Chemical Smoke Marker Emissions During Flaming and Smoldering Phases of Laboratory Open Burning of Wildland Fuels

Lee, Taehyoung; Sullivan, A.; Mack, Laura; Jimenez, José L.; Kreidenweis, Sonia M.; Onasch, T. B.; Worsnop, Douglas R.; Malm, William C.; Wold, Cyle; Hao, Wei Min; Collett, Jeffrey L.

doi:10.1080/02786826.2010.499884

Cited by 178 publications

(200 citation statements)

References 24 publications

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“…Not only is ambient OA on average much more oxidized than POA, it is often more oxidized than most SOA produced in smog-chamber experiments Holzinger et al, 2010). Parallel field observations coupling the AMS to a long residence-time (15 s) thermodenuder also reveal that both the primary and secondary components of ambient OA are relatively volatile, with half of the most oxidized organic material evaporating at 125 • C, and half of other components, including less oxidized secondary components and more reduced primary components, evaporating at 80 • C Cappa and Jimenez, 2010;Lee et al, 2011).…”

Section: Introductionmentioning

confidence: 66%

A two-dimensional volatility basis set – Part 2: Diagnostics of organic-aerosol evolution

et al. 2012

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Abstract.We discuss the use of a two-dimensional volatility-oxidation space (2-D-VBS) to describe organicaerosol chemical evolution. The space is built around two coordinates, volatility and the degree of oxidation, both of which can be constrained observationally or specified for known molecules. Earlier work presented the thermodynamics of organics forming the foundation of this 2-D-VBS, allowing us to define the average composition (C, H, and O) of organics, including organic aerosol (OA) based on volatility and oxidation state. Here we discuss how we can analyze experimental data, using the 2-D-VBS to gain fundamental insight into organic-aerosol chemistry. We first present a wellunderstood "traditional" secondary organic aerosol (SOA) system -SOA from α-pinene + ozone, and then turn to two examples of "non-traditional" SOA formation -SOA from wood smoke and dilute diesel-engine emissions. Finally, we discuss the broader implications of this analysis.

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Section: Introductionmentioning

confidence: 66%

A two-dimensional volatility basis set – Part 2: Diagnostics of organic-aerosol evolution

et al. 2012

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“…Levoglucosan is a chemical tracer for biomass burning, as it is formed during the pyrolysis of cellulose (Simoneit et al, 1998). Lee et al (2010) found that the AMS peak at m/z 60, more specifically C 2 H 4 O 2 , a fragment resulting from the breakdown of levoglucosan and other anhydrosugars, including mannosan, galactosan, arabinosan, and xylosan, is an even better indication of biomass burning than levoglucosan itself. However, Russell et al (2010) has determined that marine Org in the North Atlantic are primarily carbohydrate-like, and also shows up at m/z 60 and 44, so the AMS mass spectra cannot by themselves exclude marine sources of elevated Org.…”

Section: Org Enrichment During the Tao Cruisementioning

confidence: 99%

“…Many studies have attempted to describe background conditions by taking marine aerosol measurements from land sites and ships (Allan et al, 2004;Andreae et al, 1999;Lohmann et al, 2005;Phinney et al, 2006;Quinn and Bates, 2003;Yoon et al, 2007). During these campaigns, various criteria for "clean" marine conditions were implemented, and included parameters such as clean sector wind direction (Andreae et al, 1999;Yoon et al, 2007), particle number concentration below a certain threshold, Air Mass Back Trajectories (AMBTs) used to indicate air masses with no continental influence a certain number of days before collection took place , or volatile organic carbon (VOC) tracers (Allan et al, 2004).…”

Section: M Shank Et Al: Organic Matter and Non-refractory Aerosolmentioning

confidence: 99%

Organic matter and non-refractory aerosol over the remote Southeast Pacific: oceanic and combustion sources

