Abstract
The levels of air pollution in Macao often exceeded the levels recommended by WHO. In order for the population to take precautionary measures and avoid further health risks under high pollutant exposure, it is important to develop a reliable air quality forecast. Statistical models based on linear multiple regression (MR) and classification and regression trees (CART) analysis were developed successfully, for Macao, to predict the next day concentrations of NO2, PM10, PM2.5, and O3. All the developed models were statistically significantly valid with a 95% confidence level with high coefficients of determination (from 0.78 to 0.93) for all pollutants. The models utilized meteorological and air quality variables based on 5 years of historical data, from 2013 to 2017. Data from 2013 to 2016 were used to develop the statistical models and data from 2017 was used for validation purposes. A wide range of meteorological and air quality variables was identified, and only some were selected as significant independent variables. Meteorological variables were selected from an extensive list of variables, including geopotential height, relative humidity, atmospheric stability, and air temperature at different vertical levels. Air quality variables translate the resilience of the recent past concentrations of each pollutant and usually are maximum and/or the average of latest 24-h levels. The models were applied in forecasting the next day average daily concentrations for NO2 and PM and maximum hourly O3 levels for five air quality monitoring stations. The results are expected to be an operational air quality forecast for Macao.
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References
Cassmassi JC (1987) Development of an objective ozone forecast model for the South Coast Air Basin. Annual meeting of the Air Pollution Control Association, Conference: 80, Journal Volume: 4, New York, NY (USA), 21-26 Jun Technical Paper 87-71.3; Journal ID: ISSN 0193-9688
Choi W, Paulson SE, Casmassi J, Winer AM (2013) Evaluating meteorological comparability in air quality studies: classification and regression trees for primary pollutants in California’s South Coast Air Basin. Atmos Environ 64:150–159. https://doi.org/10.1016/j.atmosenv.2012.09.049
Clapp LJ, Jenkin ME (2001) Analysis of the relationship between ambient levels of O3, NO2 and NO as a function of NOx in the UK. Atmos Environ 35:6391–6405. https://doi.org/10.1016/S1352-2310(01)00378-8
Deng T, Chen Y, Wan Q et al (2018) Comparative evaluation of the impact of GRAPES and MM5 meteorology on CMAQ prediction over Pearl River Delta, China. Particuology 40:88–97. https://doi.org/10.1016/j.partic.2017.10.005
Durão RM, Mendes MT, Pereira MJ (2016) Forecasting O3 levels in industrial area surroundings up to 24 h in advance, combining classification trees and MLP models. Atmos Pollut Res 7:961–970
Entwistle MR, Gharibi H, Tavallali P et al (2019) Ozone pollution and asthma emergency department visits in Fresno, CA, USA, during the warm season (June–September) of the years 2005 to 2015: a time-stratified case-crossover analysis. Air Qual Atmos Heal 12:661–672. https://doi.org/10.1007/s11869-019-00685-w
He D, Zhou Z, He K et al (2000) Assessment of traffic related air pollution in urban areas of Macao. J Environ Sci 12:39–46
Kumar R, Barth MC, Pfister GG et al (2018) How will air quality change in South Asia by 2050? J Geophys Res Atmos 123:1840–1864. https://doi.org/10.1002/2017JD027357
Lee M, Brauer M, Wong P et al (2017) Land use regression modelling of air pollution in high density high rise cities: a case study in Hong Kong. Sci Total Environ 592:306–315. https://doi.org/10.1016/j.scitotenv.2017.03.094
