Global bottom-up emission inventories: methodologies, application

Report
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
How do we calculate emission inventories
Satellite observations
Continuous emissions
monitoring
E-PRTR
Cost / human investment
Source sampling
Emissions modeling
National Reports
Technology based emission
factor calculation
EDGAR, National Reports
Implied emission factor calculation
extrapolation of
existing emissions data
Reliability of emission data
1. Introduction: the EDGAR project
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)
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)
2. Methodology: calculation of emissions by country and sector
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. Example: Road transportation Italy (Diesel combustion)
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)
Basic activity data from energy statistics
Technology (passenger car) and EOP information (e.g. EURO 1 vehicles)
2. Methodology: spatial allocation
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
point sources
- Power plant location
- Production facilities
.......
area sources
- Agricultural fields
- Population/ Animal density
etc.
line sources
-Ship tracking data
- Road network density
etc.
Greenhouse gas emissions (Tg CO2 eq.)
Gg CO2-eq. per grid cell
0
0.01
0.01
0.1
0.1
1
1
10
10
25
25
50
50
100
100
200
200
Source: EC-JRC/PBL. EDGAR version 4.0. http://edgar.jrc.ec.europe.eu/,2009
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)
b)
Passenger cars, motorcycles, mopeds, light duty, buses = combined road density and population density
heavy duty vehicles = road density
3. Trends: Road transportation
3. Trends: Road transportation
3. Trends: Road transportation
3. Trends: Road transportation
3. Trends: Road transportation
3. Trends: Road transportation
3. Trends: Global emissions from agriculture (air pollutants)
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
EDGAR v4.0 Global emissions from agriculture (unit Tg)
Enteric fermentation
• Animal density maps of FAO for cattle, buffaloes, goats, swine
and sheep (FAO)
• Rural population
Example NH3 gridding
Using grid: gc_01x01_LO (grass_crop) Rows: 1084154
Using grid: crop_01x01_LO (crop) Rows: 723988
Using grid: buffalo_01x01_LO (buffaloes) Rows: 96946
Using grid: poultry_01x01_LO (poultry) Rows: 928061
Using grid: Rur_pop_perc (Rural_population) Rows: 1480982
Using grid: cattle_01x01_LO (cattle) Rows: 877516
Using grid: goats_01x01_LO (goats) Rows: 587775
Using grid: pigs_01x01_LO (pigs) Rows: 684520
Using 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)
3. Emissions from Ozone precursors (1990-2005)
3. Emissions from Ozone precursors (1990-2005)
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 2007
assessment report
Emission of NOx (as NO2) on 0.1 degree grid in EDGARv4
(ton per grid cell)
4. Satellite data and verification of emission inventories
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.
4. Satellite data and verification of emission inventories
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.
4. Satellite data and verification of emission inventories
Discussion: possible verification of inventory data through
satellite measurements?
Comparing
inventories
Direct comparison Comparing model
with satellite data results with
observations
(ground or sat.)
Annual budgets
√
-
√
Sector detail
√
?
?
Time series
√
√
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Spatial allocation
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