Analysis of the National Air Pollutant Emission Inventory (CAPSS 2016) and the Major Cause of Change in Republic of Korea

In 2016, air pollutant emissions in the Republic of Korea were 795,044 metric tons (hereafter tons) of CO, 1,248,309 tons of NOx, 358,951 tons of SOx, 611,539 tons of TSP, 233,085 tons of PM10, 100,247 tons of PM2.5, 16,401 tons of BC, 1,024,029 tons of VOCs, and 301,301 tons of NH3. Including energy production, thirteen emission sources, which comprise the national air pollutant emission inventory, were classified by their characteristics into five sectors (Energy, Industry, Road, Non-road, and Everyday Activities and Other Emission Sources) to analyze their relative contributions to the national emissions. Specifically, their contributions by pollutant were as follows: NOx (11.0%), SOx (21.9%), PM2.5 (3.2%), VOCs (0.8%), NH3 (0.5%) from the energy sector; NOx (20.2%), SOx (59.7%), PM2.5 (42.1%), VOCs (24.3%), and NH3 (14.4%) from the industry sector; NOx (36.3%), SOx (0.1%), PM2.5 (9.7%), VOCs (4.6%), and NH3 (1.7%) from the road sector; NOx (24.8%), SOx (11.5%), PM2.5 (14.3%), VOCs (4.0%), and NH3 (0.04%) from the non-road sector; and NOx (7.6%), SOx (6.7%), PM2.5 (30.6%), VOCs (66.3%), and NH3 (83.4%) from the everyday activities and other emission sources sector. The data we calculate are used as official national emissions data for the establishment, implementation, and assessment of national atmospheric environment policy to improve air quality. As critical and necessary materials, the data are also utilized on a wide range of studies on policies such as customized regional particulate matter reduction measures. Thus, it is crucial to estimate highly reliable national emissions by enhancing the emissions factors and inventory and to establish a scientific emissions testing system by using air quality modeling and satellite data.


INTRODUCTION
Air pollution, including particle pollution is becoming a serious issue worldwide.To be more specific, industrialization has led the world population, traffic, and energy consumption to increase, which is consequently exacerbating air quality.Furthermore, transboundary air pollutants are having an adverse effect not only on polluting countries but also on their neighboring ones (Li et al., 2014).This makes it clear that air pollution arising from such pollutants must be tackled from a global Korea, China, and Japan, are implementing a wide range of policies, which include energy efficiency improvement and reduction of air pollutants emissions (Wang et al., 2014).
Estimating air pollutant emissions data is vital for informing policy and research to improve the atmospheric environment.However, it is a delicate issue due to the various politico-economic interests involved.Nevertheless, these data are needed to establish policies for atmosphere management and to counter climate change and are an important tool for policy setting and outcome assessment.
Looking at the state of emissions in major developed countries, in the United States, the Environmental Protection Agency (EPA) compiles and publishes the National Emission Inventory (NEI), focusing on general air pollutants and hazardous air pollutants (HAPs).Meanwhile, in the European Union, its member states are asked to submit their own emissions data on CO 2 , CH 4 , N 2 O, SO 2 , NO x , CO, NMVOC, PFCs, and SF 6 , which are maintained and released by the European Environment Agency (EEA).Most of the members use the standardized CORINAIR system.
In the Republic of Korea, the National Air Emission Inventory and Research Center (hereafter "the NAIR") estimates the annual emissions of the air pollutants, CO, NO x , SO x , TSP, PM 10 , PM 2.5 , BC, VOCs, and NH 3 , via the Clean Air Policy Support System (CAPSS).To this end, around 300 data points are collected from 150 domestic institutions (as of 2016 emissions).Emissions are calculated by applying the emissions factors and control efficiency for each emission source/fuel to the appropriate activity level for each emission source.
The estimated emissions play the role of the official air pollutants emissions data for the Republic of Korea, which are then used as the basis to establish and analyze the expected effects of policies for air improvement, such as the combined air improvement plan, the basic plan for atmospheric environment management in the capital, special measures against particulate matter, and combined measures to control particular matter.It is also used as input data for air quality prediction models.Thus, alongside air pollution monitoring network data, emissions data are the most important basic data.
In addition, the data are used in the Korean version of Greenhouse Gas -Air pollution Interaction and Synergies (GAINS), an integrated analysis model for climate and air which is widely used in various studies in Europe and Asia (Seong et al., 2019), and also used in building an emission inventory of Southeast Asia, which were cited from the KORUS-AQ (Korea-United States Air Quality) study, a joint research project conducted by the National Institute of Environmental Research in Korea and the National Aeronautics and Space Administration (Choi et al., 2019;Goldberg et al., 2019;Miyazaki et al., 2019).
In this report, we describe the results of 2016 emissions estimates and analyze the major factors contributing to changes from 2015.

