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You are here: Home / Data and maps / Indicators / Emissions of primary particulates - outlook from LRTAP

Emissions of primary particulates - outlook from LRTAP

This content has been archived on 12 Nov 2013, reason: Content not regularly updated
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Contents
 

Assessment versions

Published (reviewed and quality assured)
  • No published assessments

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 PM2,5. Primary PM10 refers to fine particles (defined as having diameter of 10 mm or less) emitted directly to the atmosphere. Primary PM2,5 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.

The outlook presents plausible future for how much air pollution (especially RM 2,5 and PM10) is released into the atmosphere due to economic development, increased energy consumption, industrial production, traffic and agricultural activities in 43 European countries. It also explores how pollution from above mentioned sources is transported in the atmosphere, and which regions are affected. The outlook helps to identify to which extend the policy targets are achievable and what additional policy measures should be taken.

Scientific references:

Indicator definition

Definition: This indicator tracks trends and presents projections in emissions of primary particulate PM10 and PM2,5.
"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 indicator can also provide information on the sources of emissions from a number of sectors: agriculture, industry, fuel production, residential-commercial, power plants, transport, inductrial processes, waste,  and other (non energy).

Model used: GAINS/RAINS, EMEP

Ownership: UNECE Convention on Long-range Transboudary Air Pollutants 

Temporal coverage: 2000 - 2030 with 5 year span

Geographical coverage:  EU-27: Austria, Belgium, Bulgaria, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden, United Kingdom, Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Romania, Slovakia, Slovenia; By country: Albania, Armenia, Austria, Azerbaijan, Belarus, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Georgia, Germany, Greece, Hungary, Iceland, Ireland, Italy, Kazakhstan, Latvia, Lithuania, Luxembourg, Netherlands, Norway, Poland, Portugal, Republic of Moldova, Romania, Russian Federation, Serbia and Montenegro, Slovakia, Slovenia, Spain, Sweden, Switzerland, the former Yugoslav Republic of Macedonia, Turkey, Ukraine, United Kingdom.

Units

The indicator is measured in Gg of PM emissions per year.

Policy context and targets

Context description

Pan-European Policy Context
There no specific documents at the Pan-European level.

EU Policy context
There are no specific EU related emission targets set for primary PM10 and PM2,5. However, there are several Directives and Protocols that affect the emissions of primary PM10 and PM2,5, including including the 2008 Air Quality Directive and emission standards for specific mobile and stationary sources for primary PM10 and secondary PM10 precursor emissions. 

Other key EU legislation is targeted at reducing emissions of the particulate precursor pollutants from specific sources, for example:

  • transport;
  • industrial facilities and other stationary sources

EECCA policy context
However EECCA Environmental strategy does not explicitly put emphasis on the particulate mater, it highlights a need for '..optimisation of standards, accounting for environmental and combined health impacts (based on WHO4 criteria)'.  

Targets

There are no specific Pan-European emission targets set for primary  PM2,5 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).

Country group Country

NECD Targets 1990 -2010 (particulate precursors)

Gothenburg Target  1990 -2010 (particulate precursors)

EU-15 Austria

 -40%

 -40%

EU-15 Belgium

 -56%

 -53%

EU-15 Denmark

 -56%

 -52%

EU-15 Finland

 -47%

 -42%

EU-15 France

 -51%

 -49%

EU-15 Germany

 -74%

 -63%

EU-15 Greece

    9%

    5%

EU-15 Ireland

 -44%

 -32%

EU-15 Italy

 -53%

 -42%

EU-15 Luxembourg

 -34%

 -12%

EU-15 Netherlands

 -54%

 -52%

EU-15 Portugal

 -17%

   -4%

EU-15 Spain

 -45%

 -30%

EU-15 Sweden

 -45%

 -42%

EU-15 United Kingdom

 -68%

 -55%

NewEU-12 Bulgaria

 -34%

    9%

NewEU-12 Cyprus

  33%

 
NewEU-12 Czech Republic

 -75%

 -53%

NewEU-12 Estonia

 -45%

 

NewEU-12 Hungary

 -40%

 -23%

NewEU-12 Latvia

   -5%

  25%

NewEU-12 Lithuania

 -21%

 -14%

NewEU-12 Malta

 -20%

 

