Most human exposure to air pollutants happen indoors, where people spend most of their time (~90%). In the ambient atmosphere, photolysis plays a major role in initiating chemical reactions. However, indoor photolysis is less well studied. Consequently, the role that photolysis plays in indoor chemical processing, particularly in the formation of harmful species, is unclear. The major aim of this thesis was, therefore, to improve the representation of indoor lighting and attenuated sunlight in the Indoor Detailed Chemical Model (INDCM). The improved model was then used to investigate the impacts of glass type, indoor artificial light, cloudiness, time of year and latitude on indoor photolysis rates and hence indoor air chemistry.
The results show that variations in glass composition produce the highest deviations (~71%) in predicted concentrations of key indoor species (ozone, nitrous acid, nitric oxide, hydroxyl radicals, hydroperoxy radicals, organic peroxy radicals, peroxyacetyl nitrates and organic nitrates), followed by cloud level (~53%) and proximity to artificial light source (~53%), when compared to baseline conditions. These impacts were greatest for predicted hydroxyl radical concentrations, which deviated by an average of ~142% from the baseline scenario depending on the conditions studied.
Enhanced radical concentrations were found during two cleaning case studies (automated and traditional techniques), with predicted hydroxyl radical concentrations up to 1.3 × 107 and 1.5106 molecule/cm3 respectively. Furthermore, radical concentrations were found to be highest under stronger lighting conditions, persisting for several hours after the cleaning events.
This study provides a valuable contribution to the understanding of the impacts of photolysis on indoor air chemistry. Indoor artificial lights, such as LED, together with low cut-off wavelength glasses, will likely reduce the effects of photolysis indoors, but more research is needed on the health effects of different indoor air mixtures to confirm this recommendation
