"Exposure Track"-The Impact of Mobile-Device-Based Mobility Patterns on Quantifying Population Exposure to Air Pollution.
Marguerite Nyhan, Sebastian Grauwin, Rex Britter, Bruce Misstear, Aonghus McNabola, Francine Laden, Steven R H Barrett, Carlo Ratti
Author Information
Marguerite Nyhan: Massachusetts Institute of Technology , Senseable City Laboratory, Cambridge, Massachusetts 02139, United States.
Sebastian Grauwin: Massachusetts Institute of Technology , Senseable City Laboratory, Cambridge, Massachusetts 02139, United States.
Rex Britter: Massachusetts Institute of Technology , Senseable City Laboratory, Cambridge, Massachusetts 02139, United States.
Bruce Misstear: Department of Civil, Structural & Environmental Engineering, Trinity College Dublin , College Green, Dublin 2, Ireland.
Aonghus McNabola: Department of Civil, Structural & Environmental Engineering, Trinity College Dublin , College Green, Dublin 2, Ireland.
Francine Laden: Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard University , Boston, Massachusetts 02215, United States.
Steven R H Barrett: Massachusetts Institute of Technology , Department of Aeronautics & Astronautics, Cambridge, Massachusetts 02139, United States.
Carlo Ratti: Massachusetts Institute of Technology , Senseable City Laboratory, Cambridge, Massachusetts 02139, United States.
Air pollution is now recognized as the world's single largest environmental and human health threat. Indeed, a large number of environmental epidemiological studies have quantified the health impacts of population exposure to pollution. In previous studies, exposure estimates at the population level have not considered spatially- and temporally varying populations present in study regions. Therefore, in the first study of it is kind, we use measured population activity patterns representing several million people to evaluate population-weighted exposure to air pollution on a city-wide scale. Mobile and wireless devices yield information about where and when people are present, thus collective activity patterns were determined using counts of connections to the cellular network. Population-weighted exposure to PM2.5 in New York City (NYC), herein termed "Active Population Exposure" was evaluated using population activity patterns and spatiotemporal PM2.5 concentration levels, and compared to "Home Population Exposure", which assumed a static population distribution as per Census data. Areas of relatively higher population-weighted exposures were concentrated in different districts within NYC in both scenarios. These were more centralized for the "Active Population Exposure" scenario. Population-weighted exposure computed in each district of NYC for the "Active" scenario were found to be statistically significantly (p < 0.05) different to the "Home" scenario for most districts. In investigating the temporal variability of the "Active" population-weighted exposures determined in districts, these were found to be significantly different (p < 0.05) during the daytime and the nighttime. Evaluating population exposure to air pollution using spatiotemporal population mobility patterns warrants consideration in future environmental epidemiological studies linking air quality and human health.