1 Emission Source Classification and Emission Factors
To estimate national air pollutant emissions data, we established an emission source classification system by combining the CORINAIR classification system from Europe with the domestic industrial classification system for air pollutant emission sources.Thus, we classified emission sources into thirteen categories including energy production, non-industry, manufacturing industry, industrial processes, energy transport and storage, solvent use, road transport, nonroad transport, waste, agriculture, other, fugitive dust, and biomass burning.These categories were further classified into 57 subcategories, which were further categorized into 241 subgroups to estimate emissions of CO, NO x , SO x , TSP, PM 10 , PM 2.5 , BC, VOC, and NH 3 .
Emission factors are displayed as emissions per unit activity.Currently, approximately 30,000 emission factors are used in the national emissions estimate.Incidentally, while emission factors developed from research by domestic scientific research institutes such as the National Institute of Environmental Research are primarily used in the estimate, in most cases, the factors from the US EPA and the EU CORNIAIR are used except for a couple of emission sources including vehicles, construction machineries, and combustion facilities (NIER, 2015).

2 Method for Activity Level and Emission
Estimation To estimate national air pollutant emissions, we det-ermined the basic activity level after collecting 300 sets of statistics from approximately 150 institutions related to energy, industry, transport, and meteorology.There are usually three ways to validate those data: comparing the totals of raw data and registered data on the database to identify errors which might have happened when registering data in the first place; studying the previous results regarding the newly collected data and analyzing changes compared to the previous year; and comparing with other similar data.
Based on these basic data established, emissions from each emission source were calculated by applying different calculation methods to different sources.Generally, two approaches were taken to estimate emissions depending on the type of emission source: a bottomup approach and a top-down approach.
Emissions from point pollution sources were estimated using a bottom-up approach based on data collected from the Stack Emission Management System (SEMS).On the other hand, those from area sources were estimated using a top-down approach based on national statistics on fuel regarding the amount of fuel sales and LNG supply, and coal consumption except for fuel consumed in point pollution sources.Emissions from transport were also estimated by using the top-down approach based on statistics on traffic volume.Incidentally, emission factors by pollutant were taken into account in those estimates.A spatial allocation model was then used for the estimated emissions and regional emissions were estimated based on factors such as SEMS coordinates and addresses for industrial sites and traffic volume for transport, respectively (NIER, 2013).
To perform quality assurance and quality control (QA/QC) activities, the NAIR publishes a standard operating procedure (SOP) guide, which covers each stage ranging from collection of activity level data needed to estimate national emissions to validation of them, a handbook on methods for emission estimation, and an information package on emission factors.Arguably, this is necessary to ensure that the estimation methods are consistent and universal and to enhance reliability of the emission inventory (NIER, 2019a).

