4. Human activity and emission inventories
This chapter provides guidance on compiling emission inventories as part of a supplementary assessment. The primary objective is to produce an emission map of the zone. This map provides basic information on the air pollution situation in the zone, and information needed to run simple models for calculation of the concentration of air pollutants, as described in chapter 5. In this case, the specifications of the emission inventory should be determined by the input requirements of the model and hence, indirectly, by the chemical, spatial and temporal resolutions of the air quality quantities (concentrations) as specified in the Directives.
In principle, calculation of peak concentrations (both in space and in time), requires emission inventories with very high space and time resolution; however, in a number of cases, these peak concentrations may be assessed on the basis of more aggregated emission information using statistical information on the time variation of the emissions, while the emission factor methodology allows to compile emission estimates for individual sources, streets and other areas where high concentrations are expected. For secondary pollutants, such as ozone, nitrogen dioxide, and sulphate or nitrate particulates, more complex models are needed requiring data on emissions of so-called precursors, from which the pollutant is formed by chemical conversion.
A standard methodology, harmonised at the European level, has been developed and applied in the CORINAIR project and documented in the EMEP/CORINAIR Atmospheric Emission Inventory Guidebook (EMEP/CORINAIR, 1996). Within this project a complete, consistent and transparent emissions database for all of the European territory for the base years 1990 and 1994 is available. Member States may have more detailed and up-to-date emission inventories for particular zones. If however, no specific emission inventory for the zone under study is available such an inventory can be derived from the most recent CORINAIR inventory available, using the methodology described in this chapter.
The CORINAIR database can be used directly to calculate background concentrations, resulting from emissions outside the region under study.
4.1 General Methodology
An atmospheric emission inventory can be defined as a collection of data presenting an emission of a pollutant (to air) and related parameters including:
- chemical identity: characterises the chemical properties of the pollutant;
- activity or technology: characterises the cause of the emission and relates it to (human economic) activity;
- location: describes both the location on the map and the height of the release (stack height);
- time dependence: in general emission inventories store emissions as annual totals. The temporal patterns are in most cases modelled in the air quality assessment.
These parameters are presented in turn in relation to the use of the inventory for air quality assessment.
Chemical identity: the pollutants (or pollutant classes) considered in the inventory. Focusing on the air quality theme, some relevant pollutants (or pollutant classes) to be considered are: SO2, NOx, VOCs, (fine) Suspended Particulate Matter (SPM), such as PM10 and PM2.5, and CO. Among VOCs, some substances are relevant concerning their effects on health (for example benzene), others for their chemical reactivity related to ozone and other photochemical pollutants production. For the last mentioned phenomenon speciated VOC-emissions are required. Other relevant pollutants are heavy metals (HM), such as Pb, Hg, Cd, As, Ni, and persistent organic pollutants (POP) such as PAH (polycyclic aromatic hydrocarbons) and dioxins. For a number of these pollutants information is available in CORINAIR90, more will be available in CORINAIR94. In some cases however (benzene, speciated VOC's, SPM) additional information is needed, as no standardised methodology has been developed to date.
Activity or technology: an emission source nomenclature is needed that includes anthropogenic and natural activities. The SNAP97 (Selected Nomenclature for Air Pollution, version 1997) developed by the EEA (ETC/AE) and EMEP is the most complete and detailed list presently available. This nomenclature is used for the CORINAIR inventory by the 18 EEA member countries and others. SNAP97 will be presented in the 1998 revised version of the joint EMEP/CORINAIR "Atmospheric Emission Inventory Guidebook". Table 4.1 lists the main SNAP sectors and their relation to the economic sectors as defined in the EC Fifth Environmental Action Programme A more detailed list is provided in Annex 4-1. To fully define an emission source related to combustion, reference should be made to the fuel used.
SNAP is a three level hierarchical nomenclature:
- SNAP level 1 - 11 main sectors
- SNAP level 2 - 76 sub-sectors
- SNAP level 3 - 375 activities
Table 4.1 Main sectors in CORINAIR 94 and their relation to economic sectors as defined in the Fifth Environmental Action Programme.
|Main SNAP sector||Definition||Sector|
|1||Combustion in energy and transformation industries||Energy|
|2||Non-industrial combustion plants||Energy and Consumers|
|3||Combustion in manufacturing industry||Industry|
|5||Extraction and distribution of fossil fuels / geothermal energy||Energy|
|6||Solvent and other product use||Several|
|8||Other mobile sources and machinery||Transport|
|9||Waste treatment and disposal||Several|
*) SNAP97 differs from SNAP94 by that the category forestry, land use and wood stock change has been moved from sector 10 to sector 11. Sector 11 was called "Nature" in SNAP94.
