1. Atmos. Chem. Group Meeting
Rocky Mountain National Park Grand Teton National Park 2011 HR-ToF-AMS Aerosol Composition Observations
Misha Schurman
4/13/2012
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2. Atmos. Chem. Group Meeting
Outline
Motivation
Instrument Overview
ROMANS 2010 Data
Analysis and Quality Verification
Aerosol Composition Data
TETONS 2011 Data
Future Work
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3. Atmos. Chem. Group Meeting
Time-resolved concentration, size, and composition of aerosols are useful for investigating:
Pollution (Toxicity/Health Effects)
Plume Processing/Photochemistry
Nucleation/Coagulation
Radiative Forcing Properties
Direct: est. -0.1to -1.0 W/m2 from ‘fossil fuel aerosols’ (IPCC AR4)
Indirect: CCN functionality
Identifying organic aerosol components contributes to our knowledge of aerosol formation and processing, source apportionment, and appropriate mitigation strategies.
Environmental News Network
NASA EOSDIS:
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4. Relevant AMS Features
Lens focuses particle beam; nm have T.E. = 1
Skimmer discards most gases
PToF region sizes particles
Simultaneous Vaporization & Ionization
ToF-MS detection with variable flight path (V or W)
(Drewnick et al. 2005)
ToF Governing Principle:
½ mv2
Sampling:
V Mode: sizing, lower D.L.
W Mode: High res spectra, higher D.L.
chopopen - chopclosed = net aerosol
30 min HEPA/2-3 days checks inlet integrity
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5. Quality Assurance and Control
Field:
MultiChannel Plate (MCP) detector
Flow Rate
Aerodynamic Lens
Ionization Efficiency (for nitrate)
CPC-mass-based verification/correction
Sizing: PSL or DMA-NH4NO3 with known dm, ρi, and Shape factor
dva = (ρi / ρ0) * S * dm
Servo (Chopper) Position
Data Analysis:
UMR: m/z calibration, baseline check, airbeam correction, fragmentation checks
Filtering: flawed runs, calibrations, filter periods, events
HR: residuals; are ‘family’ spectra sensical?
PMF: S:N filtering, outliers in MS?
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QA/QC: RoMANS
Basic Checks
High Resolution
Na+
± 10 ppm
PMF
~5%
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7. Results: PILS, URG, AMS Comparison
ROM 2010
Results: PILS, URG, AMS Comparison
Figure 1: Time series of inorganic particulate components from HR-ToF-AMS, PILS-IC, and URG (denuder-filter system). PILS-IC agrees well with AMS (SO4: m=1.00, r2=0.88; NO3: m=1.29, r2=0.76)
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Results: Timeline
ROM 2010: High Resolution Analysis (V Mode)
Figure 2: Time series of particulate organic, sulfate, nitrate, and ammonium concentrations over the study duration.
11.5 μg/m3
0.9 μg/m3
0.2 μg/m3
0.1 μg/m3
Organics dominate and are correlated with sulfate concentrations.
Nitrate and ammonium are very low.
Organics and others not always t-correlated: evidence for multiple organic types
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Organic Aerosol Types using
Positive Matrix Factorization (PMF)?
Receptor-only factorization; iterative least-squares algorithm breaks total mass spectra into “factors” until residual is minimized.
Assume factors have constant MS profiles and time-variable contributions to total mass.
User must determine the most probable # of factors based on:
Q (quality of fit parameter) and other statistics
Dissimilarity of factors (spectral and timeline)
Comparison to ‘established’ factors : database of spectral profiles
Correlation with other aerosol components (inorganics)
Ulbrich et al. 2009
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ROM 2010: High Res (W Mode)
Results: Positive Matrix Factorization (PMF): BBOA
Figure 6: Mass spectra of organic aerosol types determined by Positive Matrix Factorization (Paatero and Tapper, 1994). Biomass Burning Organic Aerosol (BBOA) typified by levoglucosan combustion product C2H4O2+.
0.47
Fraction of Signal
BBOA indicators:
m/zs 29, 43, 44, 55,
with 60, 73
Our 60 is smaller than others, but not inconsistent with all BBOA studies, and campfires were observed nearby.
