Abstract
Air pollution is a major concern issue for most countries in the world. In Portugal and Macao, the values of nitrogen dioxide (NO2), particulate matter (PM) and ozone (O3) are frequently above the concentration thresholds accepted as “good air quality.” Portugal follows the European Union (EU) legislation (Directive 2008/50/EC) on air quality and Macao the air quality guidelines (AQG) from the WHO. Air quality forecasts are very important mitigation tools because of their ability to anticipate pollution events, and issue early warnings, allowing to take preventive measures and reduce impacts, by avoiding exposure. The work presented here refers to the statistical forecast of air pollutants for three regions: Greater Lisbon Area, Madeira Autonomous Region (both located in Portugal), and Macao Special Administrative Region (in Southern China). The presented statistical approach combines Classification and Regression Tree (CART) and multiple regression (MR) analysis to obtain optimized regression models. This consolidated methodology is now in operation for more than a decade in Portugal, and is subject to regular updates that reflect the ongoing research and the changes in the air quality monitoring network. Recently, the same methodology was applied to Macao in collaboration with the Macao Meteorological and Geophysical Bureau (SMG). Here, a statistical approach for air quality forecasting is described that has been proven to be successful, being able to forecast PM10, PM2.5, NO2, and O3 concentrations, for the next day, with a good performance. In general, all the models have shown a good agreement between the observed and forecasted concentrations (with R2 from 0.50 to 0.89), and were able to follow the concentration evolution trend. For some cases, there is a slight delay in the prediction trend. Moreover, the results obtained for pollution episodes have proven that statistical forecast can be an effective way of protecting public health.
Highlights
The Ambient Air Quality Directives of European Union (EU) set standards for key air pollutants
Its applications can fall into several broad areas, Air Quality Forecast Statistical Methods such as health alerts—many cities currently provide warnings to the public when air pollution levels exceed specified levels, being those warnings directed at specific populations that are sensitive to air pollution (Liu et al, 2018); in addition, air quality forecasts can supplement existing emission control programs or emergency responses, with cities offering free access to public transportation (Quarmby et al, 2019); on pollution episode days, to reduce vehicle emissions, and regions implementing the “No-Burn day” (AQMD, 2022); consisting of a ban period on wood-burning in residential fireplaces, stoves, or outdoor fire pits, when particulate matter concentrations are expected to reach unhealthy levels, due to air emissions and stagnant weather conditions
To predict the next-day daily average concentrations of particulate matter (PM10 and PM2.5), daily hourly maximum concentrations of ozone (O3), and daily hourly maximum concentrations of nitrogen dioxide (NO2), at air quality monitoring stations locations, forecast models were developed based on statistical methods using multiple linear regression (MR) and Classification and Regression Tree (CART) analysis
Summary
The Ambient Air Quality Directives of European Union (EU) set standards for key air pollutants. The NOVA University Lisbon (NOVA School of Science and Technology), in collaboration with the Portuguese Environment Agency (APA) and the Portuguese Institute for Sea and Atmosphere (IPMA), runs and disseminates daily air quality forecasts based on a statistical approach, first used by Cassmassi (1987) at South Coast Air Quality Management District California, USA. This statistical methodology is in operation, in Portugal (Neto et al, 2005), for more than a decade and is the subject of regular updates, reflecting the ongoing research, and the changes in the air quality monitoring network. The same methodology was extended to Madeira Autonomous Region, in Portugal, and was applied to Macao Special Administrative Region of the People’s Republic of China (MSAR), resulting from a collaboration with the Macao Meteorological and Geophysical Bureau (SMG) (Lei et al, 2019, 2020)
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