Emission type and location: in air quality assessment, point, line and area sources are usually considered. Line sources and area sources are statistical descriptions of a large number of relatively small point sources. Examples of line sources are roads, railways, and shipping routes. Urban areas can be seen as area sources. Whether or not a group of small sources can be described as line or area source, depends on the spatial resolution required. Hence, the classification of point, linear and area sources is not strict: it depends on the scope of the assessment and on cost-effective considerations. The spatial resolution of CORINAIR does not provide for allocation to line sources. Member States may have detailed local inventories which do include such sources. The Large Combustion Plant Directive reporting process yields data on point sources. These data can be and are incorporated into the CORINAIR structure. As from 2002 the IPCC Directive will call for emission reporting by individual companies, increasing the amount of data available on large point sources.
Time distribution of the emission: For microscale or local scale estimation, as well as for the simulation of air pollution episodes, high time resolution emission inventories are needed. To estimate background concentration of primary pollutants a lower temporal resolution is sufficient. Time resolution of the emissions in principle follows the time resolution of the activity rate. If required a time resolved emission pattern therefore should be derived from annual totals using statistical information on the time dependency of the activities. These can be, for example, traffic counts or seasonal and daily temperature variations. In some cases these temporal disaggregations are part of the models and in some cases the models require an hour by hour emission input. Generally, local assessments require more detail than is available from the CORINAIR database. As a screening estimate however the EMEP/CORINAIR emission factors can be used at higher spatial resolutions, provided that the activity data are available at that higher resolution.
High or low spatial resolution of emission data is one of the most important dimensions which characterises emission inventories. For microscale or local scale assessments, inventories with high spatial resolution are needed. To estimate background concentration, inventories with low spatial resolution are sufficient.
Recently, the ETC/AE produced a report describing the methodology to derive a more spatially detailed inventory from the CORINAIR inventory (Cirillo et al. 1996).
Point source emissions available in CORINAIR can be used directly. For area sources two approaches are available:
- The emission factors provided in the EMEP/CORINAIR Guidebook make no reference to spatial resolution; hence this methodology can be applied at any spatial scale. This approach needs activity data at the spatial resolution required in the assessment;
- A top-down approach uses proxy variables to estimate spatially resolved emission patterns at a higher spatial resolution than available in CORINAIR. These proxy variables are for instance population density, road length or area. In this approach a relatively small number of data has to be available at sufficient spatial resolution. Chapter 7 of the above Review Study gives details of this method.
This latter approach may be used if detailed local inventory information is lacking, or if information for different local areas is not sufficiently comparable. The uncertainties in such a method however are quite large for a particular location. The overall picture however might be quite informative.
4.2 Information on some pollutants and examples
Table 4.2 presents the (provisional) main source sector split of the
emissions in Europe as stored in the CORINAIR94 emissions inventory for the components
relevant for this document. It is clear that for SO2 industrial activities are
by far the most important source, whereas for CO, NOx and NMVOC transportation
is the most important contributing activity. Table 4.3 and 4.4 give some more detail for
the transportation sector (main SNAP sectors 07 + 08). Important issues for compiling
emission inventories for the relevant pollutants will be discussed in more detail below.
