Articles | Volume 15, issue 8
https://doi.org/10.5194/amt-15-2591-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-2591-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Apr 2022
Research article |
| 29 Apr 2022
| 29 Apr 2022
Development and application of a supervised pattern recognition algorithm for identification of fuel-specific emissions profiles
Christos Stamatis
Department of Chemical and Environmental Engineering and College of Engineering – Center for Environmental Research and Technology (CE-CERT), University of California, Riverside, Riverside, CA, USA
Kelley Claire Barsanti
CORRESPONDING AUTHOR
Department of Chemical and Environmental Engineering and College of Engineering – Center for Environmental Research and Technology (CE-CERT), University of California, Riverside, Riverside, CA, USA
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Atmos. Chem. Phys., 22, 9877–9893, https://doi.org/10.5194/acp-22-9877-2022, https://doi.org/10.5194/acp-22-9877-2022, 2022
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This article reports the measurements of organic compounds emitted from western US wildfires. We identified and quantified 240 particle-phase compounds and 72 gas-phase compounds emitted in wildfire and related the emissions to the modified combustion efficiency. Higher emissions of diterpenoids and monoterpenes were observed, likely due to distillation from unburned heated vegetation. Our results can benefit future source apportionment and modeling studies as well as exposure assessments.
James D. A. Butler, Afsara Tasnia, Deep Sengupta, Nathan Kreisberg, Kelley C. Barsanti, Allen H. Goldstein, Chelsea V. Preble, Rebecca A. Sugrue, and Thomas W. Kirchstetter
EGUsphere, https://doi.org/10.5194/egusphere-2025-2295, https://doi.org/10.5194/egusphere-2025-2295, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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Prescribed burns are controlled fires used to prevent wildfires. Smoke emissions were measured to characterize emission factors and optical properties of black and brown soot particles. Brown particles were emitted at 7–14 times that of black particles and contributed 82 % of atmospheric absorption by particles for ultraviolet light and 23 % for total solar radiation. These findings will improve inventories and climate models for prescribed burns.
William P. L. Carter, Jia Jiang, Zhizhao Wang, and Kelley C. Barsanti
EGUsphere, https://doi.org/10.5194/egusphere-2025-1183, https://doi.org/10.5194/egusphere-2025-1183, 2025
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The SAPRC Atmospheric Chemical Mechanism Generation System (MechGen) generates explicit chemical reaction mechanisms for organic compounds. MechGen has been used for decades in the development of the widely-used SAPRC mechanisms. This manuscript, detailing the software system, and a companion manuscript, detailing the chemical basis, represent the first complete documentation of MechGen. This manuscript includes examples and instructions for generating explicit and reduced mechanisms.
William P. L. Carter, Jia Jiang, John J. Orlando, and Kelley C. Barsanti
Atmos. Chem. Phys., 25, 199–242, https://doi.org/10.5194/acp-25-199-2025, https://doi.org/10.5194/acp-25-199-2025, 2025
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This paper describes the scientific basis for gas-phase atmospheric chemical mechanisms derived using the SAPRC mechanism generation system, MechGen. It can derive mechanisms for most organic compounds with C, H, O, or N atoms, including initial reactions of organics with OH, O3, NO3, and O3P or by photolysis, as well as the reactions of the various types of intermediates that are formed. The paper includes a description of areas of uncertainty where additional research and updates are needed.
Samiha Binte Shahid, Forrest G. Lacey, Christine Wiedinmyer, Robert J. Yokelson, and Kelley C. Barsanti
Geosci. Model Dev., 17, 7679–7711, https://doi.org/10.5194/gmd-17-7679-2024, https://doi.org/10.5194/gmd-17-7679-2024, 2024
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The Next-generation Emissions InVentory expansion of Akagi (NEIVA) v.1.0 is a comprehensive biomass burning emissions database that allows integration of new data and flexible querying. Data are stored in connected datasets, including recommended averages of ~1500 constituents for 14 globally relevant fire types. Individual compounds were mapped to common model species to allow better attribution of emissions in modeling studies that predict the effects of fires on air quality and climate.
Vignesh Vasudevan-Geetha, Lee Tiszenkel, Zhizhao Wang, Robin Russo, Daniel Bryant, Julia Lee-Taylor, Kelley Barsanti, and Shan-Hu Lee
EGUsphere, https://doi.org/10.5194/egusphere-2024-2454, https://doi.org/10.5194/egusphere-2024-2454, 2024
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Our laboratory experiments using two high-resolution mass spectrometers show that these OOMs can also form within the particle phase, in addition to gas-to-particle conversion processes. Our results demonstrate that particle-phase formation processes can contribute to the formation and growth of new particles in biogenic environments.
Christine Wiedinmyer, Yosuke Kimura, Elena C. McDonald-Buller, Louisa K. Emmons, Rebecca R. Buchholz, Wenfu Tang, Keenan Seto, Maxwell B. Joseph, Kelley C. Barsanti, Annmarie G. Carlton, and Robert Yokelson
Geosci. Model Dev., 16, 3873–3891, https://doi.org/10.5194/gmd-16-3873-2023, https://doi.org/10.5194/gmd-16-3873-2023, 2023
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The Fire INventory from NCAR (FINN) provides daily global estimates of emissions from open fires based on satellite detections of hot spots. This version has been updated to apply MODIS and VIIRS satellite fire detection and better represents both large and small fires. FINNv2.5 generates more emissions than FINNv1 and is in general agreement with other fire emissions inventories. The new estimates are consistent with satellite observations, but uncertainties remain regionally and by pollutant.
Yutong Liang, Christos Stamatis, Edward C. Fortner, Rebecca A. Wernis, Paul Van Rooy, Francesca Majluf, Tara I. Yacovitch, Conner Daube, Scott C. Herndon, Nathan M. Kreisberg, Kelley C. Barsanti, and Allen H. Goldstein
Atmos. Chem. Phys., 22, 9877–9893, https://doi.org/10.5194/acp-22-9877-2022, https://doi.org/10.5194/acp-22-9877-2022, 2022
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This article reports the measurements of organic compounds emitted from western US wildfires. We identified and quantified 240 particle-phase compounds and 72 gas-phase compounds emitted in wildfire and related the emissions to the modified combustion efficiency. Higher emissions of diterpenoids and monoterpenes were observed, likely due to distillation from unburned heated vegetation. Our results can benefit future source apportionment and modeling studies as well as exposure assessments.
Qi Li, Jia Jiang, Isaac K. Afreh, Kelley C. Barsanti, and David R. Cocker III
Atmos. Chem. Phys., 22, 3131–3147, https://doi.org/10.5194/acp-22-3131-2022, https://doi.org/10.5194/acp-22-3131-2022, 2022
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Chamber-derived secondary organic aerosol (SOA) yields from camphene are reported for the first time. The role of peroxy radicals (RO2) was investigated using chemically detailed box models. We observed higher SOA yields (up to 64 %) in the experiments with added NOx than without due to the formation of highly oxygenated organic molecules (HOMs) when
NOx is present. This work can improve the representation of camphene in air quality models and provide insights into other monoterpene studies.