et al. 2012

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(SO 4 ) and organics (Org), during VOCALS and the cruise show lower absolute values than those reported for previous "clean air" studies. In the marine boundary layer, average concentrations for SO 4 were 0.52 µg m −3 for the VOCALS region and 0.85 µg m −3 for the equatorial region while average Org concentrations were 0.10 and 0.07 µg m −3 , respectively. Campaign average Org/SO 4 ratios were 0.19 (VOCALS) and 0.08 (equatorial Pacific). Black carbon (BC) measurements from a single particle soot photometer (SP2) and carbon monoxide (CO) concentrations over the Southeast Pacific provided sensitive indicators of pollution. CO and BC were used to identify the least polluted air, which had average concentrations of SO 4 and Org of 0.14 and 0.01 µg m −3 , respectively, with an average Org/SO 4 of 0.08. Data from IMPEX was constrained to similar clean air criterion, and resulted in an average Org/SO 4 ratio of 0.19. Under the cleanest MBL conditions during VOCALS, identified by CO below 61 ppbv, a robust linear relationship between Org and BC concentrations revealed that even at very low pollution levels, combustion sources dominated organic aerosol, suggesting little to no marine source of submicrometer Org to the atmosphere over the eastern South Pacific. This means marine organics cannot be identified by merely setting a standard for background conditions below which anthropogenic influence can be disregarded. Other methods must be used to exclude nonmarine sources.

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“…Lee et al, 2010). These statistical models are solved by minimizing the residuals between uncertainty-weighted data and the model, so both the data and their uncertainties must be quantified in order to obtain meaningful results.…”

Section: Introductionmentioning

confidence: 99%

Peak-fitting and integration imprecision in the Aerodyne aerosol mass spectrometer: effects of mass accuracy on location-constrained fits

et al. 2015

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Abstract. The errors inherent in the fitting and integration of the pseudo-Gaussian ion peaks in Aerodyne high-resolution aerosol mass spectrometers (HR-AMSs) have not been previously addressed as a source of imprecision for these or similar instruments. This manuscript evaluates the significance of this imprecision and proposes a method for their estimation in routine data analysis.In the first part of this work, it is shown that peakintegration errors are expected to scale linearly with peak height for the constrained-peak-shape fits performed in the HR-AMS. An empirical analysis is undertaken to investigate the most complex source of peak-integration imprecision: the imprecision in fitted peak height, σ h . It is shown that the major contributors to σ h are the imprecision and bias inherent in the m/z calibration, both of which may arise due to statistical and physical non-idealities of the instrument. A quantitative estimation of these m/z-calibration imprecisions and biases show that they may vary from ion to ion, even for ions of similar m/z.In the second part of this work, the empirical analysis is used to constrain a Monte Carlo approach for the estimation of σ h and thus the peak-integration imprecision. The estimated σ h for selected well-separated peaks (for which m/z-calibration imprecision and bias could be quantitatively estimated) scaled linearly with peak height as expected (i.e. as n 1 ). In combination with the imprecision in peak-width quantification (which may be easily and directly estimated during quantification), peak-fitting imprecisions therefore dominate counting imprecisions (which scale as n 0.5 ) at high signals. The previous HR-AMS uncertainty model therefore underestimates the overall fitting imprecision even for wellresolved peaks. We illustrate the importance of this conclusion by performing positive matrix factorization on a synthetic HR-AMS data set both with and without its inclusion.In the third part of this work, the Monte Carlo approach is extended to the case of an arbitrary number of overlapping peaks. Here, a modification to the empirically constrained approach was needed, because the ion-specific m/z-calibration bias and imprecision can generally only be estimated for well-resolved peaks. The modification is to simply overestimate the m/z-calibration imprecision in all cases. This overestimation results in only a slight overestimate of σ h , while significantly reducing the sensitivity of σ h to the unknown, ion-specific m/z-calibration biases. Thus, with only the meaPublished by Copernicus Publications on behalf of the European Geosciences Union. J. C. Corbin et al.: AMS peak-integration errorssured data and an approximate estimate of the order of magnitude of m/z-calibration biases as input, conservative and unbiased estimates of peak-integration imprecisions may be obtained for each peak in any ensemble of overlapping peaks.

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Chemical Smoke Marker Emissions During Flaming and Smoldering Phases of Laboratory Open Burning of Wildland Fuels

Cited by 178 publications

References 24 publications

A two-dimensional volatility basis set – Part 2: Diagnostics of organic-aerosol evolution

A two-dimensional volatility basis set – Part 2: Diagnostics of organic-aerosol evolution

Organic matter and non-refractory aerosol over the remote Southeast Pacific: oceanic and combustion sources

Peak-fitting and integration imprecision in the Aerodyne aerosol mass spectrometer: effects of mass accuracy on location-constrained fits

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