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|Title:||Relationship between MODIS based Aerosol Optical Depth and PM10 over Croatia||Authors:||Grgurić, Sanja
Hadjimitsis, Diofantos G.
|Keywords:||MODIS AOD;PM10;PM10-AOD relationship;Aerosol;Multivariate linear regression;Artificial neural network;Croatia||Category:||Environmental Engineering||Field:||Engineering and Technology||Issue Date:||Mar-2014||Publisher:||Springer International Publishing||Source:||Central European Journal of Geosciences, 2014, Volume 6, Issue 1, pages 2-16||Abstract:||This study analyzes the relationship between Aerosol Optical Depth (AOD) obtained from Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and ground-based PM10 mass concentration distribution over a period of 5 years (2008–2012), and investigates the applicability of satellite AOD data for ground PM10 mapping for the Croatian territory. Many studies have shown that satellite AOD data are correlated to ground-based PM mass concentration. However, the relationship between AOD and PM is not explicit and there are unknowns that cause uncertainties in this relationship. The relationship between MODIS AOD and ground-based PM10 has been studied on the basis of a large data set where daily averaged PM10 data from the 12 air quality stations across Croatia over the 5 year period are correlated with AODs retrieved from MODIS Terra and Aqua. A database was developed to associate coincident MODIS AOD (independent) and PM10 data (dependent variable). Additional tested independent variables (predictors, estimators) included season, cloud fraction, and meteorological parameters — including temperature, air pressure, relative humidity, wind speed, wind direction, as well as planetary boundary layer height — using meteorological data from WRF (Weather Research and Forecast) model. It has been found that 1) a univariate linear regression model fails at explaining the data variability well which suggests nonlinearity of the AOD-PM10 relationship, and 2) explanation of data variability can be improved with multivariate linear modeling and a neural network approach, using additional independent variables.||URI:||http://ktisis.cut.ac.cy/handle/10488/8620||ISSN:||2081-9900
|DOI:||10.2478/s13533-012-0135-6||Rights:||© Springer International Publishing AG,||Type:||Article|
|Appears in Collections:||Άρθρα/Articles|
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