Statistical Forecast Applied to Two Macao Air Monitoring Stations

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Abstract

The levels of air pollution in Macao often exceeded the levels recommended by WHO. 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 (MR) analysis were developed successfully for Macao to predict the next day concentrations of PM10, PM2.5, and NO2. All the developed models were statistically significantly valid with a 95% confidence level with high coefficients of determination (from 0.89 to 0.92) for all pollutants. The models utilized meteorological and air quality variables based on 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. A wide range of meteorological and air quality variables were identified, and only some were selected as significant dependent variables. Meteorological variables were selected from an extensive list of variables, including geopotential height, relative humidity, atmospheric stability, and air temperature at different vertical levels. Air quality variables translate the resilience of the recent past concentrations of each pollutant and usually are maximum and/or the average of latest 24-hour levels. The models were applied in forecasting the next day average daily concentrations for PM10, PM2.5, and NO2 for the air quality monitoring stations. The results are expected to be an operational air quality forecast for Macao.

Original languageEnglish
Article number012018
JournalIOP Conference Series: Earth and Environmental Science
Volume489
Issue number1
DOIs
Publication statusPublished - 27 May 2020
Event15th International Conference on Atmospheric Sciences and Applications to Air Quality, ASSAQ 2019 - Kuala Lumpur, Malaysia
Duration: 28 Oct 201930 Oct 2019

Keywords

  • Air Pollutants
  • Air Quality
  • Management
  • Modelling
  • Monitoring
  • Nitrogen Dioxide
  • Ozone
  • Particulate Matter

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