27-29 June 2016 CAMx Source Apportionment Lessons...
Transcript of 27-29 June 2016 CAMx Source Apportionment Lessons...
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27-29 June 2016
CAMx – Source Apportionment – Lessons learned
G. Pirovano, P. Brotto, J. Ferreira, Y. Long, C. Emery, G. Yarwood, F. Pradelle,
S. Pillon, A. Dalla Fontana, A. Poupkou, N. Liora, D. Melas
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WG3 Source Apportionment
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CAMx simulation setup
CAMx setup
SEA SALT and DUST Model (NEMO)
BVOC (NEMO)
Temporal modulation and
chemical speciation profiles
EMISSION INVENTORY
(TNO)
EMISSION MODEL
(NCEP / GFS)
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CAMx simulation setup
CAMx setup - PSAT
PSAT - Particulate Source Apportionment Technology (Yarwood et al., 2004)
PSAT uses reactive tracers to apportion primary PM, secondary PM and gaseous precursors to secondary PM among different source categories and source regions.
• detect the role played by the different emission sources
• investigate, source by source, the influence of non linear chemical system on the evolution of the PM concentration
A similar technology has been developed for ozone (OSAT).
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CAMx simulation setup
Domain & sites..
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CAMx simulation setup
Emission categories
01 - Electric Energy production
02- BIO - Domestic heating (b. burning)
03-04 - Industrial processes
07 - Transport (all)
08 – Shipping
10 – Agriculture
11 - Dust
99 - Other Anthrop + MS2 other + SSalt + BVOC
01 - Electric Energy production
02-BIO - Domestic heating (biomass burning)
03-04 - Industrial processes
07-RTR - Transport (others)
71-RTG - Transport (gasoline)
72-RTD - Transport (heavy duty)
75-RTW - Transport (wear)
08 – Shipping
10 – Agriculture
11-DST – Dust
11-SLT – Sea Salt
11-BSO – Biogenic VOC
99 - Other Anthrop
02-OTH - Domestic heating (other)
Mandatory categories Optional categories
ALL_BC - contribution of boundary conditions
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Screening analysis
• Gridded Source Contribution Estimates
• Spatial patterns
• Seasonal differences
• Local vs regional sources
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Summer Episode – Mandatory Base case configuration - Summer mean
Model Performance Evaluation
Guido Pirovano – FAIRMODE meeting (Zagreb 27-29 June 2016) 7
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Summer Episode – Mandatory Base case configuration - Summer mean
Model Performance Evaluation
Guido Pirovano – FAIRMODE meeting (Zagreb 27-29 June 2016) 8
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Winter Episode – Mandatory Base case configuration - Winter mean
Model Performance Evaluation
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Winter Episode – Mandatory Base case configuration - Winter mean
Model Performance Evaluation
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Average Source Contribution
Estimates at receptors
• Mean Source Contribution Estimates
• Comparison with RM estimates
• Seasonal differences
• Local vs regional sources (first guess)
• Secondary PM apportionment
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Summer Episode – Optional – PM10 Base case configuration - Summer mean
Model Performance Evaluation
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Summer Episode – Optional – PM10 Base case configuration - Summer mean
Model Performance Evaluation
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Winter Episode – Optional – PM10 Base case configuration - Winter mean
Model Performance Evaluation
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Winter Episode – Optional – PM10 Base case configuration - Winter mean
Model Performance Evaluation
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Summer Episode – Optional – EC Base case configuration - Summer mean
Model Performance Evaluation
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Summer Episode – Optional – EC Base case configuration - Summer mean
Model Performance Evaluation
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Winter Episode – Optional – EC Base case configuration - Winter mean
Model Performance Evaluation
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Winter Episode – Optional – EC Base case configuration - Winter mean
Model Performance Evaluation
Guido Pirovano – FAIRMODE meeting (Zagreb 27-29 June 2016) 19
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Summer Episode – Optional – NO3-
Base case configuration - Summer mean
Model Performance Evaluation
Guido Pirovano – FAIRMODE meeting (Zagreb 27-29 June 2016) 20
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Summer Episode – Optional – NO3-
Base case configuration - Summer mean
Model Performance Evaluation
Guido Pirovano – FAIRMODE meeting (Zagreb 27-29 June 2016) 21
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Winter Episode – Optional – NO3-
Base case configuration - Winter mean
Model Performance Evaluation
Guido Pirovano – FAIRMODE meeting (Zagreb 27-29 June 2016) 22
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Paris Lens Calais Rur_bk2
mg/
m3
Media di NO3-10-ALLBC
Media di NO3-10-11-SLT
Media di NO3-10-11-DST
Media di NO3-10-11-BSO
Media di NO3-10-10-AGR
Media di NO3-10-08-SHP
Media di NO3-10-99-OTH
Media di NO3-10-07-RTR
Media di NO3-10-75-RTW
Media di NO3-10-72-RTD
Media di NO3-10-71-RTG
Media di NO3-10-34-IND
Media di NO3-10-02-OTH
Media di NO3-10-02-BIO
Media di NO3-10-01-ENI
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Winter Episode – Optional – NO3-
Base case configuration - Winter mean
Model Performance Evaluation
Guido Pirovano – FAIRMODE meeting (Zagreb 27-29 June 2016) 23
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SCEs time series and comparison with
observations
• Temporal evolution of SCEs
• Relationship between SCEs and meteorology
• SCEs and Model Performance
• Local vs regional sources (first guess)
• Secondary PM apportionment
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Winter Episode – Optional – PM10 Lens - Base case configuration - Daily time series
Model Performance Evaluation
Guido Pirovano – FAIRMODE meeting (Zagreb 27-29 June 2016) 25
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Winter Episode – Optional – OM Lens - Base case configuration - Daily time series
Model Performance Evaluation
Guido Pirovano – FAIRMODE meeting (Zagreb 27-29 June 2016) 26
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Winter Episode – Optional – EC Lens - Base case configuration - Daily time series
Model Performance Evaluation
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Winter Episode – Optional – NO3-
Lens - Base case configuration - Daily time series
Model Performance Evaluation
Guido Pirovano – FAIRMODE meeting (Zagreb 27-29 June 2016) 28
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Winter Episode – Optional – SO4=
Lens - Base case configuration - Daily time series
Model Performance Evaluation
Guido Pirovano – FAIRMODE meeting (Zagreb 27-29 June 2016) 29
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Winter Episode – Optional – NH4+
Lens - Base case configuration - Daily time series
Model Performance Evaluation
Guido Pirovano – FAIRMODE meeting (Zagreb 27-29 June 2016) 30
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PSAT Sensitivity analysis
• CAMx MPE and SCEs were not very sensitive to differences in input setup
• Changes in dispersion efficiency reduce absolute concentrations but are less effective on relative contributions
• Changes in grid resolution have different impact on local and regional sources, influencing the corresponding source contribution, particularly at urban sites.
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Further analysis
• Evaluation of both source categories and regions (urban, regional, etc…)
• Tracers?
• Improving Organic Aerosol modelling
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Open issues
• Are there systematic differences between tagged approaches and brute force methods?
• Is there an influence of OC underestimation on SCEs?
• Is there an influence of (likely) underestimation of PMcoarse on SCEs?
• How can RMs results improve CTMs MPE and SA? Use of organic tracers?...
• How can we combine RMs and CTMs results to provide more reliable and thorough SCEs?