Assessing the Influence of Secondary Organic Aerosols on Long...
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Methodology We test 3 configurations of the GEOS-Chem PAH model: (1) Default Assumes EqP; 100% of particle phase available for oxidation (2) SOA-PAH Assumes slow evaporation from SOA to gas phase; 10% of particle phase available for oxidation Two PAH emissions scenarios: (3) Combined Assumes slow evaporation from primary carbonaceous aerosols to gas phase; 10% of particle phase available for oxidation - In (2) and (3), evaporation is modeled with a rate (k) corresponding to 80% remaining on particles after 24 hours - All simulations are conducted for the PAH pyrene because of its semivolatility - Particle phase oxidation is by O 3 and NO 3 - For details on PAH model development, evaluation, and use, see Friedman & Selin 2 and Friedman, Zhang, and Selin 3 Results We evaluate each configuration’s ability to replicate PAH LRT by assessing mean (2006-2008) northern hemisphere mid-latitude (Fig. 1) and Arctic (Fig. 2) total concentrations, and mid-latitude f P (Fig. 3). Only the “Combined” configuration captures all three metrics simultaneously: Assessing the Influence of Secondary Organic Aerosols on Long-Range Atmospheric PAH Transport Carey L. Friedman and Noelle E. Selin Center for Global Change Science ([email protected]) Engineering Systems Division and Department of Earth, Atmospheric, and Planetary Sciences ([email protected]) 77 Massachusetts Ave., Cambridge, MA 02139 USA Background Polycyclic aromatic hydrocarbons (PAHs) are toxic semivolatile compounds that partition between gas and aerosol phases and undergo atmospheric long range transport (LRT). Physicochemical processes influencing PAH transport to remote regions (e.g., the Arctic) are still largely unknown. In particular, the influence of aerosols on PAH LRT and how to best model PAH gas-particle (G-P) partitioning are highly uncertain. Here, we conduct simulations using the chemical transport model GEOS-Chem under different assumptions about (1) G-P partitioning and (2) physicochemical characteristics of aerosols, to test how these variables affect PAH LRT efficiency. G-P partitioning of PAHs has been described by a number of different schemes, each employing the assumption of instantaneous equilibrium partitioning (EqP). Only one (the Dachs and Eisenreich 1 scheme, or “D&E”) considers adsorptive partitioning to black carbon (BC), and consistently performs best; hence, we use it in our model 2,3 . No partitioning scheme, however, captures observed PAH G-P distribution without fault: most underpredict PAH particulate fraction (f P ), and D&E can overpredict f P . Recently, a new hypothesis has been put forth based on laboratory work: PAHs are incorporated into SOA as it forms, which traps them and protects them from oxidation, ensuring their LRT in the particulate form 4 . SOA has never explicitly been considered in a PAH modeling study before. We develop new configurations of the GEOS-Chem PAH model to test whether incorporating SOA and a G-P scheme that traps PAHs, rather than assumes instantaneous EqP, better represents PAH LRT compared to our default model. Literature Cited: 1) Dachs & Eisenreich. 2000. Adsorption onto aerosol soot carbon dominates gas- particle partitioning of polycyclic aromatic hydrocarbons. Environ. Sci. Technol., 34:3690-3697. 2)Friedman & Selin. 2012. Long-range atmospheric transport of polycyclic aromatic hydrocarbons: A global 3D model analysis including evaluation of Arctic sources. Environ. Sci. Technol., 46:9501-9510. 3)Friedman, Zhang, & Selin. 2013. Climate change and emissions impacts on atmospheric PAH transport to the Arctic. Environ. Sci. Technol., In Press. 4) Zelenyuk et al. 2012. Synergy Between Secondary Organic Aerosols and Long- Range Transport of Polycyclic Aromatic Hydrocarbons. Environ. Sci. Technol., 46:12459-12466. Pyrene Gas phase PAH BC-bound PAH Primary OM-bound PAH K BC K OA SOA-bound PAH Gas phase PAH m ( t ) = m (0) e kt BC-bound PAH Gas phase PAH (a) “100% in SOA” (b) “SOA/gas” m ( t ) = m (0) e kt K OA Primary OM-bound PAH - Trapping PAHs in particles and protecting them from oxidation improves model-measurement agreement in remote areas compared to an instantaneous EqP scheme - Observed f P magnitude and seasonal cycle are not captured unless G-P partitioning is simulated with primary carbonaceous aerosols (especially BC) rather than SOA 0.03 0.3 3 123456789101112 “100% in SOA” SOA-PAH overestimates mid-latitude concentrations, even with particle-phase oxidation 0.0001 0.001 0.01 0.1 1 2 3 4 5 6 7 8 9 10 11 12 The default and “SOA/gas” SOA- PAH configurations severely underestimate remote Arctic concentrations 0.005 0.05 0.5 123456789101112 SOA-PAH configurations either over- or underestimate f P and do not capture seasonal variation Concentration (ng m -3 ) Concentration (ng m -3 ) Fraction in particle phase Month Month Month Measurements Default Default SOA-PAH: 100% in SOA-PAH: 100% in SOA SOA-PAH: SOA/gas SOA-PAH: SOA/gas Combined Combined Without particle-phase oxidation With particle-phase oxidation Fig. 1 Fig. 2 Fig. 3 Take–home Message
Transcript of Assessing the Influence of Secondary Organic Aerosols on Long...
