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Open Access
Research article

Macao Air Quality Forecast Using Statistical Methods

man tat lei1,2,
joana monjardino3,
luisa mendes1,
francisco ferreira3
1
Department of Sciences and Environmental Engineering, NOVA School of Science and Technology, NOVA University Lisbon, Portugal
2
Institute of Science and Environment, University of Saint Joseph, Macau, China
3
Center for Environmental and Sustainability Research, NOVA School of Science and Technology, NOVA University Lisbon, Portugal
International Journal of Environmental Impacts
|
Volume 2, Issue 3, 2019
|
Pages 249-258
Received: N/A,
Revised: N/A,
Accepted: N/A,
Available online: N/A
View Full Article|Download PDF

Abstract:

The levels of air pollution in the cities of Greater Bay Area in Southern China, including Macao, are extremely high and often exceeded the levels recommended by World Health Organization Air Quality Guidelines. In order for the population to take precautionary measures and avoid further health risks un- der high pollutant exposure, it is important to develop a reliable air quality forecast. Statistical models based on multiple regression analysis were developed successfully for Macao to predict the next-day concentrations of particulate matter (PM10 and PM2.5) for Taipa Ambient, a background representative station located within the area of Macao (32.9 km2), at Taipa Grande, the headquarter of Macao Meteorological and Geophysical Bureau. The two developed models were statistically significantly valid, with a 95% confidence level with high coefficients of determination. A wide range of meteorological and air quality variables were identified, and only some were selected as significant dependent variables. The meteorological variables such as geopotential height and relative humidity at different vertical levels were selected from an extensive list of variables. The air quality variables that translate the resilience of the recent past concentrations of each pollutant were the ones selected. The models were based in meteorological and air quality variables with five years of historical data, from 2013 to 2017. The data from 2013 to 2016 were used to develop the statistical models and data from 2017 were used for validation purposes, with high coefficients of determination between predicted and observed daily average concentrations (0.92 and 0.89 for PM10 and PM2.5 , respectively). The results are expected to be the basis for an operational air quality forecast for the region.

Keywords: air pollutants, air quality forecast, management, modelling, monitoring
References
[1] Sheng, N. & Tang, U.W., Risk assessment of traffic-related air pollution in a world heritage city. International Journal of Environmental Science and Technology, 10(1), pp. 11–18, 2013.
[2] Statistics and Census Service (DSEC), Macao in Figures. Available at http://www.dsec.gov.mo/Statistic.aspx?NodeGuid=ba1a4eab-213a-48a3-8fbb-962d15dc6f87, 2018 (accessed 1 March 2019).
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[4] World Health Organization (WHO), Health Aspects of Air Pollution with Particulate Matter, Ozone and Nitrogen Dioxide. Available at http://www.euro.who.int/__data/assets/pdf_file/0005/112199/E79097.pdf, (accessed 1 March 2019).
[5] Ministry of Ecology and Environment (MEE), Ambient Air Quality Standards. Available at http://210.72.1.216:8080/gzaqi/Document/gjzlbz.pdf, (accessed 1 March 2019).
[6] Krzyzanowski, M. & Cohen, A., Update of WHO air quality guidelines. pp. 7–13, 2008.
[7] World Health Organization (WHO), Air Quality Guidelines. Available at http://202.171.253.71/www.euro.who.int/__data/assets/pdf_file/0005/78638/E90038.pdf, (accessed 1 March 2019).
[8] World Health Organization (WHO), WHO Air Quality Guidelines for Particulate Matter, Ozone, Nitrogen Dioxide and Sulfur Dioxide. Available at http://202.171.253.72/apps.who.int/iris/bitstream/handle/10665/69477/WHO_SDE_PHE_OEH_06.02_eng.pdf?sequence=1&isAllowed=y, (accessed 1 March 2019).
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[12] Lopes, D., Ferreira, J., Hoi, K.I., Miranda, A.I., Yuen, K.V. & Mok, K.M., Weather research and forecasting model simulations over the Pearl River Delta Region. Air Quality, Atmosphere and Health, pp. 115–125, 2018.
[13] Lopes, D., Hoi, K.I., Mok, K.M., Miranda, A.I., Yuen, K.V. & Borrego, C., Air quality in the main cities of the Pearl River Delta Region. Global Nest Journal, 18(4), pp. 794–802, 2016.
[14] He, D., Zhou, Z., He, K., Hao, J., Liu, Y., Wang, Z. & Deng, Y., Assessment of traffic related air pollution in urban areas of Macao. Journal of Environmental Sciences, 12(1), pp. 39–46, 2000.
[15] Ferreira, F.C., Torres, P.M., Tente, H.S. & Neto, J.B., Ozone levels in Portugal: the Lisbon region assessment, 2004.
[16] Clapp, L.J. & Jenkin, M.E., Analysis of the relationship between ambient levels of O3, NO2 and NO as a function of NOx in the UK. Atmospheric Environment, 35(36), pp. 6391–6405, 2001.
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[21] Choi, W., Paulson, S.E., Casmassi, J. & Winer, A.M., Evaluating meteorological comparability in air quality studies: classification and regression trees for primary pollutants in California’s South Coast Air Basin. Atmospheric Environment, 64, pp. 150–159, 2013.
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[23] Cassmassi, J.C., Objective ozone forecasting in south coast air basin: updating the objective prediction models for the late 1990’s and Southern California ozone study (SCOS97-NARSTO) applications, 1997.
[24] Oduro, S.D, Ha, Q.P. & Duc, H., Vehicular emissions prediction with CART-BMARS hybrid models. Transportation Research Part D: Transport and Environment, 49, pp. 188–202, 2016.
[25] Durão, R.M., Mendes, M.T. & Pereira, M.J., Forecasting O3 levels in industrial area surroundings up to 24 h in advance, combining classification trees and MLP models. Atmospheric Pollution Research, 7, pp. 961–970, 2016.
Cite this:
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BibTex Style
MLA Style
Chicago Style
GB-T-7714-2015
Lei, M. T., Monjardino, J., Mendes, L., & Ferreira, F. (2019). Macao Air Quality Forecast Using Statistical Methods. Int. J. Environ. Impacts., 2(3), 249-258. https://doi.org/10.2495/EI-V2-N3-249-258
M. T. Lei, J. Monjardino, L. Mendes, and F. Ferreira, "Macao Air Quality Forecast Using Statistical Methods," Int. J. Environ. Impacts., vol. 2, no. 3, pp. 249-258, 2019. https://doi.org/10.2495/EI-V2-N3-249-258
@research-article{Lei2019MacaoAQ,
title={Macao Air Quality Forecast Using Statistical Methods},
author={Man Tat Lei and Joana Monjardino and Luisa Mendes and Francisco Ferreira},
journal={International Journal of Environmental Impacts},
year={2019},
page={249-258},
doi={https://doi.org/10.2495/EI-V2-N3-249-258}
}
Man Tat Lei, et al. "Macao Air Quality Forecast Using Statistical Methods." International Journal of Environmental Impacts, v 2, pp 249-258. doi: https://doi.org/10.2495/EI-V2-N3-249-258
Man Tat Lei, Joana Monjardino, Luisa Mendes and Francisco Ferreira. "Macao Air Quality Forecast Using Statistical Methods." International Journal of Environmental Impacts, 2, (2019): 249-258. doi: https://doi.org/10.2495/EI-V2-N3-249-258
Lei M. T.， Monjardino J.， Mendes L.， et al. Macao Air Quality Forecast Using Statistical Methods[J]. International Journal of Environmental Impacts， 2019， 2(3)： 249-258. https://doi.org/10.2495/EI-V2-N3-249-258