Development of land use regression, dispersion, and hybrid models for prediction of outdoor air pollution exposure in Barcelona.

Background: Air pollution is the leading environmental risk factor for health. Assessing outdoor air pollution exposure with detailed spatial and temporal variability in urban areas is crucial for evaluating its health effects.

Aim: We developed and compared Land Use Regression (LUR), dispersion (DM), and hybrid (HM) models to estimate outdoor concentrations for NO, PM, black carbon (BC), and PM (Fe, Cu, Zn) in Barcelona.

Methods: Two monitoring campaigns were conducted. In the first, NO concentrations were measured twice at 984 home addresses and in the second, NO, PM, and BC were measured four times at 34 points across Barcelona. LUR and DM were constructed using conventional techniques, while HM was developed using Random Forest (RF). Model performance was evaluated using leave-one-out cross-validation (LOOCV) and 10-fold cross-validation (10-CV) for LUR and HM, and by comparing DM and LUR estimates with routine monitoring stations. NO levels estimated by all models were externally validated using the home monitoring campaign. Agreement between models was assessed using Spearman correlation (rs) and Bland-Altman (BA) plots.

Results: Models showed moderate to good performance. LUR exhibited R of 0.62 (NO), 0.45 (PM), 0.83 (BC), and 0.85 to 0.89 (PM). DM model comparison showed R values of 0.39 (NO), 0.26 (PM), and 0.65 (BC). HM models had higher R 0.64 (NO), 0.66 (PM), 0.86 (BC), and 0.44 to 0.70 (PM). Validation for NO showed R values of 0.56 (LUR), 0.44 (DM), and 0.64 (HM). Correlations between models varied from -0.38 to 0.92 for long-term exposure, and - 0.23 to 0.94 for short-term exposure. BA plots showed good agreement between models, especially for NO and BC.

Conclusions: Our models varied substantially, with some models performing better in validation samples (NO and BC). Future health studies should use the most accurate methods to minimize bias from exposure measurement error.