Impact of surface ozone interactions on indoor air chemistry: A modeling study.

An INdoor air Detailed Chemical Model was developed to investigate the impact of ozone reactions with indoor surfaces (including occupants), on indoor air chemistry in simulated apartments subject to ambient air pollution. The results are consistent with experimental studies showing that approximately 80% of ozone indoors is lost through deposition to surfaces. The human body removes ozone most effectively from indoor air per square meter of surface, but the most significant surfaces for C6 -C10 aldehyde formation are soft furniture and painted walls owing to their large internal surfaces. Mixing ratios of between 8 and 11 ppb of C6 -C10 aldehydes are predicted to form in apartments in various locations in summer, the highest values are when ozone concentrations are enhanced outdoors. The most important aldehyde formed indoors is predicted to be nonanal (5-7 ppb), driven by oxidation-derived emissions from painted walls. In addition, ozone-derived emissions from human skin were estimated for a small bedroom at nighttime with concentrations of nonanal, decanal, and 4-oxopentanal predicted to be 0.5, 0.7, and 0.7 ppb, respectively. A detailed chemical analysis shows that ozone-derived surface aldehyde emissions from materials and people change chemical processing indoors, through enhanced formation of nitrated organic compounds and decreased levels of oxidants.

Indoor secondary organic aerosols: Towards an improved representation of their formation and composition in models.

The formation of secondary organic aerosol (SOA) indoors is one of the many consequences of the rich and complex chemistry that occurs therein. Given particulate matter has well documented health effects, we need to understand the mechanism for SOA formation indoors and its resulting composition. This study evaluates some uncertainties that exist in quantifying gas-to-particle partitioning of SOA-forming compounds using an indoor detailed chemical model. In particular, we investigate the impacts of using different methods to estimate compound vapour pressures as well as simulating the formation of highly oxygenated organic molecules (HOM) via auto-oxidation on SOA formation indoors. Estimation of vapour pressures for 136 α-pinene oxidation species by six investigated methods led to standard deviations of 28-216%. Inclusion of HOM formation improved model performance across three of the six assessed vapour pressure estimation methods when comparing against experimental data, particularly when the NO2 concentration was relatively high. We also explored the predicted SOA composition using two product classification methods, the first assuming the molecule is dominated by one functionality according to its name, and the second accounting for the fractional weighting of each functional group within a molecule. The SOA composition was dominated by the HOM species when the NO2-to-α-terpineol ratio was high for both product classification methods, as these conditions promoted formation of the nitrate radical and hence formation of HOM monomers. As the NO2-to-α-terpineol ratio decreased, peroxides and acids dominated the simple classification, whereas for the fractional classification, carbonyl and alcohol groups became more important.