Evaluating a Land Use Regression Model for Estimating Metals in Fine Particulate Matter across the Denver Metro Area:...

Few studies examine health effects of metals in ambient fine particulate matter (PM_2.5), as measurements of elemental composition are sparse. To facilitate intraurban studies in Denver, Colorado, we developed land use regression models for seven speciescopper (Cu), iron (Fe), titanium (Ti), zinc (Zn), potassium (K), calcium (Ca), and magnesium (Mg). As part of the Healthy Start Cohort study, we collected filter-based PM_2.5 using personal air samplers at 67 locations across Denver. Sample collection occurred from May 2018 through March 2019, accounting for all meteorological seasons. Exposure models were informed by 83 geospatial covariates, with traffic-related predictors as the strongest and most consistent across models. Model performance was evaluated using 10-fold cross validation and overall, varied by sampling campaign and season, with R ² values ranging from 0 to 0.63. At best, our model predicts Cu and Fe during fall (R ² = 0.56 and 0.63, respectively); whereas it fails to capture species unrelated to traffic year-round (R ² < 0.40). This highlights the influence of missing predictors (e.g., wildfire smoke, atmospheric transport and other meteorological factors) on PM_2.5 concentrations and spatial gradients. Despite limitations, resulting models enable estimation of intraurban metal exposures and support future analyses of long-term health impacts in Denver.