Development of spatiotemporal land use regression models for PM2.5 and NO2 in Chongqing, China, and exposure assessment for the CLIMB study

Abstract Limited research has been conducted in Asia on the association of maternal exposure to ambient air pollution and the increased risk of adverse pregnancy outcomes such as low birth weight and preterm birth. The aim of this study was to develop spatiotemporal land use regression (LUR) models for fine particulate matter (PM2.5) and nitrogen dioxide (NO2) in Chongqing, China, and to use the models to estimate PM2.5 and NO2 exposure for the participants in a randomized trial of complex lipid supplementation (the Complex Lipids In Mothers and Babies (CLIMB) study), before and during pregnancy. Spatiotemporal generalised additive models were developed for 2015–2016 on a daily basis incorporating measurement data from 16 sites, temporal variables on meteorology, and spatial variables produced using a geographical information system. Hold-out validation (HOV) was performed using daily and monthly averaged measurements for 2017 at 17 sites with 4 of the sites in different locations to 2015–16. The PM2.5 spatiotemporal model had good overall predictive ability (daily HOV correlation (COR)-R2 = 0.75 and HOV mean-squared-error (MSE)-R2 = 0.69; monthly HOV COR-R2 = 0.87 and HOV MSE-R2 = 0.76). The NO2 spatiotemporal model estimates had moderate-to-good correlation with measurements (daily HOV COR-R2 = 0.44; monthly HOV COR-R2 = 0.65), but estimates were subject to bias (daily HOV MSE-R2 = 0.24; monthly HOV MSE-R2 = −0.02). On this basis, we recommend that PM2.5 models are used for predicting absolute exposure and NO2 models are used for relative ranking of exposures.