Background Air pollution exposure is a leading health risk mainly due to its detrimental respiratory and cardiovascular effects. Ambient air quality varies greatly across time and space, most anthropogenic pollutants being higher in cities than rural areas. Residents of rural areas who commute to cities for work are also exposed to the air pollution there. Therefore, exposure assessments that neglect population mobility produce biased estimates. Methods In this study, we quantify the effect of recurrent mobility on long-term air pollution exposure and its attributable mortality for the pollutants NO<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12942_2025_410_Article_IEq1.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="8" /> </InlineMediaObject> <EquationSource Format="TEX">$_2$</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mn>2</mn> <mrow /> </mmultiscripts> </math></EquationSource> </InlineEquation>, O<InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12942_2025_410_Article_IEq2.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="8" /> </InlineMediaObject> <EquationSource Format="TEX">$_3$</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mn>3</mn> <mrow /> </mmultiscripts> </math></EquationSource> </InlineEquation>, PM<InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12942_2025_410_Article_IEq3.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="17" /> </InlineMediaObject> <EquationSource Format="TEX">$_{2.5}$</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mrow> <mn>2.5</mn> </mrow> <mrow /> </mmultiscripts> </math></EquationSource> </InlineEquation> and PM<InlineEquation ID="IEq4"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12942_2025_410_Article_IEq4.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="13" /> </InlineMediaObject> <EquationSource Format="TEX">$_{10}$</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mn>10</mn> <mrow /> </mmultiscripts> </math></EquationSource> </InlineEquation>, for 584 districts of Catalonia (Spain) in 2022. We use anonymized phone-based mobility data to infer the dynamic distribution of the residents of each district among the different areas, considering only recurrent mobility. We also utilise finely-resolved air quality data for the four pollutants from the bias-corrected CALIOPE model, projected over the districts. We integrate dynamic population with the air quality to calculate dynamic exposure estimates, and compute the effect of mobility on long-term exposure with respect to the static estimates. We also calculate the mortality attributable to each pollutant and the effect of mobility. Results Considering the four pollutants, between 75.9% and 86.3% of the districts present significant effects of mobility on exposure. Rural areas surrounding cities display increased exposures to NO<InlineEquation ID="IEq5"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12942_2025_410_Article_IEq1.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="8" /> </InlineMediaObject> <EquationSource Format="TEX">$_2$</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mn>2</mn> <mrow /> </mmultiscripts> </math></EquationSource> </InlineEquation>, PM<InlineEquation ID="IEq6"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12942_2025_410_Article_IEq3.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="17" /> </InlineMediaObject> <EquationSource Format="TEX">$_{2.5}$</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mrow> <mn>2.5</mn> </mrow> <mrow /> </mmultiscripts> </math></EquationSource> </InlineEquation> and PM<InlineEquation ID="IEq7"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12942_2025_410_Article_IEq4.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="13" /> </InlineMediaObject> <EquationSource Format="TEX">$_{10}$</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mn>10</mn> <mrow /> </mmultiscripts> </math></EquationSource> </InlineEquation>, and decreased exposures to O<InlineEquation ID="IEq8"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12942_2025_410_Article_IEq2.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="8" /> </InlineMediaObject> <EquationSource Format="TEX">$_3$</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mn>3</mn> <mrow /> </mmultiscripts> </math></EquationSource> </InlineEquation>. The magnitude of these effects stays under 1 <InlineEquation ID="IEq9"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12942_2025_410_Article_IEq9.gif" Format="GIF" Height="13" Rendition="HTML" Resolution="72" Type="Linedraw" Width="12" /> </InlineMediaObject> <EquationSource Format="TEX">$\upmu$</EquationSource> <EquationSource Format="MATHML"><math> <mi mathvariant="normal">μ</mi> </math></EquationSource> </InlineEquation>g/m<InlineEquation ID="IEq10"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12942_2025_410_Article_IEq10.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="8" /> </InlineMediaObject> <EquationSource Format="TEX">$^3$</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mrow /> <mn>3</mn> </mmultiscripts> </math></EquationSource> </InlineEquation> when considering the complete populations, but they increase up to 8.3 <InlineEquation ID="IEq11"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12942_2025_410_Article_IEq9.gif" Format="GIF" Height="13" Rendition="HTML" Resolution="72" Type="Linedraw" Width="12" /> </InlineMediaObject> <EquationSource Format="TEX">$\upmu$</EquationSource> <EquationSource Format="MATHML"><math> <mi mathvariant="normal">μ</mi> </math></EquationSource> </InlineEquation>g/m<InlineEquation ID="IEq12"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12942_2025_410_Article_IEq10.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="8" /> </InlineMediaObject> <EquationSource Format="TEX">$^3$</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mrow /> <mn>3</mn> </mmultiscripts> </math></EquationSource> </InlineEquation> of change when we focus on the mobile populations. However, the effects on attributable mortality are negligible. Conclusions Our work evidences the impact of cities on the air pollution exposure of people living far away from them, made possible by recurrent mobility. Our results show that correcting exposure profiles by mobility might not have a large impact at the population level when inter-area mobility is relatively low, but can be very significant for individuals and population segments with specific mobility habits, and as such should be taken into account for the design of public health policies.

The effect of recurrent mobility on air pollution exposure and mortality burden in Catalonia

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2025年7月28日

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Alejandro Navarro-Martínez,
Meriem Hajji,
Jan Mateu Armengol,
Albert Soret,
Miguel Ponce-de-León,
Alfonso Valencia

摘要

Background

Air pollution exposure is a leading health risk mainly due to its detrimental respiratory and cardiovascular effects. Ambient air quality varies greatly across time and space, most anthropogenic pollutants being higher in cities than rural areas. Residents of rural areas who commute to cities for work are also exposed to the air pollution there. Therefore, exposure assessments that neglect population mobility produce biased estimates.

Methods

In this study, we quantify the effect of recurrent mobility on long-term air pollution exposure and its attributable mortality for the pollutants NO $_2$ $_{2}$ , O $_3$ $_{3}$ , PM $_{2.5}$ $_{2.5}$ and PM $_{10}$ $_{10}$ , for 584 districts of Catalonia (Spain) in 2022. We use anonymized phone-based mobility data to infer the dynamic distribution of the residents of each district among the different areas, considering only recurrent mobility. We also utilise finely-resolved air quality data for the four pollutants from the bias-corrected CALIOPE model, projected over the districts. We integrate dynamic population with the air quality to calculate dynamic exposure estimates, and compute the effect of mobility on long-term exposure with respect to the static estimates. We also calculate the mortality attributable to each pollutant and the effect of mobility.

Results

Considering the four pollutants, between 75.9% and 86.3% of the districts present significant effects of mobility on exposure. Rural areas surrounding cities display increased exposures to NO $_2$ $_{2}$ , PM $_{2.5}$ $_{2.5}$ and PM $_{10}$ $_{10}$ , and decreased exposures to O $_3$ $_{3}$ . The magnitude of these effects stays under 1 $\upmu$ $μ$ g/m $^3$ $^{3}$ when considering the complete populations, but they increase up to 8.3 $\upmu$ $μ$ g/m $^3$ $^{3}$ of change when we focus on the mobile populations. However, the effects on attributable mortality are negligible.

Conclusions

Our work evidences the impact of cities on the air pollution exposure of people living far away from them, made possible by recurrent mobility. Our results show that correcting exposure profiles by mobility might not have a large impact at the population level when inter-area mobility is relatively low, but can be very significant for individuals and population segments with specific mobility habits, and as such should be taken into account for the design of public health policies.