Comparisons of aerosol optical depth provided by seviri satellite observations and CAMx air quality modelling

Abstract

ABSTRACT:Satellite data provide high spatial coverage and characterization of atmospheric components for vertical column. Additionally, the use of air pollution modelling in combination with satellite data opens the challenging perspective to analyse the contribution of different pollution sources and transport processes. The main objective of this work is to study the AOD over Portugal using satellite observations in combination with air pollution modelling. For this purpose, satellite data provided by Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) on-board the geostationary Meteosat-9 satellite on AOD at 550 nm and modelling results from the Chemical Transport Model (CAMx -Comprehensive Air quality Model) were analysed. The study period was May 2011 and the aim was to analyse the spatial variations of AOD over Portugal. In this study, a multi-temporal technique to retrieve AOD over land from SEVIRI was used. The proposed method takes advantage of SEVIRI's high temporal resolution of 15 minutes and high spatial resolution. CAMx provides the size distribution of each aerosol constituent among a number of fixed size sections. For post processing, CAMx output species per size bin have been grouped into total particulate sulphate (PSO4), total primary and secondary organic aerosols (POA + SOA), total primary elemental carbon (PEC) and primary inert material per size bin (CRST_1 to CRST_4) to be used in AOD quantification. The AOD was calculated by integration of aerosol extinction coefficient (Qext) on the vertical column. The results were analysed in terms of temporal and spatial variations. The analysis points out that the implemented methodology provides a good spatial agreement between modelling results and satellite observation for dust outbreak studied (10th -17th of May 2011). A correlation coefficient of r=0.79 was found between the two datasets. This work provides relevant background to start the integration of these two different types of the data in order to improve air pollution assessment.

Sources of particulate-matter air pollution and its oxidative potential in Europe

“…The initial and boundary conditions were obtained from the MOZART (Model of Ozone and Related Chemical Tracers) global model datasets MOZART-4/ GEOS-5, which are widely used in chemical transport models. The MOZART-4 data divided dust into four size bins ranging from 0.05 µm to 5.0 µm, and with the largest contribution found in dust bin 2 (0.5-1.25 µm) [90][91][92] 2− and elemental carbon) were in good agreement with data from the European air-quality database AirBase v7 and measurements from Aerodyne aerosol chemical speciation monitor or Aerodyne aerosol mass spectrometer stations 24,86,87 . The modelled OA components were reclassified into four categories to match the source apportionment of OP: (1) HOA: the sum of POA from vehicles and from other fossil fuel combustion emissions;…”

Working with the enemy? Social work education and men who use intimate partner violence

“…This supports the findings of health-related studies which suggest that a spatial resolution of about one kilometre and a temporal resolution of an hour are the minimum requirements for monitoring atmospheric events (Chow, 1995(Chow, , 1998. Second generation GEO satellites such as SEVIRI (15 min, 3 km) (Fernandes et al, 2015), GOCI (hourly, 500 m (NIR)) (Choi et al, 2012), Himawari-8 (10 min, 2 km) (Yumimoto et al, 2016) and GOES-R (15 min, 2 km) meet the hourly and sub-hourly requirements overcoming the previous temporal resolution restriction of LEO satellites albeit with a reduction in spatial resolution. Most case studies using GEO data take advantage of the enhanced temporal resolution which implies a higher probability of cloud-free measurements and fewer missed events.…”

SEVIRI Aerosol Optical Depth Validation Using AERONET and Intercomparison with MODIS in Central and Eastern Europe

“…It is much more challenging to estimate AOD from geostationary satellite retrievals than it is to do so based on data acquired from low earth orbit satellites [76]. However, advances in retrieval algorithms for geostationary satellites are found in scientific literature, with direct applications of SEVIRI data [29,56,57,59,[77][78][79][80][81][82]. The main advantage in these approaches is the higher temporal resolution that the geostationary satellites provide, e.g., [58].…”