American Association for Aerosol Research - Abstract Submission

AAAR 34th Annual Conference
October 12 - October 16, 2015
Hyatt Regency
Minneapolis, Minnesota, USA

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Model Framework to Predict Indoor Aerosol Concentrations based on Composition, Volatility, Water Uptake, and Mechanical Losses

MICHAEL WARING, Peter DeCarlo, Drexel University

Abstract Number: 277
Working Group: Indoor Aerosols

Abstract
Indoor aerosol models typically treat outdoor and indoor-emitted aerosols as static entities acted on by mechanical forces (i.e., deposition or filtration). However, indoor aerosol can also be transformed as a function of temperature (due to volatility changes) or relative humidity (due to changes in water content and subsequent aqueous phase chemistry). As such, we developed a comprehensive model framework to predict indoor aerosol concentrations with sources due to outdoor-to-indoor transport, secondary organic aerosol (SOA) formation by oxidative chemical reactions occurring indoors, and direct indoor emission. In this framework, the indoor aerosol is transformed by standard mechanical losses of envelope, surface, or filtration deposition, but also due to volatility and water content states occurring as a function of outdoor and indoor temperature and relative humidity. The model treats the total aerosol as internally and externally mixed ensembles containing these broad aerosol species: sulfate, nitrate, ammonium, chloride, black carbon, and organic aerosol (OA), which has component subgroups based on volatility. These OA components’ volatility set distinctions relate to source type, and the model considers: hydrocarbon-like OA (HOA), oxygenated organic aerosol (OOA) that is further separated into semivolatile (OOA-SV) and low-volatile (OOA-LV) components, indoor-generated SOA, etc. Aerosol behavior is modeled with the volatility basis set (VBS) for OA and component-specific factor changes due to temperature gradients for inorganic aerosol, and aerosol mass changes due to water uptake are modeled with Koehler theory. Model predictions and implications will be presented.