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https://hdl.handle.net/10217/187174

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Browsing Research Data by Author "Akherati, Ali"

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    ItemOpen Access
    Data associated with the manuscript: Investigating diesel engines as an atmospheric source of isocyanic acid in urban areas
    (Colorado State University. Libraries, 2017) Jathar, Shantanu H.; Heppding, Christopher; Link, Michael F.; Farmer, Delphine K.; Akherati, Ali; Kleeman, Michael J.; de Gouw, Joost A.; Veres, Patrick R.; Roberts, James M.
    Isocyanic acid (HNCO), an acidic gas found in tobacco smoke, urban environments and biomass burning-affected regions, has been linked to adverse health outcomes. Gasoline- and diesel-powered engines and biomass burning are known to emit HNCO and hypothesized to emit precursors such as amides that can photochemically react to produce HNCO in the atmosphere. Increasingly, diesel engines in developed countries like the United States are required to use Selective Catalytic Reduction (SCR) systems to reduce tailpipe emissions of oxides of nitrogen. SCR chemistry is known to produce HNCO as an intermediate product, and SCR systems have been implicated as an atmospheric source of HNCO. In this work, we measure HNCO emissions from an SCR system-equipped diesel engine and, in combination with earlier data, use a three-dimensional chemical transport model (CTM) to simulate the ambient concentrations and source/pathway contributions to HNCO in an urban environment. Engine tests were conducted at three different engine loads, using two different fuels and at multiple operating points. HNCO was measured using an acetate chemical ionization mass spectrometer. The diesel engine was found to emit primary HNCO (3-90 mg kg-fuel-1) but we did not find any evidence that the SCR system or other aftertreatment devices (i.e., oxidation catalyst and particle filter) produced or enhanced HNCO emissions. The CTM predictions compared well with the only available observational data sets for HNCO in urban areas but under-predicted the contribution from secondary processes. The comparison implied that diesel-powered engines were the largest source of HNCO in urban areas. The CTM also predicted that daily-averaged concentrations of HNCO reached a maximum of ~110 pptv but were an order of magnitude lower than the 1 ppbv level that could be associated with physiological effects in humans. Precursor contributions from other combustion sources (gasoline and biomass burning) and wintertime conditions could enhance HNCO concentrations but need to be explored in future work.
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    ItemOpen Access
    Dataset associated with "Particle Size Distribution Dynamics Can Help Constrain the Phase State of Secondary Organic Aerosol"
    (Colorado State University. Libraries, 2021) He, Yicong; Akherati, Ali; Nah, Theodora; Nga, Ng; Garofalo, Lauren; Farmer, Delphine; Shiraiwa, Manabu; Zaveri, Rahul; Christopher, Cappa; Pierce, Jeff; Jathar, Shantanu
    Particle phase state is a property of atmospheric aerosols that has important implications for the formation, evolution, and gas/particle partitioning of secondary organic aerosol (SOA). In this work, we use a size-resolved chemistry and microphysics model (SOM-TOMAS), updated to include an explicit treatment of particle phase state, to constrain the bulk diffusion coefficient (Db) of SOA produced from alpha-pinene ozonolysis. By leveraging data from laboratory experiments performed in the absence of a seed and under dry conditions, we find that the Db for SOA can be constrained (1-5 ×10^-15 cm^2 s^-1 in these experiments) by simultaneously reproducing the time-varying SOA mass concentrations and the evolution of the particle size distribution. Another version of our model that used the predicted SOA composition to calculate the glass transition temperature, viscosity, and, ultimately, Db (~10-15 cm^2 s^-1) of the SOA was able to reproduce the mass and size distribution measurements when we included oligomer formation (oligomers accounted for about a fifth of the SOA mass). Our work highlights the potential of a size-resolved SOA model to constrain the particle phase state of SOA by utilizing historical measurements of the evolution of the particle size distribution.