1. Uncertainty Assessment of HM Emission Inventories TFEIP/TFMM workshop on uncertainties in emission inventories and atmospheric models Stefan Reis, Jozef Pacyna, Elizabeth Pacyna & the ESPREME project team

2. 2 Background  Project Estimation of willingness-to-pay to reduce risks of exposure to heavy metals and cost-benefit analysis for reducing heavy metals occurrence in Europe (ESPREME), co-ordinated by IER, University of Stuttgart http://espreme.ier.uni-stuttgart.de  Workshop TFEIP & ESPREME Workshop Heavy Metals and POPs - Emissions, Inventories and Projections Rovaniemi/Finland, Oct 18/19, 2005 Some papers are being published in Atmos. Env.  Paper European Emission Inventories of Heavy Metals for Modelling – a Critical Review Stefan Reis, Jozef M. Pacyna, Oleg Travnikov, Elisabeth Pacyna, Thomas Pregger, Heiko Pfeiffer, Rainer Friedrich in preperation

3. 3 Scope Analyzing differences between ESPREME emission data and officially reported datasets  Emissions of As, Cd, Cr, Ni, and Pb  Emissions of Hg Improved spatial (and temporal) resolution of heavy metal emissions Atmospheric dispersion modelling of heavy metals  Wind re-suspension of heavy metals (MSC-East)  Evaluation of HM modelling results based on official and ESPREME emission data  Summary and Conclusions Acknowledgements “May we trust the model finding that emission inventories might underestimate heavy metals by a factor of 2-3 and if so, what is causing the underestimation in the emission inventories.”

4. 4 Approach What can desk studies based on openly available datasets achieve?  Investigate implied EFs based on officially reported emissions and compare to established levels in literature  Compare emissions or activities reported to different bodies/institutions (EMEP, EPER, UNFCCC, …)  Assess information provided in IIRs (if available)  Analyse independent datasets providing activity rates for sectors Limitations  Sectoral resolution of inventories (even when reported in the most disaggregated NFR levels) lack information on fuels, technologies, etc.  Statistical information on activity rates rarely available in sufficient details  Still significant gaps in EF measurements, in particular for small (area) sources E = A * EF A = Activity rate (explicit for sector, technology, time, fuel, …) EF = Emission factor = ( technology, fuel, control equipment, T, …)

5. 5 Emissions of As, Cd, Cr, Ni, and Pb (I) Cd largest difference between official and ESPREME emissions (a factor > 2) for totals. official Cd emissions from fuel combustion in utility boilers, industrial furnaces and residential and commercial units seem to be underestimated by a factor of > 3. official data for Cd emissions are most likely incomplete (not including all relevant sources) Cr official data sets seem to be underestimated by a factor ranging from 1.4 to 1.9 depending on source category Low emission factors for fuel combustion and iron and steel production, and missing sources within the category other sources main reasons for this underestimation Emissions of As and Ni seem to be generally underestimated in the official datasets by factors ranging from 1.1 to 1.9 in different countries.

6. 6 Emissions of As, Cd, Cr, Ni, and Pb (II) Pb analysis indicated for some countries a fair agreement between expert estimates and official submissions other countries report zero emissions e.g. from gasoline combustion in road transport > 50% of the anthropogenic emissions of Pb in Europe in 2000 from the combustion of gasoline (?) exemplary analysis for two large countries hints at possible explanations Measurement data for HM content of solid and liquid fuels need to be assessed, in particular with regard to their development over time and in the future

7. 7 Emissions of As, Cd, Cr, Ni, and Pb (III) Pb Difference due to the perception of ‘unleaded’ gasoline? this type of gasoline is defined as the gasoline without lead additives (!) however, there is lead as an impurity in the gasoline due to the lead content of crude oil. wide range of (literature) values regarding Pb content of gasoline (~0-1mg/l @ – 10-15 mg/l * ) future (heavier) crude oils to be exploited may have higher trace metal content (?) Assumptions: Pb content in unleaded gasoline: 15 mg/l. 75% of Pb in gasoline is emitted to the atmosphere during combustion process implied EF = 11.25 mg/l, resulting in 336 t/a for the UK from gasoline vehicles at a Pb content of only 5 mg/l # annual emissions of 150 t/a for the UK, at 0.1 mg/l only 3 t/a. in the case of Italy, between ~240-540 t/a from gasoline vehicles # limit value according to the Directive 2003/17/EC on the quality of fuels @ UKPIA/NAEI, pers. comm. * Pacyna et al., CCC 2002 How much of the decline in Pb emissions is real? (in particular with regard to the different slopes) How will this evolve in the longer term future? (diesel? Ships burning bunker fuel?) ANY Pb (and other HM) content in fuels will have a significant impact due to the amount of fuels burned! Selected countries based EMEP MSC-East figures

