Comparison of performance of land use regression models derived for Catalunya, Spain

Abstract

Land use regression (LUR) models have become a popular tool to capture small-scale variations in air pollution exposures in epidemiological analyses, and have been developed with a variety of approaches with no clear indication of the most efficient and appropriate one. We evaluated the performance of the LUR model for NO2 developed for the European Study of Cohorts for Air Pollution Effects (ESCAPE) for Catalunya, Spain, compared to two other LUR models derived locally and independently for two cohort studies (INMA-Sabadell and REGICOR-Girona) in different sub-areas of the ESCAPE domain. We made use of sampling campaigns from the three studies as independent sets of measurements by which to evaluate each model. We compared changes in R2 and measures of bias when applying each model to its own dataset vs. the independent datasets from the other studies. The three studies differed principally in their scale of analysis (from urban area only for INMA-Sabadell to large province covering urban and rural areas for ESCAPE-Catalunya and REGICOR-Girona) and sampling protocol (e.g. site selection).The LUR models performed similarly well in terms of their model adjusted R2 and cross-validation R2, ranging respectively from 0.62 and 0.63 to 0.75 and 0.73. The ESCAPE model performed well at the ESCAPE sites in Sabadell (R2 = 0.69) and moderately well at the ESCAPE sites in Girona province (R2 = 0.53). The ESCAPE model predicted the external sites less well: R2 were 0.51 and 0.36 in Sabadell and Girona province. The INMA-Sabadell and REGICOR-Girona models showed a similar pattern: the R2 for the INMA model dropped from 0.69 to 0.50 at INMA versus ESCAPE sites in Sabadell, while the R2 for the REGICOR model dropped from 0.63 to 0.44 for REGICOR versus ESCAPE sites in Girona province. The drop in performance for external sites is likely a combination of overfitting and differences in the sampling campaigns (years, site selection). Agreement between models was 53%–74% for the classification of low, medium, and high levels of air pollution predicted at cohort addresses. Despite the drop in performance, the three models still explained a substantial fraction of the variation at independent sites, especially in Sabadell, supporting their use in epidemiological studies.