NewEU-12 Poland

 -43%

 -21%

NewEU-12 Romania

     3%

     3%

NewEU-12 Slovakia

 -62%

 -31%

NewEU-12 Slovenia

 -62%

 -22%

EU-27  

 -53%

  -

CC3 Turkey

 -

 -83%

EFTA4 Iceland

 -

 -

EFTA4 Liechtenstein

 -

 -20%

EFTA4 Norway

 -

 -28%

EFTA4 Switzerland

 -

 -44%

The EU Thematic Strategy on Air Pollution [COM(2005) 446) which sets interim health and environmental objectives provides indicative ranges of needed emission reduction for the main pollutants to 2020 at the EU level, including primary particulates 59%, and particulates precursors such as SO2 emissions by 82%, NOx emissions by 60%, ammonia by 27% compared with the year 2000.

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

Key policy question

What are prospects in reducing emissions of PM across Europe?

Specific policy question

How do different sectors and processes contribute to emissions of PM10 and PM 2.5?

Methodology

Methodology for indicator calculation

The projections of the acidifying pollutants for this outlook are based on the GAINS (former RAINS) Model. Its European implementation covers 43 countries in Europe including the European part of Russia. GAINS estimates emissions, mitigation potentials and costs for six air pollutants (SO2, NOx, PM, NH3, VOC) and for the six greenhouse gases included in the Kyoto protocol.

The new GAINS model incorporates the latest version of the RAINS-Europe model (Regional Air Pollution Information and Simulation) as it has been prepared and reviewed for the CAFE programme and the 2007 revision of the NEC directive. Emissions of pollutants are calculated as a product of activity level, uncontrolled emission factor, removal efficiency of control technology applied in a given sector, and implementation level of that technology in a given emission scenario.

Overview of the model

The Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS)-Model provides a consistent framework for the analysis of co-benefits reduction strategies from air pollution and greenhouse gas sources.


The model considers emissions of:

  • Carbon dioxide (CO2)
  • Methane (CH4)
  • Nitrogen oxides (NOx)
  • Nitrous oxide (N2O)
  • Particulate matter (TSP, PM10, PM2.5 and PM1)
  • Sulfur dioxide (SO2)
  • Volatile organic compounds (VOC)

Certain versions of the GAINS Model also contain:

  • Ammonia (NH3)
  • Carbon monoxide (CO)
  • Fluorinated greenhouse gases (F-Gases)

The GAINS Model consists of several screen options, which display information pertaining to:

  • Economic Activity Pathways
    activities causing emissions (energy production & consumption, passenger & freight transport, industrial and agricultural activities, solvent use, etc.)
  • Emission Control Strategies
    the evolution of emissions and control over a given time horizon
  • Emissions Scenarios
    emissions are computed for a selected emissions scenario (combination of energy pathway and emissions control strategy), emission factors, results displays, and input values are also available under this action
  • Emission Control Costs
    displays emission control costs computed for a selected emissions scenario
  • Impacts
    presents ecosystem sensitivities and human health impacts of air pollution
  • Data Management
    provides an interactive interface where owner-specific data can be modified, updated, exported, and downloaded

The GAINS Model simultaneously addresses health and ecosystem impacts of particulate pollution, acidification, eutrophication and tropospheric ozone. Simultaneously, the GAINS Model considers greenhouse gas emission rates and the associated value per ton of CO2 equivalence. Historic emissions of air pollutants and GHGs are estimated for each country based on information collected by available international emission inventories and on national information supplied by individual countries. The GAINS Model assesses emissions on a medium-term time horizon, emission projections are specified in five year intervals through the year 2030.

Options and costs for controlling emissions are represented by several emission reduction technologies. Atmospheric dispersion processes are often modeled exogenously and integrated into the GAINS Model framework. Critical load data and critical level data are often compiled exogenously and incorporated into the GAINS modeling framework.

The model can be operated in the 'scenario analysis' mode, i.e., following the pathways of the emissions from their sources to their impacts. In this case the model provides estimates of regional costs and environmental benefits of alternative emission control strategies. The Model can also operate in the 'optimization mode' which identifies cost-optimal allocations of emission reductions in order to achieve specified deposition levels, concentration targets, or GHG emissions ceilings. The current version of the model can be used for viewing activity levels and emission control strategies, as well as calculating emissions and control costs for those strategies.

