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Few epidemiological studies have compared the associated health effects of personal exposure and residence-based estimates.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To evaluate the association between exposure to air pollution and cognitive function using exposure estimates taking mobility and location into account.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Residence-based dispersion model estimates of ambient NO\n                      <jats:sub>2<\/jats:sub>\n                      , PM\n                      <jats:sub>10<\/jats:sub>\n                      and PM\n                      <jats:sub>2.5<\/jats:sub>\n                      were assigned to 768 London-dwelling participants of the English Longitudinal Study of Ageing. The London Hybrid Exposure Model was implemented to adjust estimates per pollutant to reflect the estimated time-activity patterns of each participant based on age and residential location. Single pollutant linear mixed-effects models were fit for both exposure assessment methods to investigate the associations between assigned pollutant concentrations and cognitive function over a follow-up period of up to 15 years.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Increased long-term exposures to residence-based ambient NO\n                      <jats:sub>2<\/jats:sub>\n                      (IQR: 11.10\u2009\u00b5g\/m\n                      <jats:sup>3<\/jats:sup>\n                      ), PM\n                      <jats:sub>10<\/jats:sub>\n                      (2.35\u2009\u00b5g\/m\n                      <jats:sup>3<\/jats:sup>\n                      ), and PM\n                      <jats:sub>2.5<\/jats:sub>\n                      (2.50\u2009\u00b5g\/m\n                      <jats:sup>3<\/jats:sup>\n                      ) were associated with decreases of \u22120.10 [95% CI: \u22120.20, 0.00], \u22120.07 [\u22120.11, \u22120.02] and \u22120.14 [\u22120.21, \u22120.06], respectively, in composite memory score. Similar decreases were observed for executive function scores (\u22120.38 [\u22120.58, \u22120.18], \u22120.11 [\u22120.20, \u22120.02] and \u22120.14 [\u22120.29, 0.01], respectively). When applying personalised exposure estimates, which were substantially lower, similar decreases were observed for composite memory score per IQR, but a consistent pattern of slightly more adverse effects with executive function score was evident.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Impact Statement<\/jats:title>\n                    <jats:p>The present study constructed a framework through which time-activity information derived from a representative sample could be applied to estimates of ambient air pollution concentrations assigned to individuals in epidemiological cohort studies, with the intention of adjusting commonly used residence-based estimates to reflect population mobility and time spent in various microenvironments. Estimates of exposure were markedly lower when incorporating time-activity, likely because people in European populations spend a large proportion of their time indoors, where their exposure to ambient air pollution may be reduced through infiltration, which is not taken into account in residence-based ambient estimates. Further work into such methods could provide insights into the efficacy of personalising exposure estimates.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1038\/s41370-025-00745-7","type":"journal-article","created":{"date-parts":[[2025,1,14]],"date-time":"2025-01-14T11:43:48Z","timestamp":1736855028000},"page":"33-40","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Personalised estimation of exposure to ambient air pollution and application in a longitudinal cohort analysis of cognitive function in London-dwelling older adults"],"prefix":"10.1038","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1150-4613","authenticated-orcid":false,"given":"Dylan","family":"Wood","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dimitris","family":"Evangelopoulos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nutthida","family":"Kitwiroon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gregor","family":"Stewart","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tuan","family":"Vu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James","family":"Smith","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sean","family":"Beevers","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Klea","family":"Katsouyanni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,14]]},"reference":[{"key":"745_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.envint.2020.105974","volume":"143","author":"J Chen","year":"2020","unstructured":"Chen J, Hoek G. 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