{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T16:56:11Z","timestamp":1791219371603,"version":"4.1.0"},"reference-count":59,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T00:00:00Z","timestamp":1791158400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010269","name":"Wellcome Trust","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100010269","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Glob. Women\u2019s Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>In sub-Saharan Africa, climatic variability intersects with structural inequities\u2014biomass reliance, informal livelihoods, rapid urbanisation, and sparse regulatory monitoring\u2014to generate highly heterogeneous air pollution exposures during pregnancy. Maternal exposure assessment in the region remains dominated by coarse ambient estimates, limiting understanding of how climate and microenvironment jointly shape actual personal exposure through their influence on pollutant generation, dispersion, and proximity to emission sources.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop and implement an adaptable, climate-structured personal and ambient PM\u2082.\u2085 monitoring framework embedded within the PRECISE pregnancy cohort across The Gambia, Kenya, and Mozambique.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A purposively sampled subset of 343 pregnant or postpartum women (The Gambia\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009160; Kenya\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009105; Mozambique\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200978) completed two 5-day deployments (with usable data) of wearable multi-parameter monitors in opposing dry and wet seasons. Devices recorded minute-resolved PM\u2082.\u2085, temperature, humidity, and mobility (GPS\/accelerometry). Fixed-site monitors were installed at six health facilities to characterise background exposure and seasonal dynamics. Calibration combined repeated pre-deployment co-location with reference instrumentation and in-country inter-sensor harmonisation using Deming regression. Quality assurance incorporated a \u226575% ambient air pollution data completeness threshold, encrypted transfer, geolocation validation, and cross-site reproducibility checks. Meteorological data (NASA POWER\/MERRA-2) were integrated to contextualise climatic drivers.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The protocol generated 3,190 person-days of personal exposure data together with 328 unique days of continuous ambient data. Ambient datasets achieved high completeness in The Gambia and Kenya (&amp;gt;85% data capture); Mozambique ambient capture was lower due to site-level connectivity constraints. Ambient monitoring periods were included only when \u226575% of expected observations were available; periods not meeting this threshold were excluded from primary analyses. Inter-sensor agreement was robust (R\n                      <jats:sup>2<\/jats:sup>\n                      \u2009&amp;gt;\u20090.72 pre-deployment; &amp;gt;0.75 in-country). Ambient monitoring characterised seasonal background variability and enabled evaluation of ambient\u2013personal concordance under contrasting climatic regimes. Integration of personal and ambient datasets supported microenvironmental partitioning and climate-contextual interpretation of exposure patterns.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This integrated monitoring framework extends the operational and methodological capacity for high-resolution exposure assessment through the generation of high-resolution, climate-structured exposure data within pregnancy cohorts in under-monitored African settings, advancing equitable exposure science and strengthening climate-informed maternal and newborn health research.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Significance statement<\/jats:title>\n                    <jats:p>This study establishes an adaptable, climate-structured exposure measurement architecture that reduces exposure misclassification and enables more precise exposure\u2013response inference in maternal health research conducted in under-monitored environments<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fgwh.2026.1849628","type":"journal-article","created":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T15:58:55Z","timestamp":1791215935000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["An integrated climate-structured personal and ambient air pollution monitoring framework embedded within a pregnancy and postpartum cohort in Sub-Saharan Africa"],"prefix":"10.3389","volume":"7","author":[{"given":"Liberty","family":"Makacha","sequence":"first","affiliation":[{"name":"Department of Women and Children\u2019s Health, King\u2019s College London","place":["London, United Kingdom"]},{"name":"Place Alert Labs, Midlands State University","place":["Gweru, Zimbabwe"]},{"name":"Centre for Sexual Health and HIV AIDS Research Zimbabwe (CeSHHAR)","place":["Harare, Zimbabwe"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jovito","family":"Nune","sequence":"additional","affiliation":[{"name":"ISGlobal, Barcelona Institute for Global Health, Universitat de Barcelona","place":["Barcelona, Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"P. 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