지리정보시스템(GIS) 및 존재인구를 이용한 초미세먼지(PM2.5) 노출평가
Existing Population Exposure Assessment Using PM2.5 Concentration and the Geographic Information System
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Background: The concentration of air pollutants as measured by the Air Quality Monitoring System (AQMS) is not an accurate population exposure level since actual human activities and temporal and spatial variability need to be considered. Therefore, to increase the accuracy of exposure assessment, the population should be considered. However, it is difficult to obtain population data due to limitations such as personal information. Objectives: The existing population defined in this study is the number of people in each region's grid. The purpose is to provide a methodology for evaluating exposure to PM2.5 through existing population data provided by the National Geographic Information Institute. Methods: The selected study period was from October 26 to October 28, 2021. Using PM2.5 concentration data measured at the Sensor-based Air Monitoring Station (SAMS) installed in Guro-gu and Wonju-si, the concentration for each grid was estimated by applying inverse distance weights through QGIS version 3.22. Considering the existing population, population-weighted average concentration (PWAC) was calculated and the exposure level of the population was compared by region. Results: The outdoor PM2.5 concentration as measured through the SAMS was high in Wonju-si on all three days. Wonju-si showed an average 22% higher PWAC than Guro-gu. As a result of comparing the PWAC and outdoor PM2.5 concentration by region, the PWAC in Guro-gu was 1~2% higher than the observed value, but it was almost the same. Conversely, observations of Wonju-si were 10.1%, 11.3%, and 8.2% higher than PWAC. Conclusions: It is expected that the Geographic Information System (GIS) method and the existing population will be used to evaluate the exposure level of a population with a narrow activity radius in further research. In addition, based on this study, it is judged that research on exposure to environmental pollutants and risk assessment methods should be expanded.
Abstract
Background: The concentration of air pollutants as measured by the Air Quality Monitoring System (AQMS) is not an accurate population exposure level since actual human activities and temporal and spatial variability need to be considered. Therefore, to increase the accuracy of exposure assessment, the population should be considered. However, it is difficult to obtain population data due to limitations such as personal information. Objectives: The existing population defined in this study is the number of people in each region's grid. The purpose is to provide a methodology for evaluating exposure to PM2.5 through existing population data provided by the National Geographic Information Institute. Methods: The selected study period was from October 26 to October 28, 2021. Using PM2.5 concentration data measured at the Sensor-based Air Monitoring Station (SAMS) installed in Guro-gu and Wonju-si, the concentration for each grid was estimated by applying inverse distance weights through QGIS version 3.22. Considering the existing population, population-weighted average concentration (PWAC) was calculated and the exposure level of the population was compared by region. Results: The outdoor PM2.5 concentration as measured through the SAMS was high in Wonju-si on all three days. Wonju-si showed an average 22% higher PWAC than Guro-gu. As a result of comparing the PWAC and outdoor PM2.5 concentration by region, the PWAC in Guro-gu was 1~2% higher than the observed value, but it was almost the same. Conversely, observations of Wonju-si were 10.1%, 11.3%, and 8.2% higher than PWAC. Conclusions: It is expected that the Geographic Information System (GIS) method and the existing population will be used to evaluate the exposure level of a population with a narrow activity radius in further research. In addition, based on this study, it is judged that research on exposure to environmental pollutants and risk assessment methods should be expanded.