Zachary C. J. Decker, Michael A. Robinson, Kelley C. Barsanti, Ilann Bourgeois, Matthew M. Coggon, Joshua P. DiGangi, Glenn S. Diskin, Frank M. Flocke, Alessandro Franchin, Carley D. Fredrickson, Georgios I. Gkatzelis, Samuel R. Hall, Hannah Halliday, Christopher D. Holmes, L. Gregory Huey, Young Ro Lee, Jakob Lindaas, Ann M. Middlebrook, Denise D. Montzka, Richard Moore, J. Andrew Neuman, John B. Nowak, Brett B. Palm, Jeff Peischl, Felix Piel, Pamela S. Rickly, Andrew W. Rollins, Thomas B. Ryerson, Rebecca H. Schwantes, Kanako Sekimoto, Lee Thornhill, Joel A. Thornton, Geoffrey S. Tyndall, Kirk Ullmann, Paul Van Rooy, Patrick R. Veres, Carsten Warneke, Rebecca A. Washenfelder, Andrew J. Weinheimer, Elizabeth Wiggins, Edward Winstead, Armin Wisthaler, Caroline Womack, and Steven S. Brown
Atmos. Chem. Phys., 21, 16293–16317, https://doi.org/10.5194/acp-21-16293-2021, https://doi.org/10.5194/acp-21-16293-2021, 2021
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To understand air quality impacts from wildfires, we need an accurate picture of how wildfire smoke changes chemically both day and night as sunlight changes the chemistry of smoke. We present a chemical analysis of wildfire smoke as it changes from midday through the night. We use aircraft observations from the FIREX-AQ field campaign with a chemical box model. We find that even under sunlight typical
nighttimechemistry thrives and controls the fate of key smoke plume chemical processes.
Sabrina Chee, Kelley Barsanti, James N. Smith, and Nanna Myllys
Atmos. Chem. Phys., 21, 11637–11654, https://doi.org/10.5194/acp-21-11637-2021, https://doi.org/10.5194/acp-21-11637-2021, 2021
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We explored molecular properties affecting atmospheric particle formation efficiency and derived a parameterization between particle formation rate and heterodimer concentration, which showed good agreement to previously reported experimental data. Considering the simplicity of calculating heterodimer concentration, this approach has potential to improve estimates of global cloud condensation nuclei in models that are limited by the computational expense of calculating particle formation rate.
Isaac Kwadjo Afreh, Bernard Aumont, Marie Camredon, and Kelley Claire Barsanti
Atmos. Chem. Phys., 21, 11467–11487, https://doi.org/10.5194/acp-21-11467-2021, https://doi.org/10.5194/acp-21-11467-2021, 2021
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This is the first mechanistic modeling study of secondary organic aerosol (SOA) from the understudied monoterpene, camphene. The semi-explicit chemical model GECKO-A predicted camphene SOA yields that were ~2 times α-pinene. Using 50/50 α-pinene + limonene as a surrogate for camphene increased predicted SOA mass from biomass burning fuels by up to ~100 %. The accurate representation of camphene in air quality models can improve predictions of SOA when camphene is a dominant monoterpene.
Dagny A. Ullmann, Mallory L. Hinks, Adrian M. Maclean, Christopher L. Butenhoff, James W. Grayson, Kelley Barsanti, Jose L. Jimenez, Sergey A. Nizkorodov, Saeid Kamal, and Allan K. Bertram
Atmos. Chem. Phys., 19, 1491–1503, https://doi.org/10.5194/acp-19-1491-2019, https://doi.org/10.5194/acp-19-1491-2019, 2019
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We measured the viscosity and diffusion of organic molecules in secondary organic aerosol (SOA) generated from the ozonolysis of limonene. The results suggest that the mixing times of large organics in the SOA studied are short (< 1 h) for conditions found in the planetary boundary layer. The results also show that the Stokes–Einstein equation gives accurate predictions of diffusion coefficients of large organics within the studied SOA up to a viscosity of 102 to 104 Pa s.
Coty N. Jen, Lindsay E. Hatch, Vanessa Selimovic, Robert J. Yokelson, Robert Weber, Arantza E. Fernandez, Nathan M. Kreisberg, Kelley C. Barsanti, and Allen H. Goldstein
Atmos. Chem. Phys., 19, 1013–1026, https://doi.org/10.5194/acp-19-1013-2019, https://doi.org/10.5194/acp-19-1013-2019, 2019
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Wildfires in the western US are occurring more frequently and burning larger land areas. Smoke from these fires will play a greater role in regional air quality and atmospheric chemistry than in the past. To help fire and climate modelers and atmospheric experimentalists better understand how smoke impacts the environment, we have separated, identified, classified, and quantified the thousands of organic compounds found in smoke and related their amounts emitted to fire conditions.
Lindsay E. Hatch, Albert Rivas-Ubach, Coty N. Jen, Mary Lipton, Allen H. Goldstein, and Kelley C. Barsanti
Atmos. Chem. Phys., 18, 17801–17817, https://doi.org/10.5194/acp-18-17801-2018, https://doi.org/10.5194/acp-18-17801-2018, 2018
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We demonstrate the use of solid-phase extraction (SPE) disks for the untargeted analysis of gas-phase intermediate volatility and semi-volatile organic compounds emitted from biomass burning. SPE and Teflon filter samples collected from laboratory fires were analyzed by two-dimensional gas chromatography, with distinct differences in the observed chromatographic profiles as a function of
fuel type. Fuel-dependent emissions and volatility differences among benzenediol isomers were captured.
Adrian M. Maclean, Christopher L. Butenhoff, James W. Grayson, Kelley Barsanti, Jose L. Jimenez, and Allan K. Bertram
Atmos. Chem. Phys., 17, 13037–13048, https://doi.org/10.5194/acp-17-13037-2017, https://doi.org/10.5194/acp-17-13037-2017, 2017
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Using laboratory data, meteorological fields and a chemical transport model, we investigated how often mixing times are < 1 h within SOA in the planetary boundary layer (PBL). Based on viscosity data for alpha-pinene SOA generated using mass concentrations of ~1000 µg m −3, mixing times in biogenic SOA are < 1h most of the time.
Qijing Bian, Shantanu H. Jathar, John K. Kodros, Kelley C. Barsanti, Lindsay E. Hatch, Andrew A. May, Sonia M. Kreidenweis, and Jeffrey R. Pierce
Atmos. Chem. Phys., 17, 5459–5475, https://doi.org/10.5194/acp-17-5459-2017, https://doi.org/10.5194/acp-17-5459-2017, 2017
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In this paper, we perform simulations of the evolution of biomass-burning organic aerosol in laboratory smog-chamber experiments and ambient plumes. We find that in smog-chamber experiments, vapor wall losses lead to a large reduction in the apparent secondary organic aerosol formation. In ambient plumes, fire size and meteorology regulate the plume dilution rate, primary organic aerosol evaporation rate, and secondary organic aerosol formation rate.
