Macao air quality forecast using statistical methods

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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.
Original languageEnglish
Pages (from-to)249-258
JournalInternational Journal of Environmental Impacts: Management, Mitigation and Recovery
Issue number3
Publication statusPublished - 19 Jul 2019


  • air pollutants
  • air quality forecast
  • management
  • modelling
  • monitoring


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