{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T14:11:26Z","timestamp":1773929486848,"version":"3.50.1"},"posted":{"date-parts":[[2026,3,17]]},"group-title":"Public and Global Health","reference-count":62,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T00:00:00Z","timestamp":1773705600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,3,17]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:sec>\n                  <jats:title>Introduction<\/jats:title>\n                  <jats:p>\n                    Particulate Matter (PM\n                    <jats:sub>2.5<\/jats:sub>\n                    ) exposure contributes to the global disease burden, yet its monitoring remains sparse and uneven and is limited in many limited ground monitoring network settings. Road-traffic proxy indicators can provide indirect estimates of PM\n                    <jats:sub>2.5<\/jats:sub>\n                    where measurements are limited but require context-specific validation. We evaluated three PM\n                    <jats:sub>2.5<\/jats:sub>\n                    road-traffic related proxies:(I) population-Weighted Road Network Density (WRND), (ii) Euclidean (straight line) distance from highways (EH), and (iii) Euclidean distance from main roads (EM).\n                  <\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>\n                    We validated proxies using high-resolution outdoor filtered PM\n                    <jats:sub>2.5<\/jats:sub>\n                    personal exposure measurements collected over 1 year from 343 postpartum participants in The Gambia, Kenya, and Mozambique. Village-level spatial patterns for the PM\n                    <jats:sub>2.5<\/jats:sub>\n                    -proxy relationship were mapped using 5 km hexagonal aggregated tessellations. Proxy-PM\n                    <jats:sub>2.5<\/jats:sub>\n                    associations were assessed using Spearman correlation, and predictive utility was tested using country-specific and global Random Forest (RF) models (3-fold cross-validation), reporting R\n                    <jats:sup>2<\/jats:sup>\n                    , RMSE, and feature importance\n                  <\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>\n                    Spatial mapping showed heterogeneous proxy\u2013PM\n                    <jats:sub>2.5<\/jats:sub>\n                    relationships across and within sites, with elevated PM\n                    <jats:sub>2.5<\/jats:sub>\n                    occurring in both low- and high-proxy contests. WRND\u2013PM\n                    <jats:sub>2.5<\/jats:sub>\n                    correlations were weak overall and statistically significant only in Mozambique (r = 0.351;\n                    <jats:italic>p = 0.005<\/jats:italic>\n                    ), with non-significant associations in Kenya (r = \u22120.041;\n                    <jats:italic>p = 0.673<\/jats:italic>\n                    ) and The Gambia (r = \u22120.020;\n                    <jats:italic>p = 0.909<\/jats:italic>\n                    ). EH\u2013PM\n                    <jats:sub>2.5<\/jats:sub>\n                    correlations were positive in The Gambia (r = 0.335;\n                    <jats:italic>p = 0.053<\/jats:italic>\n                    ) and Mozambique (r = 0.292;\n                    <jats:italic>p = 0.020<\/jats:italic>\n                    ) but negative and significant in Kenya (r = \u22120.224;\n                    <jats:italic>p = 0.018<\/jats:italic>\n                    ).Single-variable RF models performed poorly across all countries (R\n                    <jats:sup>2<\/jats:sup>\n                    &lt; 0.45) and the Global model (R\n                    <jats:sup>2<\/jats:sup>\n                    =0.42). Combining proxies improved performance in Kenya (R\n                    <jats:sup>2<\/jats:sup>\n                    =0.52; RMSE=31.7\u00b5g\/m\n                    <jats:sup>3<\/jats:sup>\n                    ) and Mozambique (R\n                    <jats:sup>2<\/jats:sup>\n                    =0.60; RMSE=8.9 \u00b5g\/m\n                    <jats:sup>3<\/jats:sup>\n                    ), Global R\n                    <jats:sup>2<\/jats:sup>\n                    =0.46; RMSE=29.1 \u00b5g\/m\n                    <jats:sup>3<\/jats:sup>\n                    ), although in The Gambia, the combined model (R\n                    <jats:sup>2<\/jats:sup>\n                    =0.53; RMSE=37.6 \u00b5g\/m\n                    <jats:sup>3<\/jats:sup>\n                    ) did not exceed the best single-proxy model.\n                  <\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>\n                    Road-network proxies provide context-dependent signals of personal PM\n                    <jats:sub>2.5<\/jats:sub>\n                    exposure, and predictive performance is strengthened when proxies are combined in a hybrid model.\n                  <\/jats:p>\n                <\/jats:sec>","DOI":"10.64898\/2026.03.13.26348337","type":"posted-content","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:45:15Z","timestamp":1773794715000},"source":"Crossref","is-referenced-by-count":0,"title":["Performance of Road-Traffic-Based Exposure Proxies Against Personal PM\n                  <sub>2.5<\/sub>\n                  Measurements in Three Sub-Saharan African Countries"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-1332-8606","authenticated-orcid":false,"given":"Handsome Bongani","family":"Nyoni","sequence":"first","affiliation":[{"name":"Place Alert Labs, Surveying and Geomatics Department, Faculty of the Built Environment, Art and 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