Lindsay E. Hatch, Robert J. Yokelson, Chelsea E. Stockwell, Patrick R. Veres, Isobel J. Simpson, Donald R. Blake, John J. Orlando, and Kelley C. Barsanti
Atmos. Chem. Phys., 17, 1471–1489, https://doi.org/10.5194/acp-17-1471-2017, https://doi.org/10.5194/acp-17-1471-2017, 2017
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The most comprehensive database of gaseous biomass burning emissions to date was compiled. Four complementary instruments were deployed together during laboratory fires. The results generally compared within experimental uncertainty and highlighted that a range of measurement approaches are required for adequate characterization of smoke composition. Observed compounds were binned based on volatility, and priority recommendations were made to improve secondary organic aerosol predictions.
Anna L. Hodshire, Michael J. Lawler, Jun Zhao, John Ortega, Coty Jen, Taina Yli-Juuti, Jared F. Brewer, Jack K. Kodros, Kelley C. Barsanti, Dave R. Hanson, Peter H. McMurry, James N. Smith, and Jeffery R. Pierce
Atmos. Chem. Phys., 16, 9321–9348, https://doi.org/10.5194/acp-16-9321-2016, https://doi.org/10.5194/acp-16-9321-2016, 2016
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Processes that control the growth of newly formed particles are not well understood and limit predictions of aerosol climate impacts. We combine state-of-the-art measurements at a central-US site with a particle-growth model to investigate the species and processes contributing to growth. Observed growth was dominated by organics, sulfate salts, or a mixture of these two. The model qualitatively captures the variability between different days.
T. Yli-Juuti, K. Barsanti, L. Hildebrandt Ruiz, A.-J. Kieloaho, U. Makkonen, T. Petäjä, T. Ruuskanen, M. Kulmala, and I. Riipinen
Atmos. Chem. Phys., 13, 12507–12524, https://doi.org/10.5194/acp-13-12507-2013, https://doi.org/10.5194/acp-13-12507-2013, 2013
K. C. Barsanti, A. G. Carlton, and S. H. Chung
Atmos. Chem. Phys., 13, 12073–12088, https://doi.org/10.5194/acp-13-12073-2013, https://doi.org/10.5194/acp-13-12073-2013, 2013
M. Xie, K. C. Barsanti, M. P. Hannigan, S. J. Dutton, and S. Vedal
Atmos. Chem. Phys., 13, 7381–7393, https://doi.org/10.5194/acp-13-7381-2013, https://doi.org/10.5194/acp-13-7381-2013, 2013
A. Mahmud and K. Barsanti
Geosci. Model Dev., 6, 961–980, https://doi.org/10.5194/gmd-6-961-2013, https://doi.org/10.5194/gmd-6-961-2013, 2013
Related subject area
Subject: Gases | Technique: Laboratory Measurement | Topic: Data Processing and Information Retrieval
An algorithm for automatic fitting and formula assignment in atmospheric mass spectra
Determination of pressure baseline corrections for clumped-isotope signals with complex peak shapes
Atmospheric H2 observations from the NOAA Cooperative Global Air Sampling Network
Application of fuzzy c-means clustering for analysis of chemical ionization mass spectra: insights into the gas phase chemistry of NO3-initiated oxidation of isoprene
Wall loss of semi-volatile organic compounds in a Teflon bag chamber for the temperature range of 262–298 K: mechanistic insight on temperature dependence
Obtaining accurate non-methane hydrocarbon data for ambient air in urban areas: comparison of non-methane hydrocarbon data between indirect and direct methods
Reconstruction of high-frequency methane atmospheric concentration peaks from measurements using metal oxide low-cost sensors
Cavity ring-down spectroscopy of water vapor in the deep-blue region
Orbitool: a software tool for analyzing online Orbitrap mass spectrometry data
Dynamic infrared gas analysis from longleaf pine fuel beds burned in a wind tunnel: observation of phenol in pyrolysis and combustion phases
High-precision measurements of nitrous oxide and methane in air with cavity ring-down spectroscopy at 7.6 µm
Mapping and quantifying isomer sets of hydrocarbons ( ≥ C12) in diesel exhaust, lubricating oil and diesel fuel samples using GC × GC-ToF-MS
Measurement of alkyl and multifunctional organic nitrates by proton-transfer-reaction mass spectrometry
Uncertainty budgets of major ozone absorption cross sections used in UV remote sensing applications
New and improved infrared absorption cross sections for chlorodifluoromethane (HCFC-22)
High spectral resolution ozone absorption cross-sections – Part 1: Measurements, data analysis and comparison with previous measurements around 293 K
High spectral resolution ozone absorption cross-sections – Part 2: Temperature dependence
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OH clock determination by proton transfer reaction mass spectrometry at an environmental chamber
Water isotopic ratios from a continuously melted ice core sample
Valter Mickwitz, Otso Peräkylä, Frans Graeffe, Douglas Worsnop, and Mikael Ehn
Atmos. Meas. Tech., 18, 1537–1559, https://doi.org/10.5194/amt-18-1537-2025, https://doi.org/10.5194/amt-18-1537-2025, 2025
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Stephan Räss, Peter Nyfeler, Paul Wheeler, Will Price, and Markus Christian Leuenberger
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Gabrielle Pétron, Andrew M. Crotwell, John Mund, Molly Crotwell, Thomas Mefford, Kirk Thoning, Bradley Hall, Duane Kitzis, Monica Madronich, Eric Moglia, Donald Neff, Sonja Wolter, Armin Jordan, Paul Krummel, Ray Langenfelds, and John Patterson
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Hydrogen (H2) is a gas in trace amounts in the Earth’s atmosphere with indirect impacts on climate and air quality. Renewed interest in H2 as a low- or zero-carbon source of energy may lead to increased production, uses, and supply chain emissions. NOAA measurements of weekly air samples collected between 2009 and 2021 at over 50 sites in mostly remote locations are now available, and they complement other datasets to study the H2 global budget.
Rongrong Wu, Sören R. Zorn, Sungah Kang, Astrid Kiendler-Scharr, Andreas Wahner, and Thomas F. Mentel
Atmos. Meas. Tech., 17, 1811–1835, https://doi.org/10.5194/amt-17-1811-2024, https://doi.org/10.5194/amt-17-1811-2024, 2024
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Recent advances in high-resolution time-of-flight chemical ionization mass spectrometry (CIMS) enable the detection of highly oxygenated organic molecules, which efficiently contribute to secondary organic aerosol. Here we present an application of fuzzy c-means (FCM) clustering to deconvolve CIMS data. FCM not only reduces the complexity of mass spectrometric data but also the chemical and kinetic information retrieved by clustering gives insights into the chemical processes involved.
