Upload
arabella-goodman
View
216
Download
0
Tags:
Embed Size (px)
Citation preview
Improving the Quality of HM Emission Inventories
Expert estimates for Heavy Metals from the ESPREME Project
TFEIP - Thessaloniki Oct 2006
Background
ProjectEstimation 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
WorkshopTFEIP & 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
PaperEuropean 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
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
Emissions of As, Cd, Cr, Ni, and Pb (I)
0
1000
2000
3000
4000
5000
6000
As Cd Cr Ni
EMEP Official (with gap filling)
ESPREME
Analyzing differences between ESPREME emission data and officially reported datasets
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 residentialand 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.
0
10
20
30
40
50
60
70
80
90
100
EM
EP
ES
PR
EM
E
EM
EP
ES
PR
EM
E
EM
EP
ES
PR
EM
E
EM
EP
ES
PR
EM
E
EM
EP
ES
PR
EM
E
As Cd Cr Ni Pb
% s
hare
of to
tal e
mis
sions
Gasoline combustion
Cement production and othersources
Waste disposal
Iron and steel production
Non-ferrous metals
Fossil fuel combustion to produceheat and electricity
Emissions of As, Cd, Cr, Ni, and Pb (II)
Pb Italy 2000
0
200
400
600
800
1000
1200
1400
1600
1800
2000
EMEP official ESPREME
Mg
Waste disposal
Gasoline combustion in mobilesources
Industrial combustion andprocesses
Combustion in commercial boilersand other sources
Analyzing differences between ESPREME emission data and officially reported datasets
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
Pb UK 2000
0
100
200
300
400
500
600
EMEP official UK NAEI ESPREME
Mg
Waste disposal
Gasoline combustion inmobile sources
Industrial processes
Power plants, commercial,residential and industrialcombustion
Emissions of As, Cd, Cr, Ni, and Pb (III)
Analyzing differences between ESPREME emission data and officially reported datasets
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 tothe 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
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
11000
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
United Kingdom
Netherlands
Italy
Greece
Germany
# 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?How will this evolve in the longer term future?(diesel? ships?)
Selected countries based EMEP MSC-East figures
Emissions of Hg
Analyzing differences between ESPREME emission data and officially reported datasets
Species of Hg by sector
0
10
20
30
40
50
60
70
C
oa
l co
mb
ust
ion
: P
ow
er
Pla
nts
C
oa
l co
mb
ust
ion
:R
esi
de
ntia
l an
d c
om
me
rcia
l
Oil
com
bu
stio
n
Ce
me
nt p
rod
uct
ion
No
n-f
err
ou
s m
eta
ls: L
ea
d
No
n-f
err
ou
s m
eta
ls: Z
inc
Iro
n &
ste
el p
rod
uct
ion
Ca
ust
ic s
od
a
Wa
ste
Oth
er
sou
rce
s
Mg
Hg0Hg 2+Hg part
• 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.
Improving the Spatial Resolution
Analyzing differences between ESPREME emission data and officially reported datasets
• 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)
Ni
As Cd
Cr Hg0
Hg Hg2+
Hgpart
ConclusionsSignificant 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 meteorologicalparameters into the emission distribution can help to improve the temporal representation
Lessons learnedWhat 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 sector
1A1, 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).
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