Does not resemble Biogenic SOA (rules out traffic/BVOC mix, etc.) †
Figure 2: “Spectra of…PMF factors (interpreted as the denoted source profiles) calculated by…PMF.” (Lanz et al. 2007) †
Jülich Plant Aerosol Chamber
O3-OH BSOA experiment
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† (Kiendler-Scharr et al., 2009)

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Results: PMF: SV- and LV-OOA
ROM 2010: High Res (W Mode)
†“Spectra of…PMF factors (interpreted as the denoted source profiles) calculated by…PMF.” (Lanz et al. 2007)
*(Kiendler-Scharr et al., 2009) †
Figure 6: Mass spectra of organic aerosol types determined by Positive Matrix Factorization (Paatero and Tapper, 1994). Semi-Volatile Oxidized Organic Aerosol (SV-OOA) has moderately oxidized CH3CO+; and Low Volatility Oxidized Organic Aerosol (LV-OOA) is dominated by CO2+. 28 * * † †
Classic SV- and LV-OOA spectral markers consistent with urban sources. Both quite oxidized (from 43/44 ratio and frag fraction)
No mass spec indicators of Biogenic SOA influence (27, 29, w/ 43,44)
Future delta analysis will look for BSOA influence (vs ‘plain’ urban)
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† (Kiendler-Scharr et al., 2009)

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ROM 2010: High Resolution (W Mode)
Results: PMF Factor Timeline
LV-OOA dominates
SV-OOA events with and w/o commensurate BBOA event
BBOA events from nearby campfires
Factor’s mass spec is static: to explore oxidations changes, size distributions, define factor-dominated periods
Figure 7: Timeline of organic aerosol factor concentrations. Semi-Volatile Oxidized Organic Aerosol (SV-OOA) and Low Volatility Oxidized Organic Aerosol (LV-OOA) are generally correlated with each other in varying proportions. Biomass Burning Organic Aerosol (BBOA) is low with high concentrations events. Pie chart shows study average fractional contributions.
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Results: Positive Matrix Factorization (PMF)
ROM 2010: High Resolution (W Mode)
f43 and f44 for periods dominated by the given factor:
Ng et al., 2011
Triangle plot describes degree of oxidation: upper left corner is most oxidized
Markers sized by time; linear movement during certain periods indicate oxidation of existing or introduction of new air mass?
PMF-derived factors agree well with other studies
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Results: Diurnal Profiles
ROM 2010: High Resolution 1 2
Figure 3: Diurnal concentrations averaged over the study duration.
Mean influenced by outliers (NO3, SO4, NH4)
Inconsistent diurnal variation: Event driven mean
Consistent variation in organics:
Afternoon up-slopes from front range/ boundary layer compression may cause LV-OOA
Second SV-OOA feature from BBOA signal ‘misattribution’?
Regular campfires at local camp cause 10pm BBOA feature
CO2(44) and C2H3O(43) driving overall organic diurnal feature
C2H4O2(60) enhancement at 22h supports BBOA pattern (scattered 16h drive mean increase)
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ROM 2010: High Resolution Analysis
Results: Wind Direction and Species Concentration
Inorganic species associated with SE winds.
Organics show little trend with wind direction.
Complex terrain makes wind direction an inadequate tracer for air mass origin and precludes HYSPLIT use.
We have other evidence for organic sources and types: MS, r2 w/ inorganics, size distributions
Figure 4: Concentrations of inorganic species with wind direction.
Figure 8: Concentrations of organic aerosol types with wind direction. BBOA spikes reflect isolated fires; LV-OOA and SV-OOA are disperse, suggest SE directionality.
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Results: PMF Factor ‘Size Distributions’
SMPS size distribution indicating AMS lens transmission range
Ezra Levin
Organics + Sulfate smaller than Ammonium + Nitrate
May indicate different mixtures of OA and inorganics
ALL
LV-OOA
SV-OOA
LV-OOA events have larger particles (~450 nm) than other organic types (~350 nm).
LV-OOA more monodisperse – has undergone ageing and coagulation?
SV-OOA ~350 nm fairly broad distribution, smaller than inorgs
BBOA slightly smaller (~300 nm)
BBOA more poly disperse – closer to source, less processing/coagulation time?
BBOA
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Results: Case Studies 2 1
8/6-8/2010: 10 pm BBOA events, afternoon enhancement on day 2: BBOA events start small (~200 nm), become more poly disperse.
7/8-11/10: Afternoon SV-OOA events feature broader size distribution.
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Results: PMF Factors and Inorganic Species
ROM 2010: HR Analysis (W Mode)
Timeline Correlation (r2)
LVOOA
SVOOA
BBOA
SO4
0.77
0.18
0.05
NO3
0.33
0.41
0.02
NH4
0.76
0.72
0.03
NO3 - NH4: r2 = 0.89
SO4 - NH4: r2 = 0.97
SO4 - NO3 : r2 = 0.34
Figure 10: Time series of organic factors and inorganic species
LV-OOA, sulfate, and ammonium are correlated
SV-OOA and ammonium are correlated; maybe nitrate (low concentration)
Inorganics correlated with each other; size evidence of mixture w/ LV-OOA
BBOA events are higher in amplitude, not coincident with other species.
