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Institute of Energy Economics and the Rational Use of Energy University of Stuttgart 1 Jochen Theloke, IER, University of Stuttgart TFMM 7 th Meeting,

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Presentation on theme: "Institute of Energy Economics and the Rational Use of Energy University of Stuttgart 1 Jochen Theloke, IER, University of Stuttgart TFMM 7 th Meeting,"— Presentation transcript:

1 Institute of Energy Economics and the Rational Use of Energy University of Stuttgart 1 Jochen Theloke, IER, University of Stuttgart TFMM 7 th Meeting, FMI, Helsinki Progress within ESPREME on heavy metal emission inventories FP SSP-1 - Policy Oriented Research - Contract No.: Jan Dec 2006

2 Institute of Energy Economics and the Rational Use of Energy University of Stuttgart 2 Aim: Provision of information, that can contribute to the development of a strategy for reducing the occurrence of heavy metals in the environment in Europe. This strategy should be effective efficient Europe-wide and should make use of the state-of-the-art in integrated assessment modelling.

3 Institute of Energy Economics and the Rational Use of Energy University of Stuttgart 3 University of Stuttgart, Institute of Energy Economics and the Rational Use of Energy (IER) co-ordinator Norwegian Institut of Air Research (NILU) Institute of Ecology of Industrial Areas (IETU) Swedish Environmental Research Institut (IVL) Institute of Occupational Medicine (IOM) EMEP Meteorological Synthesizing Centre – East (MSC-E) Institute for Atmospheric Pollution (CNR-IIA) Czech Hydrometeorological Institute (CHMI) ETZEL, Hungary NILU Polska The ESPREME Project Team

4 Institute of Energy Economics and the Rational Use of Energy University of Stuttgart 4 Approach The approach consists of the following steps: 1.Consolidate, improve and provide European wide emission data of heavy metals (Hg, Cd, Pb, Ni, As and Cr) 2.Collect systematic data on the possibilities to reduce emissions. 3.Improve models for the transport of HM in the atmosphere, soil and water and apply them to simulate the transport of HM in these media; modeling results will be evaluated vs. measurement data. 4.Collect data on thresholds and information on exposure–response relationships. 5.Estimate the willingness-to-pay to avoid damage from HM exposure by transferring values from available contingent valuation studies. 6.Set up an integrated assessment model (IAM). 7.Carry out runs of the IAM to identify cost effectiveness strategies, i.e. bundles of measures that achieve compliance with thresholds and cost-benefit analyses to identify bundles of measures, where the difference between benefits and costs is maximised. 8.Conduct a feasibility study to identify approaches and further research needs for macro-economic assessment of HM abatement strategies.

5 Institute of Energy Economics and the Rational Use of Energy University of Stuttgart 5 IER Water/Soil Model (WP05)  HM Optimisation Model (WP07) EMEP MSC-E HM-Model (WP04) Feasibility Study (WP08) SR-Relationships Basemaps,... costs benefits (by country, sector,...) HM abatement strategies for Europe policy deployment (WP09) HM Emissions (WP02) Abatement measures Stock/Activity data (WP03) direct releases into water/soil HM via air (by landcover) SR-Relationships Basemaps,... Exposure-Response Functions (WP06) ESPREME Model Framework

6 Institute of Energy Economics and the Rational Use of Energy University of Stuttgart 6 ESPREME HM emissions to air – Expert emission inventory for the base case 2000 and scenarios of future development, BAU+Climate and MTFR, for % -22% -23% -20% -40% -43% -41% -42% -57%

7 Institute of Energy Economics and the Rational Use of Energy University of Stuttgart 7 ESPREME Hg total emission projection and sectoral contribution to 2010 BAU+Climate total Hg emissions -36% -54%

8 Institute of Energy Economics and the Rational Use of Energy University of Stuttgart 8 ESPREME Hg speciation of emissions for the base case 2000 by sector

