Acknowledgement
본 연구는 환경부의 재원으로 한국환경산업기술원의 환경성 질환 예방관리 핵심 기술개발사업(과제번호: 2021003320008) 및 환경부, 환경보건학회 환경보건센터 "2023년 환경보건 전문인력 양성사업 위탁사업(환경보건학회)"에서 지원 받아 수행된 결과이며 이에 감사드립니다.
References
- Sokhi RS, Moussiopoulos N, Baklanov A, Bartzis J, Coll I, Finardi S, et al. Advances in air quality research - current and emerging challenges. Atmos Chem Phys. 2022; 22(7): 4615-4703. https://doi.org/10.5194/acp-22-4615-2022
- World Health Organization (WHO). Monitoring air pollution levels is key to adopting and implementing WHO's global air quality guidelines. Available: https://www.who.int/news/item/10-10-2023-monitoring-air-pollution-levels-is-key-to-adopting-and-implementing-who-s-global-air-uality-guidelines [Accessed 9 December 2023].
- World Health Organization (WHO). Ambient (outdoor) air pollution. Available: https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health [Accessed 27 November 2023].
- Yang Z, Mahendran R, Yu P, Xu R, Yu W, Godellawattage S, et al. Health effects of long-term exposure to ambient PM2.5 in Asia-Pacific: a systematic review of cohort studies. Curr Environ Health Rep. 2022; 9(2): 130-151. https://doi.org/10.1007/s40572-022-00344-w
- Schwartz J, Wei Y, Yitshak-Sade M, Di Q, Dominici F, Zanobetti A. A national difference in differences analysis of the effect of PM2.5 on annual death rates. Environ Res. 2021; 194: 110649.
- Han F, Yang X, Xu D, Wang Q, Xu D. Association between outdoor PM2.5 and prevalence of COPD: a systematic review and meta-analysis. Postgrad Med J. 2019; 95(1129): 612-618. https://doi.org/10.1136/postgradmedj-2019-136675
- Lai S, Zhao Y, Ding A, Zhang Y, Song T, Zheng J, et al. Characterization of PM2.5 and the major chemical components during a 1-year campaign in rural Guangzhou, Southern China. Atmos Res. 2016; 167: 208-215. https://doi.org/10.1016/j.atmosres.2015.08.007
- Sun R, Zhou Y, Wu J, Gong Z. Influencing factors of PM2.5 pollution: disaster points of meteorological factors. Int J Environ Res Public Health. 2019; 16(20): 3891.
- Maji KJ, Sarkar C. Spatio-temporal variations and trends of major air pollutants in China during 2015-2018. Environ Sci Pollut Res Int. 2020; 27(27): 33792-33808. https://doi.org/10.1007/s11356-020-09646-8
- Ministry of Environment. Air quality standards. Available: https://www.me.go.kr/mamo/web/index.do?menuId=586 [Accessed 26 December 2023].
- Yi SJ, Kim H, Kim SY. Exploration and application of regulatory PM10 measurement data for developing long-term prediction models in South Korea. J Korean Soc Atmos Environ. 2016; 32(1): 114-126. https://doi.org/10.5572/KOSAE.2016.32.1.114
- Air Korea. Inquiry of final confirmed measurement data. Available: https://www.airkorea.or.kr/web/pastSearch?pMENU_NO=123 [Accessed 27 November 2023].
- Lin YC, Chi WJ, Lin YQ. The improvement of spatial-temporal resolution of PM2.5 estimation based on micro-air quality sensors by using data fusion technique. Environ Int. 2020; 134: 105305.
- Munir S, Mayfield M, Coca D, Jubb SA, Osammor O. Analysing the performance of low-cost air quality sensors, their drivers, relative benefits and calibration in cities-a case study in Sheffield. Environ Monit Assess. 2019; 191(2): 94.
- Dubey R, Patra AK, Joshi J, Blankenberg D, Kolluru SSR, Madhu B, et al. Evaluation of low-cost particulate matter sensors OPC N2 and PM Nova for aerosol monitoring. Atmos Pollut Res. 2022; 13(3): 101335.
- Hofman J, Nikolaou M, Shantharam SP, Stroobants C, Weijs S, La Manna VP. Distant calibration of low-cost PM and NO2 sensors; evidence from multiple sensor testbeds. Atmos Pollut Res. 2022; 13(1): 101246.
