Satellite data are fundamental for air quality monitoring. Despite this, several algorithms are present in literature leaving to the reader a choice on the best approach without considering the physical – spatial – temporal inconsistencies concerning the problem of relating satellite with ground-based data. In order to overcome this dilemma, the present work was carried out developing a general methodological approach to overcome the aforementioned inconsistencies obtaining in any region the best retrieval. In this respect, the particulate matter phenomenology (both at ground and vertically) was considered in order to select the best approach for the AOD to PM conversion starting from the knowledge of PM properties, meteorology and vertical behaviour. In this respect, multiple years of particulate matter vertical profiles, chemical composition, hygroscopicity, meteorology and optical properties were used to define an optimized satellite AOD-ground PM relationship. A hybrid algorithm (physically based and statistical) was developed to be applied over the Milan conurbation (Po Valley) to determine ground-level PM1, PM2.5 and PM10 concentrations from satellite aerosol optical depth (AOD) data. The developed algorithm (based on aerosol optical depth, mixing height, wind speed and PM concentrations of the previous day) showed high accuracy enabling to predict ground particulate matter concentrations with very low RMSE in prediction (5.48 μg/m3, 9.89 μg/m3 and 10.64 μg/m3 for PM1, PM2.5 and PM10) and rather high R2 (≥0.83 on the evaluation set of data).
|Titolo:||Satellite AOD conversion into ground PM10, PM2.5 and PM1 over the Po valley (Milan, Italy) exploiting information on aerosol vertical profiles, chemistry, hygroscopicity and meteorology|
|Data di pubblicazione:||2019|
|Appare nelle tipologie:||1.1 Articolo in rivista|