Predicting Thermal Behavior of Secondary Organic Aerosols

• Published in: Environmental science & technology 2017-09, Vol.51 (17), p.9911-9919
• Main Authors: Offenberg, John H., Lewandowski, Michael, Kleindienst, Tadeusz E., Docherty, Kenneth S., Jaoui, Mohammed, Krug, Jonathan, Riedel, Theran P., Olson, David A.
• Format: Article
• Language: English
• Subjects: Aerosols; Air Pollutants; Air quality; Aromatic hydrocarbons; Artificial neural networks; Atomic properties; Atomic structure; Carbon; Chemical bonds; Decomposition reactions; Experiments; Functional groups; Hydrocarbons; Hydrogen Peroxide; Mathematical models; Measurement; Molecular weight; Neural networks; Neural stem cells; Nitrogen oxides; Oxidation; Oxidation-Reduction; Oxides; Oxygen; Oxygen atoms; Parameterization; Photolysis; Photooxidation; Predictions; Predictive control; Relative humidity; Ring structures; Statistical methods; Statistical models; Temperature; Thermal decomposition; Thermodynamic properties
• ISSN: 0013-936X; 1520-5851
• DOI: 10.1021/acs.est.7b01968

Volume concentrations of secondary organic aerosol (SOA) are measured in 139 steady-state, single precursor hydrocarbon oxidation experiments after passing through a temperature controlled inlet. The response to change in temperature is well predicted through a feedforward Artificial Neural Network. The most parsimonious model, as indicated by Akaike’s Information Criterion, Corrected (AIC,C), utilizes 11 input variables, a single hidden layer of 4 tanh activation function nodes, and a single linear output function. This model predicts thermal behavior of single precursor aerosols to less than ±5%, which is within the measurement uncertainty, while limiting the problem of overfitting. Prediction of thermal behavior of SOA can be achieved by a concise number of descriptors of the precursor hydrocarbon including the number of internal and external double bonds, number of methyl- and ethyl- functional groups, molecular weight, and number of ring structures, in addition to the volume of SOA formed, and an indicator of which of four oxidant precursors was used to initiate reactions (NO x photo-oxidation, photolysis of H2O2, ozonolysis, or thermal decomposition of N2O5). Additional input variables, such as chamber volumetric residence time, relative humidity, initial concentration of oxides of nitrogen, reacted hydrocarbon concentration, and further descriptors of the precursor hydrocarbon, including carbon number, number of oxygen atoms, and number of aromatic ring structures, lead to over fit models, and are unnecessary for an efficient, accurate predictive model of thermal behavior of SOA. This work indicates that predictive statistical modeling methods may be complementary to descriptive techniques for use in parametrization of air quality models.