Emissions of primary particulates - outlook from WBCSD

Assessment versions

Published (reviewed and quality assured)

• No published assessments

Rationale

Justification for indicator selection

In recent years scientific evidence has been strengthened by many epidemiological studies that indicate there is an association between long and short-term exposure to fine particulate matter and various serious health impacts. Fine particles have adverse effects on human health and can be responsible for and/or contribute to a number of respiratory problems. Fine particles in this context refer to the sum of primary PM₁₀ and PM_2,5. Primary PM₁₀ refers to fine particles (defined as having diameter of 10 mm or less) emitted directly to the atmosphere. Primary PM_2,5 refers to fine particles (defined as having diameter of 2,5mm). A large fraction of the urban population is exposed to levels of fine particulate matter in excess of limit values set for the protection of human health. There have been a number of recent policy initiatives that aim to control particulate concentrations and thus protect human health.

Why to assess emissions of primary particulates from transport sector?

Transport is one of the major contributors to the pollution of primary particulates.

Scientific references

• EEA Core set of indicators (CSI)
• EECCA core set of indicators Guidelines for the application of environmental indicators in Eastern Europe , Caucasus and Central Asia

Indicator definition

Definition: Generally, the indicator 'Emissions of primary particulates include PM 10 and PM 2,5. "PM10" means particulate matter which passes through a size-selective inlet with a 50 % efficiency cut-off at 10 mm aerodynamic diameter; "PM2,5" means particulate matter which passes through a size-selective inlet with a 50 % efficiency cut-off at 2,5mm aerodynamic diameter.
The outlook from IEA/SMP model provides information about PM-10 from the transport sector.

Model used: IEA/SMP

Ownership:  World Business Council for Sustainable Development 

Temporal coverage: 1990 - 2050

Geographical coverage: ^{OECD Europe: Austria, Belgium, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, Turkey, the United Kingdom; OECD North America: USA, Canada, Mexico; Former Soviet Union: Armenia, Azerbaijan, Belarus, Estonia, Georgia, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, Republic of Moldova, Russian Federation, Tajikistan, Turkmenistan, Ukraine, Uzbekistan
Eastern Europe: Albania, Bosnia and Herzegovina, Bulgaria, Croatia, the Former Yugoslav Republic of Macedonia, Poland, Romania, Slovakia, Slovenia, Serbia and Montenegro; India; China}

Units

The indicator is measured in thousand tonnes per year and for the form of average numbers gram per kilometer is used. 

Policy context and targets

Context description

EU policy context

There are no specific european emission targets set for primary PM₁₀ and PM_2,5. However, there are several Directives and Protocols that affect the emissions of primary PM₁₀ and PM_2,5, including air quality standards for PM in the First Daughter Directive to the Framework Directive on Ambient Air Quality and emission standards for specific mobile and stationary sources for primary PM  precursor emissions.

EECCA policy context
Implement transport strategies for sustainable development in order to       ...to improve urban air quality, including through the development of better vehicle technologies that are more environmentally sound, affordable and socially acceptable ( EECCA Strategy)

Targets

There are no specific Pan-European emission targets set for primary  PM_2,5 and PM₁₀. However, emissions of the precursors NOx, SOx and NH3 are covered by the NECD and the Gothenburg Protocol to the UNECE LRTAP Convention. Both instruments contain emission ceilings (limits) that countries must meet by 2010.

Table 1. Percentage reduction required by 2010 compared to 1990 levels by country, for aggregated emissions of the secondary particulate precursors NOx, SOx and NH3 (individual pollutant emission ceilings weighted by particulate formation potential factors prior to aggregation).

Other related targets

• Member States shall take the measures necessary to ensure that concentrations of PM₁₀ in ambient air do not exceed the limit values.

The margins of tolerance laid down in Section I of Annex III shall apply in accordance with Article 8 of Directive 96/62/EC.

• Member States shall ensure that measuring stations to supply data on concentrations of PM_2,5 are installed and operated. Each Member State shall choose the number and the siting of the stations at which PM_2,5 is to be measured as representative of concentrations of PM_2,5 within that Member State. Where possible sampling points for PM_2,5 shall be co-located with sampling points for PM₁₀.
• Action plans for PM₁₀ prepared in accordance with Article 8 of Directive 96/62/EC and general strategies for decreasing concentrations of PM₁₀ shall also aim to reduce concentrations of PM_2,5.
•  Member States may designate zones or agglomerations within which limit values for PM₁₀ are exceeded owing to concentrations of PM₁₀ in ambient air due to the resuspension of particulates following the winter sanding of roads.

EECCA level

• Implement transport strategies for sustainable development in order to       ...improve urban air quality, including through the development of better vehicle technologies that are more environmentally sound, affordable and socially acceptable ( EECCA Strategy)

Links to other policy links

Council Directive 1999/30/EC of 22 April 1999 relating to limit values for sulphur dioxide, nitrogen dioxide and oxides of nitrogen, particulate matter and lead in ambient air

Related policy documents

• 1999 Protocol to Abate Acidification, Eutrophication and Ground-level Ozone
  Convention on Long-range Transboundary Air Pollution 1999 Protocol to Abate Acidification, Eutrophication and Ground-level Ozone, amended on 4 May 2012.
• UNECE Convention on Long-range Transboundary Air Pollution
  UNECE Convention on Long-range Transboundary Air Pollution.