Longkun He, Wenli Liu, Yatai Li, Jixuan Wang, Mikinori Kuwata, and Yingjun Liu
Atmos. Meas. Tech., 17, 755–764, https://doi.org/10.5194/amt-17-755-2024, https://doi.org/10.5194/amt-17-755-2024, 2024
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We experimentally investigated vapor wall loss of n-alkanes in a Teflon chamber across a wide temperature range. Increased wall loss was observed at lower temperatures. Further analysis suggests that lower temperatures enhance partitioning of n-alkanes to the surface layer of a Teflon wall but slow their diffusion into the inner layer. The results are important for quantitative analysis of chamber experiments conducted at low temperatures, simulating wintertime or upper-tropospheric conditions.
Song Gao, Yong Yang, Xiao Tong, Linyuan Zhang, Yusen Duan, Guigang Tang, Qiang Wang, Changqing Lin, Qingyan Fu, Lipeng Liu, and Lingning Meng
Atmos. Meas. Tech., 16, 5709–5723, https://doi.org/10.5194/amt-16-5709-2023, https://doi.org/10.5194/amt-16-5709-2023, 2023
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We optimized and conducted an experimental program for the real-time monitoring of non-methane hydrocarbon instruments using the direct method. Changing the enrichment and specially designed columns further improved the test effect. The results correct the measurement errors that have prevailed for many years and can lay a foundation for the evaluation of volatile organic compounds in the regional ambient air and provide direction for the measurement of low-concentration ambient air pollutants.
Rodrigo Andres Rivera Martinez, Diego Santaren, Olivier Laurent, Gregoire Broquet, Ford Cropley, Cécile Mallet, Michel Ramonet, Adil Shah, Leonard Rivier, Caroline Bouchet, Catherine Juery, Olivier Duclaux, and Philippe Ciais
Atmos. Meas. Tech., 16, 2209–2235, https://doi.org/10.5194/amt-16-2209-2023, https://doi.org/10.5194/amt-16-2209-2023, 2023
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A network of low-cost sensors is a good alternative to improve the detection of fugitive CH4 emissions. We present the results of four tests conducted with two types of Figaro sensors that were assembled on four chambers in a laboratory experiment: a comparison of five models to reconstruct the CH4 signal, a strategy to reduce the training set size, a detection of age effects in the sensors and a test of the capability to transfer a model between chambers for the same type of sensor.
Qing-Ying Yang, Eamon K. Conway, Hui Liang, Iouli E. Gordon, Yan Tan, and Shui-Ming Hu
Atmos. Meas. Tech., 15, 4463–4472, https://doi.org/10.5194/amt-15-4463-2022, https://doi.org/10.5194/amt-15-4463-2022, 2022
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Water vapor absorption in the near-UV region is essential to describe the energy budget of Earth; however, there is little spectroscopic information available. And accurate near-UV water absorption is also required in both ground-based observations and satellite missions for trace gas species. Here, we provide the high-resolution spectra of water vapor around 415 nm measured with cavity ring-down spectroscopy. These absorption lines have never been experimentally verified before.
Runlong Cai, Yihao Li, Yohann Clément, Dandan Li, Clément Dubois, Marlène Fabre, Laurence Besson, Sebastien Perrier, Christian George, Mikael Ehn, Cheng Huang, Ping Yi, Yingge Ma, and Matthieu Riva
Atmos. Meas. Tech., 14, 2377–2387, https://doi.org/10.5194/amt-14-2377-2021, https://doi.org/10.5194/amt-14-2377-2021, 2021
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Orbitool is an open-source software tool, mainly coded in Python, with a graphical user interface (GUI), specifically developed to facilitate the analysis of online Orbitrap mass spectrometric data. It is notably optimized for long-term atmospheric measurements and laboratory studies.
Catherine A. Banach, Ashley M. Bradley, Russell G. Tonkyn, Olivia N. Williams, Joey Chong, David R. Weise, Tanya L. Myers, and Timothy J. Johnson
Atmos. Meas. Tech., 14, 2359–2376, https://doi.org/10.5194/amt-14-2359-2021, https://doi.org/10.5194/amt-14-2359-2021, 2021
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We have developed a novel method to identify and characterize the gases emitted in biomass burning fires in a time-resolved fashion. Using time-resolved infrared spectroscopy combined with time-resolved thermal imaging in a wind tunnel, we were able to capture the gas-phase dynamics of the burning of plants native to the southeastern United States.
Jing Tang, Bincheng Li, and Jing Wang
Atmos. Meas. Tech., 12, 2851–2861, https://doi.org/10.5194/amt-12-2851-2019, https://doi.org/10.5194/amt-12-2851-2019, 2019
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A high-sensitivity CH4 and N2O sensor based on mid-IR (7.6 µm) cavity ring-down spectroscopy was developed. The effect of temperature fluctuation on measurement sensitivity was analyzed and corrected, and detection limits of 5 pptv for CH4 and 9 pptv for N2O were experimentally achieved. Separate and continuous measurements of CH4 and N2O concentrations of indoor and outdoor air at different locations showed the spatial and temporal concentration variations of CH4 and N2O in air.
Mohammed S. Alam, Soheil Zeraati-Rezaei, Zhirong Liang, Christopher Stark, Hongming Xu, A. Rob MacKenzie, and Roy M. Harrison
Atmos. Meas. Tech., 11, 3047–3058, https://doi.org/10.5194/amt-11-3047-2018, https://doi.org/10.5194/amt-11-3047-2018, 2018
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Diesel fuel, lubricating oil and diesel exhaust emissions all contain a very complex mixture of chemical compounds with diverse molecular structures. The GC × GC-ToF-MS analytical method is a very powerful way of separating and identifying those compounds. This paper describes the allocation of compounds into groups with similar molecular structures and chemical properties, which facilitates the intercomparison of very complex mixtures such as are found in diesel fuel, oil and emissions.
Marius Duncianu, Marc David, Sakthivel Kartigueyane, Manuela Cirtog, Jean-François Doussin, and Benedicte Picquet-Varrault
Atmos. Meas. Tech., 10, 1445–1463, https://doi.org/10.5194/amt-10-1445-2017, https://doi.org/10.5194/amt-10-1445-2017, 2017
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A commercial PTR-ToF-MS has been optimized in order to allow the measurement of individual organic nitrates in the atmosphere. This has been accomplished by shifting the distribution between different ionizing analytes. The proposed approach has been proved to be appropriate for the online detection of individual alkyl nitrates and functionalized nitrates.
Mark Weber, Victor Gorshelev, and Anna Serdyuchenko
Atmos. Meas. Tech., 9, 4459–4470, https://doi.org/10.5194/amt-9-4459-2016, https://doi.org/10.5194/amt-9-4459-2016, 2016
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Ozone absorption cross sections measured in the laboratory using spectroscopic means can be a major source of uncertainty in atmospheric ozone retrievals. In this paper we assess the overall uncertainty in three published UV ozone cross-section datasets that are most popular in the remote sensing community. The overall uncertainties were estimated using Monte Carlo simulations. They are important for traceability of atmospheric ozone measuring instruments to common metrological standards.
