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24 Mar 2009
Earth Sciences DepartmentBarcelona Supercomputing Center
DAURE 4th scientific meeting
BSC-CNS Model results
J.M. Baldasano, O. Jorba, M.T. Pay, M. Piot, E. López
224 Mar 2009
How do we estimate PM Emissions
PM 10= factorPMtoPM10�PST
PM 2 . 5= factorPMtoPM 2 .5�PST
E j �k �= E �k ��factor j
Ej(k): PM2.5 emission for species j
E(k): PM2.5 emission
factorj: speciation factor for species jfollowing CB-4 – aero3 chemical mechanism
PST = factorActividad�FactorEmisiónCB
‐4 aero3
Speciation
2
324 Mar 2009
Example for Snap-07 sector : Road Transport
● Exhaust Emissions● Hot emissions● Cold start emissions
PM10= PM2.5 PM10= PM2.5 PMcoarse=PM10PMcoarse=PM10--PM2.5= 0PM2.5= 0● Non-exhaust emissions
● Particles from Tyre● Particles from Brake wear● Particles from road surface wear
PM2.5/PM10= 0.55PM2.5/PM10= 0.55● PM2.5 Speciation
Species: POA PEC PSO4 PNO3 PMFINE
factorj 0.3637 0.6130 0.0044 0.0006 0.0183
424 Mar 2009
PM emission distribution by SNAP sectors
Agriculture 10
Extraction and distribution of fossil fuels and geothermal energy 05
Combustion in manufacturing industry 03
Combustion in energy and transformation industries 01Non-industrial combustion plants 02
Production processes 04
Solvents and other product use 06Road transport 07Other mobile sources and machinery 08Waste treatment and disposal 09
Other sources 11
DescriptionSNAP sector
43,000
2,000
59,000
2,414
0,000
75,000
1,000
0,103
0,000
6,450
0,000
11,800
2,173
0,000
12,870
0,000
0,078
0,000
36,550
2,000
47,200
0,241
0,000
62,130
1,000
0,025
0,000
0,000
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
01 02 03 04 05 06 07 08 09 10 11
kt/year
SNAP Sectors
Particles Emission
PST PM_Coarse PM2.5
New sources included1) Traffic emission from small cities.
2) Agriculture and livestock emission coming from a top-down disaggregation EMEP emission database, specially in the case of NH4
+ and NO3-.
3
524 Mar 2009
PM emission distribution by SNAP sectors
Agriculture 10
Extraction and distribution of fossil fuels and geothermal energy 05
Combustion in manufacturing industry 03
Combustion in energy and transformation industries 01Non-industrial combustion plants 02
Production processes 04
Solvents and other product use 06Road transport 07Other mobile sources and machinery 08Waste treatment and disposal 09
Other sources 11
DescriptionSNAP sector
43,000
2,000
59,000
2,414
0,000
75,000
1,000
0,103
0,000
6,450
0,000
11,800
2,173
0,000
12,870
0,000
0,078
0,000
36,550
2,000
47,200
0,241
0,000
62,130
1,000
0,025
0,000
0,000
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
01 02 03 04 05 06 07 08 09 10 11
kt/year
SNAP Sectors
Particles Emission
PST PM_Coarse PM2.5
624 Mar 2009
We extract data from these grid cells through bilinear interpolation
BCN site
MODEL CELL vs OBSERVATION
4
724 Mar 2009
MODEL CELL vs OBSERVATION
We extract data from these grid cells through bilinear interpolation
MSY site
824 Mar 2009
● Emissions of Isoprene and monoterpene (Guenther et al., 1983; Parra et al., 2004)
● Innsbruck observations:● Problems with modeled Isoprene● Good behavior with monoterpenes
MODEL vs OBS: Isoprene-monoterpene. Checking model biogenic emissions
5
924 Mar 2009
● Emissions of Isoprene and monoterpene (Guenther et al., 1983; Parra et al., 2004)
● Innsbruck observations:● Problems with modeled Isoprene● Good behavior with monoterpenes
MODEL vs OBS: Isoprene-monoterpene. Checking model biogenic emissions
1024 Mar 2009
● Emissions of Isoprene and monoterpene (Guenther et al., 1983; Parra et al., 2004)
● Innsbruck observations:● Problems with modeled Isoprene● Good behavior with monoterpenes
Source: Parra et al., 2004
MODEL vs OBS: Isoprene-monoterpene. Checking model biogenic emissions
6
1124 Mar 2009
● Emissions of Isoprene and monoterpene (Guenther et al., 1983; Parra et al., 2004)
● Innsbruck observations:● Problems with modeled Isoprene● Good behavior with monoterpenes
Source: Parra et al., 2004
MODEL vs OBS: Isoprene-monoterpene. Checking model biogenic emissions
Sensitivity and uncertainty of emission model
1224 Mar 2009
● Emissions of Isoprene and monoterpene (Guenther et al., 1983; Parra et al., 2004)
● Innsbruck observations:● Problems with modeled Isoprene● Good behavior with monoterpenes
Source: Parra et al., 2004
MODEL vs OBS: Isoprene-monoterpene. Checking model biogenic emissions
Sensitivity and uncertainty of emission model
1- DAURE campaign during February and March – low emission factors for Isoprene2- Meteorological conditions well captured by the model, but some underestimation of temperature at MSY site= Factor combination that leads to a large underestimation of modeled isoprene concentrations
7
1324 Mar 2009
Agriculture and livestocksTests: Analyzing the impact of the inclusion of agriculture and livestock emissions and
improvement of traffic emissions in small cities.Cv2 which includes these new emission improvements.Cv1 which does not include these new emission improvements.