Table 4.2 Main sector split of European total emissions in 19941 (%, totals for EU member states).
|Main SNAP sector||SO2||NOx||NMVOC||CO|
|Combustion in energy and transformation industries||51||19||1||1|
|Non-industrial combustion plants||7||4||3||12|
|Combustion in manufacturing industry||17||9||0||6|
|Extraction and distribution of fossil fuels / geothermal energy||0||1||5||0|
|Solvent and other product use||0||0||23||0|
|Other mobile sources and machinery||2||15||5||7|
|Waste treatment and disposal||0||1||1||5|
|Table 4.3 Emissions from the sector Transport (SNAP sectors 07 + 08) in 1990 (Gg)||SO2||NOx||NMVOC||CO|
|Off road vehicles and machines||153||1,147||418||1,690|
|Air traffic (LTO + taxing)||20||179||71||174|
|Share in European total (%)||4.6||56.7||34.2||59.0|
|Table 4.4 Emission by road transport in Europe 1990 (Gg).
|European road traffic||718||7,846||6,766||38,919|
|Share in European total (%)||2.6||43.8||31.1||55.8|
4.2.1 Sulphur dioxide
As is shown in table 4.5 an assessment of ambient SO2 concentrations should primarily take into account the emissions from stationary combustion in larger installations. About 85% of the emissions of SO2 in Europe originate from these sources. Since these emissions are largely emitted from high stacks, the ground level concentrations are influenced by sources at larger distances. This might average out major short time fluctuations, so it is expected that annual total emissions are sufficient for the assessment of SO2 air quality. In considering short term exceedance statistics, particularly around sources with lower emission heights, this will be no longer valid, and time variations in emission strength should be considered explicitly. Note, however, that the variability of meteorological conditions will at least be as important for ambient concentrations as the variability in emission. Such variability might be assessed by using models that estimate higher percentiles in the long term frequency distribution of hourly average concentrations, using average emissions as an input.
Table 4.5 Top SNAP level 3 activities, causing 90% of cumulative SO2 emissions in 1990 (%).
|1||public power and cogeneration - combustion plants 300 mw||49.1%|
|2||commercial, instit. and resid. - combustion plants < 50 mw||59.4%|
|3||industrial combustion - plants < 50 mw||67.7%|
|4||industrial combustion - plants 300 mw||74.1%|
|5||industrial combustion - plants 50 mw and < 300 mw||78.5%|
|6||nature - volcanoes||80.6%|
|7||public power and cogener. - combus Plants 50 and < 300 mw||81.9%|
|8||industrial combustion - refinery processes furnaces||83.3%|
|9||industrial combustion - sinter plant||84.6%|
|10||district heating - combustion plants 50 mw and < 300 mw||85.8%|
|11||district heating - combustion plants < 50 mw||86.7%|
|12||other mob. sources - marine activities: national sea traffic||87.6%|
|13||industrial combustion - cement||88.4%|
|14||production proc. - sulphuric acid||89.1%|
|15||road trans. - heavy duty vehic. and buses : rural driving||89.7%|
|16||commercial, instit. and resid. - combustion plants 50 mw||90.3%|
4.2.2 Nitrogen dioxide
Table 4.6 presents the major contributors to NOx emissions in Europe at the SNAP level 3 (activities). At this level, large power plants are the largest source activity for this pollutant. However, road transport and other mobile source activities come into play as the second through seventh and 9th highest activities emitting almost 40% of all NOx. At SNAP level 1, road transport contributed more than 50 % in the EU-12 countries in 1990, and locally in cities this may be even higher. Since these sources are low level sources, emitting in many cases directly into the living areas of densely populated regions, assessment of air quality with respect to nitrogen oxides should in most cases concentrate on mobile sources and especially on road traffic.
An inventory for NO2 should hence concentrate on urban scale and on road traffic. Detailed emission estimates, both in high temporal and spatial resolutions, can be obtained from traffic density data and emission factors as published in EMEP/CORINAIR Guidebook chapters on traffic emissions, and other literature. The methodology is available in the Copert II software system (Ahlvik et al, 1997), which in fact can be used at higher resolutions. A typical value for the percentage of NOx directly emitted as NO2 would be 5 %.
Software tools like the CAR model (Eerens et al, 1993), Mobile5 (USEPA, 1993), OSPM (Berkowicz et al, 1997) and CTB can be used to estimate emissions and concentrations from traffic densities, based upon either default emission factors or, when available, more specific emission factors for the region under study. These and other air pollution models for the calculation of concentrations are described in a COST 615 inventory.(Schatzmann et al., 1996)
Table 4.5 Top SNAP level 3 activities, causing 90% of cumulative SO2 emissions in 1990 (%).
