Study of spatial and temporal variation of atmospheric optical parameters and their relation with PM 2.5 concentration over Europe using GIS technologies

University essay from Lunds universitet/Institutionen för naturgeografi och ekosystemvetenskap

Abstract: The purpose of this study was to examine the use of remote sensing aerosol data as an estimator of ground level fine particulate matter concentration (PM 2.5). In order to examine this possible relation, daily MODIS Aerosol Optical Depth (AOD) data were used, collected for an entire year. The analysis involved manipulation of pollution and meteorological data, such as the PM 2.5 concentration which resulted from a regional photochemical model and meteorological parameters like wind speed (WS), planetary boundary layer height (PBL) and relative humidity (RH), which in turn resulted from the application of a prognostic meteorological model for the whole of Europe. Statistical regression analysis was performed for the aforementioned data in several locations of big urban agglomerations all over Europe, where the problem of particulate matter air pollution is higher, as well as its impact on man and the environment. Furthermore, the relation of AOD with PM 2.5 and meteorological parameters was also examined using PM 2.5 measurements of two operational air pollution stations located in Attica, Greece. The study confirmed a conclusion reached by other relevant studies, that the relationship between AOD and PM 2.5 is highly variable for different regions and for different time scales (Engel-Cox, 2004; Hu et al, 2013). A strong correlation of AOD – PM 2.5 was established for winter and autumn in most locations. During spring and especially summer the regression models did not produce good results for most of the places that were applied. The study also confirmed that the use of meteorological data can improve the PM 2.5 to AOD correlation. AOD or AOD/PBL was the most dominant factor in the regression analysis only in 40 % of the cases with good results. In 60 % of cases, one of the meteorological factors (RH, WS or PBL) was the most important factor in the regression equation.

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