Improving the Quality of HM Emission Inventories

1. Improving the Quality of HM Emission Inventories TFEIP - Thessaloniki Oct 2006 Expert estimates for Heavy Metals from the ESPREME Project

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 ProjectionsRovaniemi/Finland, Oct 18/19, 2005 A ‘Special Section’ within Atmos. Env. has been proposed, 5 papers submitted • Paper European Emission Inventories of Heavy Metals for Modelling – a Critical ReviewStefan Reis, Jozef M. Pacyna, Oleg Travnikov, Elisabeth Pacyna, Thomas Pregger,Heiko Pfeiffer, Rainer Friedrich Submitted to Atmos. Env. in Sep 2006

3. Scope Analyzing differences between ESPREME emission data and officiallyreported 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

4. Analyzing differences between ESPREME emission data and officially reported datasets Emissions of As, Cd, Cr, Ni, and Pb (I) • Cd • largest difference between official and expert emissions (a factor of more than 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 more than 3. • official data for Cd emissions are most likely incomplete with respect to the inclusion of all important sources of these emissions • 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 factorsranging from 1.1 to 1.9 in different countries.

5. Analyzing differences between ESPREME emission data and officially reported datasets 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 illustrates the scope of the problem

6. Analyzing differences between ESPREME emission data and officially reported datasets Emissions of As, Cd, Cr, Ni, and Pb (III) • Pb • Hypothesis: Error 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 assumptions regarding Pb content of gasoline (~0-1mg/l@ – 10-15 mg/l*) • future (heavier) crude oils to be exploited with higher trace metal content • Playing with numbers: • 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 in336 t/a for the UK from gasoline vehicles • at Pb content of only 5 mg/l# annual emissions of 150 t/a for the UK • in the case of Italy, between ~240-540 t/a from gasoline vehicles Selected countries based EMEP MSC-East figures How much of the decline in Pb emissions is real?How will this evolve in the longer term future?(diesel? ships?) # limit value according to the Directive 2003/17/EC on the quality of fuels@ UKPIA/NAEI, pers. comm.* Pacyna et al., CCC 2002

7. Analyzing differences between ESPREME emission data and officially reported datasets 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. • Hg0: elemental mercury, 146 t (61%) • Hg2+: gaseous divalent mercury, 76 t (32%) • Hgpart:of Hg on particles, 17 t (7%) • Hg speciation/profiles need a detailed sectoral resolution of emissions.

8. Analyzing differences between ESPREME emission data and officially reported datasets 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 (<50, 50-150 and >150 m effective emission height) As Cd Hg2+ Hg Hgpart Hg0 Ni Cr

9. Conclusions Significant uncertainties in current officially reported HM inventories • due tomissing 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 distinctsource groups with assigned emission heights provides a better spatial representation • information on stack heights and other parameters for Large Point Sourcesin particular would further improve this • further advances in integrating temperature profiles and other meteorologicalparameters into the emission distribution can help to improve the temporal representation

10. Lessons learned What is needed to conduct in-depth assessments? • Detailed sectoral (SNAP 3 / NRF-2 L2) inventory submissions & methods used to compile these • Additional information on major source groups (activity rates, up-to-date EF, control equipment) • Harmonisation between national and other projections of future activities/technologies • Better/more detailed scenario descriptions What is missing? • Current sectoral reporting structure does not provide sufficient detail, e.g. for the energy sector1A1, 1A2 (lacking information on fuel types, technology, size) - except for some productionprocesses (2Ax) • LPS data reported e.g. to EPER/EPRTR could include basic parameters (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).

11. Acknowledgements • The main part of the work was financed under the EC 6th 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. http://espreme.ier.uni-stuttgart.de