|1||public power and cogeneration - combustion plants 300 mw||18.5%|
|2||road transport - passenger cars : rural driving||28.0%|
|3||road trans. - heavy duty vehicles and buses : rural driving||35.6%|
|4||road transport - passenger cars : urban driving||42.3%|
|5||road transport - passenger cars : highway driving||47.9%|
|6||road trans. - heavy duty vehicles and buses : highway driving||53.5%|
|7||other mob. sources - off road vehic. and machines: agriculture||57.6%|
|8||commercial, instit. and resid. - combustion plants < 50 mw||61.6%|
|9||road trans. - heavy duty vehicles and buses : urban driving||65.3%|
|10||industrial combustion - plants < 50 mw||68.5%|
|11||other mob. sources - marine activities: national sea traffic||71.1%|
|12||industrial combustion - plants 300 mw||73.5%|
|13||industrial combustion - cement||75.8%|
|14||industrial combustion - plants 50 mw and < 300 mw||77.4%|
|15||other mob. sources - off road vehicles and machines: industry||78.8%|
|16||road transport - light duty vehicles < 3.5 t : urban driving||80.2%|
|17||road transport - light duty vehicles < 3.5 t : rural driving||81.3%|
|18||other mob. sources - railways||82.4%|
|19||other mob. sources - airports (lto cycles and ground act.)||83.4%|
|20||industrial combustion - sinter plant||84.4%|
|21||road transport - heavy duty vehicles > 3.5 t and buses||85.3%|
|22||public power and cogener. - combus. plants 50 and < 300 mw||86.2%|
|23||road transport - passenger cars||87.1%|
|24||w.t.d. - open burning of agricultural wastes (except 10.03)||88.0%|
|25||other mobile sources - marine activities: national fishing||88.8%|
|26||production processes - nitric acid||89.4%|
|27||other mobile sources - off road vehicles and machines||89.9%|
|28||road transport - light duty vehicles < 3.5 t : highway driving||90.5%|
Emission data on lead are incomplete at the European and the EU level within CORINAIR. It is expected however that road traffic will also in the case of lead be a major contributor to emissions. These emissions depend on the legislation on lead content in gasolines and the availability and use of unleaded gasolines. Most exceedances of the air quality limits, however, are expected around metal industries, because of emissions from stock piles and from stacks. Member States may have specific emission inventories for these sources.
Contrary to the case of NO2, the environmental effects of lead are long term exposure effects. Hence long term average air pollutant concentrations are relevant to the assessment of air quality with respect to Pb. This means that also long term average emissions are sufficient and hence less detailed traffic density information is needed as compared to the case of NO2.
Table 4.7 gives some values for emission
factors and the assumptions underlying them. These factors should be modified if the
assumptions do not hold for the region under study. At small scales and for low speeds,
the COPERT methodology can be applied. As many countries decrease the amount of lead in
gasolines, the problem will be decreased too.
Table 4.7 Examples of Pb emission factors for road traffic For different assumptions the emission factors should be adapted accordingly; for instance, fuel lead content is different in the Member States.
|Lead content of gasoline||140||mg/litre|
|Percentage of leaded gasoline sales (national average)||25||%|
|Percentage of vehicle km's using gasoline as a fuel||65||%|
|Percentage emitted to the atmosphere||75||%|
|Average speed||Emission factor||Unit|
Even less information is available for the emissions of particulate matter on a Community-wide scale.(PM10). In a recent study (Berdowski et al., 1996) particulate emissions in Europe have been estimated. The most important sources are stationary combustion of solid fuels, road transport and production processes. Table 4.8 presents the European (EU15) totals for 1990 as derived from this study. Major uncertainties may still exist in these estimates.
A number of Member States have developed more recent estimates.
Table 4.8 PM10 emissions (EU15, 1990) per source category as estimated by TNO.
|SNAP sectors||Source sector||emission (kton/year)|
|1, 2, 3||Total stationary combustion||1,350|
|7, 8||Total transport||670|
|9||Waste processing plants||100|
|4, 5||Total process emissions||460|
4.3 Uncertainty Assessment
In the joint EMEP/CORINAIR Atmospheric Emission Inventory Guidebook and particularly in the chapter on "Verification concepts" suggestions are provided in detail for procedures and techniques that can be used to assess the validity of the emission data included in inventories. The text box in chapter 2 (from the EMEP/CORINAIR Guidebook) defines relevant concepts in this respect.