3 Record of Major Improvements in Emissions
The methodology for estimating air pollutant emissions was reviewed by the National Emissions Data Management Committee of the NAIR based on relevant domestic and overseas research results.Further-more, past emissions were re-estimated using the latest methodology in the event of major changes in emissions due to the addition of new substances or the discovery of new emission sources in order to ensure the consistency of emission trends analysis.To estimate the national emissions in 2016 in a more accurate manner, several improvements were made to the estimation methods.For example, new PM emissions factors for vehicles on gasoline and LPG multi-point injection (MPI) engines were applied to estimate road transport emissions.Also, new PM emissions factors for twowheeled vehicles (with four-stroke engines) were applied, and NO x and NH 3 emissions factors for small diesel vehicles (Euro 3 and Euro 4 emission standards) were updated to the present 2016 COPERT emission factors.Moreover, new emissions factors for CO, HC, NO x , PM, NH 3 , and SO x for hybrid vehicles and NO x emissions factors for such diesel vehicles as passenger cars, RVs, freight cars, special cars, and buses (before Euro 3 emission standards), which reflected the actual road driving conditions, were applied as well.When it came to non-road transport emissions, emissions factors for CO, HC, NO x , and PM for construction machineries (2015 model year onwards) reflecting the Tier 4 emission standards were applied.

1. 1 Emissions per Substance and Emission Source
In 2016, the nationwide emissions of air pollutants included 795,044 tons of CO, 1,248,309 tons of NO x , 358,951 tons of SO x , 611,539 tons of TSP, 233,085 tons of PM 10 , 100,247 tons of PM 2.5 , 16,401 tons of BC, 1,024,029 tons of VOCs, and 301,301 tons of NH 3 (Table 1) (NIER, 2019b).

1. 2 Analysis on Changes in Emissions compared
to the Previous Year On an annual basis, the OECD (Organization for Economic Cooperation and Development) asks its member states to submit national emissions estimates for CO, NO x , SO x , PM 10 , PM 2.5 and NMVOC (Non-methane Volatile Organic Compounds) from mobile and stationary sources, collects and makes the data public (https:// stats.oecd.org/).Republic of Korea also submits its national emissions estimates based on the CAPSS annually.However, a few emission sources on the domestic classification system are not included in the OECD submission criteria, resulting in gaps between the annual total national emissions estimates and those submitted to the OECD.
Table 2 represents national air pollutant emissions for the OECD member states.According to the OECD, Canada saw a 4.4% decrease in NMVOCs emissions while SO x emissions dropped in the UK (-28.4%), the US (-19.1%),France (-11.7%), and Germany (-7.3%), respectively.Meanwhile, PM 2.5 emissions in Republic of Korea and CO emissions in Japan increased by 2.4% and 10.9%, respectively while PM 10 and PM 2.5 emissions estimates in Japan were not provided with.

