Navarro-Martínez Alejandro, Hajji Meriem, Armengol Jan Mateu, Soret Albert, Ponce-de-León Miguel, Valencia Alfonso
Barcelona Supercomputing Center (BSC), Plaça Eusebi Güell 1-3, 08034, Barcelona, Spain.
Department of Fluid Mechanics, Universitat Politècnica de Catalunya, 08034, Barcelona, Spain.
Int J Health Geogr. 2025 Jul 28;24(1):19. doi: 10.1186/s12942-025-00410-0.
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.
In this study, we quantify the effect of recurrent mobility on long-term air pollution exposure and its attributable mortality for the pollutants NO , O , PM and PM , 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.
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 , PM and PM , and decreased exposures to O . The magnitude of these effects stays under 1 g/m when considering the complete populations, but they increase up to 8.3 g/m of change when we focus on the mobile populations. However, the effects on attributable mortality are negligible.
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.
空气污染暴露是主要的健康风险，主要是因为其对呼吸和心血管系统有有害影响。环境空气质量随时间和空间变化很大，大多数人为污染物在城市中的含量高于农村地区。通勤到城市工作的农村居民也会接触到城市的空气污染。因此，忽视人口流动性的暴露评估会产生有偏差的估计。
在本研究中，我们量化了2022年西班牙加泰罗尼亚584个地区中，反复流动对长期空气污染暴露及其可归因死亡率的影响，涉及污染物一氧化氮（NO）、臭氧（O₃）、细颗粒物（PM₂.₅）和粗颗粒物（PM₁₀）。我们使用匿名的基于手机的流动数据来推断每个地区居民在不同区域之间的动态分布，仅考虑反复流动。我们还利用经过偏差校正的CALIOPE模型提供的四种污染物的高分辨率空气质量数据，将其投影到各个地区。我们将动态人口与空气质量相结合，以计算动态暴露估计值，并相对于静态估计值计算流动对长期暴露的影响。我们还计算了每种污染物的可归因死亡率以及流动的影响。
考虑到这四种污染物，75.9%至86.3%的地区显示出流动对暴露有显著影响。城市周边的农村地区对一氧化氮、细颗粒物和粗颗粒物的暴露增加，对臭氧的暴露减少。考虑全部人口时，这些影响的幅度保持在1微克/立方米以下，但当我们关注流动人群时，变化幅度会增加到8.3微克/立方米。然而，对可归因死亡率的影响可以忽略不计。
我们的研究证明了城市对居住在远离城市地区的人们的空气污染暴露的影响，这种影响是由反复流动造成的。我们的结果表明，当区域间流动性相对较低时，通过流动性校正暴露概况在人口层面可能不会产生很大影响，但对于具有特定流动习惯的个人和人群细分可能非常重要，因此在公共卫生政策设计中应予以考虑。