8. 8 Emissions of As, Cd, Cr, Ni, and Pb (IV) Main causes of differences identified: Some countries report zero emissions from sectors with significant activity rates (e.g. Iron & steel production, non-ferrous metal production, cement production); even assuming state-of-the-art equipment for emission control, emissions will be > 0 (missing sources) Implied emission factors derived from official emissions reported are in some cases only a fraction of EFs provided in the EMEP/CORINAIR Emission Inventory Guidebook; without detailed information on technologies, processes, emission control equipment implemented, fuels used, it is difficult to identify small differences as “errors”, but some EFs used are not attainable by current processes/technologies For source groups where EFs may vary significantly depending on the type and quality of fuel used (e.g. domestic wood combustion, combustion of hard coal, lignite and oil with different HM contents) some countries have not estimated any emissions, indicating the need for research and measurements, as these sources may contribute a fair share of emissions Example Cd emissions in Germany in 2000: EMEP Official (NFR02, L1; from EMEP WEBDAB): ~29.5 t 81% industrial production processes, 12% energy use, 7% solvent use (paint application) ESPREME estimates (Pacyna et al.): 66.3 t 73% energy use (mainly combustion of coal and oil), 21% industrial production, 6% other

9. 9 Emissions of Hg Hg emission data received from national authorities have then been checked by ESPREME emission experts for completeness and comparability completeness of data regarded mainly the inclusion of all major source categories which may emit mercury to the atmosphere. no major omissions have been detected in the reported data; all major source categories in all countries reporting the emission data were included. in the majority of the cases, emission factors estimated on the basis of national emission data reported to the project were within the range of emission factors proposed in the EIGB. Hg 0 : elemental mercury, 146 t (61%) Hg 2+ : gaseous divalent mercury, 76 t (32%) Hg part : of Hg on particles, 17 t (7%) Hg speciation/profiles need a detailed sectoral resolution of emissions.

10. 10 Improving the Spatial Resolution creating maps for the 50x50 km grid based on detailed sectoral distribution factors, road networks, land-use data, point source information assigning source sectors to low, medium and high ( 150 m effective emission height) Ni As Cd Cr Hg 0 Hg Hg 2+ Hg part

11. 11 Conclusions Significant uncertainties in current officially reported HM inventories  due to missing sources (e.g. Pb from gasoline combustion, re-suspension)  reported emissions of Hg seem to be more robust than those of other metals  main problem for validation and verification is the completeness in reporting, lacking a consistent dataset without gaps (need to use ‘expert estimates’ for modelling)  ‘gap’ between bottom-up calculation of expert estimates and often only aggregated inventory ‘sectors’ Improved spatial and temporal resolution of heavy metal emissions  applying improved methods to distribute sectoral emissions, including distinct source groups with assigned emission heights provides a better spatial representation  information on stack heights and other parameters for Large Point Sources in particular would further improve this  further advances in integrating temperature profiles and other meteorological parameters into the emission distribution can help to improve the temporal representation

12. 12 Lessons learned What would be needed to conduct in-depth assessments?  Detailed sectoral (SNAP 3 / NRF-2 L2) inventory submissions & methods used to compile these, i.e. Informative Inventory Reports giving some insight into technologies, assumptions as to EFs, sources of information for activity rates and anticipated development of key sources  Additional information on major source groups (activity rates, up-to-date EF, control equipment)  Harmonisation between national and other projections of future activities/technologies What is missing?  Current sectoral reporting structure does not provide sufficient detail, e.g. for the energy sector 1A1, 1A2 (lacking information on fuel types, technology, size) – except for some production processes (NFR codes 2Axx)  LPS data reported e.g. to EPER/EPRTR could include basic parameters ( stack height, T, …)  For some source groups, general lack of information (e.g. residential combustion, wood combustion), some results available, but further measurements needed (?)  With major emission sources being reduced, the small fractions may make the difference,where knowledge about EFs is limited or non-existent (measurements/analysis needed).

13. 13 Acknowledgements The main part of the work was financed under the EC 6 th Framework Programme within the ESPREME project. Discussions in the frame of a workshop co-organized by ESPREME and the UNECE Task Force Emission Inventories and Projections (TFEIP) on Heavy Metals and POPs in Rovaniemi in October 2005 have greatly contributed to the scientific discussions around this work. Publications:  Stefan Reis, Jozef M. Pacyna, Oleg Travnikov, Elisabeth Pacyna, Thomas Pregger, Heiko Pfeiffer, Rainer Friedrich (2007) European Emission Inventories of Heavy Metals for Modelling – a Critical Review. (in preperation)  Pacyna E., Pacyna J.M., Fudala J., Strzelecka-Jastrzabc E., Hlawiczka S., Panasiuk D., Nitter S., Pregger T., Pfeiffer H., Friedrich R. (2007) Current and future emissions of selected heavy metals to the atmosphere from anthropogenic sources in Europe. Atmospheric Environment, doi:10.1016/j.atmosenv.2007.07.040 http://espreme.ier.uni-stuttgart.de