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RoMANS: Conclusions
Oxidized organics (LV-OOA and SV-OOA) dominate submicron particulate mass, with some biomass burning organic aerosol (BBOA):
LV-OOA and SV-OOA transport from the Front Range is indicated by afternoon concentration increase probably associated with upslope flow, highly oxidized nature, large mode and relatively narrow size distribution, and correlation with inorganic tracers.
BBOA largely from nearby campfires.
Organic nitrogen is negligible (average 0.03 µg/m3, ON =(Org/OM:OC)*N:C*(14/12))).
Inorganic species also indicate transport from the Front Range:
Afternoon concentration increase.
SE wind direction.
Concentration correlation with SV- and LV-OOA.
Internal mixture of LV-OOA and Inorganics indicated by consistent size distributions and time-series correlation. SV-OOA is mixed with these, possibly externally.
External mixture between BBOA and other species suggested by inconsistent time-series and size distributions.
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QA/QC: Tetons
Problem with W mode: low AB makes low sensitivity (high noise). Got great fits, so can use for chemical info, but:
Use V mode for quantitation
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Results: AMS Timeline
TET 2011: UMR (V Mode)
Study Avg
1.6 μg/m3
0.3 μg/m3
0.2 μg/m3
0.08 μg/m3
D.L. = µg/m3
Organics dominate
All species have low concentrations
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Results: AMS Timeline
TET 2011: UMR (V Mode)
Various mixtures of org/inorgs – composition constant for days-weeks
In general neutral or even “basic”; acidic event on 8/15/11 coincident with high concentrations of all species
Acidic 8/15/11 event
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Results: Diurnal Profiles
TET 2011: High Res (W Mode)
Total Organics 1 1
Some nighttime enhancement – boundary layer compression?
Midday organic and sulfate increase – transport?
1: Mean influenced by event/s, but only for CO2 (not 43, etc.); indicates spectral change, different OA type?
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Results: Organic Aerosol: LV-OOA
TET 2011: High Res (W Mode)
Oxidized: all mass from most oxidized fragment
LV-OOA signatures: 28, 44>43, 55
43/44 daily fluctuations: less oxidized in afternoon?
No BSOA spectral markers
Organic event (8/15/11): increase in 44/43
SV-OOA?
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TETONS: Conclusions
Low species concentrations (organics 1.6µg/m3; inorgs µg/m3)
Afternoon and nighttime enhancement: compare with other datasets
Linear relationships (though varied slopes) b/w organics, inorganics may indicate internal mixtures (also, quite oxidized)
Mass spectral features = LV-OOA
8/15/11 Event: Mass spectrum and CO2 diurnal pattern indicate enhanced degree of oxidation.
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Future Work
TET PMF
Delta fragment series analysis (ROM and TET)
Delta describes important series of fragments
Each m/z has a delta value, weighted avg of m/z ranges
Patterns emerge for biogenic, diesel, other sources
(Kiendler-Scharr et al., 2009)
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Acknowledgements
Collett Group Kriedenweis Group National Park Service Salvation Army High Peak Camp Grand Targhee Ski Resort
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References
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Alfarra, M.R., Prevot, A.S.H., Szidatt, S., Sandradewi, J., Weimer, S., Lanz, V.S., Schreiber, D., Mohr, M., and Baltensperger, U. (2007). “Identification of the mass spectral signature of organic aerosols from wood burning emissions.” Environ. Sci. Technol. 14:
Drewnick, F., S. S. Hings, P. DeCarlo, J. T. Jayne, M. Gonin, K. Fuhrer, S. Weimer, J. L. Jimenez, K. L. Demerjian, S. Borrmann and D. R. Worsnop (2005). "A New Time-of-Flight Aerosol Mass Spectrometer (TOF-AMS): Instrument Description and First Field Deployment." Aerosol Science and Technology 39(7):
Farmer, D. K., Matsunaga, A., Docherty, K. S., Surratt, J. D., Seinfeld,J. H., Ziemann, P. J., and Jimenez, J. L.(2010), “Response of anaerosol mass spectrometer to organonitrates and organosulfatesand implications for atmospheric chemistry.” P. Natl. Acad. Sci.107(15), 6670–6675
Fry, J. L., A. Kiendler-Scharr, A. W. Rollins, P. J. Wooldridge, H. F. S. S. Brown3, W. Dub´e, A. Mensah, M. d. Maso, R. Tillmann, H.-P. Dorn, T. Brauers and R. C. Cohen (2009). "Organic nitrate and secondary organic aerosol yield from NO3 oxidation of beta-pinene evaluated using a gas-phase kinetics/aerosol partitioning model." Atmos. Chem. Phys. 9:
Kiendler-Scharr, A., Zhang, Q., Hohaus, T., Kleist, E., Mensah, A., Mentel, T. F., Spindler, C., Uerlings, R., Tillmann, R., and Wildt, J.: Aerosol Mass Spectrometric Features of Biogenic SOA: Observations from a Plant Chamber and in Rural Atmospheric Environments, Environ. Sci. Technol., 43, 8166–8172, doi: /es901420b, 2009.