Jeremy J. Harrison
Atmos. Meas. Tech., 9, 2593–2601, https://doi.org/10.5194/amt-9-2593-2016, https://doi.org/10.5194/amt-9-2593-2016, 2016
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Using infrared sounders on satellite platforms to monitor concentrations of atmospheric HCFC-22, a stratospheric-ozone-depleting molecule which is still increasing in the atmosphere, crucially requires accurate laboratory spectroscopic data. This manuscript describes a new high-resolution infrared absorption cross-section data set for remote-sensing purposes; this improves upon the one currently available in the HITRAN and GEISA databases.
V. Gorshelev, A. Serdyuchenko, M. Weber, W. Chehade, and J. P. Burrows
Atmos. Meas. Tech., 7, 609–624, https://doi.org/10.5194/amt-7-609-2014, https://doi.org/10.5194/amt-7-609-2014, 2014
A. Serdyuchenko, V. Gorshelev, M. Weber, W. Chehade, and J. P. Burrows
Atmos. Meas. Tech., 7, 625–636, https://doi.org/10.5194/amt-7-625-2014, https://doi.org/10.5194/amt-7-625-2014, 2014
L. Huang, A. Chivulescu, D. Ernst, W. Zhang, A.-L. Norman, and Y.-S. Lee
Atmos. Meas. Tech., 6, 1685–1705, https://doi.org/10.5194/amt-6-1685-2013, https://doi.org/10.5194/amt-6-1685-2013, 2013
J. Schmitt, B. Seth, M. Bock, C. van der Veen, L. Möller, C. J. Sapart, M. Prokopiou, T. Sowers, T. Röckmann, and H. Fischer
Atmos. Meas. Tech., 6, 1425–1445, https://doi.org/10.5194/amt-6-1425-2013, https://doi.org/10.5194/amt-6-1425-2013, 2013
P. Barmet, J. Dommen, P. F. DeCarlo, T. Tritscher, A. P. Praplan, S. M. Platt, A. S. H. Prévôt, N. M. Donahue, and U. Baltensperger
Atmos. Meas. Tech., 5, 647–656, https://doi.org/10.5194/amt-5-647-2012, https://doi.org/10.5194/amt-5-647-2012, 2012
V. Gkinis, T. J. Popp, T. Blunier, M. Bigler, S. Schüpbach, E. Kettner, and S. J. Johnsen
Atmos. Meas. Tech., 4, 2531–2542, https://doi.org/10.5194/amt-4-2531-2011, https://doi.org/10.5194/amt-4-2531-2011, 2011
Cited articles
Abdi, H. and Williams, L. J.: Principal component analysis, WIREs Comput.
Stat., 2, 433–459, https://doi.org/10.1002/wics.101, 2010. a
Alvarado, M. J. and Prinn, R. G.: Formation of ozone and growth of aerosols in
young smoke plumes from biomass burning: 1. Lagrangian parcel studies,
J. Geophys. Res.-Atmos., 114, D09306,
https://doi.org/10.1029/2008JD011144, 2009. a
Andreae, M. O.: Emission of trace gases and aerosols from biomass burning – an updated assessment, Atmos. Chem. Phys., 19, 8523–8546, https://doi.org/10.5194/acp-19-8523-2019, 2019. a
Andreae, M. O., Browell, E. V., Garstang, M., Gregory, G. L., Harriss, R. C.,
Hill, G. F., Jacob, D. J., Pereira, M. C., Sachse, G. W., Setzer, A. W.,
Dias, P. L. S., Talbot, R. W., Torres, A. L., and Wofsy, S. C.:
Biomass-burning emissions and associated haze layers over Amazonia, J. Geophys. Res.-Atmos., 93, 1509–1527,
https://doi.org/10.1029/JD093iD02p01509, 1988. a
Chen, J., Anderson, K., Pavlovic, R., Moran, M. D., Englefield, P., Thompson, D. K., Munoz-Alpizar, R., and Landry, H.: The FireWork v2.0 air quality forecast system with biomass burning emissions from the Canadian Forest Fire Emissions Prediction System v2.03, Geosci. Model Dev., 12, 3283–3310, https://doi.org/10.5194/gmd-12-3283-2019, 2019. a
Dennison, P. E., Brewer, S. C., Arnold, J. D., and Moritz, M. A.: Large
wildfire trends in the western United States, 1984–2011, Geophys.
Res. Lett., 41, 2928–2933, https://doi.org/10.1002/2014GL059576,
2014. a, b
Dong, Y. and Peng, C.-Y. J.: Principled missing data methods for researchers,
SpringerPlus, 2, 222, https://doi.org/10.1186/2193-1801-2-222, 2013. a, b
Elkan, C.: Using the Triangle Inequality to Accelerate K-Means, in: Proceedings
of the Twentieth International Conference on International Conference on
Machine Learning, ICML'03, p. 147–153, 21–24 August2003, Washington DC, USA, AAAI Press, 2003. a
Fu, P., Kawamura, K., and Barrie, L. A.: Photochemical and Other Sources of
Organic Compounds in the Canadian High Arctic Aerosol Pollution during
Winter−Spring, Environ. Sci. Technol., 43, 286–292,
https://doi.org/10.1021/es803046q, 2009. a
Gewers, F. L., Ferreira, G. R., Arruda, H. F. D., Silva, F. N., Comin, C. H.,
Amancio, D. R., and Costa, L. D. F.: Principal Component Analysis: A Natural
Approach to Data Exploration, ACM Comput. Surv., 54, 1–34, https://doi.org/10.1145/3447755,
2021. a, b
Goode, J. G., Yokelson, R. J., Ward, D. E., Susott, R. A., Babbitt, R. E.,
Davies, M. A., and Hao, W. M.: Measurements of excess O3, CO2, CO, CH4, C2H4,
C2H2, HCN, NO, NH3, HCOOH, CH3COOH, HCHO, and CH3OH in 1997 Alaskan biomass
burning plumes by airborne Fourier transform infrared spectroscopy (AFTIR),
J. Geophys. Res.-Atmos., 105, 22147–22166,
https://doi.org/10.1029/2000JD900287, 2000. a
Hastie, T., Tibshirani, R., and Friedman, J.: The Elements of Statistical
Learning, Springer, New York, 145 pp., https://doi.org/10.1007/978-0-387-84858-7, 2009. a
Hatch, L. E., Luo, W., Pankow, J. F., Yokelson, R. J., Stockwell, C. E., and Barsanti, K. C.: Identification and quantification of gaseous organic compounds emitted from biomass burning using two-dimensional gas chromatography–time-of-flight mass spectrometry, Atmos. Chem. Phys., 15, 1865–1899, https://doi.org/10.5194/acp-15-1865-2015, 2015. a
Hatch, L. E., Yokelson, R. J., Stockwell, C. E., Veres, P. R., Simpson, I. J., Blake, D. R., Orlando, J. J., and Barsanti, K. C.: Multi-instrument comparison and compilation of non-methane organic gas emissions from biomass burning and implications for smoke-derived secondary organic aerosol precursors, Atmos. Chem. Phys., 17, 1471–1489, https://doi.org/10.5194/acp-17-1471-2017, 2017. a
Hatch, L. E., Rivas-Ubach, A., Jen, C. N., Lipton, M., Goldstein, A. H., and Barsanti, K. C.: Measurements of I/SVOCs in biomass-burning smoke using solid-phase extraction disks and two-dimensional gas chromatography, Atmos. Chem. Phys., 18, 17801–17817, https://doi.org/10.5194/acp-18-17801-2018, 2018. a
Hatch, L. E., Jen, C. N., Kreisberg, N. M., Selimovic, V., Yokelson, R. J.,
Stamatis, C., York, R. A., Foster, D., Stephens, S. L., Goldstein, A. H., and
Barsanti, K. C.: Highly Speciated Measurements of Terpenoids Emitted from
Laboratory and Mixed-Conifer Forest Prescribed Fires, Environ. Sci.
