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By ignoring time activity, population exposure misclassification could potentially lead to loss of precision or bias in epidemiological studies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>We aimed to assess how residential-based air pollution exposures compared with \u201creal\u201d tracking-based exposures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We conducted two tracking campaigns in Switzerland (CH) and the Netherlands (NL) with 686 participants followed for 2 weeks with GPS trackers whilst keeping time activity diaries. In addition, we simulated mobility and commuting tracks for the same subjects using agent-based modeling (ABM) with information from census and travel survey data to estimate mobility-integrated air pollution exposures. Exposures were calculated by overlaying residential address, measured (GPS) and modeled (ABM) tracks with annual average hourly NO\n                      <jats:sub>2<\/jats:sub>\n                      and PM\n                      <jats:sub>2.5<\/jats:sub>\n                      concentration surfaces.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      We found strong agreements between residential and tracking-based exposures in CH for both pollutants (\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009&gt;\u20090.76) and NL for NO\n                      <jats:sub>2<\/jats:sub>\n                      (\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.79), and weaker agreement in NL for PM\n                      <jats:sub>2.5<\/jats:sub>\n                      (\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.56). Similarly, the agreement between ABM and tracking-based exposures was strong for NO\n                      <jats:sub>2<\/jats:sub>\n                      (\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009&gt;\u20090.77 in CH and NL), while for PM\n                      <jats:sub>2.5<\/jats:sub>\n                      it was stronger in CH (\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.80) than in NL (\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.54). The highest correlations were between residential and ABM exposures (\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009&gt;\u20090.96 for both pollutants). Using information commonly available even in large administrative cohorts, we found that exposures derived from the tracking campaigns agreed well with ABM in our two study areas.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Significance<\/jats:title>\n                    <jats:p>Our study supports the use of residential exposures in epidemiological studies on long-term health effects of air pollution, whilst acknowledging that ABM, especially if the work location is known, can be a useful tool to estimate mobility-integrated exposures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Impact statement<\/jats:title>\n                    <jats:p>\n                      <jats:list list-type=\"bullet\">\n                        <jats:list-item>\n                          <jats:p>Our research supports the use of residential exposures in studies investigating the long-term health effects of air pollution, whilst acknowledging that agent-based modeling, especially if the work location is known, is valuable for estimating mobility-integrated exposures. Our findings are broadly applicable to air pollution epidemiology, in particular, studies of large populations that rely on exposure modeling.<\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1038\/s41370-025-00836-5","type":"journal-article","created":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T08:33:59Z","timestamp":1766738039000},"page":"469-478","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Comparison of residential and mobility-integrated air pollution exposures from tracking campaigns and agent-based modelling in Switzerland and the Netherlands"],"prefix":"10.1038","volume":"36","author":[{"given":"Kees","family":"de Hoogh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin","family":"Fl\u00fcckiger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicole","family":"Probst-Hensch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ayoung","family":"Jeong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Medea","family":"Imboden","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aletta","family":"Karsies","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oliver","family":"Schmitz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roel","family":"Vermeulen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kalliopi","family":"Kyriakou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aisha","family":"Ndiaye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youchen","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Derek","family":"Karssenberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danielle","family":"Vienneau","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gerard","family":"Hoek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,26]]},"reference":[{"key":"836_CR1","doi-asserted-by":"publisher","first-page":"1600419","DOI":"10.1183\/13993003.00419-2016","volume":"49","author":"GD Thurston","year":"2017","unstructured":"Thurston GD, Kipen H, Annesi-Maesano I, Balmes J, Brook RD, Cromar K, et al. 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The Dutch tracking campaign received an ethics exemption from the Medische Ethische Toetsingscommissie (METC) Utrecht, as no health-related data was collected from the participating subjects (WAG\/mb\/20\/007879). The Swiss tracking campaign was approved by the Ethikkommission Nordwest- und Zentralschweiz (EKNZ) as it recruited from the EKNZ-approved ongoing COVCO-Basel study (Nr. 2020-00927 and Nr. 2021-01704, respectively). Informed consent was obtained from all participants in the Dutch and Swiss tracking campaigns.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}}]}}