Lanz, V. A., Alfarra, M. R., Baltensperger, U., Buchmann, B., Hueglin, C., and Prévôt, A. S. H. (2007). “Source apportionment of submicron organic aerosols at an urban site by factor analytical modeling of aerosol mass spectra.” Atmos. Chem. Phys. 7:
Lanz, V.A., M. R. Alfarra, U. Baltensperger, B. Buchmann, C. Hueglin, S. Szidat, M.N. Wehrli, L. Wacker, S. Weimer, A. Caseiro, H.Puxbaum, A. S. H. Prevot (2008). “Source Attribution of Submicron Organic Aerosols during Wintertime Inversions by Advanced Factor Analysis of Aerosol Mass Spectra.” Environmental Science & Technology 42(1):
Malm, W. C., B. A. Schichtel, M. G. Barna, K. A. Gebhart, J. L. Collett Jr. and S. M. Kreidenweis (2007). Source Apportionment of Sulfur and Nitrogen Species at Rocky Mountain National Park using Modeled Conservative Tracer Releases and Tracers of Opportunity. Air and Waste Managament Association. Pittsburgh, PA.
Mohr, C., S. Weimer, R. Richter, P. F. DeCarlo, R. Chirico, M. F. Heringa, A. S. H. Prévôt, and U. Baltensperger (2009). "Source apportionment of ambient aerosol applying PMF on AMS mobile and stationary data." Geophysical Research Abstracts 11.
Neff, J. C., E. A. Holland, F. J. Dentener, W. H. McDowell and K. M. Russell (2002). "The origin, composition and rates of organic nitrogen deposition: A missing piece of the nitrogen cycle?" Biogeochemistry 57-58(1):
Schneider, J., S. Weimer, F. Drewnick, S. Borrmann, G. Helas, P. Gwaze, O. Schmid, M. O. Andreae and U. Kirchner (2006). "Mass spectrometric analysis and aerodynamic properties of various types of combustion-related aerosol particles." International Journal of Mass Spectrometry 258:
Ulbrich, I. M., M. R. Canagaratna, Q. Zhang, D. R. Worsnop and J. L. Jimenez (2009). "Interpretation of organic components from Positive Matrix Factorization of aerosol mass spectrometric data." Atmos. Chem. Phys., 9:
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Zhang, Q., M. R. Alfarra, D. R. Worsnop, J. D. Allan, H. Coe, M. R. Canagaratna and J. L. Jimenez (2005c). "Deconvolution and Quantification of Hydrocarbon-like and Oxygenated Organic Aerosols Based on Aerosol Mass Spectrometry." Environmental Science & Technology 39(13):
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ROM 2010: UMR (V Mode)
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Supplementary
Unscaled
TET IE/AB
ROM diurnal O/C
Detec. Limit (µm/m3)*
Resolution (m/Δm)
Mass Range (m/z)
HR-ToF-AMS V Mode
0.003
~2000
1-1200
HR-ToF-AMS W Mode
0.05
~4000
*typical for NO3. Values from Aerodyne Research, Inc.
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31. Is AMS appropriate for the campaign?
Strengths
Quantitative
High Time Resolution
Size + Composition
HR = unequivocal fragment ID
Good Detection Limits
Weaknesses
Limited particle size range
Extensive fragmentation hinders parent ID
Direct AON not possible
Time intensive data analysis
Detec. Limit (µm/m3)*
Resolution (m/Δm)
Mass Range (m/z)
HR-ToF-AMS V Mode
0.003
~2000
1-1200
HR-ToF-AMS W Mode
0.05
~4000
*typical for NO3. Values from Aerodyne Research, Inc.
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TET Spare Figs
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