Technol., 53, 9418–9428, https://doi.org/10.1021/acs.est.9b02612,
2019. a, b, c, d, e, f, g, h, i
Holder, A. L., Gullett, B. K., Urbanski, S. P., Elleman, R., O'Neill, S.,
Tabor, D., Mitchell, W., and Baker, K. R.: Emissions from prescribed burning
of agricultural fields in the Pacific Northwest, Atmos. Environ.,
166, 22–33,
2017. a
Hu, Y. Q., Fernandez-Anez, N., Smith, T. E. L., and Rein, G.: Review of
emissions from smouldering peat fires and their contribution to regional haze
episodes, Int. J. Wild. Fire, 27, 293–312,
https://doi.org/10.1071/WF17084, 2018. a
Jaffe, D. A., O’Neill, S. M., Larkin, N. K., Holder, A. L., Peterson, D. L.,
Halofsky, J. E., and Rappold, A. G.: Wildfire and prescribed burning impacts
on air quality in the United States, J. Air Waste Manage.
Assoc., 70, 583–615, https://doi.org/10.1080/10962247.2020.1749731, 2020. a, b
Jain, A. K.: Data clustering: 50 years beyond K-means, Pattern Recogn.
Lett., 31, 651–666, https://doi.org/10.1016/j.patrec.2009.09.011,
award winning papers from the 19th International Conference on Pattern
Recognition (ICPR), 2010. a, b
Jakobsen, J. C., Gluud, C., Wetterslev, J., and Winkel, P.: When and how should
multiple imputation be used for handling missing data in randomised clinical
trials – a practical guide with flowcharts, BMC Med. Res.
Method., 17, 162, https://doi.org/10.1186/s12874-017-0442-1, 2017. a
Jen, C. N., Liang, Y., Hatch, L. E., Kreisberg, N. M., Stamatis, C.,
Kristensen, K., Battles, J. J., Stephens, S. L., York, R. A., Barsanti,
K. C., and Goldstein, A. H.: High Hydroquinone Emissions from Burning
Manzanita, Environ. Sci. Technol. Lett., 5, 309–314,
https://doi.org/10.1021/acs.estlett.8b00222, 2018. a
Johnson, K. J. and Synovec, R. E.: Pattern recognition of jet fuels:
comprehensive GC × GC with ANOVA-based feature selection and principal
component analysis, Chemometr. Intell. Lab., 60,
225–237, https://doi.org/10.1016/S0169-7439(01)00198-8, fourth
International Conference on Environ metrics and Chemometrics held in Las
Vegas, NV, USA, 18–20 September 2000, 2002. a, b, c
Jolliffe, I.: Principal Component Analysis, Springer, New York, 188 pp., https://doi.org/10.1007/b98835, 2002. a
Keane, R. E. and Lutes, D.: First-Order Fire Effects Model (FOFEM), 1–5,
Springer International Publishing, Cham,
https://doi.org/10.1007/978-3-319-51727-8_74-1, 2018. a
Kochanski, A. K., Pardyjak, E. R., Stoll, R., Gowardhan, A., Brown, M. J., and
Steenburgh, W. J.: One-Way Coupling of the WRF–QUIC Urban Dispersion
Modeling System, J. Appl. Meteorol. Climatol., 54, 2119–2139, https://doi.org/10.1175/JAMC-D-15-0020.1, 2015. a
Koss, A. R., Sekimoto, K., Gilman, J. B., Selimovic, V., Coggon, M. M., Zarzana, K. J., Yuan, B., Lerner, B. M., Brown, S. S., Jimenez, J. L., Krechmer, J., Roberts, J. M., Warneke, C., Yokelson, R. J., and de Gouw, J.: Non-methane organic gas emissions from biomass burning: identification, quantification, and emission factors from PTR-ToF during the FIREX 2016 laboratory experiment, Atmos. Chem. Phys., 18, 3299–3319, https://doi.org/10.5194/acp-18-3299-2018, 2018. a, b
Lever, J., Krzywinski, M., and Altman, N.: Principal component analysis, Nat.