Liu JC, Peng RD (2018) Health effect of mixtures of ozone, nitrogen dioxide, and fine particulates in 85 US counties. Air Qual Atmos Heal 11:311–324. https://doi.org/10.1007/s11869-017-0544-2
Lopes D, Hoi KI, Mok KM et al (2016) Air quality in the main cities of the pearl river delta region. Glob Nest J 18:794–802
Martinez NM, Montes LM, Mura I, Franco JF (2018) Machine Learning Techniques for PM 10 Levels Forecast in Bogotá. In: 2018 ICAI Workshops (ICAIW). IEEE, pp 1–7. doi: https://doi.org/10.1109/ICAIW.2018.8554995
Oduro SD, Ha QP, Duc H (2016) Vehicular emissions prediction with CART-BMARS hybrid models. Transp Res Part D Transp Environ 49:188–202. https://doi.org/10.1016/j.trd.2016.09.012
Reid N, Yap D, Bloxam R (2008) The potential role of background ozone on current and emerging air issues: an overview. Air Qual Atmos Heal 1:19–29. https://doi.org/10.1007/s11869-008-0005-z
Sheng N, Tang UW (2013) Risk assessment of traffic-related air pollution in a world heritage city. Int J Environ Sci Technol 10:11–18. https://doi.org/10.1007/s13762-012-0030-1
SMG (2014) Climate in Macao. SMG/ Macao Meteorological and Geophysical Bureau. Available at: http://www.smg.gov.mo/smg/climate/e_climaintro.htm. Accessed 1 June 2019
SMG (2019) Annual summary of air quality in Macao – 2018. SMG/ Macao Meteorological and Geophysical Bureau. Available at: http://www.smg.gov.mo/smg/airQuality/pdf/IQA_2018_PT.pdf. Accessed 1 June 2019
Tong CHM, Yim SHL, Rothenberg D et al (2018a) Assessing the impacts of seasonal and vertical atmospheric conditions on air quality over the Pearl River Delta region. Atmos Environ 180:69–78. https://doi.org/10.1016/j.atmosenv.2018.02.039
Tong CHM, Yim SHL, Rothenberg D et al (2018b) Projecting the impacts of atmospheric conditions under climate change on air quality over the Pearl River Delta region. Atmos Environ 193:79–87. https://doi.org/10.1016/j.atmosenv.2018.08.053
US EPA (2003) Guidelines for Developing an Air Quality (Ozone and PM2.5) Forecasting Program. doi: EPA-456/R-03-002. Available at: https://nepis.epa.gov/Exe/ZyPURL.cgi?Dockey=2000F0ZT.TXT. Accessed 1 June 2019
WHO (2018) Ambient ( outdoor ) air quality and health. https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health. Accessed 2 Jul 2019
WHO (2019) Air pollution and health: summary. https://www.who.int/airpollution/ambient/about/en/. Accessed 2 Jul 2019
Wiśniewska K, Lewandowska AU, Staniszewska M (2019) Air quality at two stations (Gdynia and Rumia) located in the region of Gulf of Gdansk during periods of intensive smog in Poland. Air Qual Atmos Heal 12:879–890. https://doi.org/10.1007/s11869-019-00708-6
Xie J, Liao Z, Fang X et al (2019) The characteristics of hourly wind field and its impacts on air quality in the Pearl River Delta region during 2013–2017. Atmos Res 227:112–124. https://doi.org/10.1016/j.atmosres.2019.04.023
Zhang J, Ding W (2017) Prediction of air pollutants concentration based on an extreme learning machine: the case of Hong Kong. Int J Environ Res Public Health 14:1–19. https://doi.org/10.3390/ijerph14020114
Zheng J, Zhang L, Che W et al (2009) A highly resolved temporal and spatial air pollutant emission inventory for the Pearl River Delta region , China and its uncertainty assessment. Atmos Environ 43:5112–5122. https://doi.org/10.1016/j.atmosenv.2009.04.060
Funding
The work developed was supported by The Macao Meteorological and Geophysical Bureau (SMG). The research work of CENSE is financed by the Fundação para a Ciência e Tecnologia, I.P., Portugal (UID/AMB/04085/2019).
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Lei, M.T., Monjardino, J., Mendes, L. et al. Macao air quality forecast using statistical methods. Air Qual Atmos Health 12, 1049–1057 (2019). https://doi.org/10.1007/s11869-019-00721-9
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DOI: https://doi.org/10.1007/s11869-019-00721-9