1. 3 Analysis on Changes in Emissions Compared
to the Previous Year Although air pollutant emissions have been estimated since 1999, directly comparing with past data is difficult due to annual additions of new emission sources or improvements in estimation methods as mentioned above.Since 2007, anthracite coal imports have been added to the emissions estimate, CleanSYS emissions data have been used, and the VOCs' emission factors have been changed, resulting in large shifts in emissions for the related substances.In 2011, improvements to emission estimates continued to be pursued, with the addition of PM 2.5 emissions and new emission sources such as industrial processes, improvement of the car emission factors for transport, and use of control efficiency of oil mist collection facilities in the energy transport and storage category.In 2012, the estimation methodology was improved in the non-road transport (construction machinery) category, and the activity levels of the food and drinks manufacturing (whiskey and other spirits) and VOCs emission factors were improved.In 2014, fishing vessels and leisure boats were added to the ships category, and the methodology for the road sector was also improved, such as using NO x emissions factors that reflected the actual road driving conditions.In 2016, NO x emissions factors for diesel vehicles (before Euro 3 emission standards) were improved by reflecting the actual road driving conditions, and PM emissions factors for MPI gasoline and LPG vehicles were introduced based on research findings.
In this report, the main causes of change in emissions from 2015 to 2016 are analyzed and described by classifying emission sources into five sectors such as Energy, Industry, Road, Non-road, and Everyday Activities and Other Emission Sources based on NO x , SO x , VOCs and NH 3 contributing to the formation of primary and secondary PM 2.5 , as shown in Table 3.Further details on emissions per pollutant by emission source can be found in Appendices.
The public power generation category's contributions to the emissions in the energy sector by pollutant were as follows: NO x (79.7%),SO x (90.8%),PM 2.5 (80.3%),VOCs (62.7%), and NH 3 (51.2%).Specifically, emissions of NO x , SO x , and PM 2.5 decreased by 5.6% (2015: 116,250 tons → 2016: 109,721 tons), 0.03% (2015: 71,515 tons → 2016: 71,497 tons), and 13.3% (2015: 2,989 tons → 2016: 2,593 tons), respectively, compared to the previous year while VOCs and NH 3 emissions increased by 7.5% (2015: 4,497 tons → 2016: 4,832 tons) and 27.0% (2015: 557 tons → 2016: 708 tons), respectively.While there were increases in fuel consumption such as bituminous coal and LNG compared to the previous year, the emissions by pollutant decreased because tighter standards in environmental management forced each power plant to use reduction catalysts and to improve desulfurization facilities for NO x and SO x reduction and dust collectors such as electric precipitators (ESP) to remove PM 2.5 .
The contributions of the private power generation category to the emissions in the energy sector by pollutant were as follows: NO x (17.4%),SO x (7.4%), PM 2.5 (16.0%),VOCs (29.6%), and NH 3 (37.3%).Emissions of NO x , SO x , PM 2.5 , VOCs, and NH 3 all increased by 5.8%
These changes in the emissions were made because of fluctuations in the number of recent cars registered and vehicle kilometers traveled (VKT) by vehicle type (Table 7).Incidentally, improvements in emissions factors for PM 2.5 and NH 3 led to marked changes in emissions of the pollutants from each vehicle type.
The contributions of the passenger cars category to the emissions in the road sector by pollutant were as follows: NO x (9.1%), SO x (35.4%),PM 2.5 (1.5%), VOCs (33.4%), and NH 3 (89.8%).Emissions of NO x , SO x , and PM 2.5 increased by 13.8% (2015: 36,193 (2015: 56 tons → 2016: 154 tons), respectively, compared to the previous year.This was due to the fact that the number of RVs registered increased by 10.6% (2015: 4.547 million units → 2016: 5.088 million units) with an increase of 15.7% in the VKT of them (2015: 62.720 billion km → 2016: 72.848 billion km), leading to the increases in the emissions.

