Links between global-scale emission estimatesand national emission inventories
STEVEN J. SMITH
Joint Global Change Research InstituteCollege Park, MD

Expert Meeting on Short-Lived Climate Forcers
Geneva, Switzerland
May 28, 2018

1Background
Global emission inventories are necessary for global Earth-system models,
Human-Earth System models, and comparative analysis across countries.
National inventories are a key component of efforts to limit pollution and its
impacts on human health and the environment.
Inventories are particularly important for short-lived species that have highspatial heterogeneity.
Aerosol Emissions: BC, OC, (other PM2.5), (dust)
Aerosol and ozone precursors: SO2, NOX, CO, NH3, NMVOC
Inventories of CO2, CH4 are also used for carbon-cycle and atmospheric chemistry studies.

The substantial amount of work that goes into developing and updating
national inventories makes these useful for the development of global
inventories.
2

A Variety of Approaches to using of national
inventory information in global estimates
Independent Estimates§e.g., EDGAR. (Sources: EMEP/EEA 2013 guidebook, literature, IEA energy data)

Pros: Independent estimate with consistent methodology across countries
Cons: May not have most up to date regional information or location-specificdetails. Must use global proxy databases for gridding.

Mosaic Emissions Grids§e.g., HTAPv2. (regional gridded air pollution inventories “stitched” together with EDGAR
gridded data as default where other information not available), MIX

Pros: Detailed regional information consistent with air quality studies.
Cons: Gridded data not always available for the same year, nor for mostrecent or earlier years. No detailed sector info (and maybe not consistent)

Mosaic Aggregate Regional/Country Emissions by Sector§e.g., HTAPv1, CEDS, RCP (also GAINS-extensive country consultations)

Pros: Can use most up to date regional emissions information
Cons: Inconsistent methods, generally global grid proxies (e.g., EDGAR)

3

Community Emissions Data System (CEDS)
Advertisement
Timely, reproducible, consistent, “research” estimates for emissions of aerosol (BC,OC), precursor compounds (SO2, NOx, NH3, CH4, CO, NMVOC), and CO2.

Methodology§Hybrid of bottom-up emission estimates & calibration to inventories (e.g.
EPA, EMEP, etc.)§Mapped to spatial grids for CMIP6 and other research§

Calibrated to country-level inventories (most species but not BC/OC)§

Drawing heavily on existing work (EDGAR, GAINS, country inventories)§Seasonality and VOC speciation•

Aggregate data (country/sector) : in Journal Article supplement
https://www.geosci-model-dev.net/11/369/2018/gmd-11-369-2018.html•••

Gridded data: https://esgf-node.llnl.gov/search/input4mips/
GitHub: https://github.com/JGCRI/CEDS/
Project Web Site: http://www.globalchange.umd.edu/CEDS/

Emissions [Tg SO2/year]

CMIP6 Anthropogenic Emissions (1750 – 2014)SO2●

●

●

●

100●

●
●

50●
●

●

●
●

0● ●

●

●

4
1750 1800 1850 1900 1950 2000

CEDS System Diagram
1. Data Collection
Activity Data
fuel consumption,
processing activity,and other drivers(1750 – 20xx)

Emissions
Inventories
Where
Available

4. Historical Extension

Historical
Extension
activity data andemission factors(1750 – 20xx)

2. Develop Default
Emissions

3. Calibrate to Existing
Inventories

Default Emission
Estimatesby year, country, fuel,and sector(1960/1970 – 20xx)

Default Emission
Factors
Bottom Up
Emissions/Emissions
Factors by fuel andsector

5. Final Emissions

Final Emissions
by country, year, fuel,process, and sector

Emission Factor
Scaling
interpolation andextrapolation

6. Other Data Products

GriddedEmissions

SpatialProxy &Emissions
Data

Uncertainty
Estimates

5Issues With Connecting Country Inventories
to Global Datasetso Heterogeneous data formats• There is no common format for either aggregate or gridded emissions data
• Custom data processing needed for each dataseto Inconsistent sector definitions• Definitions of sectors are sometime unclear• Not all inventories include all sectors – important to know where to gap fill
• Some air pollutant sectors (e.g. off road) have incomplete global driver datao General lack of fuel-specific data

