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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

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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-.

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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

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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

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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

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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

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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)‏

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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

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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

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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

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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