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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 \u201cActive Population Exposure\u201d was evaluated using population activity patterns and spatiotemporal PM2.5 concentration levels, and compared to \u201cHome Population Exposure\u201d, 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 \u201cActive Population Exposure\u201d scenario. Population-weighted exposure computed in each district of NYC for the \u201cActive\u201d scenario were found to be statistically significantly (p &amp;lt; 0.05) different to the \u201cHome\u201d scenario for most districts. In investigating the temporal variability of the \u201cActive\u201d population-weighted exposures determined in districts, these were found to be significantly different (p &amp;lt; 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.<\/jats:p>","DOI":"10.1021\/acs.est.6b02385","type":"journal-article","created":{"date-parts":[[2016,8,12]],"date-time":"2016-08-12T00:08:47Z","timestamp":1470960527000},"page":"9671-9681","source":"Crossref","is-referenced-by-count":158,"title":["\u201cExposure\nTrack\u201d\u2500The Impact of\nMobile-Device-Based Mobility Patterns on Quantifying Population Exposure\nto Air Pollution"],"prefix":"10.1021","volume":"50","author":[{"given":"Marguerite","family":"Nyhan","sequence":"first","affiliation":[{"name":"Massachusetts Institute of Technology , , , ,","place":["Cambridge, Massachusetts, United States, 02139"]},{"name":"Harvard University , , , ,","place":["Boston, Massachusetts, United States, 02215"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastian","family":"Grauwin","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology , , , ,","place":["Cambridge, Massachusetts, United States, 02139"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rex","family":"Britter","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology , , , ,","place":["Cambridge, Massachusetts, United States, 02139"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bruce","family":"Misstear","sequence":"additional","affiliation":[{"name":"Trinity College Dublin , , College Green , ,","place":["Dublin, Ireland, 2"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aonghus","family":"McNabola","sequence":"additional","affiliation":[{"name":"Trinity College Dublin , , College Green , ,","place":["Dublin, Ireland, 2"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francine","family":"Laden","sequence":"additional","affiliation":[{"name":"Harvard University , , , ,","place":["Boston, Massachusetts, United States, 02215"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steven R. 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