 The The current version (June 2008) allows access to

  • the recent set of activity data and projections for all European countries that has been developed for the revision of the NEC directive,
  • computations of emissions, emission projections and control costs for the air pollutants (SO2, NOx, PM, NH3, VOC),
  • emissions, control measures and emission control costs of the optimized policy scenarios that are analyzed for the NEC review,
  • computation and display of concentration and deposition fields of selected air pollutants,
  • computation and display of health and environmental impacts of air pollutants,
  • emission inventories and projections for CO2,
  • estimates for the other greenhouse gases (CH4, N2O, HFC, PFC, SF6).
  • Atmospheric dispersion processes over Europe for all pollutants are modelled on the basis of results of the European EMEP model developed at the Norwegian Meteorological Institute (Simpson et al., 2003).Atmospheric dispersion processes over Europe for all pollutants are modelled on the basis of results of the European EMEP model developed at the Norwegian Meteorological Institute (Simpson et al., 2003).

For more information see: http://gains.iiasa.ac.at/gains/docu.EU/index.menu?page=448 (requires regstration)

Calculation of Particulate Matter with RAINS model

Together with the existing modules dealing with the emissions of the precursor emissions of secondary aerosols such as sulphur dioxide (SO2), nitrogen oxides (NOx), ammonia (NH3) and volatile organic compounds (VOC), a special PM extraction of RAIMS enables the comparison of the potentials and costs for controlling primary emissions of fine particles with those of secondary aerosols and to find costminimal approaches for reducing ambient levels of particulate matter.

The emissions of particulate matter (PM) in the RAINS model are calculated for three different size classes: the fine fraction (PM2.5), the coarse fraction (PM10 - PM2..5) and large particles (PM_>10 hm). Summed up, these three fractions represent total suspended particles (TSP).

Fine particles are emitted from a large number of sources with large differences in their technical and economic properties. The methodology distinguishes 392 source categories for stationary energy combustion, industrial processes, mobile sources and agriculture. For each of these sectors, the study explores the applicable options for reducing PM emissions, their efficiency and their costs.

Emissions characteristics of the individual sectors are strongly determined by country-specific conditions. The methodology estimates emission control costs of standard technologies under the specific conditions characteristic for the various European countries. Based on the assumption of the general availability of control technologies with equal technical properties and costs, a number of country-specific circumstances (level of technological advancement, installation size distribution, labor costs, etc.) are used to estimate the costs for the actual operation of pollution control equipment.

For the individual source sectors, emissions are estimated based on statistical information on economic activity and emission factors that reflect hypothetical emissions if no control measures were applied. These emission factors were taken from the literature and were, to the maximum possible extent, adapted to the country-specific conditions. Actual emissions are calculated taking into account the application of emission control measures in a given sector, for which also costs are estimated.

It needs to be emphasized that these preliminary estimates are still associated with considerable uncertainties, and more work, involving national experts, will be necessary to obtain a verified and generally accepted European data base to estimate the potential for further reductions of fine particles in Europe.

 Overview of the EMEP model

The European Monitoring and Evaluation Program (EMEP) developed a Unified EMEP model in order to provide, on a regular basis, governments and other parties under the Convention on Long Range Transboundary Air Pollution with scientific information that can support the continuing development and evaluation of the protocols under the convention. Unified EMEP model combines several models. For integrated assessment analyses it uses RAINS model developed and maintained at the Center for Integrated Assessment Modeling (CIAM). It also uses MSC-W model for atmospheric dispersion and deposition of acidifying compounds, compounds causing eutrophication, ground level ozone and particulate matter developed by the Meteorological Synthesizing Centre West (MSC-W), one of the centers under the EMEP programme. Additionally it uses EMEP Chemical Transport Models for the regional atmospheric dispersion and deposition of heavy metals (Cd, Pb, Hg) and selected persistent organic compounds (PCB, PAH, HCB, PCDD/Fs, g-HCH). The latest model version has been documented in the EMEP Status report I, Part I (Simpson et. al., 2003) and the EMEP Status report 2004 (Tarrasón et al., 2004) where a few updates are described.

The model domain covers Europe and the Atlantic Ocean. The model grid has a horizontal resolution of 50 km at 60 0N, which is consistent with the resolution of emission data reported to CLRTAP. In the vertical, the model has 20 sigma layers reaching up to 100 hPa. The unified model uses 3-hourly resolution meteorological data from the PARLAM-PS model, a dedicated version of the HIRLAM (high resolution limited area model) numerical weather prediction model.

