M. Schaap, A. Apituley, R. Koelemeijer, R. Timmermans, G. de Leeuw Mapping the PM2.5 distribution in the Netherlands using MODIS AOD.

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M. Schaap, A. Apituley, R. Koelemeijer, R. Timmermans, G. de Leeuw Mapping the PM2.5 distribution in the Netherlands using MODIS AOD

AT2, 01 October 2008, HelsinkiMapping the PM2.5 distribution in the Netherlands using MODIS AOD2 Introduction Satellite derived AOD may be used to gain insight in the regional PM2.5 distribution Goal: To assess the relationship between AOD and PM2.5 in the Netherlands. Can we use AOD data at all? If yes, what is the relation? When does it apply? Can we extrapolate the relation at Cabauw to the Netherlands and estimate PM2.5 concentrations from satellite data?

AT2, 01 October 2008, HelsinkiMapping the PM2.5 distribution in the Netherlands using MODIS AOD3 Cabauw The combination of the instrumentation at Cabauw provides an unique opportunity to study the AOD-PM2.5 relationship in the Netherlands.

AT2, 01 October 2008, HelsinkiMapping the PM2.5 distribution in the Netherlands using MODIS AOD4 Instruments used in this study AOD: Sun-photometer (CIMEL) AERONET Level 1.5 PM2.5: TEOM-FDMS Backscatter profile: aerosol LIDAR Clouds:combination of Cabauw instrumentation Period: 1 August 2006 – 31 May 2007

AT2, 01 October 2008, HelsinkiMapping the PM2.5 distribution in the Netherlands using MODIS AOD5 AOD and PM2.5: Timeseries

AT2, 01 October 2008, HelsinkiMapping the PM2.5 distribution in the Netherlands using MODIS AOD6 Typical vertical profile in periods with good correlation Stable nice weather conditions typical for smog conditions Continental airmasses Cloud free Well mixed boundary layer

AT2, 01 October 2008, HelsinkiMapping the PM2.5 distribution in the Netherlands using MODIS AOD7 Typical vertical profile in periods with bad correlation A need for improved cloud detection! AOD meas.

AT2, 01 October 2008, HelsinkiMapping the PM2.5 distribution in the Netherlands using MODIS AOD8 Cloud detection using the LIDAR and Angstrom coef.

AT2, 01 October 2008, HelsinkiMapping the PM2.5 distribution in the Netherlands using MODIS AOD9 Influence of cloud screening

AT2, 01 October 2008, HelsinkiMapping the PM2.5 distribution in the Netherlands using MODIS AOD10 Time of day Time windowPM 2.5 = a * AOD + bR2R2 PM 2.5 = a * AODR2R2 aba

AT2, 01 October 2008, HelsinkiMapping the PM2.5 distribution in the Netherlands using MODIS AOD11 Air mass origin PM 2.5 Average PM 2.5 level associated with AOD measurements TEOM-FDMSMODISAERONET LIDAR Average N

AT2, 01 October 2008, HelsinkiMapping the PM2.5 distribution in the Netherlands using MODIS AOD12 Application to MODIS data… MODIS validationMODIS AOD-PM2.5

AT2, 01 October 2008, HelsinkiMapping the PM2.5 distribution in the Netherlands using MODIS AOD13 Estimated PM2.5 distribution over the Netherlands There appear to be unrealisitc gradients in the AOD distribution within the Netherlands

AT2, 01 October 2008, HelsinkiMapping the PM2.5 distribution in the Netherlands using MODIS AOD14 Conclusions First inspection of the AERONET (L1.5) AOD and PM2.5 data yields a low correlation between the two properties AOD correlates well with PM2.5 under stable fair weather conditions with continental air masses AERONET L1.5 contains significant cloud contamination Improved cloud detection using LIDAR eliminates many “outliers” Comparison to L2.0 provides confidence in our cloud-screening method and that of AERONET Strength of correlation increases when focusing around noon. Mapping of the regional PM2.5 distribution yields concentrations that are about 45% higher than the long term average The uncertainty associated with the AOD data may be higher or of similar magnitude as the spatial variability within the country. The good temporal correlation shows that AOD can be used for monitoring PM2.5 changes in time

AT2, 01 October 2008, HelsinkiMapping the PM2.5 distribution in the Netherlands using MODIS AOD15 Comparison to L2.0 provides confidence in our cloud- screening method and that of AERONET