1. 4 Non-Road Sector Emissions
The non-road sector consisted of categories including the ships and the construction machineries, and its contributions to the national emissions by pollutant were as follows: NO x (24.8%),SO x (11.5%),PM 2.5 (14.3%),VOCs (4.0%), and NH 3 (0.04%).Emissions of NO x , SO x , PM 2.5 , VOCs, and NH 3 all increased by 1.8%    8 and Fig. 5).
Incidentally, the number of forklifts and excavators registered increased by 4.4% and 2.4%, respectively and working hours of the two increased by 2.4% each; conversely, the number of old machineries registered, relatively large emitters, to which the US Tier 1 emissions standards applied, decreased while that of advanced machineries to which the Tier 4 emissions standards could apply increased, resulting in the changes in the emissions (Table 9).
The solvent use category (other solvent use, painting facilities, etc.) accounted for 82.2% of VOCs emissions in the everyday activities and other emission sources sector with a 0.5% increase (2015: 555,359 tons → 2016: 558,004 tons), which was found to be due to a 1.2% increase of supply of paints (2015: 808,000 kL → 2016: 818,057 kL) compared to the previous year.
Agriculture (fertilizer use, livestock excrement management, etc.) accounted for 94.3% of NH 3 emissions in the everyday activities and other emission sources sector and saw a 2.5% increase (2015: 231,263 tons → 2016: 237,017 tons) from a year earlier; this was found to be a result of an increase of 2.6% in the number of livestock such as cattle and pigs (2015: 189.417 million animals → 2016: 194.318 million animals) compared  to the previous year.The fugitive dust category included paved road dust, or resuspended dust from vehicles running on the roads, and dust emitted into the air from industrial processes, not from certain exhaust systems in industries.Fugitive dust accounted for 56.4% of PM 2.5 emissions in the everyday activities and other emission sources sector, increasing by 0.2% (2015: 17,248 tons → 2016: 17,286 tons) compared to the previous year.Paved road dust, which accounted for 41% of fugitive dust emissions, saw a 6.2% increase in PM 2.5 emissions (2015: 6,671 tons → 2016: 7,087 tons) compared to the previous year.This was because of increases both in the number of cars registered and in the VKT in the road transport including passenger cars with the number of rain days with 0.254 mm or more (US EPA) decreasing by 3.6% (2015: 130 days → 2016: 125 days) compared to the previous year.
The biomass burning category included the category of burning in everyday life such as open burning of municipal solid waste, and its contributions to emissions in the everyday activities and other emission sources sector by pollutant were as follows: NO x (9.5%), PM 2.5 (39.5%), and VOCs (12.9%).Emissions of NO x , PM 2.5 , and VOCs increased by 2.0% (2015: 8,883 tons → 2016: 9,059 tons), 0.5% (2015: 12,060 tons → 2016: 12,124 tons), and 1.9% (2015: 86,012 tons → 2016: 87,687 tons), respectively, compared to the previous year.This was because the cultivation area for industrial crops (sesame, perilla, groundnut, etc.) expanded by 8.3% (2015: 72,298 ha → 2016: 78,276 ha) compared to the previous year, and the amount of incineration consequently increased.
sions data other than those directly measured by the Tele-Monitoring System (TMS) on the smokestack.More recently, in a bid to increase the reliability of emissions data, we are introducing the Community Multiscale Air Quality Modeling (CMAQ) system, a 3-dimentional chemistry transport model, in which emissions data are entered to simulate the concentrations of pollutants, which are then to be compared with those measured from the surface and satellites.
The data we calculate are used as official national emissions data for the establishment, implementation, and assessment of national atmospheric environment policy to improve air quality.As critical and necessary materials, the data are also utilized on a wide range of studies on policies such as customized regional particulate matter reduction measures.Thus, it is crucial to estimate highly reliable national emissions by enhancing the emissions factors and inventory and to establish a scientific emissions testing system by using air quality modeling and satellite data.

Trends in CO emissions
(units: tons/year)

Fig. 2 .
Fig. 2. Emissions in the energy sector by pollutant in 2015 and 2016.

Fig. 3 .
Fig. 3. Emissions in the industry sector by pollutant in 2015 and 2016.

Fig. 4 .
Fig. 4. Emissions in the road sector by pollutant in 2015 and 2016.

Fig. 5 .
Fig. 5. Emissions in the non-road sector by pollutant in 2015 and 2016.

Fig. 6 .
Fig. 6.Emissions in the everyday activities and other emission sources sector by pollutant in 2015 and 2016.

Table 1 .
2016 emissions and the relative contribution of air pollutants per major emission source category.(units: tons/year)

Table 2 .
National air pollutant emissions for the OECD member states.

Table 3 .
Emission source classification by sector and category.

Table 5 .
Changes in emissions and percentage in the industry sector by pollutant.(units: tons/year)

Table 6 .
Changes in emissions and percentage in the road sector by pollutant.(units: tons/year)

Table 7 .
Changes in the number of registered cars and VKT by vehicle type.

Table 9 .
Changes in the number of registered construction machines and working hours by machine type.

Table 10 .
Changes in emissions and percentage in the everyday activities and other emission sources sector by pollutant.(units: tons/year)