A description of methods for deriving air pollution land use regression model predictor variables from remote sensing data in Ulaanbaatar, Mongolia

摘要

Air pollution is a major risk factor for death and disease, particularly in low-and middle-income countries (LMIC) where concentrations are relatively high and large populations are exposed. High-quality exposure assessment is integral to both air pollution epidemiologic studies and impact assessments. Land use regression (LUR) modelling is a powerful exposure assessment technique that uses the relationships between air pollution concentrations at discrete monitoring locations and the surrounding characteristics of those locations to model small-scale spatial concentration gradients within cities. Regardless of whether they are calibrated based on local measurements or transferred from another location, LUR models require spatially resolved data on predictor variables that may be unavailable or of insufficient quality in many settings. We describe methods for deriving LUR model predictors, including land cover, road locations, and ger (Mongolian yurt) locations, from satellite data and high-resolution imagery in Ulaanbaatar, Mongolia. These methods may allow LUR models to be developed for more locations in LMIC, potentially improving the quality of air pollution exposure assessments in those settings.