Atmospheric composition from space: its role in environmental assessment Robert Koelemeijer...
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![Page 1: Atmospheric composition from space: its role in environmental assessment Robert Koelemeijer Netherlands Environmental Assessment Agency (MNP)](https://reader037.fdocuments.us/reader037/viewer/2022110100/56649dd05503460f94ac4ef5/html5/thumbnails/1.jpg)
Atmospheric composition from space: its role in environmental assessment
Robert Koelemeijer
Netherlands Environmental Assessment Agency (MNP)
![Page 2: Atmospheric composition from space: its role in environmental assessment Robert Koelemeijer Netherlands Environmental Assessment Agency (MNP)](https://reader037.fdocuments.us/reader037/viewer/2022110100/56649dd05503460f94ac4ef5/html5/thumbnails/2.jpg)
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MNP: bridge between science and policy
• How does air quality develop, and do we understand this?
• Do we meet (future) policy goals? (national emission ceilings,
EU air quality limit values)
• What measures can be taken to meet the goals, what are the
costs and benefits of measures?
• Our “Clients”: national government, but also international bodies (European Topic Center Air and Climate Change EEA, CLRTAP convention (EMEP), European Commission, European Parliament, OECD, UNEP)
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How do we work? Main tools:
• Emission inventories – Large point sources: individually registered
– Other sources: emission = activity * emission_factor
Assessment of future developments (activities, policy measures) emission projections assessment of compliance with national emission ceilings
• Chemical transport models + ground based measurements assessment of concentration levels and compliance with (future) air quality limit values.
• “Options document”: compilation of all technically feasible emission reduction measures and costs cost-curves
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Role of satellite data
• Validation of (combination of) models/emissions (source-
strengths and locations)
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Jonson et al., 2007. NO2 EMEP-GOME
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Jonson et al., 2006. HCHO, EMEP-GOME
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Role of satellite data
• Validation of (combination of) models/emissions (source-
strengths and locations)
• Short-term air quality forecast (US-studies, ECMWF-GEMS, in
NL: SMOGPROG)
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Role of satellite data
• Validation of (combination of) models/emissions (source-
strengths and locations)
• Short-term air quality forecast
• Observe global trends and illustrating the large-scale picture
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Richter et al., 2005. Global NO2-trend GOME
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Sciamachy NO2-column
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Project: Mapping PM2.5 in Europe (MNP, TNO, RIVM)
Source: Airbase
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Can we improve mapping of PM using satellite information (in addition to ground network and models)?
Ingredients:
• MODIS data of Aerosol Optical Thickness (AOD), and “fine fraction” of the AOD (AODF)
• Year: 2003, domain: Europe
• Ground-based measurements: AERONET (AOD) + AirBase (PM)
• Model: Lotos-Euros (TNO / RIVM / MNP)
Approach:
• Validation against AERONET
• Mapping of PM, using ground-based and satellite measurements
and models
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Validation against AERONET
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Findings of validation against AERONET
• MODIS AOD (collection 4) shows a seasonal bias over Europe
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Findings of validation against AERONET
• Relative difference more or less constant
“AOD_modis * 0.7 = AOD_AERONET” (collection 4-data)
“AODF_modis * 0.9 = AOD_AERONET” (collection 4-data)
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Findings of validation against AERONET
• MODIS AOD and AODF agree within error-bounds quoted in
literature after removal of bias (±0.05 ± 20%, 1σ-error)
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Findings of validation against AERONET
• MODIS correlates fairly good in time and space with AERONET– Average time-correlation: 0.72 (AOD) and 0.66 (AODF)
(34 stations, whole 2003)
– Spatial correlation yearly averages: 0.64 (AOD) and 0.72 (AODF)
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Cloud contamination?
• PCCD = Potentially Cloud Contaminated Data
• PCCD-matrix for MODIS and AATSR
1/3rd of MODIS AOD data may suffer from cloud-contamination
Half of AATSR AOD data may suffer from cloud contamination
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MODIS AOD, 2003
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MODIS AODF, 2003
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Top right: modeled PM2.5Top left: MODIS AODF
Bottom: merged PM2.5
PM2.5_merged = a1 AODF + a2 PM2.5_model
a1 and a2 found by least-squares fitting to measured PM2.5 (AirBase)
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Assimilation experiment
• Lotos/Euros Chemical Transport Model• Ensemble Kalman Filtering
Example: AOD distribution at 26 march 2003
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Modeled, measured and assimilated AOD
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Differences between fitting and assimilation method
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Findings of PM2.5 mapping study
• MODIS AOD (collection 4) shows a large bias over Europe, but
agrees within error-bounds quoted in literature after removal of
the bias (note: for clear-sky situations)
• MODIS data can still suffer from residual cloud-contamination
(perhaps about 1/3rd of all data)
• First attempts were made to use MODIS data for mapping PM2.5
distributions in Europe
• Satellite measurements of AOD can be used to improve mapping
of PM2.5 in Europe, but more extensive validation and further
improvement of retrieval algorithms will be necessary
Koelemeijer et al., Atm. Env. 40, 5304-5315, 2006
Schaap et al.,, Atm. Env. 42, 2187-2197, 2008
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Correlation PM2.5 – AOD at Cabauw-NL
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PM2.5 AOD
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AO
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Summary
• Application of satellite data:
– Validation of models and emissions
– Get information about countries / areas where ground-based measurements are not (made) available
• Strong points:
– Method independent of country borders
– Global coverage
– High communicative value
• Weaknesses:
– Only data under (near) cloud-free conditions
– Sensitive to assumptions on atmospheric state / surface
• Use in policy oriented reports could benefit from:
– Easy access to yearly-averaged data + published