Statistical Methods to Forecast Air Quality in Taipa Ambient and Taipa Residential of Macao

Air pollution is a major concern issue on Macao since the concentration levels of several of the most common pollutants are frequently above the internationally recommended values. The low air quality episodes impacts on human health paired with highly populated urban areas are important motivations to develop forecast methodologies in order to anticipate pollution episodes, allowing establishing warnings to the local community to take precautionary measures and avoid outdoor activities during this period. Using statistical methods (multiple linear regression (MLR) and classification and regression tree (CART) analysis) we were able to develop forecasting models for the main pollutants (NO₂, PM_2.5, and O₃) enabling us to know the next day concentrations with a good skill, translated by high coefficients of determination (0.82–0.90) on a 95% confidence level. The model development was based on six years of historical data, 2013 to 2018, consisting of surface and upper-air meteorological observations and surface air quality observations. The year of 2019 was used for model validation. From an initially large group of meteorological and air quality variables only a few were identified as significant dependent variables in the model. The selected meteorological variables included geopotential height, relative humidity and air temperature at different altitude levels and atmospheric stability characterization parameters. The air quality predictors used included recent past hourly levels of mean concentrations for NO₂ and PM_2.5 and maximum concentrations for O₃. The application of the obtained models provides the expected daily mean concentrations for NO₂ and PM_2.5 and maximum hourly concentrations O₃ for the next day in Taipa Ambient air quality monitoring stations. The described methodology is now operational, in Macao, since 2020.