PDS 65: Exposure assessment: implications for epidemiology, Exhibition Hall (PDS), Ground floor, August 27, 2019, 1:30 PM - 3:00 PM Background: A major challenge in epidemiological studies is producing accurate short-term air pollution predictions at fine spatial and temporal resolution across large geographic areas. The aim of this study is to develop spatio-temporal models to predict daily concentrations on a 25m grid for nitrogen dioxide (NO2), particulate matters including PM2.5 and PM10, and ozone (O3) for Great Britain from 2010–2015. Daily estimates can be averaged over other time periods (e.g. weekly, monthly, pregnancy trimester, annual) for different health analysis needs. Method: We developed generalised additive models (GAM) with penalised splines to describe spatial and temporal variations in daily concentrations of the pollutants. The models included Geographic Information System (GIS)-derived local-scale predictors and daily estimates (on a ~10km grid) from a chemical transport model (CTM). Model validation was performed using five-fold cross-validation. Results: The spatio-temporal model performance of O3 was strong (cross-validation variance (CV R2)=~0.82 and ~0.91 for daily and annual averaged estimates, respectively). The daily PM models also had high predictive accuracy (CV R2=~0.76 for both pollutants), however the models performed differently in annual estimates (CV R2=~0.66 and ~0.79 for PM2.5 and PM10, respectively). The predictive ability of daily NO2 models was relatively low (CV R2=~0.59), but was improved in annual estimates (CV R2=~0.73). For all pollutants, models performed consistently across study years, though the performance varied by site types, with weaker performance at traffic sites (CV R2=~0.56) compared to background sites (CV R2=~0.72). Frequent predictors that were included in the final models were mostly traffic-related. Conclusion: Our models overall performed well in estimating air pollution concentrations at fine spatial resolution in Great Britain. The models are currently being used in producing daily pollution surfaces. Daily values will be extracted from point of interest and used in air pollution exposure health studies (e.g. time-series analysis of daily hospital admissions).