Browsing by Author "Dibb, Jack E."
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Item Identifying Chemical Aerosol Signatures using Optical Suborbital Observations: How much can optical properties tell us about aerosol composition?(Copernicus Publications, 2021-09-24) Kacenelenbogen, Meloë S. F.; Tan, Qian; Burton, Sharon P.; Hasekamp, Otto P.; Froyd, Karl D.; Shinozuka, Yohei; Beyersdorf, Andreas J.; Ziemba, Luke; Thornhill, Kenneth L.; Dibb, Jack E.; Shingler, Taylor; Sorooshian, Armin; Espinosa, Reed W.; Martins, Vanderlei; Jimenez, Jose L.; Campuzano-Jost, Pedro; Schwarz, Joshua P.; Johnson, Matthew S.; Redemann, Jens; Schuster, Gregory L.Improvements in air quality and Earth’s climate predictions require improvements of the aerosol speciation in chemical transport models, using observational constraints. Aerosol speciation (e.g., organic aerosols, black carbon, sulfate, nitrate, ammonium, dust or sea salt) is typically determined using in situ instrumentation. Continuous, routine surface network aerosol composition measurements are not uniformly widespread over the globe. Satellites, on the other hand, can provide a maximum coverage of the horizontal and vertical atmosphere but observe aerosol optical properties (and not aerosol speciation) based on remote sensing instrumentation. Combinations of satellite-derived aerosol optical properties can inform on air mass aerosol types (AMTs e.g., clean marine, dust, polluted continental). However, these AMTs are subjectively defined, might often be misclassified and are hard to relate to the critical parameters that need to be refined in models. In this paper, we derive AMTs that are more directly related to sources and hence to speciation. They are defined, characterized, and derived using simultaneous in situ gas-phase, chemical and optical instruments on the same aircraft during the Study of Emissions and Atmospheric Composition, Clouds, and Climate Coupling by Regional Surveys (SEAC4RS, US, summer of 2013). First, we prescribe well-informed AMTs that display distinct aerosol chemical and optical signatures to act as a training AMT dataset. These in situ observations reduce the errors and ambiguities in the selection of the AMT training dataset. We also investigate the relative skill of various combinations of aerosol optical properties to define AMTs and how much these optical properties can capture dominant aerosol speciation. We find distinct optical signatures for biomass burning (from agricultural or wildfires), biogenic and dust-influence AMTs. Useful aerosol optical properties to characterize these signatures are the extinction angstrom exponent (EAE), the single scattering albedo, the difference of single scattering albedo in two wavelengths, the absorption coefficient, the absorption angstrom exponent (AAE), and the real part of the refractive index (RRI). We find that all four AMTs studied when prescribed using mostly airborne in situ gas measurements, can be successfully extracted from at least three combinations of airborne in situ aerosol optical properties (e.g., EAE, AAE and RRI) over the US during SEAC4RS. However, we find that the optically based classifications for BB from agricultural fires and polluted dust include a large percentage of misclassifications that limit the usefulness of results relating to those classes. The technique and results presented in this study are suitable to develop a representative, robust and diverse source-based AMT database. This database could then be used for widespread retrievals of AMTs using existing and future remote sensing suborbital instruments/networks. Ultimately, it has the potential to provide a much broader observational aerosol data set to evaluate chemical transport and air quality models than is currently available by direct in situ measurements. This study illustrates how essential it is to explore existing airborne datasets to bridge chemical and optical signatures of different AMTs, before the implementation of future spaceborne missions (e.g., the next generation of Earth Observing System (EOS) satellites addressing Aerosol, Cloud, Convection and Precipitation (ACCP) designated observables).Item Overview of the Alaskan Layered Pollution and Chemical Analysis (ALPACA) Field Experiment(ACS, 2024-02-21) Simpson, William R.; Mao, Jingqiu; Fochesatto, Gilberto J.; Law, Kathy S.; DeCarlo, Peter F.; Schmale, Julia; Pratt, Kerri A.; Arnold, Steve R.; Stutz, Jochen; Dibb, Jack E.; Creamean, Jessie M.; Weber, Rodney J.; Williams, Brent J.; Alexander, Becky; Hu, Lu; Yokelson, Robert J.; Shiraiwa, Manabu; Decesari, Stefano; Anastasio, Cort; D’Anna, Barbara; Gilliam, Robert C.; Nenes, Athanasios; St. Clair, Jason; Trost, Barbara; Flynn, James H.; Savarino, Joel; Conner, Laura D.; Kettle, Nathan; Heeringa, Krista M.; Albertin, Sarah; Baccarini, Andrea; Barret, Brice; Battaglia, Michael A.; Bekki, Slimane; Brado, T. J.; Brett, Natalie; Brus, David; Campbell, James R.; Cesler-Maloney, Meeta; Cooperdock, Sol; Cysneiros de Carvalho, Karolina; Delbarre, Hervé; DeMott, Paul J.; Dennehy, Conor J. S.; Dieudonné, Elsa; Dingilian, Kayane K.; Donateo, Antonio; Doulgeris, Konstantinos