ABSTRACT
Using central air monitors to assign air pollution exposures to subjects in epidemiological studies is a common procedure, but exposure misclassification and bias can occur. Monitor-based exposure assessment is limited both spatially and temporally, failing to capture the acute changes in pollution concentration levels over distance and time. Prevalence of smartphones and recent developments of small mobile monitors offer opportunities to vastly improve personal-level exposure assessment. Smartphone monitoring systems offer several distinct advantages, such as low cost, small size, and the ease of generating large and robust data. This proposed project will 1) assess the accuracy of smartphone mobile monitors via comparisons to reference instruments under different experimental conditions; 2) record multiple repeated measurements within New York City and generate crowd-sourced data; 3) and develop and improve land use regression models by incorporating high-resolution geographical information and time-dependent variables. This study will greatly improve our understanding of capabilities of smartphone monitors in measuring and modeling ambient air pollution concentrations and offer insight into potential usage in future epidemiological studies.
Using central air monitors to assign air pollution exposures to subjects in epidemiological studies is a common procedure, but exposure misclassification and bias can occur. Monitor-based exposure assessment is limited both spatially and temporally, failing to capture the acute changes in pollution concentration levels over distance and time. Prevalence of smartphones and recent developments of small mobile monitors offer opportunities to vastly improve personal-level exposure assessment. Smartphone monitoring systems offer several distinct advantages, such as low cost, small size, and the ease of generating large and robust data. This proposed project will 1) assess the accuracy of smartphone mobile monitors via comparisons to reference instruments under different experimental conditions; 2) record multiple repeated measurements within New York City and generate crowd-sourced data; 3) and develop and improve land use regression models by incorporating high-resolution geographical information and time-dependent variables. This study will greatly improve our understanding of capabilities of smartphone monitors in measuring and modeling ambient air pollution concentrations and offer insight into potential usage in future epidemiological studies.