Methodology

Methodology for indicator calculation

PM emissions were calculated within the IEA/SMP model for transport sector. PM_2,5 are excluded from calculation procedures.

At a world regional average level of aggregation, there is no information in the model about where vehicles are traveling (e.g. urban v. rural) or how various emissions translate into atmospheric concentrations. The emissions trends are included to provide a general directional sense of whether total emissions from road vehicles increasing or decreasing over time. Five types of pollutants are tracked: nitrogen oxides (NOx), particulate matter (PM-10), carbon monoxide (CO), hydrocarbons (HC or VOC) and lead (Pb). A pollutant emission tracking has been developed only for road vehicles - no tracking for rail, air or shipping.

The approach used for light-duty vehicles has been to rely primarily on existing tailpipe emissions standards for new vehicles around the world, and the announced plans for phase-in of future, generally tighter, standards. For the developing world, in cases where information on existing or planned future standards was unavailable, simple assumptions were made regarding adoption of standards similar to the EU system (EURO 1 through EURO 5) in the future, at a certain time-lag after these have been implemented in Europe.

For other road vehicles (2/3 wheelers, trucks and buses), since the model does not track new vehicles or stock turnover, but only the existing stock of vehicles, estimates are based on assumed average emissions across the vehicle stock, and evolution of this average.

Several types of data are thus needed to generate estimates: average emissions for new and existing vehicles in the base year (2000), and estimated emissions of new vehicles, or in the case of non-LDVs estimated improvements in the stock average, in the future. While the new LDV emissions estimates are based primarily on current and future emissions standards, other sources were needed for the estimates related to existing vehicles. Few such sources exist that cover non-OECD regions.

The best source found for average in-use emissions in 2000 was a very recent (and as of May 2004, still unpublished) report from an OECD Environment Directorate study, part of their MOVE II project. The IEA has not been involved in that study, but obtained relevant estimates through internal communication. The study is highly relevant since it generates average emissions for all types of road vehicles by region (with a similar but not identical regional classification system as used here), vehicle type, vehicle vintage, and emissions control category (with seven categories, from uncontrolled up through the equivalent of EURO IV control levels). However, the authors warn that estimates are not final and could change. The IEA also relied on input from SMP Workstreams 2 and 3, including both data and review of the estimates and projections contained herein.

An important difference between the projections here and the OECD projections is the assumption here that all world regions will eventually adopt the same emissions standards being implemented in OECD regions. The OECD report restricts improvements to those emissions standards already announced or nearly finalized. This leads to a large difference in the projection - if developing regions do not continue to follow the OECD country lead (with some regions such as Africa and the Middle East assumed not to adopt any standards at all), then total emissions for each of the four pollutants in the developing world rises over time, rather than dropping in the projections are used here, with the assumption of a 10-15 year lag time in adopting OECD emissions standards in the developing world (described in more detail here).

Overview of the SMP Spreadsheet Model

(The flowchart on the page 4 of the IEA/SMP spreadsheet provides an example of the logic behind the model on the basis of light-duty vehicles(e.g. automobiles)).

The model was produced in a framework of the Sustainable Mobility Project (SMP) implemented by the World Business Council for Sustainable Development and International Energy Agency.
The IEA/SMP Transport Spreadsheet Model is designed to handle all transport modes and most vehicle types. It produces projections of vehicle stocks, travel, energy use and other indicators through 2050 for a reference case and for various policy cases and scenarios. It is designed to have some technology-oriented detail and to allow fairly detailed bottom-up modeling. The SMP spreadsheet model 1.60 is the most recent version and is available for a more detailed inspection (and use, though no user guide has been prepared and there are no plans, at this time, of providing on-going usersupport for the model. A very basic outline of how to use the model is provided in the first sheet of the model spreadsheet).
The model does not include any representation of economic relationships (e.g., elasticities) nor does it track costs. Rather, it is an "accounting" model, anchored by the "ASIF" identity:

• Activity (passenger and freight travel)
• Structure (travel shares by mode and vehicle type)
• Intensity (fuel efficiency)
• Fuel type = fuel use by fuel type (and CO2 emissions per unit fuel use).

