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Air Quality, Atmosphere & Health

, Volume 12, Issue 9, pp 1049–1057 | Cite as

Macao air quality forecast using statistical methods

  • Man Tat LeiEmail author
  • Joana Monjardino
  • Luisa Mendes
  • David Gonçalves
  • Francisco Ferreira
Article

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 linear multiple regression (MR) and classification and regression trees (CART) analysis were developed successfully, for Macao, to predict the next day concentrations of NO2, PM10, PM2.5, and O3. All the developed models were statistically significantly valid with a 95% confidence level with high coefficients of determination (from 0.78 to 0.93) for all pollutants. The models utilized meteorological and air quality variables based on 5 years of historical data, from 2013 to 2017. Data from 2013 to 2016 were used to develop the statistical models and data from 2017 was used for validation purposes. A wide range of meteorological and air quality variables was identified, and only some were selected as significant independent 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-h levels. The models were applied in forecasting the next day average daily concentrations for NO2 and PM and maximum hourly O3 levels for five air quality monitoring stations. The results are expected to be an operational air quality forecast for Macao.

Keywords

Particulate matter PM2.5 PM10 NO2 O3 Macao 

Notes

Funding information

The work developed was supported by The Macao Meteorological and Geophysical Bureau (SMG). The research work of CENSE is financed by the Fundação para a Ciência e Tecnologia, I.P., Portugal (UID/AMB/04085/2019).

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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Sciences and Environmental Engineering, NOVA School of Science and TechnologyNOVA University LisbonLisbonPortugal
  2. 2.Institute of Science and EnvironmentUniversity of Saint JosephMacauChina
  3. 3.Center for Environmental and Sustainability Research, NOVA School of Science and TechnologyNOVA University LisbonLisbonPortugal

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