Method., 14, 641–642, https://doi.org/10.1038/nmeth.4346, 2017. a
Lindaas, J., Pollack, I. B., Garofalo, L. A., Pothier, M. A., Farmer, D. K.,
Kreidenweis, S. M., Campos, T. L., Flocke, F., Weinheimer, A. J., Montzka,
D. D., Tyndall, G. S., Palm, B. B., Peng, Q., Thornton, J. A., Permar, W.,
Wielgasz, C., Hu, L., Ottmar, R. D., Restaino, J. C., Hudak, A. T., Ku,
I.-T., Zhou, Y., Sive, B. C., Sullivan, A., Collett Jr., J. L., and Fischer,
E. V.: Emissions of Reactive Nitrogen From Western U.S. Wildfires During
Summer 2018, J. Geophys. Res.-Atmos., 126,
e2020JD032657, https://doi.org/10.1029/2020JD032657, 2021. a
Liu, X., Huey, L. G., Yokelson, R. J., Selimovic, V., Simpson, I. J.,
Müller, M., Jimenez, J. L., Campuzano-Jost, P., Beyersdorf, A. J., Blake,
D. R., Butterfield, Z., Choi, Y., Crounse, J. D., Day, D. A., Diskin, G. S.,
Dubey, M. K., Fortner, E., Hanisco, T. F., Hu, W., King, L. E., Kleinman, L.,
Meinardi, S., Mikoviny, T., Onasch, T. B., Palm, B. B., Peischl, J., Pollack,
I. B., Ryerson, T. B., Sachse, G. W., Sedlacek, A. J., Shilling, J. E.,
Springston, S., St. Clair, J. M., Tanner, D. J., Teng, A. P., Wennberg,
P. O., Wisthaler, A., and Wolfe, G. M.: Airborne measurements of western U.S.
wildfire emissions: Comparison with prescribed burning and air quality
implications, J. Geophys. Res.-Atmos., 122, 6108–6129,
https://doi.org/10.1002/2016JD026315, 2017. a, b
McKenzie, D., O’Neill, S. M., Larkin, N. K., and Norheim, R. A.: Integrating
models to predict regional haze from wildland fire, Ecol. Modell.,
199, 278–288, https://doi.org/10.1016/j.ecolmodel.2006.05.029, 2006. a
McMeeking, G. R., Kreidenweis, S. M., Carrico, C. M., Lee, T., Collett Jr.,
J. L., and Malm, W. C.: Observations of smoke-influenced aerosol during the
Yosemite Aerosol Characterization Study: Size distributions and chemical
composition, J. Geophys. Res.-Atmos., 110, D09206,
https://doi.org/10.1029/2004JD005389, 2005. a
McNeish, D.: Missing data methods for arbitrary missingness with small samples,
J. Appl. Stat., 44, 24–39,
https://doi.org/10.1080/02664763.2016.1158246, 2017. a
Miller, J. D., Safford, H. D., Crimmins, M., and Thode, A. E.: Quantitative
Evidence for Increasing Forest Fire Severity in the Sierra Nevada and
Southern Cascade Mountains, California and Nevada, USA, Ecosystems, 12,
16–32, https://doi.org/10.1007/s10021-008-9201-9, 2009. a, b
Nelson, K. J., Connot, J., Peterson, B., and Martin, C.: The LANDFIRE Refresh
Strategy: Updating the National Dataset, Fire Ecol., 9, 80–101,
https://doi.org/10.4996/fireecology.0902080, 2013. a
Ottmar, R.: Consume 3.0 – A Software Tool for Computing Fuel Consumption, Fire
Sci. Brief, p. 6,
https://www.firescience.gov/projects/briefs/98-1-9-06_FSBrief55.pdf (last access: 6 April 2022),
2009. a
Park, R. J., Jacob, D. J., Kumar, N., and Yantosca, R. M.: Regional visibility
statistics in the United States: Natural and transboundary pollution
influences, and implications for the Regional Haze Rule, Atmos.
Environ., 40, 5405–5423, 2006. a
Pavlovic, R., Chen, J., Anderson, K., Moran, M. D., Beaulieu, P.-A., Davignon,
D., and Cousineau, S.: The FireWork air quality forecast system with
near-real-time biomass burning emissions: Recent developments and evaluation
of performance for the 2015 North American wildfire season, J.
Air Waste Manage. Assoc., 66, 819–841,
https://doi.org/10.1080/10962247.2016.1158214, 2016. a
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel,
O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J.,
Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.:
Scikit-learn: Machine Learning in Python, J. Machine Learn.
Res., 12, 2825–2830, 2011. a
Permar, W., Wang, Q., Selimovic, V., Wielgasz, C., Yokelson, R. J., Hornbrook,
R. S., Hills, A. J., Apel, E. C., Ku, I.-T., Zhou, Y., Sive, B. C., Sullivan,
A. P., Collett Jr., J. L., Campos, T. L., Palm, B. B., Peng, Q., Thornton,
J. A., Garofalo, L. A., Farmer, D. K., Kreidenweis, S. M., Levin, E. J. T.,
DeMott, P. J., Flocke, F., Fischer, E. V., and Hu, L.: Emissions of Trace
Organic Gases From Western U.S. Wildfires Based on WE-CAN Aircraft
Measurements, J. Geophys. Res.-Atmos., 126,
e2020JD033838, https://doi.org/10.1029/2020JD033838, 2021. a
Prichard, S., Larkin, N. S., Ottmar, R., French, N. H., Baker, K., Brown, T.,
Clements, C., Dickinson, M., Hudak, A., Kochanski, A., Linn, R., Liu, Y.,
Potter, B., Mell, W., Tanzer, D., Urbanski, S., and Watts, A.: The Fire and
Smoke Model Evaluation Experiment – A Plan for Integrated, Large
Fire – Atmosphere Field Campaigns, Atmosphere, 10, 2,
https://doi.org/10.3390/atmos10020066, 2019. a
Prichard, S. J., O'Neill, S. M., Eagle, P., Andreu, A. G., Drye, B., Dubowy,
J., Urbanski, S., and Strand, T. M.: Wildland fire emission factors in North
America: synthesis of existing data, measurement needs and management
applications, Int. J. Wildl. Fire, 29, 132–147,
https://doi.org/10.1071/WF19066, 2020. a, b
Reeves, M. C., Ryan, K. C., Rollins, M. G., and Thompson, T. G.: Spatial fuel
data products of the LANDFIRE Project, Int. J. Wildl.
Fire, 18, 250–267, https://doi.org/10.1071/WF08086, 2009. a
Sekimoto, K., Koss, A. R., Gilman, J. B., Selimovic, V., Coggon, M. M., Zarzana, K. J., Yuan, B., Lerner, B. M., Brown, S. S., Warneke, C., Yokelson, R. J., Roberts, J. M., and de Gouw, J.: High- and low-temperature pyrolysis profiles describe volatile organic compound emissions from western US wildfire fuels, Atmos. Chem. Phys., 18, 9263–9281, https://doi.org/10.5194/acp-18-9263-2018, 2018. a, b
Selimovic, V., Yokelson, R. J., Warneke, C., Roberts, J. M., de Gouw, J., Reardon, J., and Griffith, D. W. T.: Aerosol optical properties and trace gas emissions by PAX and OP-FTIR for laboratory-simulated western US wildfires during FIREX, Atmos. Chem. Phys., 18, 2929–2948, https://doi.org/10.5194/acp-18-2929-2018, 2018. a, b, c
Simoneit, B. R.: Biomass burning – a review of organic tracers for smoke
from incomplete combustion, Appl. Geochem., 17, 129–162,
2002. a
