Generalization of Constrained Mixed-Effect Modeling Framework with Ensemble Learning to Broader Geographic Areas for Predicting Nitrogen Oxides at High Spatiotemporal Resolution

Background: Spatiotemporal models developed for a specific region typically provide high spatial and temporal resolution in predicting exposures locally; however, it is challenging to directly apply them to broader regions in large epidemiological studies.Aim: To extend our southern California (CA) nitrogen oxides (NOx) spatiotemporal modeling framework (Li et al., 2017) to the entire state, evaluate its performance, and recommend key parameters to tune for future spatiotemporal model extension applications.Methods: In addition to our southern CA model data, we incorporated data from 105 ambient monitoring stations to cover CA. We conducted sensitivity analyses to determine the optimal number and aggregation distance to use in reconstructing temporal basis functions (temporal variability) and Thiessen polygons (spatial effects), respectively. We conducted ensemble and 10-fold cross validation (CV) to determine model prediction performance against from long-term ambient monitoring data and from short-term ...