The emissions consist of girded annual national emissions of sulphur dioxide, nitrogen oxides, ammonia, non-methane volatile organic compounds and carbon monoxide. They are available in each cell of the 50 * 50 km2 model grid and distributed temporally according to monthly and daily factors derived from data provided by the University of Stuttgart (IER). Concentrations of 71 species are computed in the latest version of the Unified EMEP model (56 are advected and 15 are short-lived and not advected). Four secondary and two primary PM compounds are included in the model. The sulphur and nitrogen chemistry is coupled to the photo-chemistry, which allows a more sophisticated description of e.g. the oxidation of sulphur dioxide to sulphate, also including oxidant limitations.

Dry deposition is calculated using the resistance analogy and is a function of the pollutant type, meteorological conditions and surface properties. Parametrisation of wet deposition processes includes both in-cloud and sub-cloud scavenging of gases and particles using scavenging coefficients.

For more information see: http://www.emep.int/

Use of Scenarios

The LRTAP uses GAINS/RAINS and EMEP to calculate global and European emissions for two categories of scenarios: 'current legislation'  and  'maximum technically feasible reduction' (MFR) scenarios.

The 'current legislation' (CLE) scenario reflects the current perspectives of individual countries on economic development and takes into account the anticipated effects of presently decided emission control legislation.

The 'maximum technically feasible reduction' (MFR) scenario outlines the scope for emission reduction offered by a full implementation of the best available emission control technologies. Considering this calculation as a theoretical analysis of the long-term reduction potential, practical limitations on the penetration of most advanced emission control measures imposed by the gradual turnover of existing capital stock especially in the short run are not taken in to account, and the obviously high costs of such a theoretical emission control strategy are not estimated. On the other hand, in this scenario the potential for emission reductions offered by structural changes, such as increased energy efficiency measures, fuel substitution, more efficient production technologies or reduced transport demand is not considered. Earlier studies have shown that the emission reduction potential of such measures is considerable and that some of them could be even cost-effective (e.g., Rentz et al., 1994, Van Vuuren et al., 2005). The latest version of the model allows to build scenarios which accounts for structural changes. Such set of scenarios is developed by the IISAA in order to identify new NEC for the EU.

 

Methodology for gap filling

The input data for RAINS model comes from different international sources as main data sets. National data were used for verification of the international data sources and for gap filling (see data sets).

Methodology references

Uncertainties

Methodology uncertainty

A methodology has been developed to estimate uncertainties of emission calculations based on uncertainty estimates for the individual parameters of the calculation (Suutari et al., 2001). It was found that uncertainties in modeled national emissions of SO2 and NOx in Europe typically lie in the range between 10 and 30 percent (Outlook from RAINS model). In general, the uncertainties are strongly dependent on the potential for error compensation. This compensation potential is larger (and uncertainties are smaller) if calculated emissions are composed of a larger number of similar-sized source categories, where the errors in input parameters are not correlated with each other. Thus, estimates of national total emissions are generally more certain than estimates of sectoral emissions.

The uncertainty in input parameters showed that the actual uncertainties are critically influenced by the specific situation (pollutant, year, country). Generally, however, the emission factor is an important contributor to the uncertainty in estimates of historical emissions, while uncertainty in the activity data dominates the future estimates.

For more information see http://www.iiasa.ac.at/rains/review/suutari.pdf.

Data sets uncertainty

These preliminary estimates are still associated with considerable uncertainties, and more work, involving national experts, will be necessary to obtain a verified and generally accepted European data base to estimate the potential for further reductions of fine particles in Europe.

Rationale uncertainty

N.A.

Further work

Short term work

Work specified here requires to be completed within 1 year from now.

Long term work

Work specified here will require more than 1 year (from now) to be completed.

General metadata

Responsibility and ownership

EEA Contact Info

Anita Pirc Velkavrh

Ownership

No owners.

Identification

Indicator code
Outlook 007
Specification
Version id: 1

Permalinks

Permalink to this version
63e30e5d517031062714f7ca7bde090e
Permalink to latest version
27NX92OUXK

Classification

DPSIR: Pressure
Typology: Descriptive indicator (Type A - What is happening to the environment and to humans?)

Geographical coverage

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