Long-term historical trends in air pollutant emissions in South Korea (2000–2018)

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Abstract

This study aims to understand the impact of previous air quality improvement policies on historical emission changes by examining long-term emission trends in Korea. Annual emissions from 2000 to 2018 were estimated using Korea’s official emissions inventory, the Clean Air Policy Support System (CAPSS). To ensure a consistent comparison, standardization of the method for calculating emissions and unification of the reported emission sectors were conducted each year. Furthermore, Korea’s emissions history was compared with that of neighboring countries, such as China and Japan. The annual emissions of these countries were acquired from the HTAPv3 emissions inventory, an international long-term emission trend study. For comparison, the emission source classification of Korea was matched with that of HTAPv3. As a result of the analysis, NO _x and SO _x emissions in Korea have shown decreasing trends, whereas VOCs (volatile organic compounds) have indicated a gradual increasing trend since 2000. Compared to the previous period of implementing South Korea’s air quality improvement policy, changes in NOx and SOx emissions, which are combustion-related pollutants, showed a relationship with the policy’s timeline. However, non-combustion-related pollutants such as VOCs did not exhibit such a relationship. It was concluded that the related policies were not as effective in reducing VOCs as planned in the policy. By comparing the emission trends of Japan, Korea, and China, it was confirmed that Japan was the first country to experience a decrease in combustion-related pollutants emissions, followed by Korea and China. Additionally, combustion-related pollutants decreased in all three countries, whereas VOCs decreased only in Japan. VOC is a precursor material generating secondary PM _2.5 and Ozone; considering that, if relevant policies are additionally implemented to control future PM _2.5 concentrations, and to reduce emissions efficiently and effectively, Japan’s VOC reduction policies can be applied to Korea’s emission reduction policies. These results are expected to serve as important references when establishing future air quality improvement policies in Korea.

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HTAP_v2.2: a mosaic of regional and global emission grid maps for 2008 and 2010 to study hemispheric transport of air pollution

G. Janssens-Maenhout, M. Crippa, D Guizzardi … (2015)

The mandate of the Task Force Hemispheric Transport of Air Pollution (TF HTAP) under the Convention on Long-Range Transboundary Air Pollution (CLRTAP) is to improve the scientific understanding of the intercontinental air pollution transport, to quantify impacts on human health, vegetation and climate, to identify emission mitigation options across the regions of the Northern Hemisphere, and to guide future policies on these aspects. The harmonization and improvement of regional emission inventories is imperative to obtain consolidated estimates on the formation of global-scale air pollution. An emissions data set has been constructed using regional emission grid maps (annual and monthly) for SO 2 , NO x , CO, NMVOC, NH 3 , PM 10 , PM 2.5 , BC and OC for the years 2008 and 2010, with the purpose of providing consistent information to global and regional scale modelling efforts. This compilation of different regional gridded inventories – including that of the Environmental Protection Agency (EPA) for USA, the EPA and Environment Canada (for Canada), the European Monitoring and Evaluation Programme (EMEP) and Netherlands Organisation for Applied Scientific Research (TNO) for Europe, and the Model Inter-comparison Study for Asia (MICS-Asia III) for China, India and other Asian countries – was gap-filled with the emission grid maps of the Emissions Database for Global Atmospheric Research (EDGARv4.3) for the rest of the world (mainly South America, Africa, Russia and Oceania). Emissions from seven main categories of human activities (power, industry, residential, agriculture, ground transport, aviation and shipping) were estimated and spatially distributed on a common grid of 0.1° × 0.1° longitude-latitude, to yield monthly, global, sector-specific grid maps for each substance and year. The HTAP_v2.2 air pollutant grid maps are considered to combine latest available regional information within a complete global data set. The disaggregation by sectors, high spatial and temporal resolution and detailed information on the data sources and references used will provide the user the required transparency. Because HTAP_v2.2 contains primarily official and/or widely used regional emission grid maps, it can be recommended as a global baseline emission inventory, which is regionally accepted as a reference and from which different scenarios assessing emission reduction policies at a global scale could start. An analysis of country-specific implied emission factors shows a large difference between industrialised countries and developing countries for acidifying gaseous air pollutant emissions (SO 2 and NO x ) from the energy and industry sectors. This is not observed for the particulate matter emissions (PM 10 , PM 2.5 ), which show large differences between countries in the residential sector instead. The per capita emissions of all world countries, classified from low to high income, reveal an increase in level and in variation for gaseous acidifying pollutants, but not for aerosols. For aerosols, an opposite trend is apparent with higher per capita emissions of particulate matter for low income countries.

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Korean National Emissions Inventory System and 2007 Air Pollutant Emissions

DaeGyun Lee, Yong-Mi Lee, Kee-Won Jang … (2011)

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CAMS-REG-v4: a state-of-the-art high-resolution European emission inventory for air quality modelling

Jeroen Kuenen, Stijn Dellaert, Antoon Visschedijk … (2022)

Abstract. This paper presents a state-of-the-art anthropogenic emission inventory developed for the European domain for an 18-year time series (2000–2017) at a 0.05∘ × 0.1∘ grid resolution, specifically designed to support air quality modelling. The main air pollutants are included: NOx, SO2, non-methane volatile organic compounds (NMVOCs), NH3, CO, PM10 and PM2.5, and also CH4. To stay as close as possible to the emissions as officially reported and used in policy assessment, the inventory uses the officially reported emission data by European countries to the UN Framework Convention on Climate Change, the Convention on Long-Range Transboundary Air Pollution and the EU National Emission Ceilings Directive as the basis where possible. Where deemed necessary because of errors, incompleteness or inconsistencies, these are replaced with or complemented by other emission data, most notably the estimates included in the Greenhouse gas Air pollution Interaction and Synergies (GAINS) model. Emissions are collected at the high sectoral level, distinguishing around 250 different sector–fuel combinations, whereafter a consistent spatial distribution is applied for Europe. A specific proxy is selected for each of the sector–fuel combinations, pollutants and years. Point source emissions are largely based on reported facility-level emissions, complemented by other sources of point source data for power plants. For specific sources, the resulting emission data were replaced with other datasets. Emissions from shipping (both inland and at sea) are based on the results from a separate shipping emission model where emissions are based on actual ship movement data, and agricultural waste burning emissions are based on satellite observations. The resulting spatially distributed emissions are evaluated against earlier versions of the dataset as well as against alternative emission estimates, which reveals specific discrepancies in some cases. Along with the resulting annual emission maps, profiles for splitting particulate matter (PM) and NMVOCs into individual components are provided, as well as information on the height profile by sector and temporal disaggregation down to the hourly level to support modelling activities. Annual grid maps are available in csv and NetCDF format (https://doi.org/10.24380/0vzb-a387, Kuenen et al., 2021).