Korean Journal of Agricultural and Forest Meteorology (한국농림기상학회지)

Korean Society of Agricultural and Forest Meteorology (한국농림기상학회)
권호 표지
DOI QR코드

DOI QR Code

Air Pollution and Weather Data by Si-Gun-Gu in South Korea

시군구별 대기오염 및 기상 데이터

  • Yun, Seong Do (Department of Agricultural Economics, Mississippi State University) ;
  • Kim, Seung Gyu (Department of Agricultural Economics, Kyungpook National University)
  • 윤성도 (미시시피주립대학교 농업경제학과) ;
  • 김승규 (경북대학교 농업경제학과)
  • Received : 2020.08.10
  • Accepted : 2020.09.01
  • Published : 2020.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

Studies in socioeconomic impacts of air pollution are inevitable to merge data of the air pollutant density, weather, and socioeconomic variables. Due to their spatiotemporal disparities in units, to combine these data are time and effort consuming generically. The data described in this article aims to provide the major variables of air pollution and weather at the Si-Gun-Gu level to meet the data needs from social science. The latest (August 2020) data distributed are the balanced panel of 250 Si-Gun-Gu in South Korea for 2001-2018. The weather variables in this data are directly applicable to other social science topics, which are not limited to air pollution research.

대기오염의 사회경제적 효과에 대한 연구에는 측정된 대기오염 물질, 기상 자료, 그리고 사회경제적 데이터의 병합이 필요하다. 이들 자료들의 시간적·공간적 범위와 단위가 상이하기 때문에 분석에 필요한 데이터 가공에 많은 시간과 노력이 요구된다. 본 데이터의 구축은 사회과학 분야에서 널리 사용되는 대표적인 대기오염 및 기상 변수를 시군구 단위로 제공하는 것을 목표로 한다. 2020년 8월 기준 배포 버전 데이터의 시간적 범위는 2001년부터 2018년이며, 공간적 범위는 250개 시군구로서 패널 형태의 자료를 제공한다. 본 데이터의 기상 변수들은 대기오염 관련 분석뿐만 아니라 다양한 사회과학의 연구에서 사용할 수 있는 주요 변수들을 포함하고 있다.

Keywords

References

  1. AirKorea, 2020: Korea Environment Corporation https://www.airkorea.or.kr/
  2. Begueria, S., S. M. Vicente‐Serrano, M. Tomas‐Burguera, and M. Maneta, 2016: Bias in the variance of gridded data sets leads to misleading conclusions about changes in climate variability. International Journal of Climatology 36(9), 3413-3422. https://doi.org/10.1002/joc.4561
  3. CC BY 4.0, Creative Commons Attribution 4.0 International license, https://creativecommons.org/licenses/by/4.0/
  4. Cressie, N., 2015: Statistics for Spatial Data. John Wiley & Sons.
  5. Deschênes, O., and M. Greenstone, 2007: The economic impacts of climate change: Evidence from agricultural output and random fluctuations in weather. American Economic Review 97(1), 354-385. https://doi.org/10.1257/aer.97.1.354
  6. Geoservice, 2020: Geoservice Corporation http://www.gisdeveloper.co.kr/
  7. Hsiang, S., 2016: Climate econometrics. Annual Review of Resource Economics 8(1), 43-75. https://doi.org/10.1146/annurev-resource-100815-095343
  8. KMA (Korea Meteorological Administration), 2020: Climate Information Portal. https://data.kma.go.kr/
  9. Lee, C. J., and M. S. Hong, 2019: Spatiotemporal Variations of Fine Particulates in and around the Korean Peninsula. Journal of Korean Society for Atmospheric Environment 35(6), 675-682. https://doi.org/10.5572/KOSAE.2019.35.6.675
  10. Schlenker, W., and M. Roberts, 2009: Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change. Proceedings of the National Academy of Sciences 106(37), 15594-15598.
  11. WHO (World Health Organization), 2020: Ambient Outdoor Air Quality Database https://www.who.int/airpollution/data/cities/en/
  12. Yeo, M. J., and Y. P. Kim, 2019: Trends of the PM10 concentrations and high PM10 concentration cases in Korea. Journal of Korean Society for Atmospheric Environment 35(2), 249-264. https://doi.org/10.5572/KOSAE.2019.35.2.249
  13. Yun, S. D., and B. M. Gramig, 2019: Agro-Climatic data by county: A spatially and temporally consistent U.S. dataset for agricultural yields, weather and soils. Data 4(2), 66pp. https://doi.org/10.3390/data4020066
  14. Yun, S. D., and S. G. Kim, 2020a: Air pollution and weather data by Si-Gun-Gu in South Korea (2001 - 2018). https://github.com/ysd2004/AirKorea, DOI:10.5281/zendo.3685164.
  15. Yun, S. D., and S. G. Kim, 2020b: Mismatch of perception and data: Air pollution, medical expenses, and consumption in South Korea. Environmental and Resource Economics Review 29(2), 113-144. https://doi.org/10.15266/KEREA.2020.29.2.113