Methodology
We test 3 configurations of the GEOS-Chem PAH model:
(1) Default
Assumes EqP; 100% of particle phase available for oxidation
(2) SOA-PAH
Assumes slow evaporation from SOA to gas phase; 10% of particle phase available for oxidation
Two PAH emissions scenarios:
(3) Combined
Assumes slow evaporation from primary carbonaceous aerosols to gas phase; 10% of particle phase available for oxidation
- In (2) and (3), evaporation is modeled with a rate (k) corresponding to 80% remaining on particles after 24 hours
- All simulations are conducted for the PAH pyrene because of its semivolatility- Particle phase oxidation is by O3 and NO3- For details on PAH model development, evaluation, and use, see Friedman & Selin2
and Friedman, Zhang, and Selin3
ResultsWe evaluate each configuration’s ability to replicate PAH LRT by assessing mean (2006-2008) northern hemisphere mid-latitude (Fig. 1) and Arctic (Fig. 2) total concentrations, and mid-latitude fP (Fig. 3). Only the “Combined” configuration captures all three metrics simultaneously:
Assessing the Influence of Secondary Organic Aerosols on Long-Range Atmospheric PAH TransportCarey L. Friedman and Noelle E. Selin Center for Global Change Science ([email protected])Engineering Systems Division and Department of Earth, Atmospheric, and Planetary Sciences ([email protected])77 Massachusetts Ave., Cambridge, MA 02139 USA
BackgroundPolycyclic aromatic hydrocarbons (PAHs) are toxic semivolatile compounds that partition between gas and aerosol phases and undergo atmospheric long range transport (LRT). Physicochemical processes influencing PAH transport to remote regions (e.g., the Arctic) are still largely unknown. In particular, the influence of aerosols on PAH LRT and how to best model PAH gas-particle (G-P) partitioning are highly uncertain. Here, we conduct simulations using the chemical transport model GEOS-Chem under different assumptions about (1) G-P partitioning and (2) physicochemical characteristics of aerosols, to test how these variables affect PAH LRT efficiency.
G-P partitioning of PAHs has been described by a number of different schemes, each employing the assumption of instantaneous equilibrium partitioning (EqP). Only one (the Dachs and Eisenreich1 scheme, or “D&E”) considers adsorptive partitioning to black carbon (BC), and consistently performs best; hence, we use it in our model2,3. No partitioning scheme, however, captures observed PAH G-P distribution without fault: most underpredict PAH particulate fraction (fP), and D&E can overpredict fP.
Recently, a new hypothesis has been put forth based on laboratory work: PAHs are incorporated into SOA as it forms, which traps them and protects them from oxidation, ensuring their LRT in the particulate form4.
SOA has never explicitly been considered in a PAH modeling study before. We develop new configurations of the GEOS-Chem PAH model to test whether incorporating SOA and a G-P scheme that traps PAHs, rather than assumes instantaneous EqP, better represents PAH LRT compared to our default model.
Literature Cited:1) Dachs & Eisenreich. 2000. Adsorption onto aerosol soot carbon dominates gas-
particle partitioning of polycyclic aromatic hydrocarbons. Environ. Sci. Technol., 34:3690-3697.
2)Friedman & Selin. 2012. Long-range atmospheric transport of polycyclic aromatic hydrocarbons: A global 3D model analysis including evaluation of Arctic sources. Environ. Sci. Technol., 46:9501-9510.
3)Friedman, Zhang, & Selin. 2013. Climate change and emissions impacts on atmospheric PAH transport to the Arctic. Environ. Sci. Technol., In Press.
4) Zelenyuk et al. 2012. Synergy Between Secondary Organic Aerosols and Long-Range Transport of Polycyclic Aromatic Hydrocarbons. Environ. Sci. Technol., 46:12459-12466.
Pyrene
Gas phase PAH
BC-bound PAH
Primary OM-bound PAH
KBC
KOA
SOA-bound PAH Gas phase PAH
m(t) = m(0) e kt
BC-bound PAH
Gas phase PAH
(a) “100% in SOA” (b) “SOA/gas”
m(t) = m(0) e kt
KOA
Primary OM-bound PAH
- Trapping PAHs in particles and protecting them from oxidation improves model-measurement agreement in remote areas compared to an instantaneous EqP scheme
- Observed fP magnitude and seasonal cycle are not captured unless G-P partitioning is simulated with primary carbonaceous aerosols (especially BC) rather than SOA
0.03
0.3
3
1 2 3 4 5 6 7 8 9 10 11 12
“100% in SOA” SOA-PAH overestimates mid-latitude concentrations, even with particle-phase oxidation
0.0001
0.001
0.01
0.1
1 2 3 4 5 6 7 8 9 10 11 12
The default and “SOA/gas” SOA-PAH configurations severely underestimate remote Arctic concentrations
0.005
0.05
0.5
1 2 3 4 5 6 7 8 9 10 11 12
SOA-PAH configurations either over- or underestimate fP and do not capture seasonal variation
Concentration (ng m-3)
Concentration (ng m-3)
Fraction in particle phase
Month
Month
Month
Measurements Default DefaultSOA-PAH: 100% in SOA-PAH: 100% in SOASOA-PAH: SOA/gas SOA-PAH: SOA/gasCombined Combined
Without particle-phase oxidation With particle-phase oxidation
Fig. 1
Fig. 2
Fig. 3
Take–home Message
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