- Cai J, Ge Y, Li H, Yang C, Liu C, Meng X, et al. Application of land use regression to assess exposure and identify potential sources in PM2.5, BC, NO2 concentrations. Atmos Environ (1994). 2020; 223: 117267.
- Colvile RN, Gomez-Perales JE, Nieuwenhuijsen MJ. Use of dispersion modelling to assess road-user exposure to PM2.5 and its source apportionment. Atmos Environ. 2003; 37(20): 2773-2782. https://doi.org/10.1016/S1352-2310(03)00217-6
- Son JY, Kim YS, Cho YS, Lee JT. Prediction approaches of personal exposure from ambient air pollution using spatial analysis: a pilot study using Ulsan cohort data. J Korean Soc Atmos Environ. 2009; 25(4): 339-346. https://doi.org/10.5572/KOSAE.2009.25.4.339
- Santiago JL, Borge R, Sanchez B, Quaassdorff C, de la Paz D, Martilli A, et al. Estimates of pedestrian exposure to atmospheric pollution using high-resolution modelling in a real traffic hot-spot. Sci Total Environ. 2021; 755(Pt 1): 142475.
- Kim JY, Choe PG, Oh Y, Oh KJ, Kim J, Park SJ, et al. The first case of 2019 novel coronavirus pneumonia imported into Korea from Wuhan, China: implication for infection prevention and control measures. J Korean Med Sci. 2020; 35(5): e61.
- Edwards L, Rutter G, Iverson L, Wilson L, Chadha TS, Wilkinson P, et al. Personal exposure monitoring of PM2.5 among US diplomats in Kathmandu during the COVID-19 lockdown, March to June 2020. Sci Total Environ. 2021; 772: 144836.
- Chen K, Wang M, Huang C, Kinney PL, Anastas PT. Air pollution reduction and mortality benefit during the COVID-19 outbreak in China. Lancet Planet Health. 2020; 4(6): e210-e212. https://doi.org/10.1016/S2542-5196(20)30107-8
- Giani P, Castruccio S, Anav A, Howard D, Hu W, Crippa P. Short-term and long-term health impacts of air pollution reductions from COVID-19 lockdowns in China and Europe: a modelling study. Lancet Planet Health. 2020; 4(10): e474-e482. https://doi.org/10.1016/S2542-5196(20)30224-2
- Lokhandwala S, Gautam P. Indirect impact of COVID-19 on environment: a brief study in Indian context. Environ Res. 2020; 188: 109807.
- Pope CA 3rd, Burnett RT, Thun MJ, Calle EE, Krewski D, Ito K, et al. Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. JAMA. 2002; 287(9): 1132-1141. https://doi.org/10.1001/jama.287.9.1132
- Yoo SK, Kim BY. A decision-making model for adopting a cloud computing system. Sustainability. 2018; 10(8): 2952.
- Kim D, Min G, Choe Y, Shin J, Woo J, Kim D, et al. Evaluation of population exposures to PM2.5 before and after the outbreak of COVID-19. J Environ Health Sci. 2021; 47(6): 521-529. https://doi.org/10.5668/JEHS.2021.47.6.521
- Frigge M, Hoaglin DC, Iglewicz B. Some implementations of the boxplot. Am Stat. 1989; 43(1): 50-54. https://doi.org/10.1080/00031305.1989.10475612
- Kim SJ, Kim TJ, Kim CS. Estimating the method of the number of visitors of water-friendly park using GPS location information. Ecol Resilient Infrastruct. 2020; 7(3): 171-180.
- Woo J, Min G, Kim D, Cho M, Sung K, Won J, et al. Existing population exposure assessment using PM2.5 concentration and the geographic information system. J Environ Health Sci. 2022; 48(6): 298-305. https://doi.org/10.5668/JEHS.2022.48.6.298
- Park J, Jo W, Cho M, Lee J, Lee H, Seo S, et al. Spatial and temporal exposure assessment to PM2.5 in a community using sensor-based air monitoring instruments and dynamic population distributions. Atmosphere. 2020; 11(12): 1284.
- Park J, Kim E, Choe Y, Ryu H, Kim S, Woo BL, et al. Indoor to outdoor ratio of fine particulate matter by time of the day in house according to time-activity patterns. J Environ Health Sci. 2020; 46(5): 504-512.
