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Open AccessArticle

Statistical Forecast of Pollution Episodes in Macao during National Holiday and COVID-19

1
Department of Sciences and Environmental Engineering, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal
2
Institute of Science and Environment, University of Saint Joseph, Macau 999078, China
3
Center for Environmental and Sustainability Research, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(14), 5124; https://doi.org/10.3390/ijerph17145124
Received: 27 May 2020 / Revised: 11 July 2020 / Accepted: 14 July 2020 / Published: 15 July 2020
(This article belongs to the Special Issue Air Pollution Meteorology)
Statistical methods such as multiple linear regression (MLR) and classification and regression tree (CART) analysis were used to build prediction models for the levels of pollutant concentrations in Macao using meteorological and air quality historical data to three periods: (i) from 2013 to 2016, (ii) from 2015 to 2018, and (iii) from 2013 to 2018. The variables retained by the models were identical for nitrogen dioxide (NO2), particulate matter (PM10), PM2.5, but not for ozone (O3) Air pollution data from 2019 was used for validation purposes. The model for the 2013 to 2018 period was the one that performed best in prediction of the next-day concentrations levels in 2019, with high coefficient of determination (R2), between predicted and observed daily average concentrations (between 0.78 and 0.89 for all pollutants), and low root mean square error (RMSE), mean absolute error (MAE), and biases (BIAS). To understand if the prediction model was robust to extreme variations in pollutants concentration, a test was performed under the circumstances of a high pollution episode for PM2.5 and O3 during 2019, and the low pollution episode during the period of implementation of the preventive measures for COVID-19 pandemic. Regarding the high pollution episode, the period of the Chinese National Holiday of 2019 was selected, in which high concentration levels were identified for PM2.5 and O3, with peaks of daily concentration exceeding 55 μg/m3 and 400 μg/m3, respectively. The 2013 to 2018 model successfully predicted this high pollution episode with high coefficients of determination (of 0.92 for PM2.5 and 0.82 for O3). The low pollution episode for PM2.5 and O3 was identified during the 2020 COVID-19 pandemic period, with a low record of daily concentration for PM2.5 levels at 2 μg/m3 and O3 levels at 50 μg/m3, respectively. The 2013 to 2018 model successfully predicted the low pollution episode for PM2.5 and O3 with a high coefficient of determination (0.86 and 0.84, respectively). Overall, the results demonstrate that the statistical forecast model is robust and able to correctly reproduce extreme air pollution events of both high and low concentration levels. View Full-Text
Keywords: air pollution; air quality forecast; modelling; pollution episodes; national holiday; COVID-19 air pollution; air quality forecast; modelling; pollution episodes; national holiday; COVID-19
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MDPI and ACS Style

Lei, M.T.; Monjardino, J.; Mendes, L.; Gonçalves, D.; Ferreira, F. Statistical Forecast of Pollution Episodes in Macao during National Holiday and COVID-19. Int. J. Environ. Res. Public Health 2020, 17, 5124.

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