Air pollution modelling for birth cohorts: a time-space regression model

BACKGROUND: To investigate air pollution effects during pregnancy or in the first weeks of life, models are needed that capture both the spatial and temporal variability of air pollution exposures. METHODS: We developed a time-space exposure model for ambient NO(2) concentrations in Bern, Switzerland. We used NO(2) data from passive monitoring conducted between 1998 and 2009: 101 rural sites (24,499 biweekly measurements) and 45 urban sites (4350 monthly measurements). We evaluated spatial predictors (land use; roads; traffic; population; annual NO(2) from a dispersion model) and temporal predictors (meteorological conditions; NO(2) from continuous monitoring station). Separate rural and urban models were developed by multivariable regression techniques. We performed ten-fold internal cross-validation, and an external validation using 57 NO(2) passive measurements obtained at study participant’s homes. RESULTS: Traffic related explanatory variables and fixed site NO(2) measurements were the most relevant predictors in both models. The coefficient of determination (R(2)) for the log transformed models were 0.63 (rural) and 0.54 (urban); cross-validation R(2)s were unchanged indicating robust coefficient estimates. External validation showed R(2)s of 0.54 (rural) and 0.67 (urban). CONCLUSIONS: This approach is suitable for air pollution exposure prediction in epidemiologic research with time-vulnerable health effects such as those occurring during pregnancy or in the first weeks of life. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12940-016-0145-9) contains supplementary material, which is available to authorized users.