참고문헌 (32)
- "Air Quality in Europe 2019"[URL]
- "9 out of 10 People Worldwide Breathe Polluted Air, but More Countries are Taking Action"[URL]
- Cohen AJ, "Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015", Lancet, 389(10082), pp.1907~1918 (2017)
- World Health Organization, "Health Effects of Particulate Matter" (2013), World Health Organization Regional Office for Europe
- 강충민, "서울시 미세입자 (PM2.5)의 호흡기질환 사망과의 연관성 연구", 한국대기환경학회지, 22(5), pp.554~563 (2006), 한국대기환경학회
- Lee SL, "Association between air pollution and asthma admission among children in Hong Kong", Clin Exp Allergy, 36(9), pp.1138~1146 (2006)
- "Atmospheric Environment Standards"[URL]
- 이선주, "PM10 장기노출 예측모형 개발을 위한 국가 대기오염측정자료의 탐색과 활용", 한국대기환경학회지, 32(1), pp.114~126 (2016), 한국대기환경학회[DOI]
- "About Air Korea"[URL]
- 김정환, "국내 기준성 대기오염물질의 권역별 장기 추이 및 원인 분석 : PM10과 오존을 중심으로", 한국대기환경학회지, 34(1), pp.1~15 (2018), 한국대기환경학회[DOI]
- World Health Organization, "Health risk assessment of air pollution : general principles" (2016), World Health Organization;
- 손지영, "공간분석 기법을 이용한 대기오염 개인노출추정 방안 소개 및 적용의 사례", 한국대기환경학회지, 25(4), pp.339~346 (2009), 한국대기환경학회
- Lin YC, "5 estimation based on micro-air quality sensors by using data fusion technique", Environ Int, 134, pp.105305~ (2020)
- 전용택, "일부 공단지역 내 이산화질소의 개인노출 농도 및 건강위해성평가", 한국환경보건학회지, 34(3), pp.199~206 (2008), 한국환경보건학회
- Gariazzo C, "A multi-city air pollution population exposure study : combined use of chemical-transport and random-forest models with dynamic population data", Sci Total Environ, 724, pp.138102~ (2020)
- 김경태, "유동인구 추정 시 통신 자료의 활용에 관한 연구", 서울도시연구, 16(3), pp.177~187 (2015), 서울연구원[DOI]
- Yoo S, "A decision-making model for adopting a cloud computing system", Sustainability, 10(8), pp.2952~ (2018)
- Park J, "Assessment of PM2.5 population exposure of a community using sensor-based air monitoring instruments and similar time-activity groups", Atmos Pollut Res, 11(11), pp.1971~1981 (2020)
- "National Statistical Map"[URL]
- 구자용, "지역별 기후지도 제작을 위한 내삽 과정에서 격자의 해상도에 관한 연구", 국토지리학회지, 46(4), pp.489~500 (2012), 국토지리학회
- 김동준, "서울시 구로구에서 COVID-19 발생 전ㆍ후 초미세먼지(PM2.5) 농도 변화에 따른 인구집단 노출평가", 한국환경보건학회지, 47(6), pp.521~529 (2021), 한국환경보건학회[DOI]
- Heo J, "Advanced exposure assessment based on monitoring sensors estimation for particulate matter in population", pp.2022
- Gupta P, "Impact of California fires on local and regional air quality: the role of a low-cost sensor network and satellite observations", Geo-Health, 2(6), pp.172~181 (2018)
- Liu HY, "Performance assessment of a low-cost PM2.5 sensor for a near four-month period in Oslo, Norway", Atmosphere, 10(2), pp.41~ (2019)
- Ahmed SO, "Spatiotemporal interpolation of air pollutants in the Greater Cairo and the Delta, Egypt", Environ Res, 160, pp.27~34 (2018)
- Alimissis A, "Spatial estimation of urban air pollution with the use of artificial neural network models", Atmos Environ, 191, pp.205~213 (2018)
- National Institute of Environmental Research, "Analysis and Interpretation of the Causes of High Particulate Matter Pollution in Gangwon Province" (2018), National Institute of Environmental Research
- 조성환, "강원도 춘천과 영월에서 측정한 미세먼지 농도 특성 및 고농도 원인 분석", 한국대기환경학회지, 32(1), pp.100~113 (2016), 한국대기환경학회[DOI]
- Brody M, "The effects of increasing population granularity in PM2.5 population-weighted exposure and mortality risk assessment", Environ Health Perspect, 129(12), pp.127703~ (2021)
- Singh V, "An approach to predict population exposure to ambient air PM2.5 concentrations and its dependence on population activity for the megacity London", Environ Pollut, 257, pp.113623~ (2020)
- Leung DYC, "Outdoor-indoor air pollution in urban environment:challenges and opportunity", Front Environ Sci, 2(69), pp.1~7 (2015)
- 강동화, "주거건물의 실내외 미세먼지 측정에 의한 실외 미세먼지의 실내 영향도 평가 기초연구", 한국건축친환경설비학회 논문집, 9(6), pp.452~459 (2015), 한국건축친환경설비학회