- Park J, Ryu H, Kim E, Choe Y, Heo J, Lee J, et al. Assessment of PM2.5 population exposure of a community using sensor-based air monitoring instruments and similar time-activity groups. Atmos Pollut Res. 2020; 11(11): 1971-1981. https://doi.org/10.1016/j.apr.2020.08.010
- Doreswamy, Harishkumar KS, Yogesh KM, Gad I. Forecasting air pollution particulate matter (PM2.5) using machine learning regression models. Procedia Comput Sci. 2020; 171: 2057-2066. https://doi.org/10.1016/j.procs.2020.04.221
- Ma X, Chen T, Ge R, Xv F, Cui C, Li J. Prediction of PM2.5 concentration using spatiotemporal data with machine learning models. Atmosphere. 2023; 14(10): 1517.
- Brody M, Golub A, Potashnikov V. The effects of increasing population granularity in PM2.5 population-weighted exposure and mortality risk assessment. Environ Health Perspect. 2021; 129(12): 127703.
- Gariazzo C, Pelliccioni A, Bolignano A. A dynamic urban air pollution population exposure assessment study using model and population density data derived by mobile phone traffic. Atmos Environ. 2016; 131: 289-300. https://doi.org/10.1016/j.atmosenv.2016.02.011
- Dantas G, Siciliano B, Franca BB, da Silva CM, Arbilla G. The impact of COVID-19 partial lockdown on the air quality of the city of Rio de Janeiro, Brazil. Sci Total Environ. 2020; 729: 139085.
- Bi J, Carmona N, Blanco MN, Gassett AJ, Seto E, Szpiro AA, et al. Publicly available low-cost sensor measurements for PM2.5 exposure modeling: guidance for monitor deployment and data selection. Environ Int. 2022; 158: 106897.
- Schilt U, Barahona B, Buck R, Meyer P, Kappani P, Mockli Y, et al. Low-cost sensor node for air quality monitoring: field tests and validation of particulate matter measurements. Sensors (Basel). 2023; 23(2): 794.
- Coker ES, Buralli R, Manrique AF, Kanai CM, Amegah AK, Gouveia N. Association between PM2.5 and respiratory hospitalization in Rio Branco, Brazil: demonstrating the potential of low-cost air quality sensor for epidemiologic research. Environ Res. 2022; 214(Pt 1): 113738.
- Khan J, Kakosimos K, Raaschou-Nielsen O, Brandt J, Jensen SS, Ellermann T, et al. Development and performance evaluation of new AirGIS - a GIS based air pollution and human exposure modelling system. Atmos Environ. 2019; 198: 102-121. https://doi.org/10.1016/j.atmosenv.2018.10.036
- Alimissis A, Philippopoulos K, Tzanis CG, Deligiorgi D. Spatial estimation of urban air pollution with the use of artificial neural network models. Atmos Environ. 2018; 191: 205-213. https://doi.org/10.1016/j.atmosenv.2018.07.058
- Adong P, Bainomugisha E, Okure D, Sserunjogi R. Applying machine learning for large scale field calibration of low-cost PM2.5 and PM10 air pollution sensors. Appl AI Lett. 2022; 3(3): e76.
- Chauhan A, Singh RP. Decline in PM2.5 concentrations over major cities around the world associated with COVID-19. Environ Res. 2020; 187: 109634.
- Seo JH, Jeon HW, Sung UJ, Sohn JR. Impact of the COVID-19 outbreak on air quality in Korea. Atmosphere. 2020; 11(10): 1137.
- Kwon OM, Jeong HC. COVID-19 confirmed cases and subway passengers. New Phys Sae Mulli. 2022; 72(11): 873-878. https://doi.org/10.3938/NPSM.72.873
- Sannigrahi S, Kumar P, Molter A, Zhang Q, Basu B, Basu AS, et al. Examining the status of improved air quality in world cities due to COVID-19 led temporary reduction in anthropogenic emissions. Environ Res. 2021; 196: 110927.
- Kazakos V, Taylor J, Luo Z. Impact of COVID-19 lockdown on NO2 and PM2.5 exposure inequalities in London, UK. Environ Res. 2021; 198: 111236.