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Predicting Thermal Behavior of Secondary Organic Aerosols
Offenberg, J., M. Lewandowski, Tad Kleindienst, K. Docherty, M. Jaoui, J. Krug, T. Riedel, AND D. Olson. Predicting Thermal Behavior of Secondary Organic Aerosols. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, 51(17):9911-9919, (2017). https://doi.org/10.1021/acs.est.7b01968
In order to predict the temporal and spatial distribution of aerosols, particularly Secondary Organic Aerosols (SOA), it is important to understand the distribution of organic compounds between the gas and particle phases. Key thermodynamic properties describing the gas to particle partitioning of organic compounds include saturation vapor pressures and the enthalpies of vaporization. During the past several decades, several techniques have been developed to probe thermodynamic properties of low volatility organic molecules often found in atmospheric aerosols. Evaluating the thermodynamic behavior of these compounds can be challenging, in part due to the low concentrations that need to be measured. Given that the number of organic molecules in the atmosphere may be in the range of tens of thousands or more, experimentally determined thermodynamic properties of compounds of atmospheric relevance is alarmingly limited. The goal of this work is to begin to bridge the gap between expected behavior of single-components, through simple mixtures, toward complex, multiorigin atmospheric organic aerosols.
Volume concentrations of steady-state secondary organic aerosol (SOA) were measured in 139 steadystate single precursor hydrocarbon oxidation experiments after passing through a temperature controlled inlet tube. Higher temperatures resulted in greater loss of particle volume, with all experiments following linear relationships between natural log of concentration vs. temperature-1. Negatives of observed slopes are converted to effective enthalpies of vaporization (DHeff) which rangefrom 6 to 67 kJ mol-1.These values depend upon the properties of the parent hydrocarbon (e.g. number of carbon atoms, number of internal or external double bonds, presence of aromatic or non-aromatic ring structures), as well as conditions of the experiment (relative humidity, oxidant system, oxidant concentrations) and the products of the complex reactions (e.g. aerosol loading). The observed response to change in temperature can be well predicted through a feedforward Artificial Neural Network. The most parsimonious model, as indicated by consensus of several Information Criteria, is comprised of 13input variables, a single hidden layer of 3 tanh activation function nodes, and a single linear output function. This model predicts the thermal behavior of single precursor aerosols to less than +/- 5%, which is within the laboratory measurement uncertainty, while limiting the problem of overfitting. The selected model reveals that prediction of the thermal behavior of SOA can be performed by a concise number of molecular descriptors of the reactant hydrocarbon, and a general description of theconditions of laboratory oxidation, namely the oxidant in the experiment and the mass of SOA formed. The inclusion of detailed experimental conditions, such as reacted hydrocarbon concentration (D HC), chamber relative humidity, chamber volumetric residence time, and/or initial oxidant concentration lead to over-fitted models. Additional input variables are not necessary for an efficient, accurate predictive model of the thermal behavior of the SOA produced. This work indicates that similar predictive modelling methods may be advantageous over current descriptive techniques for assignment of input parameters into air quality models.
Record Details:Record Type: DOCUMENT (JOURNAL/PEER REVIEWED JOURNAL)
Organization:U.S. ENVIRONMENTAL PROTECTION AGENCY
OFFICE OF RESEARCH AND DEVELOPMENT
NATIONAL EXPOSURE RESEARCH LABORATORY
EXPOSURE METHODS & MEASUREMENT DIVISION