TY - JOUR
T1 - Air Quality Forecast by Statistical Methods
T2 - Application to Portugal and Macao
AU - Mendes, Luísa
AU - Monjardino, Joana
AU - Ferreira, Francisco
N1 - info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04085%2F2020/PT#
PY - 2022/3/10
Y1 - 2022/3/10
N2 - 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.
AB - 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.
KW - air quality
KW - classification and regression trees
KW - multiple regression
KW - nitrogen dioxide
KW - ozone
KW - particulate matter
UR - http://www.scopus.com/inward/record.url?scp=85127470316&partnerID=8YFLogxK
U2 - 10.3389/fdata.2022.826517
DO - 10.3389/fdata.2022.826517
M3 - Article
C2 - 35360510
AN - SCOPUS:85127470316
SN - 2624-909X
VL - 5
SP - 1
EP - 17
JO - Frontiers in Big Data
JF - Frontiers in Big Data
M1 - 826517
ER -