• Emissions by fuel and sector would enable analysis and allow better calibration• (Thank you U.K. for providing both!)o Lack of activity data
• Activity data is not always publicly available, making consistency difficult to check• While global inventories use fuel consumption as driver data, some sectors incountry inventories may use other types of activity data (e.g. vehicle-km, etc.).
• It would be very valuable for global inventories if country inventories could report
combustion emission factors and process emissions intensities (physical units).

o Limited or no uncertainty analysis• Not traditionally a priority for country em inventories used for regulatory purposes.
• For scientific purposes, uncertainty is highly desired.

6Issues: Inventory Errors, Gaps and Biases
Regulatory air pollutant inventories are generally based on extensive guidance
and processes, including, for example, testing regimes for road vehicles andreporting requirements• Given their regulatory purposes, adherence to a well-defined, publicly accessible,
and approved process is important.

o For many scientific purposes, the most accurate dataset is desired, notnecessarily the dataset most well aligned with a country inventory• There are a number of documented cases where observational (+model) evidence
indicates that current processes have lead to biases in regulatory inventories both in
USA and Europe (and likely elsewhere).
• How much evidence is required to depart from the “official” inventory data?

o There are known issues and gaps in some country data

• Thus, for example, leading to the EMEP data “as used by models”• Alternative approach is GAINS, where extensive consultations and expert judgment
are used in the process of emissions estimation• Shipping emissions consistency (”international” vs ”domestic”) are a significant issue(in and between country inventories, and between country and global inventories)• Fugitive emissions are difficult to quantify in all cases• Different goals/hierarchies for air quality vs climate emissions at the country level7

Consistency in Country Inventories: What isgoing on here?
Because CEDS consistently processed inventory and driver data, we can examine
implied emission factors in country-level inventories.
Road Vehicle – Gasoline Implied EF
Different assumptions ordifferent vehicle emission
characteristics?

FranceCO Emission Factor [g/g]

USA
0.6

Belgium
CanadaDenmark
France0.4

Germany
Italy
Netherlands

0.2

NorwaySpain
SwedenUK

0.0

United Kingdom
1970

1980

Data shown where values exist in inventory

1990

Year20002010 2015

United States8

Issues: Primary Aerosol Emissions
There are some specific issues related to primary aerosol emissions.
• Climate modeling is primarily focused on sub-micron primary BC and OC emissions(e.g. BC1, OC1)
• Air quality inventories primarily quantify PM2.5

o Composition of PM2.5 is not detailed in most existing inventories• Current regulatory regimes focus on total PM2.5, regardless of composition• The anthropogenic BC and OC portion of PM2.5 is particularly important for climatechange studies• The best way to use country estimates of PM2.5 to improve BC and OC are not clear• Need to better determine: PM2.5 -> PM1.0 = BC + OM + ? (+ Brown Carbon?)o Biomass Consumption• A major source of aerosol particles is solid biomass consumption, particularly
traditional biomass (but also wood fuels in higher income countries)• Biomass consumption statistics range widely in quality• Data sources are not always well documented on an international scale• Annual trends in international statistics can be incomplete or inconsistent over time(some trends are likely per-capita extrapolations)o Be careful about use of defaults• Need to be cautious when using default emission factors (illusion of certainty)

9

Summary - Future Directions (Initial thoughts)
o Develop common data formats and definitions for country-level inventory data
• This would greatly advance analysis, use, and comparison• Should be coupled with API (Application Programing Interface) data accesso Better document sectoral definitions• Is a particular issue for research inventories• Issue: fuel comb and process (e.g. IPCC guidelines) vs facility emissions (country level)o Improved methods for inclusion of high quality country/regional gridded
emissions data/proxies into global inventories• This would improve the quality of global inventory data for recent years• Need to develop methods to smoothly merge the higher quality gridded data for recentyears with the less available proxy data for earlier years for time series model analysis

o Confront inventories with observations• Observations are critical for many species, particularly BC/OC!
• Particularly where real-world emissions conditions vary widely (fugitive emissions,carbonaceous aerosols in nearly all cases, biologic sources (NOx, NH3)), in-situ andremote observations will be necessary to better constrain emissionso Document/research methods to harmonize estimates of PM2.5 and BC/OC• Kudos to Klimont et al. (2017) for providing PM10, PM2.5, PM1, BC1, OC1, OM110

