Upgrading the Estimation of Daily PM10 Concentrations Utilizing Prediction Variables Reflecting Atmospheric Processes

This paper formulates a Multiple Linear Regression Model (MLRM), for the estimation of daily PM10 concentrations in background urban areas. 24-hour backward air mass trajectories, NO2 concentrations and gridded (1 degrees x 1 degrees resolution) Aerosol Optical Depth (AOD) observations from MODIS were used in order to compose the model's predictor variables. As a supplement to local combustion/non-combustion contributions, the suggested method intends to comprise and quantify the effect that transboundary PM sources and wind dispersion on particulate air pollution levels. The proposed technique was implemented at a background sampling site in Birmingham (United Kingdom) and the results were compared with the outcome of a Simple Linear Regression Model (SLRM) which contained only one predictor variable expressing local combustion. Various statistical indices signified the upgraded performance of the MLRM, in comparison with SLRM, thus the participation of long range transport and wind dispersion variables in the MLRM was successful. According to the MLRM's findings, anthropogenic combustion (traffic, heating) is the strongest source of PM10 in the selected background urban area, followed by local non-combustion emissions and long range transport. Extreme PM2.5 intrusions from continental Europe also emerged.