Global bottom-up emission inventories:

Global bottom-up emission inventories: methodologies, application in atmospheric chemistry modeling

and possible verification through satellite measurements

John van Aardenne

European Commission DG Joint Research Centre

"The views expressed are purely those of the presenters and may not in any circumstances be regarded as stating an official position of the European Commission."

EDGAR project:

Ulrike Doering (JRC)Greet Maenhout (JRC)Suvi Monni (JRC)Jos Olivier (PBL)Valerio Pagliari (JRC),Jeroen Peters (PBL), Fulgencio SanMartin (now at OECD)Lorenzo Orlandini (now at DG AGRI), John van Aardenne (JRC, project leader)

http://edgar.jrc.ec.europa.eu

Reliability of emission data

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Continuous emissions monitoring

extrapolation of existing emissions data

Source sampling

Emissions modeling

Implied emission factor calculation

Technology based emission factor calculation

How do we calculate emission inventories

E-PRTR

EDGAR, National Reports

National Reports

Satellite observations

The Emissions Database for Global Atmospheric Research (EDGAR v4) provides global past and present day anthropogenic emissions of greenhouse gases and air pollutants by country and on spatial grid

Inventories for policy purposes: - monitoring the progress/compliance in meeting specific emission targets- scenario studies in emissions reduction options (2000-2100 period)

Inventories for scientific purposes:- understanding processes that lead to anthropogenic and natural emissions- understanding change in atmospheric composition due to emissions trends

Science for policy:- impact modeling: human health, ecosystems- assessment of transport of air pollutants across countries and continents (e.g HTAP)

1. Introduction: the EDGAR project

Years 1970 - 2005

CO2 CH4 N2O HFCs PFCs SF6

CFCs HCFCs

CO SO2 NOX NMVOC NH3

PM2.5 PM10 OC BC

2. Methodology: source categories

Main groups:

- Energy: Fuel Combustion (IPCC 1A)

- Fugitive emissions from fuel (IPCC 1B)

- Industrial Processes (non-combustion, IPCC 2)

- Product Use (IPCC 3)

- Agriculture (including Savanna burning), (IPCC 4)

- Land Use Change and Forestry (IPCC 5)

- Waste (IPCC 6)

- Other anthropogenic sources (fossil fuel fires) (IPCC 7)

Technology based emission factor approach (see for example on road transport)

x: compound, c: country, s: sector, yr: year, AD: activity data, TECH: technology , EOP: % of technologies that are controlled by end-of-pipe abatement measures, EF: uncontrolled emission factor by sector & technology, RED: reduction % on the uncontrolled EF by the installed abatement measure.

Methodology follows state-of-the-art in large scale emission inventory calculations, IPCC 2006 Guidelines and EEA/EMEP Guidelines

2. Methodology: calculation of emissions by country and sector

AD: activity data, consumption of fuels for combustion (TJ diesel in road transport, with data from International Energy Statistics (IEA, DOE, UN)

TECH: technology information: share of activity by technology (% of diesel used by passenger cars, trucks, from literature analysis, own studies)

EF: uncontrolled emission factor (kg NO2 / TJ diesel, with datafrom IPCC and EEA/EMEP Emission Inventory Guidelines and literature analysis)

EOP: share of the specific emission abatement measures (% of diesel passengers equipped with emission regulations (e.g. EURO II, from scientific literature, country reports and own studies)

REDEOP: removal efficiency of abatement measure (% of uncontrolled EF removed, with datafrom IPCC and EEA/EMEP Emission Inventory Guidelines and scientific literature)

Technology (passenger car) and EOP information (e.g. EURO 1 vehicles)Basic activity data from energy statistics

2. Example: Road transportation Italy (Diesel combustion)

Spatial allocation

(xi, yi) = representing the lower left corner of each 0.1 grid cell, AD: activity data (e.g. natural gas in power plant sector), for some maps technology data is gridded (e.g. underground hard coal mines), C: country, yr: year, Indicator (spatial grid, e.g. road density map). Grid cells share countries and include point source from different countries(no loss of emission information during gridding)

Indicator (spatial proxy) is assigned to Activity data and Technologies (note : in most cases no need to adjust data to match EDGAR datasets)

2. Methodology: spatial allocation

line sources

point sources

area sources

- Ship tracking data- Road network densityetc.

- Power plant location- Production facilities.......

- Agricultural fields- Population/ Animal

densityetc.

2. Methodology: spatial allocation

Greenhouse gas emissions (Tg CO2 eq.)

Source: EC-JRC/PBL. EDGAR version 4.0. http://edgar.jrc.ec.europe.eu/,2009

Gg CO2-eq. per grid cell0 0.01

0.01 0.10.1 1

1 1010 2525 5050 100

100 200200

3. Example: Global emissions from road transportation

EDGAR v4.0 Global emissions from road transportation (unit Tg)Road transport NOx emissions in Annex I and non-Annex I countries (Tg NO2)

Allocation on grid: proxy data = road density (modified from VMAP, 2007) and population density (modified from CIESIN, 2007)

a) Passenger cars, motorcycles, mopeds, light duty, buses = combined road density and population density b) heavy duty vehicles = road density

3. Trends: Road transportation

3. Trends: Global emissions from agriculture (air pollutants)

EDGAR v4.0 Global emissions from agriculture (unit Tg)

Spatial allocation of agricultural emissions

Agricultural soils • IRRI rice map• soil acidity map• soil type• thermal climate zones• cropland and grassland maps of Hyde

Agricultural waste burning • Hyde cropland map

Enteric fermentation • Animal density maps of FAO for cattle, buffaloes, goats, swine and sheep (FAO)• Rural population

Example NH3 griddingUsing grid: gc_01x01_LO (grass_crop) Rows: 1084154Using grid: crop_01x01_LO (crop) Rows: 723988Using grid: buffalo_01x01_LO (buffaloes) Rows: 96946Using grid: poultry_01x01_LO (poultry) Rows: 928061Using grid: Rur_pop_perc (Rural_population) Rows: 1480982Using grid: cattle_01x01_LO (cattle) Rows: 877516Using grid: goats_01x01_LO (goats) Rows: 587775Using grid: pigs_01x01_LO (pigs) Rows: 684520Using grid: sheeps_01x01_LO (sheeps_0.1x0.1) Rows: 748986

3. Trends: Agriculture (air pollutants)

NH3 from agriculture, 1970 Majority of NH3 emissions occur in manure management and N application to soils

Global emissions have almost doubled between 1970 and 2005, strong growth especially in Asia

In 2005, emission intensity similar in Asia and Europe

3. Emissions from Ozone precursors (1990-2005)

4. Satellite data and verification of emission inventories

Tropospheric NO2 columns for 2004-2005 determined from the SCIAMACHY satellite instrument (Figure taken from HTAP 2007assessment report

Emission of NOx (as NO2) on 0.1 degree grid in EDGARv4(ton per grid cell)


Jianzhong Ma,, Andreas Richter, John P. Burrows, Hendrik Nüß and John A. van Aardenne, Comparison of model-simulated tropospheric NO2, over China with GOME-satellite data, Atmospheric Environment. Volume 40, Issue 4, Pages 593-604, 2006.


Comparinginventories

Direct comparison with satellite data

Comparing model results with observations (ground or sat.)

Annual budgets √ - √

Sector detail √ ? ?

Time series √ √ √

Spatial allocation √ √ √

Discussion: possible verification of inventory data through satellite measurements?

Global bottom-up emission inventories:

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