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MSC-W Emission Estimates: Why and How?

MSC-W Emission Estimates: Why and How?. Vigdis Vestreng, EMEP/MSC-W. 7 th Joint UNECE Task Force & EIONET WS on Emission Inventories and Projections, Thessaloniki 31 Oct – 2 Nov 2006. Methodology for gap filling and replacements of reported emission data.

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MSC-W Emission Estimates: Why and How?

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  1. MSC-W Emission Estimates: Why and How? Vigdis Vestreng, EMEP/MSC-W 7th Joint UNECE Task Force & EIONET WS on Emission Inventories and Projections, Thessaloniki 31 Oct – 2 Nov 2006

  2. Methodology for gap filling and replacements of reported emission data • Inventory Review 2006. Emission data reported to LTRAP Convention and NECD. EMEP note 1/2006 ETC-ACC: Elisabeth Rigler, Martin Adams MSC-W: Vigdis Vestreng II. Chapter 2 in EMEP joint report 1/2006; Emissions: progress towards the emission ceilings in the Gothenburg Protocol ETC/ACC: Elisabeth Rigler MSC-W: Vigdis Vestreng, Leonor Tarrasón,Heiko Klein, Anna Carlin Benedictow III. Chapter 3 in EMEP report 1/2004 JRC: phillip Thunis; CONCAWE: Les white MSC-W: Leonor Tarrasón, Heiko Klein, Vigdis Vestreng IV. MSC-W presentation in the Projection WS MSC-W: Jan Eiof Jonson

  3. Motivation & Requirements • Assessments of pollution impact on human health, exceedances of critical loads and climate • Need complete and good quality emission data in the whole of the modelling domain • Spatially distributed consistent sector emission data

  4. Step 1 of 6. Input from Stage 1 and 2 review • Review on NFR level flag problems with consistency and comparability. • IIRs needed to verify replacement of reported data

  5. Step 2 of 6. Identification of data format and gaps

  6. Step 3 of 6. Conversion to SNAP SNAP sectors in the modelling work (NFR to SNAP, table IIIB) A. we wish to use as much as possible gridded data reported by countries and up to now most reported gridded data is in SNAP (only 5 countries reported in NFR) B. we wish to be able to compare directly with the emissions gridded data estimated in cooperation with IIASA for CAFÉ (1990, 1995, 2000, 2010, 2020 data)

  7. Step 4 of 6. Identification of possible inconsistent data Both PM 2.4 and PM10 needs to be reported

  8. Step 5 of 6. IIR consultation; Example: Denmark • SNAP 1 seems inconsistent across time. Possible outliers in 1991 and 1996 • IIR explanation: e.g. High fuel consumption in 1996 due to large electricity export. Also 2003 vs 2004: Low rainfall in Norway and Sweden in 2003.

  9. Step 6 of 6. Sources of emission data to complete official data

  10. Results statistics Amount of reported, replaced and gaps of national total emission in the EMEP inventory (Unit: %, average values in brackets)

  11. Methodology to spatially distribute emission data Use of ancillary data to distribute sector emissions: • Large Point Source Information (LPS, both location and intensities) • Population distribution (POP, common with IIASA) • Information from the CEPMEIP project (TNO, land-use and road maps) • Information on national gridded sector emissions (GS) The method secures the consistency of emission sector distribution across Europe per component. Precursor gases and primary PM emissions are consistentlydistributed.

  12. Conclusions Strong need for MSC-W estimates due to • Lack of officially reported data (e.g. only 13 countries reported gridded sector data for at least one year) • Inconsistent and incomplete (in terms of sources included) reporting MSC-W estimates are needed to be able to perform impact studies for health, air pollution and climate.

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