END

11

ADDITIONAL SLIDES ON CEDS

12

Consistency in Country Inventories: What isgoing on here?
Because CEDS consistently processed inventory and driver data, we can examine
implied emission factors in country-level inventories.
Road Vehicle – Gasoline Implied EF
Belgium

FranceCO Emission Factor [g/g]USA

Canada0.6

Denmark
France0.4

Germany
Italy
Netherlands

0.2

NorwaySpainSwedenUK0.0

United Kingdom1970

1980

1990

Year

2000

2010 2015

United States

13

CEDS goals
Timely, reproducible, consistent, “research” estimates for emissions of aerosol (BC,OC) and aerosol precursor compounds (SO2, NOx, NH3, CH4, CO, NMVOC)
Instead of thisProduce thisX sector
X region

X sector
X fuelX country
X state/province

Historical
Estimate
2000

2010

2015
2000

2002

2004

2006

2008

2010

2012

2014

CEDS produces:
Uncertainty essential• SO2, NOX, CO, OC, BC, NH3, NMVOC, CO2, CH4
for estimates of more
recent years.
• 1750 – 2014
• Aggregate annual estimates (221 regions/countries, 8 fuels, 55 sectors)• Gridded data (0.5° x 0.5° , monthly seasonality, 9 sectors)
14System Diagram
1. Data Collection
Activity Data
fuel consumption,
processing activity,and other drivers(1750 – 20xx)

Emissions
Inventories
Where
Available

4. Historical Extension

Historical
Extension
activity data andemission factors(1750 – 20xx)

2. Develop Default
Emissions

3. Calibrate to Existing
Inventories

Default Emission
Estimatesby year, country, fuel,and sector(1960/1970 – 20xx)

Default Emission
Factors
Bottom Up
Emissions/Emissions
Factors by fuel andsector

5. Final Emissions

Final Emissions
by country, year, fuel,process, and sector

Emission Factor
Scaling
interpolation andextrapolation

6. Other Data Products

GriddedEmissions

SpatialProxy &Emissions
Data

Uncertainty
Estimates

15Methodology
1) Estimates by Country, Sector, Fuel§Hybrid of bottom-up emission estimates & calibration to inventories (e.g.
EPA, EMEP, etc.)

2) Map to spatial grid§Country values mapped to spatial grids for 14 “intermediate” gridding
sectors. (Chosen where we have EDGAR, and other global grids.)§

Other than population, we lack spatial information for earlier years.
§Mostly use time-changing EDGAR grids over 1970 – 2008, then constant§

Residential emissions distribution merges to population grid by 1900

3) Add Seasonality§

Global gridded monthly profiles (mostly from ECLIPSE) applied bygridding sector4) Aggregate for gridded data release§Aggregated to 9 final gridding sectors, month (including aircraft)

16

CEDS CMIP6 – Improvements Relative ToCMIP5o More robust emission trends• Consistent methodology across all years• All emission species use same driver data
• Consistently calibrated to country-level inventories where availableo Annual data resolves important socio-economic eventso Emissions estimates out to 2014 to capture recent trends as best as possible• Albeit with additional uncertainty (which is now being estimated)o 1850 Emissions – CO higher and NOX lower, due to explicit representation of biomass
and coal emissions for all species.

o New sectors included• Residential waste burning• Flaring (from ECLIPSE project)• Fossil-fuel Fires (from EDGAR)

o Reproducible emissions generation process• CEDS data system and most input data will be released as open source software• Updated data such as new country inventories and energy driver data can be readily
incorporated to allow annual updates• Modular system facilitates data updates (e.g., “drag and drop” capability)
17

GitHub Wiki https://github.com/JGCRI/CEDS/wiki

Extensive Wiki Documentation

18

GitHub – Tracking Issues

https://github.com/JGCRI/CEDS/issues
19