Various indicators are tracked and characterized by coefficients per unit travel, per vehicle or per unit fuel use as appropriate.
The modes, technologies, fuels, regions and basic variables are included in the spreadsheet model. Not all technologies or variables are covered for all modes. Apart from energy use, the model tracks emissions of CO2, and CO2-equivalent GHG emissions (from vehicles as well as upstream), PM, NOx, HC, CO and Pb. Projections of safety (fatalities and injuries) are also incorporated.
The most detailed segment of the model covers light-duty vehicles. The flow chart  on the page 4 of the Model Documentation provides an overview of the key linkages in the light-duty vehicle section of the model. For other passenger modes (such as buses, 2-wheelers), the approach is similar, however there is no stock model. Stocks are projected directly; vehicle sales needed to achieve these stocks is not currently tracked.
Overview of the projections, regions and viraibales used by the IEA/SMP transport spreadsheet model is peresented in the table below:

Sectors / Modes

Vehicle
Technologies/
Fuels

Regions

Variables

Key model assumptions for the reference case

The reference case projects one possible set of future conditions, based on recent trends in various important indicators and other variables. Adjustments are made for expected deviations from recent trends due to factors such as existing policies, population projections, income projections and expected availability of new technologies. Expectations for other future changes in trends, such as saturations in vehicle ownership, are also incorporated.

In general, no major new policies are assumed to be implemented beyond those already implemented in 2003. An exception to this is where there is clear evidence of what might be called "policy trajectories" - future policy actions that are either explicit or implicit in other trends. For example, a clear trend is emerging in the developing world to adopt vehicle emissions standards of a form similar to those already implemented in OECD countries. It is assumed that this "policy trajectory" will continue in the future. In contrast, no such policy trajectory is evident for reduced light-duty vehicle (LDV) fuel consumption; we therefore only incorporate existing fuel consumption programmes through the year they currently end; we assume a return after that date to historical (non-policy-driven) trends in fuel consumption.

In general, the model tried to avoid introducing significant changes in trends after 2030. We run the trends assumed to exist in 2030 out to 2050 in order to see the net effects and directions in that latter year of actions and events that often occurred years earlier.

For more infomation click here.

Calculation of emissions

Pollutant emissions tracking was implemented in the model to allow the Sustainable Mobility Project to better understand the vehicle emissions trends that result from the projection of vehicle sales, stocks and travel. At a world regional average level of aggregation, there is no information in the model about where vehicles are traveling (e.g. urban v. rural) or how various emissions translate into atmospheric concentrations. The emissions trends are included to provide a general directional sense of whether total emissions from road vehicles increasing or decreasing over time. Five types of pollutants are tracked: nitrogen oxides (NOx), particulate matter (PM-10), carbon monoxide (CO), hydrocarbons (HC or VOC) and lead (Pb). Note that for lead, a different approach is used which is discussed after the other pollutants. Pollutant emissions tracking have been developed only for road vehicles - no tracking for rail, air or shipping.

The approach used for light-duty vehicles has been to rely primarily on existing tailpipe emissions standards for new vehicles around the world, and the announced plans for phase-in of future, generally tighter, standards. For the developing world, in cases where information on existing or planned future standards was unavailable, simple assumptions were made regarding adoption of standards similar to the EU system (EURO 1 through EURO 5) in the future, at a certain time-lag after these have been implemented in Europe.

Methodology for gap filling

No methodology for gap filling has been specified. Probably this info has been added together with indicator calculation.

Methodology references

• The IEA/SMP Transport Spreadsheet Model

Data specifications

EEA data references

• No datasets have been specified here.

External data references

• Input data for the IEA/SPM model - secondary data from different sources
• Input data to EIA/SMP model - GDP (WEO)
• Output from IEA/SMP model - emissions of primary particulates
• Input data to IEA/SMP model - Average Pollutant Emissions for Existing Vehicles in 2000 (g/km) - output from the report OECD Environment Directorate study, (part of the MOVE II project)

Data sources in latest figures

Uncertainties

Methodology uncertainty

Uncertainties related to indicator calculation

All data should be based on movements on national territory, regardless of the nationality of the vehicle. It is unknown what the assumptions are regarding movement of the transport when the assigned regions.  

Uncertainties related to IEA/SMP transport model

The model does not include any representation of economic relationships (e.g.,
elasticities) nor does it track costs. The IEA has a cost-optimization model capable of this, the ETP model, but this model was not employed in the SMP's work due to its lack of transparency and its complexity.

Data sets uncertainty

The table below provides a simplified picture of what types of variables and the level of
detail modelled for each major transport mode in the IEA/SMP transport spreadsheet model. As can be seen in the next table, there is a range of coverage by mode, as well as variations in the quality of the data available (indicated by x or i). In general, there is better data available for light-duty vehicles than for other modes, though for non-OECD regions most data is quite poor, except for aggregate estimates of transport energy consumption. New vehicle characteristics are only tracked for light-duty vehicles; existing stock is used as the basic vehicle indicator for all other modes.

The reference case includes the modes and variables identified in the table below:

Modes and Variables Covered in the Reference Case Projection

 Auto
 
 

 Air
 
 

 Truck
 
 

 Frt
 Rail
 

 Pass
 Rail
 

 Buss
 
 

 Mini-
 bus
 

 2-3
 wheel
 

 Water
 
 

Note: * = have data of fair to good reliability; i = have data but incomplete or of poor reliability; blank = have nothing or have not attempted to project. Note that data of fair reliability is available for energy use across all road vehicles in non-OECD countries, but breaking this out into various road modes (cars, trucks, buses, 2- wheelers) is difficult and relatively unreliable.
For more information click here.

Rationale uncertainty

No uncertainty has been specified