Atmospheric composition from space: its role in environmental assessment

Robert Koelemeijer

Netherlands Environmental Assessment Agency (MNP)

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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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0.5

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1.5

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1-Mar 16-Mar 31-Mar 16-Apr 1-May 16-May 1-Jun

PM2.5 AOD

PM

2.5

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g/m

3 )

AO

D

Date

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1-Aug 16-Aug 31-Aug 15-Sep 1-Oct

PM2.5 AOD

PM

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(g

/m3)

AO

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Date

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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