Simpson, I. J., Akagi, S. K., Barletta, B., Blake, N. J., Choi, Y., Diskin, G. S., Fried, A., Fuelberg, H. E., Meinardi, S., Rowland, F. S., Vay, S. A., Weinheimer, A. J., Wennberg, P. O., Wiebring, P., Wisthaler, A., Yang, M., Yokelson, R. J., and Blake, D. R.: Boreal forest fire emissions in fresh Canadian smoke plumes: C1-C10 volatile organic compounds (VOCs), CO2, CO, NO2, NO, HCN and CH3CN, Atmos. Chem. Phys., 11, 6445–6463, https://doi.org/10.5194/acp-11-6445-2011, 2011. a
Stamatis, C.: christos-stamatis/supervised_pattern_recognition: 70(v3.0), Zenodo [code], https://doi.org/10.5281/zenodo.6336170, 2022. a
Stockwell, C. E., Yokelson, R. J., Kreidenweis, S. M., Robinson, A. L., DeMott, P. J., Sullivan, R. C., Reardon, J., Ryan, K. C., Griffith, D. W. T., and Stevens, L.: Trace gas emissions from combustion of peat, crop residue, domestic biofuels, grasses, and other fuels: configuration and Fourier transform infrared (FTIR) component of the fourth Fire Lab at Missoula Experiment (FLAME-4), Atmos. Chem. Phys., 14, 9727–9754, https://doi.org/10.5194/acp-14-9727-2014, 2014. a, b, c, d, e, f
Stockwell, C. E., Veres, P. R., Williams, J., and Yokelson, R. J.: Characterization of biomass burning emissions from cooking fires, peat, crop residue, and other fuels with high-resolution proton-transfer-reaction time-of-flight mass spectrometry, Atmos. Chem. Phys., 15, 845–865, https://doi.org/10.5194/acp-15-845-2015, 2015. a, b, c
Urbanski, S.: Wildland fire emissions, carbon, and climate: Emission factors,
Forest Ecol. Manage., 317, 51–60, 2014. a
Urbanski, S. P.: Combustion efficiency and emission factors for wildfire-season fires in mixed conifer forests of the northern Rocky Mountains, US, Atmos. Chem. Phys., 13, 7241–7262, https://doi.org/10.5194/acp-13-7241-2013, 2013. a
Urbanski, S. P., Hao, W. M., and Baker, S.: Chapter 4 Chemical Composition of
Wildland Fire Emissions, Vol. 8, Wildland Fires and Air Pollution,
pp. 79–107, Elsevier, 2008. a
Vogelmann, J. E., Kost, J. R., Tolk, B., Howard, S., Short, K., Chen, X.,
Huang, C., Pabst, K., and Rollins, M. G.: Monitoring Landscape Change for
LANDFIRE Using Multi-Temporal Satellite Imagery and Ancillary Data,
IEEE J. Sel. Top. Appl.,
4, 252–264, https://doi.org/10.1109/JSTARS.2010.2044478, 2011. a
Wan, X., Kawamura, K., Ram, K., Kang, S., Loewen, M., Gao, S., Wu, G., Fu, P.,
Zhang, Y., Bhattarai, H., and Cong, Z.: Aromatic acids as biomass-burning
tracers in atmospheric aerosols and ice cores: A review, Environ.
Pollut., 247, 216–228, 2019. a
Ward, D. E. and Hardy, C. C.: Smoke emissions from wildland fires, Environ.
Int., 17, 117–134, 1991. a
Welke, J. E., Manfroi, V., Zanus, M., Lazzarotto, M., and Alcaraz Zini, C.:
Differentiation of wines according to grape variety using multivariate
analysis of comprehensive two-dimensional gas chromatography with
time-of-flight mass spectrometric detection data, Food Chem., 141,
3897–3905, https://doi.org/10.1016/j.foodchem.2013.06.100, 2013. a, b, c
Westerling, A. L., Hidalgo, H. G., Cayan, D. R., and Swetnam, T. W.: Warming
and Earlier Spring Increase Western U.S. Forest Wildfire Activity, Science,
313, 940–943, https://doi.org/10.1126/science.1128834, 2006. a
Yokelson, R. J., Goode, J. G., Ward, D. E., Susott, R. A., Babbitt, R. E.,
Wade, D. D., Bertschi, I., Griffith, D. W. T., and Hao, W. M.: Emissions of
formaldehyde, acetic acid, methanol, and other trace gases from biomass fires
in North Carolina measured by airborne Fourier transform infrared
spectroscopy, J. Geophys. Res.-Atmos., 104,
30109–30125, https://doi.org/10.1029/1999JD900817, 1999. a
Yokelson, R. J., Burling, I. R., Gilman, J. B., Warneke, C., Stockwell, C. E., de Gouw, J., Akagi, S. K., Urbanski, S. P., Veres, P., Roberts, J. M., Kuster, W. C., Reardon, J., Griffith, D. W. T., Johnson, T. J., Hosseini, S., Miller, J. W., Cocker III, D. R., Jung, H., and Weise, D. R.: Coupling field and laboratory measurements to estimate the emission factors of identified and unidentified trace gases for prescribed fires, Atmos. Chem. Phys., 13, 89–116, https://doi.org/10.5194/acp-13-89-2013, 2013. a, b
Zangrando, R., Barbaro, E., Zennaro, P., Rossi, S., Kehrwald, N. M., Gabrieli,
J., Barbante, C., and Gambaro, A.: Molecular Markers of Biomass Burning in
Arctic Aerosols, Environ. Sci. Technol., 47, 8565–8574,
https://doi.org/10.1021/es400125r, 2013. a
Zhang, Q., Jimenez, J. L., Canagaratna, M. R., Allan, J. D., Coe, H., Ulbrich,
I., Alfarra, M. R., Takami, A., Middlebrook, A. M., Sun, Y. L., Dzepina, K.,
Dunlea, E., Docherty, K., DeCarlo, P. F., Salcedo, D., Onasch, T., Jayne,
J. T., Miyoshi, T., Shimono, A., Hatakeyama, S., Takegawa, N., Kondo, Y.,
Schneider, J., Drewnick, F., Borrmann, S., Weimer, S., Demerjian, K.,
Williams, P., Bower, K., Bahreini, R., Cottrell, L., Griffin, R. J.,
Rautiainen, J., Sun, J. Y., Zhang, Y. M., and Worsnop, D. R.: Ubiquity and
dominance of oxygenated species in organic aerosols in
anthropogenically-influenced Northern Hemisphere midlatitudes, Geophys.
Res. Lett., 34, L13801, https://doi.org/10.1029/2007GL029979, 2007.
a
Zhang, Y., Kong, S., Sheng, J., Zhao, D., Ding, D., Yao, L., Zheng, H., Wu, J.,
Cheng, Y., Yan, Q., Niu, Z., Zheng, S., Wu, F., Yan, Y., Liu, D., and Qi, S.:
Real-time emission and stage-dependent emission factors/ratios of specific
volatile organic compounds from residential biomass combustion in China,
Atmos. Res., 248, 105189, 2021. a
Ziółkowska, A., Wąsowicz, E., and Jeleń, H. H.: Differentiation of wines
according to grape variety and geographical origin based on volatiles
profiling using SPME-MS and SPME-GC/MS methods, Food Chem., 213,
714–720, https://doi.org/10.1016/j.foodchem.2016.06.120, 2016. a, b
Short summary
Building on the identification of hundreds of gas-phase chemicals in smoke samples from laboratory and field studies, an algorithm was developed that successfully identified chemical patterns that were consistent among types of trees and unique between types of trees that are common fuels in western coniferous forests. The algorithm is a promising approach for selecting chemical speciation profiles for air quality modeling using a highly reduced suite of measured compounds.
Building on the identification of hundreds of gas-phase chemicals in smoke samples from...
