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

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  • Man Tat Lei

    (Department of Sciences and Environmental Engineering, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal
    Institute of Science and Environment, University of Saint Joseph, Macau 999078, China)

  • Joana Monjardino

    (Center for Environmental and Sustainability Research, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal)

  • Luisa Mendes

    (Department of Sciences and Environmental Engineering, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal)

  • David Gonçalves

    (Institute of Science and Environment, University of Saint Joseph, Macau 999078, China)

  • Francisco Ferreira

    (Center for Environmental and Sustainability Research, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal)

Abstract

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 (NO 2 ), particulate matter (PM 10 ), PM 2.5 , but not for ozone (O 3 ) 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 (R 2 ), 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 PM 2.5 and O 3 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 PM 2.5 and O 3 , with peaks of daily concentration exceeding 55 μg/m 3 and 400 μg/m 3 , respectively. The 2013 to 2018 model successfully predicted this high pollution episode with high coefficients of determination (of 0.92 for PM 2.5 and 0.82 for O 3 ). The low pollution episode for PM 2.5 and O 3 was identified during the 2020 COVID-19 pandemic period, with a low record of daily concentration for PM 2.5 levels at 2 μg/m 3 and O 3 levels at 50 μg/m 3 , respectively. The 2013 to 2018 model successfully predicted the low pollution episode for PM 2.5 and O 3 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.

Suggested Citation

  • Man Tat Lei & Joana Monjardino & Luisa Mendes & David Gonçalves & Francisco Ferreira, 2020. "Statistical Forecast of Pollution Episodes in Macao during National Holiday and COVID-19," IJERPH, MDPI, vol. 17(14), pages 1-19, July.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:14:p:5124-:d:385060
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    References listed on IDEAS

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    1. Fu, Shihe & Gu, Yizhen, 2017. "Highway toll and air pollution: Evidence from Chinese cities," Journal of Environmental Economics and Management, Elsevier, vol. 83(C), pages 32-49.
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    1. Thomas M. T. Lei & Martin F. C. Ma, 2023. "The Relationship between Roadside PM Concentration and Traffic Characterization: A Case Study in Macao," Sustainability, MDPI, vol. 15(14), pages 1-14, July.
    2. Soo-Min Choi & Hyo Choi, 2022. "Artificial Neural Network Modeling on PM 10 , PM 2.5 , and NO 2 Concentrations between Two Megacities without a Lockdown in Korea, for the COVID-19 Pandemic Period of 2020," IJERPH, MDPI, vol. 19(23), pages 1-22, December.

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