9 Institute of Energy Economics and the Rational Use of Energy University of Stuttgart 9 Method for generation of heavy metal emissions in spatial and temporal resolution Annual emissions from NILU (country totals) in coarse sectoral resolution Sectoral disaggregation to SNAP level 3 and spatial (NUTS level 1, 2 or 3) disaggregation by indicators of the IER GENEMIS Emission Model Disaggregated annual emission table as input for the IER GENEMIS Emission Model GIS intersection ECM Temporal profiles Land use dataLPS data base Digital road maps Temporally (hourly/daily) and spatially (50 x 50 km, EMEP grid) resolved heavy metal emission data sets

10 Institute of Energy Economics and the Rational Use of Energy University of Stuttgart 10 Spatially resolved annual emissions of total Hg for the 2010 BAU+Climate scenario (following page: same for As, Cd, Cr and Ni)

11 Institute of Energy Economics and the Rational Use of Energy University of Stuttgart 11

12 Institute of Energy Economics and the Rational Use of Energy University of Stuttgart 12 HM releases to agricultural soils Based on „Assessment and reduction of heavy metal input into agro-ecosystems” (AROMIS, concerted action, co-ord KTBL) Inputs –Fertilisers (mineral and organic like compost, sewage sludge and livestock manure) –Plant protection products –Livestock feeds Data of different quality and comprehensiveness are provided for 21 countries No data for As and Hg available Inputs to soil

13 Institute of Energy Economics and the Rational Use of Energy University of Stuttgart 13 Data availibility from the AROMIS data base

14 Institute of Energy Economics and the Rational Use of Energy University of Stuttgart 14 Inventories of HM releases to soil Due to limitation of AROMIS: different inventories necessary Inventory directly derived from AROMIS –Explicitly Reported Values (ERV) 11 countries Derived inventories for the whole of the geographical scope –Relate flows [mass/time] to activity indicator per input category –Identify activity-specific minimum and maximum flows –Apply these flows to the other countries according to their respective activities Inventories –Minimum pan-European Emission (MinEE) –Maximum pan-European Emission (MaxEE) Inputs to soil

15 Institute of Energy Economics and the Rational Use of Energy University of Stuttgart t/a 2515 t/a t/a 4548 t/a Inputs to soil

16 Institute of Energy Economics and the Rational Use of Energy University of Stuttgart t/a 2515 t/a t/a 4548 t/a Inputs to soil Inputs to air

17 Institute of Energy Economics and the Rational Use of Energy University of Stuttgart 17 HM into soils: conclusions Total amounts from 40 countries for MinEE and MaxEE inventories –At least as much as for air –Presumably more relevant for human health due to direct application to soils –Difference in bioavailability between atmospheric and agricultural inputs? Pan-European emission inventories based on rough assumptions The data situation on HM releases to soil is poor Input-related activity data are currently lacking –E.g., some mineral fertilisers, organic fertilisers, pesticides, and sewage sludge Inputs to soil

18 Institute of Energy Economics and the Rational Use of Energy University of Stuttgart 18 All facilities for which emissions of heavy metals directly into water are reported at present in the EPER database Data from EPER Inputs to water

19 Institute of Energy Economics and the Rational Use of Energy University of Stuttgart 19 All facilities for which emissions of heavy metals indirectly into water are reported at present in the EPER database Data from EPER Inputs to water

20 Institute of Energy Economics and the Rational Use of Energy University of Stuttgart 20 Total emissions from EPER data base 2001 [kg/a] Heavy metal Air Water direct indirect As Cd Cr Hg Ni Pb

21 Institute of Energy Economics and the Rational Use of Energy University of Stuttgart 21 Contact: Rainer Jochen Till Bachmann Further information: TFEIP & ESPREME Workshop Heavy Metals and POPs Emissions, Inventories and Projections Rovaniemi/Finland Oct 18/19, 2005


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