The available data is not always sufficient to develop quantitative statistical measures of the data accuracy; in these cases subjective rating schemes and evaluations are used to describe the relative confidence associated with specific estimates.
Uncertainty analysis: uncertainty estimates for emissions data are important for assessing both the inherent uncertainty of the emissions estimates for individual facilities and the range of emissions magnitude represented by all sources in a study area; to proceed in these analyses, information on the distribution of parameter values, or at least on their range, is needed. The aim is to evaluate the variability, and hence the uncertainty, related to the emission estimation. The chapter "Verification concepts" of the EMEP/CORINAIR Guidebook provides a methodology for representing the overall quality of the databases. A data quality rating procedure is recommended. Each emission factor is assigned a data quality rating according to the following definitions. This table includes a rough indication of the error range associated with each quality rating.
The EMEP/CORINAIR Guidebook presents a default
table for quality ratings for each relevant pollutant at the level of the 11 main SNAP
sectors in CORINAIR.
|Rating||Definition||typical error ranges|
|A||an estimate based on a large number of measurements made at a large number of facilities that fully represent the sector;||± 10 to 30 %|
|B||an estimate based on a large number of measurements made at a large number of facilities that represent a large part of the sector;||± 20 to 60 %|
|C||an estimate based on a number of measurements made at a small number of representative facilities, or an engineering judgement based on a number of relevant facts;||± 50 to 150 %|
|D||an estimate based on a single measurements, or an engineering calculation derived from a number of relevant facts and some assumptions;||± 100 to 300 %|
|E||an estimate based on an engineering calculation derived from assumptions only.||± order of magnitude|
The quality of a regional emission inventory compiled according to this guidance also depends on the quality of the additional data used in the procedure. Error propagation theory might be applied to estimate the uncertainties of the resulting inventory, using the error estimates of the table above. It is expected that the use of the proxy variables in the top down approach will not add dramatically to the uncertainties, provided that a proper choice is made as to which proxy variable is used for each activity.
In UK emission inventories estimated uncertainties are: (Ken Stevenson, AEA Technology, private communication)
- SO2: + 10%
- NOx: + 30%
- NMVOC: + 50%
- CO: + 40%
- PM10: + 50%
- P. Ahlvik, S. Eggleston, N. Gorißen, D. Hassel, A.-J. Hickman, R. Joumard, L. Ntziachristos, R. Rijkeboer, Z. Samaras and K.-H. Zierock (1997) COPERT II Computer Programme to Calculate Emissions from Road Transport - Methodology and Emission Factors. EEA Technical Report. European Environment Agency, Copenhagen.
- J.J. Berdowski et al.(1996) Particulate matter emissions (PM10 - PM2.5 - PM0.1) in Europe in 1990 and 1993, TNO-MEP R96/430, Apeldoorn, the Netherlands.
- R. Berkowicz, O. Hertel, N.N. Sørensen and J.A. Michelsen (1997) Modeling air pollution from traffic in urban areas. In: "Flow and Dispersion trough Obstacles" R.J.Perkins, S.E. Belcher, Eds. Clarendon, Oxford.
- H.C. Eerens, C.J. Sliggers, K.D. van den Hout (1993) The CAR model: the Dutch method to determine city street air quality. Atm. Env. 27B, 389-399.
- EMEP/CORINAIR (1996). Atmospheric Emission Inventory Guidebook (2 volumes). EEA, Copenhagen. Available on CD-ROM or on EEA Web-site http://www.eea.eu.int/.
- M.C. Cirillo, R. De Lauretis, R. Del Ciello (1996) Review Study of European Urban Inventories. EEA Topic Report 30/1996. EEA, Copenhagen.
- M. Schatzmann, S. Rafailidas, R. Britter, M. Arend (1996) Database, monitoring and modelling of urban air pollution - Inventory of models and data sets. EC COST 615 Action. EC DGXII, Brussels.
- US EPA, Office of Air and Radiation (1993) Mobile5 software and documentation; URL http://www.epa.gov/OMSWWW.
1 As available in April 1997
For references, please go to www.eea.europa.eu/soer or scan the QR code.
This briefing is part of the EEA's report The European Environment - State and Outlook 2015. The EEA is an official agency of the EU, tasked with providing information on Europe's environment.
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