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 under 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 (PM 10 and PM 2.5 ) for Taipa Ambient, a background representative station located within the area of Macao (32.9 km 2 ), 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 PM 10 and PM 2.5 , respectively). The results are expected to be the basis for an operational air quality forecast for the region.
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.
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