Modeling Indoor Inorganic Aerosol Concentrations During the ATHLETIC Campaign with IMAGES

In 2018, the ATHLETIC campaign was conducted at the University of Colorado Dal Ward Athletic Center and characterized dynamic indoor air composition in a gym environment. Among other parameters, inorganic particle and gas-phase species were alternatingly measured in the gym’s supply duct and weight room. The Indoor Model of Aerosols, Gases, Emissions, and Surfaces (IMAGES) uses the inorganic aerosol thermodynamic equilibrium model, ISORROPIA, to estimate the partitioning of inorganic aerosols and corresponding gases. In this study herein, measurements from the ATHLETIC campaign were used to evaluate IMAGES’ performance. Ammonia emission rates, nitric acid deposition, and particle deposition velocities were related to observed occupancy, which informed these rates in IMAGES runs. Initially, modeled indoor inorganic aerosol concentrations were not in good agreement with measurements. A parametric investigation revealed that lowering the temperature or raising the relative humidity used in the ISORROPIA model drove the semivolatile species more toward the particle phase, substantially improving modeled-measured agreement. One speculated reason for these solutions is that aerosol water was enhanced by increasing the RH or decreasing the temperature. Another is that thermodynamic equilibrium was not established in this indoor setting or that the thermodynamic parametrizations in ISORROPIA are less accurate for typical indoor settings. This result suggests that applying ISORROPIA indoors requires further careful experimental validation.

This work applies an indoor aerosol model, IMAGES, that estimates the partitioning of inorganic aerosol components and their corresponding gas-phase species with ISORROPIA by leveraging measurements from a university athletic center and derived relationships between occupancy and nitric acid deposition, particle deposition, and ammonia emissions. This study highlights that applying ISORROPIA indoors can sometimes result in inaccurate gas-particle partitioning. However, forcing the model to predict increased particle water by either adjusting relative humidity up or temperature down will result in accurate gas-particle partitioning.