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Background <p>Air pollution exposure assessment during pregnancy often relies on static models based solely on residential addresses. These models may underestimate true exposure and distort assessments of environmental inequalities by neglecting daily mobility. This study aimed to quantify exposure misclassification due to mobility and to assess how it varies across socioeconomic groups in Strasbourg, France.</p> Methods <p>We analyzed data from 497 pregnant women enrolled in the MOBIFEM cohort. Exposure to nitrogen dioxide (NO₂), particulate matter ≤ 10&#xa0;μm (PM₁₀), and ≤ 2.5&#xa0;μm (PM₂.₅), was modeled using six scenarios: one static (residential address only) and five dynamic scenarios incorporating frequented locations and travel itineraries. A local socioeconomic deprivation index was used to stratify the analyses. High-resolution hourly pollutant concentrations were estimated using the ADMS-Urban dispersion model.</p> Results <p>Dynamic models incorporating mobility data consistently yielded significantly higher mean exposure estimates than the static residential model. The degree of underestimation was greatest among women living in the least deprived neighborhoods (Tertile 1). For instance, the largest discrepancies in NO₂ exposure between static and dynamic models were observed in this group (T-value = -4.72, <i>p</i> &lt; 0.001).</p> Conclusion <p>Relying solely on a residential address leads to substantial underestimation of personal air pollution exposure during pregnancy and may distort the characterization of social inequalities in exposure. In our study area, neglecting mobility disproportionately affected the least deprived women, likely due to longer commuting distances. These findings underscore the importance of incorporating individual mobility data into exposure assessment to improve the accuracy of environmental health risk analyses and inform public health policies.</p>

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Time-activity and daily mobility patterns during pregnancy and inequalities in air pollution exposure in perinatal outcomes: a cohort study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月13日

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  • Valentin Simoncic,
  • Romain Wenger,
  • Phillipe Deruelle,
  • Nicolas Sananes,
  • Charles Schillinger,
  • Loriane Huber,
  • Séverine Deguen,
  • Wahida Kihal-Talantikite

摘要

Background

Air pollution exposure assessment during pregnancy often relies on static models based solely on residential addresses. These models may underestimate true exposure and distort assessments of environmental inequalities by neglecting daily mobility. This study aimed to quantify exposure misclassification due to mobility and to assess how it varies across socioeconomic groups in Strasbourg, France.

Methods

We analyzed data from 497 pregnant women enrolled in the MOBIFEM cohort. Exposure to nitrogen dioxide (NO₂), particulate matter ≤ 10 μm (PM₁₀), and ≤ 2.5 μm (PM₂.₅), was modeled using six scenarios: one static (residential address only) and five dynamic scenarios incorporating frequented locations and travel itineraries. A local socioeconomic deprivation index was used to stratify the analyses. High-resolution hourly pollutant concentrations were estimated using the ADMS-Urban dispersion model.

Results

Dynamic models incorporating mobility data consistently yielded significantly higher mean exposure estimates than the static residential model. The degree of underestimation was greatest among women living in the least deprived neighborhoods (Tertile 1). For instance, the largest discrepancies in NO₂ exposure between static and dynamic models were observed in this group (T-value = -4.72, p < 0.001).

Conclusion

Relying solely on a residential address leads to substantial underestimation of personal air pollution exposure during pregnancy and may distort the characterization of social inequalities in exposure. In our study area, neglecting mobility disproportionately affected the least deprived women, likely due to longer commuting distances. These findings underscore the importance of incorporating individual mobility data into exposure assessment to improve the accuracy of environmental health risk analyses and inform public health policies.