NH3 emissions (mole/s) NO2 emissions (mole/s)
Emissions from March 10th 2004 at 9 UTC
• Additional sources implemented in current emission files:• SNAP10 sector (agriculture)• Emission module for small cities (CO, NH3, NMVOC, NOx, SOx, PM)
1424 Mar 2009
MODEL aerosol comparison: Barcelona
Emissions from paved roads, unpaved roads, heavy construction operations, etc. not considered: PM10 levels are largely underestimated and difficult to capture
SO42- underestimated. Levels follow the tendency of CIEMAT observations.
Secondary Organic Aerosols largely understimated: large uncertainties on aero-3 soa parameterization.
Modeled NO3- and NH4
+ are understimated: Agriculture and livestock emission not included.
Plots source: Univ. of Colorado
Model: CALIOPE (http://www.bsc.es/caliope)
8
1524 Mar 2009
MODEL aerosol comparison: Barcelona
Emissions from paved roads, unpaved roads, heavy construction operations, etc. not considered: PM10 levels are largely underestimated and difficult to capture
SO42- underestimated. Levels follow the tendency of CIEMAT observations.
Secondary Organic Aerosols largely understimated: large uncertainties on aero-3 soa parameterization.
Modeled NO3- and NH4
+ are understimated: Agriculture and livestock emission not included.
Plots source: Univ. of Colorado
Model: CALIOPE (http://www.bsc.es/caliope)
BarcelonaBarcelona
MontsenyMontseny
NONO33-- -- May 2004May 2004
CSICCSICCv1Cv1Cv2Cv2
BarcelonaBarcelona
MontsenyMontseny
NHNH44+ + -- May 2004May 2004
CSICCSICCv1Cv1Cv2Cv2
1624 Mar 2009
● Systematic underestimation:● Emissions from agriculture and livestock not considered● Dynamics on mountainous terrain
MODEL aerosol comparison: Montseny
9
1724 Mar 2009
● Systematic underestimation:● Emissions from agriculture and livestock not considered● Dynamics on mountainous terrain
MODEL aerosol comparison: Montseny
1824 Mar 2009
● Systematic underestimation:● Emissions from agriculture and livestock not considered● Dynamics on mountainous terrain
Meteorology: good mean behaviour but clear underestimation of upslope winds
MODEL aerosol comparison: Montseny
10
1924 Mar 2009
Comparison with other modeling studies
CMAQ evaluation. NO3- PM predictions are very sensitive to NHx, and
thus the NH3 emissions need serious attention.Dennis, 2004
CAMx4, CMAQ-CB4, CMAQ-SARP99 comparison. SO42- were more
consistent among the three models; NO3- and organic matter have
higher discrepancies.
Boyland and Baker, 2004
CMAQ evaluation. Performance of NH4+ showed deficiencies in fall,
which were related to NH3 emission. Appel et al. 2008
Model performance for NO3- is strongly dependent on model
performance for NHx, SO42- and TNO3
Yu et al. 2005
CAMx evaluation. SO42- predictions were reasonable, but nitrate was
significantly overpredicted.Morris et al.,2004b
CMAQ and CAMx did not perform well for NO3-. Better results are
obtained for SO42-.Morris et al.,2004a
VISTAS and WRAP model comparison. SO42- performance reasonably
well. NO3- levels showed discrepancies, may need better NH3 emissions.Tonnesen, 2003
Summary of evaluation studiesReferences
“The current performance of air quality models for PM is poor”…”There is a dire need for improving model inputs and model formulations in order to obtain acceptable model performance”…”3-D air quality models are the best tools available to address the PM source-receptors relationships because they take into account the non-linearities that affect the formation of secondary PM”
Seigneur, 2003.
2024 Mar 2009
Future tasks
● Improve emission sources:● Agriculture and livestock emissions● Sea salt aerosols● Mineral dust from European continent● Fugitive emissions: paved road emissions● Impact of water content
● Implement the new DREAM model configuration (size distribution 8 bins).
● Provide an updated model simulation for the DAURE campaign:● Hindcast simulation● New emission updates● CMAQv4.7 – CB05-aero4
● Run some tests proposed by José Luis concerning NH3model sensitivity
11
2124 Mar 2009
Papers proposal
● As leading authors:● Modeling evaluation of aerosol chemical speciation in the
northeastern Spain: results from the DAURE campaign
● As contributors:● Those papers requiring further analysis and model sensitivity
runs.
Thanks for your attention
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