GlobEmission (ITT 6721) new ESA contract starting on Oct. 11 KNMI/BIRA/FMI/TNO/VITO

GlobEmission (ITT 6721)new ESA contract starting on Oct. 11

KNMI/BIRA/FMI/TNO/VITO

Status of emission inventories

Status of current emission inventories

Inventory Spatial resolution Temporal resolution

Last update Period Remarks

EDGAR 3.2 1 x 1 annual 2005 2000 updated every 5 years

GEIA v.1 1 x 1 annual 1995 1985

GFED v.3 1 x 1 monthly 2010 5 year fire emissions, satellite data involved

GICC 1 x 1 monthly 2010 1997-2005 biomass burning

MACC 0.5 x 0.5 monthly 2010 1990-2010 under construction

MEGAN 1 x 1 monthly 2009 2000 biogenic emissions

POET 1 x 1 annual/monthly 2003 1990-2000

RETRO 0.5 x 0.5 monthly 2005 1960-2000

UNFCCC per country annual 2008 1990-2008 greenhouse gases

Global inventories

Temporal resolution: monthly or annualUpdated: 2 inventories within the last 5 years (in blue)Spatial resolution: 0.5 or 1 degree

Status of current emission inventories

Inventory Spatial resolution Temporal resolution

Last update Period Remarks

Regional inventories: Europe

EMEP 50 x 50 km annual 2007 1970-2020 Country totals updated yearly,Spatial distrib. updated every 5 yr

TNO-MACC 0.125 x 0.0625 annual 2007 2003-2007 EMEP country totals with improved spatial distribution

Regional inventories: Asia

INTEX-B 0.5 x 0.5 annual 2006 2006

REAS 0.5 x 0.5 annual 2007 1995,2000 with predictions for 1980-2020

Regional inventories: North America

CGEIC 1 x 1 annual 1985 1985 part of EDGAR

NEI (EPA) USA state annual 2008 2008 updated every 3 years

Regional inventories

Temporal resolution: annualUpdated: 3 inventories within the last 5 years (in blue)Spatial resolution: ~0.1 – 1.0 degree

User requirements

Committed end users• European Environmental Agency (EEA)• University of Edinburgh• Min. of Environmental Protection of China (MEP)• Indian Inst. of Tropical Meteorology (IITM)• South African Weather service (SAWS)• National institute for Env. Studies Japan (NIES)

Specific user requirements:

• Species: NOx, CH4, CO, NMVOC, SO2, PM, O3

• Accuracy: better than 30% - 80 %

• Spatial resolution: 1 km - 50 km

• Time resolution: daily – annual

User Requirements: Regions

User Requirements: Temporal/Spatial

Do the bottom-up inventories match the user requirements ?

• Species : – Number of species is more than enough

• Accuracy:– Usual sufficient– Some regions are unreliable

• Spatial:– Sometimes a higher resolution is requested– Africa is under-represented in the regional inventories

• Temporal:– More recent emission estimates needed– Higher temporal resolution needed: monthly or daily

Can GlobEmission using satellite observations improve this ?

• Species : – Focus on a few targetted species (–)

• Accuracy:– Probably sufficient, but it still has to prove itself (±)– All regions are treated similar (+)

• Spatial:– Sometimes a higher resolution is requested (±)– Regional inventory dedicated to South-Africa (+)

• Temporal:– Emission estimates will be made available almost immediately (+

+)– Monthly temporal resolution possible (+)

Project Overview

GlobEmission:Inversion of satellite observations

Concentrations EmissionsSatellite observations

GlobEmission: Approach

• Based on satellite observations using inversion techniques

• Complementary to bottom-up inventories (not replacing)

• Focus on a limited number of species:– NOx, CH4, CO, NMVOC, SO2, PM

• Validation with existing inventories and model results

• Goal: to demonstrate the validity of the concept

Service implementationDedicated services for the following four types of emission estimates:

1. Global • Inversion of HCHO, CHOCHO on a global domain• CO inventory assessment

2. Regional • NO2 (and O3) and SO2 over South Africa, China, India (high resolution)

2b. High resolution Emission Maps• Spatial disaggregation to create high resolution maps over South Africa

3. European • Inversion of NOx in Europe

• Verification of SO2 and CO inventories in Europe (and O3)

4. Aerosol-related• Aerosol inversion over Europe, South Africa, China and Japan• Forest Fire emissions

Global Emission Estimates

Service provider : BIRA-IASB

Main users: NIES, Univ. Edinburgh

Main tasks :• Inversion of HCHO, CHOCHO on a global domain

• Derivation of global NMVOC emissions constrained by HCHO retrievals.

• Derivation of global natural emissions of isoprene using HCHO column data.

• Derivation of the missing biogenic source of CHOCHO over land using combined HCHO and CHOCHO retrievals.

• Evaluation and improvement of the anthropogenic CO emission inventories.

Regional Emission EstimatesService provider : KNMIMain users: SAWS (South Africa), IITM (India), MEP (China)Main tasks :• Derivation of NO2 (and O3) and SO2 emissios using CHIMERE and UV-VIS

satellite data• On a 25×25 km2 resolution over China, India and South Africa

Service provider : VITOMain users: SAWS (South Africa)Main tasks :• The E-MAP tool is used to grid air pollutants (e.g. NOx, SO2, PM10) as input to

air quality models for different regions and at different resolutions.• This tool will be extended towards South Africa as to allow spatial

disaggregation of (low-resolution) regional emission inventories for South Africa derived from satellite observations to high-resolution regional inventories

High resolution emission inventories for the “Regional Emission Estimates” of South Africa

Left: NOx point source emissions stemming from industrial production processes (SNAP 4)Right: NOx traffic emissions (SNAP 7) disaggregated on a 5x5 km² grid.

European Emissions Estimates

Service provider : TNO and KNMIMain users: EEAMain tasks :• Derivation of sectoral emission estimates for NOx, SO2 and CO for the years

2009-2010 per country and per year, using LOTOS-EUROS model and satellite data.

• Investigating the effect of a new NOx emission inventory on ozone formation

• Verication of existing EMEP NOx, SO2 and CO emission inventories.

FMI: Emission estimates related to aerosols

Service provider : FMI

Main users: MEP, SAWS, NIES, Univ. Edinburgh, EEA

Main tasks :• Aerosol inversion over Europe, South Africa, China and Japan using

SILAM and satellite data

• Deriving Forest Fire emissions

End