Development and Evaluation of Land-Use Regression Models Using Modeled Air Quality Concentrations

Abstract

Land-use regression (LUR) models have emerged as a preferred methodology for estimating individual exposure to ambient air pollution in epidemiologic studies in absence of subject-specific measurements. Although there is a growing literature focused on LUR evaluation, further research is needed to identify strengths and limitations of LUR modeling and strategies for improvement. In particular, LUR models have several limitations and among these are the needs for comprehensive monitoring data from a large number of sites, and the inability to link sources of emissions with measured elevated concentrations. In contrast, air quality models are designed to provide this linkage and have a long history of use by regulatory agencies in developing pollution mitigation strategies. Thus, the linkage of LUR techniques with available air quality modeling tools may benefit evaluation and enhancement of LUR techniques. In this study, we evaluated the fitted LUR models in several different ways and examined the implications of alternate LUR development strategies on model performance for benzene, particulate matter (PM2.5), and nitrogen oxides (NOx).