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Background <p>Residence-based air pollution exposure assessments ignore daily human mobility and may misrepresent exposure levels and disparities across population groups.</p> Objective <p>We hypothesize that incorporating high-resolution mobility trajectories into exposure modeling will reveal higher average PM<sub>2.5</sub> exposures and uncover sociodemographic disparities that traditional residence-based methods underestimate or conceal.</p> Methods <p>We analyzed 155,000 trip records from 990 Boston-area participants (June–December 2023) collected via smartphone GPS, linked to PM<sub>2.5</sub> measurements from 294 calibrated PurpleAir air quality sensors collected at 2-min intervals. For each stay location, we computed a daily adjusted exposure as the average PM<sub>2.5</sub> within a 4 km buffer minus the region’s daily average. We compared these mobility-informed exposures to home-based estimates, assessed temporal (weekday vs. weekend, peak vs. off-peak) and spatial variability (Moran’s I), and used weighted least squares regressions and t-tests to evaluate differences across race, income, education, age, and occupation.</p> Results <p>Mobility-informed exposures averaged 0.10 µg/m<sup>3</sup> higher than residence-based estimates on weekdays (up to 0.45 µg/m<sup>3</sup> on high-pollution days). Employed and higher-income individuals, as well as White participants, experienced significantly elevated exposures during peak travel hours (up to +0.30 µg/m<sup>3</sup>; <i>p</i> &lt; 0.01). Spatial clustering of mobility exposures was stronger on weekdays (Moran’s I = 0.4) than weekends (I = 0.2), and regression coefficients confirmed systematic underestimation by traditional methods.</p> Significance <p>These findings demonstrate that neglecting mobility systematically underestimates exposure levels and obscures environmental injustices.</p> Impact statement <p>Integrating dynamic mobility data with hyperlocal air quality monitoring provides a refined framework for accurate exposure assessment, informing equitable public health policies and targeted interventions.</p>

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Mobility-driven estimate reveals elevated air pollution exposure and socioeconomic disparities beyond residence-based approaches in Boston

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月8日

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  • Nail F. Bashan,
  • Yang Zhang,
  • Michelle L. Bell,
  • Qi R. Wang

摘要

Background

Residence-based air pollution exposure assessments ignore daily human mobility and may misrepresent exposure levels and disparities across population groups.

Objective

We hypothesize that incorporating high-resolution mobility trajectories into exposure modeling will reveal higher average PM2.5 exposures and uncover sociodemographic disparities that traditional residence-based methods underestimate or conceal.

Methods

We analyzed 155,000 trip records from 990 Boston-area participants (June–December 2023) collected via smartphone GPS, linked to PM2.5 measurements from 294 calibrated PurpleAir air quality sensors collected at 2-min intervals. For each stay location, we computed a daily adjusted exposure as the average PM2.5 within a 4 km buffer minus the region’s daily average. We compared these mobility-informed exposures to home-based estimates, assessed temporal (weekday vs. weekend, peak vs. off-peak) and spatial variability (Moran’s I), and used weighted least squares regressions and t-tests to evaluate differences across race, income, education, age, and occupation.

Results

Mobility-informed exposures averaged 0.10 µg/m3 higher than residence-based estimates on weekdays (up to 0.45 µg/m3 on high-pollution days). Employed and higher-income individuals, as well as White participants, experienced significantly elevated exposures during peak travel hours (up to +0.30 µg/m3; p < 0.01). Spatial clustering of mobility exposures was stronger on weekdays (Moran’s I = 0.4) than weekends (I = 0.2), and regression coefficients confirmed systematic underestimation by traditional methods.

Significance

These findings demonstrate that neglecting mobility systematically underestimates exposure levels and obscures environmental injustices.

Impact statement

Integrating dynamic mobility data with hyperlocal air quality monitoring provides a refined framework for accurate exposure assessment, informing equitable public health policies and targeted interventions.