.3%
Subtotal 22,288 36,873 40,311 40,816 59,407 45.6%
Waste Waste incineration 42,907 44,612 53,173 55,520 55,366 -0.3%
Others 3,601 3,449 3,901 3,468 3,039 -12.4%
Subtotal 46,508 48,061 57,074 58,988 58,405 -1.0%
Others Forest fires and other fires 637 551 648 624 901 44.4%
Biomass burning Open burning 4,974 4,807 4,488 4,361 4,231 -3.0%
Crop residue incineration 60,450 61,154 61,408 63,497 62,729 -1.2%
Grilled meat and fish 107 147 154 110 137 24.6%
Wood stoves and boilers 28,154 17,406 17,071 16,858 16,581 -1.6%
Traditional fireplaces 3,046 1,687 1,638 1,608 1,569 -2.5%
Charcoal kilns 4,757 1,254 1,254 1,254 1,254 0.0%
Subtotal 101,487 86,454 86,012 87,687 86,500 -1.4%
Total 1,015,059 992,256 1,010,771 1,024,029 1,047,585 2.3%

Appendix 1. 
Continued. (i) Trends in NH3 emissions. (unit: metric tons/year)
Emission source category 2013 2014 2015 2016 2017 Change (%) (2017-2016)
First-level Second-level
Energy production Public power generation 1,117 798 557 708 440 -37.8%
District heating 122 145 128 158 192 21.5%
Oil refining 181 174 198 177 151 -14.5%
Private power generation 325 308 496 516 547 5.8%
Subtotal 1,745 1,425 1,379 1,559 1,330 -14.7%
Non-industry Commercial and public facilities 507 498 567 582 580 -0.3%
Residential facilities 667 618 641 698 723 3.6%
Agricultural·livestock·fishery facilities 218 164 143 134 125 -6.9%
Subtotal 1,392 1,280 1,351 1,415 1,429 1.0%
Manufacturing industry Combustion facilities 84 57 67 130 122 -5.7%
Process furnaces 309 229 233 254 245 -3.3%
Others 407 431 327 288 320 11.0%
Subtotal 800 717 627 672 688 2.4%
Industrial process Petroleum industry 21,936 22,368 23,384 24,496 24,953 1.9%
Iron and steel industry 1,531 1,691 1,728 1,728 1,724 -0.3%
Ammonia consumption 11,584 13,984 14,320 16,265 16,301 0.2%
Subtotal 35,051 38,043 39,432 42,489 42,977 1.1%
Road transport Passenger cars 9,631 9,906 9,863 4,554 3,914 -14.1%
Taxis 102 104 1.4%
Vans 8 8 7 18 16 -10.5%
Buses 10 12 14 27 29 6.3%
Freight cars 90 83 88 162 160 -1.1%
Special cars 2 2 2 3 3 -7.5%
RVs 48 52 56 154 160 4.0%
Two-wheeled vehicles 48 49 50 51 52 1.7%
Subtotal 9,839 10,113 10,078 5,071 4,437 -12.5%
Non-road transport Railroads 16 14 12 11 10 -3.6%
Ships 114 13 14 14 15 2.2%
Agricultural machinery 54 53 53 53 54 1.4%
Construction machinery 37 36 38 39 41 5.3%
Subtotal 220 116 117 117 120 2.4%
Waste Others 23 23 22 22 22 0.3%
Agriculture Fertilizer use 21,691 20,172 19,901 19,553 17,754 -9.2%
Livestock manure management 209,426 207,781 211,362 217,464 226,582 4.2%
Subtotal 231,117 227,953 231,263 237,017 244,335 3.1%
Others Animals 12,785 12,832 12,882 12,924 12,945 0.2%
Biomass burning Open burning 3 2 2 2 2 -3.0%
Crop residue incineration 5 5 5 5 5 -4.0%
Wood stoves and boilers 10 6 6 6 6 -1.5%
Traditional fireplaces 3 2 2 2 2 -2.5%
Subtotal 20 16 15 15 15 -2.7%
Total 292,993 292,517 297,167 301,301 308,298 2.3%


SUPPLEMENTARY MATERIALS

Table S1. 
Configurations of (A) WRF and (B) CMAQ models in this study.
(A) WRF (B) CMAQ
Description Description
Version WRF v3.4.1 Version Version 4.7.1
Microphysics WSM6 Boundary condition Default profile
Short wave radiation Dudhia Chemical mechanism SAPRC99
Land-surface model NOAH Chemical solver EBI
PBL scheme YSU 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).