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

作者：Proietti Elena; Delgado Eckert Edgar; Vienneau Danielle^*; Stern Georgette; Tsai Ming Yi; Latzin Philipp; Frey Urs; Roosli Martin

来源：Environmental Health, 2016, 15(1): 61.

DOI：10.1186/s12940-016-0145-9

• Air pollution
• NO2
• Exposure
• Pregnancy
• Birth cohort

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 NO2 concentrations in Bern, Switzerland. We used NO2 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 NO2 from a dispersion model) and temporal predictors (meteorological conditions; NO2 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 NO2 passive measurements obtained at study participant's homes.
Results: Traffic related explanatory variables and fixed site NO2 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.

• 出版日期2016-5-25