Particulate air pollution (PM) has been shown by many studies to cause adverse health effects. Traditionally PM exposure was estimated using ambient concentrations. Lately, studies have revealed that this approach poorly reflects differences between individual’s exposures and as such results in exposure misclassification.
This thesis aims to improve personal exposure predictions by building a model (MEPEX model), which takes into account the temporal and spatial variability of ambient PM, as well as visited microenvironments. For the composition of this model, existing approaches for model components were evaluated, compared and developed. A temporally adjusted land-use regression (LUR-adj) model for predictions of ambient PM2.5 and PM10 was built, validated, and compared to estimates from a dispersion model. Ratios were developed to adjust ambient concentrations for cycling and in-bus transport microenvironments. Additionally, modelling approaches for the home indoor microenvironment were compared, using monitoring data. A secondary aim was to evaluate the performance of different approaches for personal exposure assessment by comparing varying levels of model sophistication.
Validation of the LUR-adj model showed good model fit (IA > 0.5) and low error (NMSE < 1) for short-term predictions of PM2.5 and PM10 at locations in London. In comparison to predictions of a dispersion model (ADMS-urban), LUR-adj estimates of PM10 produced better results for model performance parameters at the majority of 26 predicted locations.
MEPEX model predictions of monitored daily personal exposure for an individual in London resulted in an R2 of 0.439 for PM2.5 and 0.403 for PM10. Predictions using modelled home outdoor concentrations in comparison were lower with R2 of 0.173 for PM2.5 and 0.086 for PM10. These results provide the first quantifiable evidence that personal exposure models of PM2.5 and PM10 can reduce exposure misclassification compared to estimates based only on ambient PM.