M.; Edwards, Kasey C.; Fahey, Kathleen; Fang, Ting; Guo, Fangzhou; Heinlein, Laura M. D.; Holen, Andrew L.; Huff, Deanna; Ijaz, Amna; Johnson, Sarah; Kapur, Sukriti; Ketcherside, Damien T.; Levin, Ezra; Lill, Emily; Moon, Allison R.; Onishi, Tatsuo; Pappaccogli, Gianluca; Perkins, Russell; Pohorsky, Roman; Raut, Jean-Christophe; Ravetta, Francois; Roberts, Tjarda; Robinson, Ellis S.; Scoto, Federico; Selimovic, Vanessa; Sunday, Michael O.; Temime-Roussel, Brice; Tian, Xinxiu; Wu, Judy; Yang, YuhanThe Alaskan Layered Pollution And Chemical Analysis (ALPACA) field experiment was a collaborative study designed to improve understanding of pollution sources and chemical processes during winter (cold climate and low-photochemical activity), to investigate indoor pollution, and to study dispersion of pollution as affected by frequent temperature inversions. A number of the research goals were motivated by questions raised by residents of Fairbanks, Alaska, where the study was held. This paper describes the measurement strategies and the conditions encountered during the January and February 2022 field experiment, and reports early examples of how the measurements addressed research goals, particularly those of interest to the residents. Outdoor air measurements showed high concentrations of particulate matter and pollutant gases including volatile organic carbon species. During pollution events, low winds and extremely stable atmospheric conditions trapped pollution below 73 m, an extremely shallow vertical scale. Tethered-balloon-based measurements intercepted plumes aloft, which were associated with power plant point sources through transport modeling. Because cold climate residents spend much of their time indoors, the study included an indoor air quality component, where measurements were made inside and outside a house to study infiltration and indoor sources. In the absence of indoor activities such as cooking and/or heating with a pellet stove, indoor particulate matter concentrations were lower than outdoors; however, cooking and pellet stove burns often caused higher indoor particulate matter concentrations than outdoors. The mass-normalized particulate matter oxidative potential, a health-relevant property measured here by the reactivity with dithiothreiol, of indoor particles varied by source, with cooking particles having less oxidative potential per mass than pellet stove particles.Item Why do models overestimate surface ozone in the Southeast United States(Copernicus Publications, 2016-11-01) Travis, Katherine R.; Jacob, Daniel J.; Fisher, Jenny A.; Kim, Patrick S.; Marais, Eloise A.; Zhu, Lei; Yu, Karen; Miller, Christopher C.; Yantosca, Robert M.; Sulprizio, Melissa P.; Thompson, Anne M.; Wennberg, Paul O.; Crounse, John D.; St. Clair, Jason; Cohen, Ronald C.; Laughner, Joshua L.; Dibb, Jack E.; Hall, Samuel R.; Ullmann, Kirk; Wolfe, Glenn M.; Pollack, Illana B.; Peischl, Jeff; Neuman, Jonathan A.; Zhou, XianliangOzone pollution in the Southeast US involves complex chemistry driven by emissions of anthropogenic nitrogen oxide radicals (NOx ≡ NO + NO₂) and biogenic isoprene. Model estimates of surface ozone concentrations tend to be biased high in the region and this is of concern for designing effective emission control strategies to meet air quality standards. We use detailed chemical observations from the SEAC⁴RS aircraft campaign in August and September 2013, interpreted with the GEOS-Chem chemical transport model at 0.25° × 0.3125° horizontal resolution, to better understand the factors controlling surface ozone in the Southeast US. We find that the National Emission Inventory (NEI) for NOx from the US Environmental Protection Agency (EPA) is too high. This finding is based on SEAC4RS observations of NOx and its oxidation products, surface network observations of nitrate wet deposition fluxes, and OMI satellite observations of tropospheric NO₂ columns. Our results indicate that NEI NOx emissions from mobile and industrial sources must be reduced by 30–60 %, dependent on the assumption of the contribution by soil NOx emissions. Upper-tropospheric NO₂ from lightning makes a large contribution to satellite observations of tropospheric NO2 that must be accounted for when using these data to estimate surface NOx emissions. We find that only half of isoprene oxidation proceeds by the high-NOx pathway to produce ozone; this fraction is only moderately sensitive to changes in NOx emissions because isoprene and NOx emissions are spatially segregated. GEOS-Chem with reduced NOx emissions provides an unbiased simulation of ozone observations from the aircraft and reproduces the observed ozone production efficiency in the boundary layer as derived from a regression of ozone and NOx oxidation products. However, the model is still biased high by 6 ± 14 ppb relative to observed surface ozone in the Southeast US. Ozonesondes launched during midday hours show a 7 ppb ozone decrease from 1.5 km to the surface that GEOS-Chem does not capture. This bias may reflect a combination of excessive vertical mixing and net ozone production in the model boundary layer.