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<p>Particulate Matter (PM<sub>2.5</sub>) exposure contributes to the global disease burden, yet its monitoring remains sparse and uneven, with limited ground sensor infrastructure. Road-traffic proxy indicators can provide indirect estimates of PM<sub>2.5</sub> where measurements are limited but require context-specific validation. We evaluated three PM<sub>2.5</sub> 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). We validated these proxies using high-resolution outdoor filtered PM<sub>2.5</sub> personal exposure measurements collected over 1 year from 343 postpartum participants in The Gambia, Kenya, and Mozambique. Proxy-PM<sub>2.5</sub> 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<sup>2</sup>, RMSE, and feature importance. Spatial mapping showed heterogeneous proxy–PM<sub>2.5</sub> relationships across and within sites, with elevated PM<sub>2.5</sub> occurring in both low- and high-proxy contexts. wRND–PM<sub>2.5</sub> correlations were weak overall and statistically significant only in Mozambique (<i>r</i> = 0.351; <i>p</i> = 0.005), with non-significant associations in Kenya (<i>r</i> = − 0.041; <i>p</i> = 0.673) and The Gambia (<i>r</i> = − 0.020; <i>p</i> = 0.909). EH–PM<sub>2.5</sub> correlations were positive in The Gambia (<i>r</i> = 0.335; <i>p</i> = 0.053) and Mozambique (<i>r</i> = 0.292; <i>p</i> = 0.020) but negative and significant in Kenya (<i>r</i> = − 0.224; <i>p</i> = 0.018). Single-variable RF models performed poorly across all countries (R<sup>2</sup> &lt; 0.45) and the Global model (R<sup>2</sup> = 0.42). Combining proxies improved performance in Kenya (R<sup>2</sup> = 0.52; RMSE = 31.7&#xa0;µg/m<sup>3</sup>) and Mozambique (R<sup>2</sup> = 0.60; RMSE = 8.9&#xa0;µg/m<sup>3</sup>), Global R<sup>2</sup> = 0.46; RMSE = 29.1&#xa0;µg/m<sup>3</sup>), although in The Gambia, the combined model (R<sup>2</sup> = 0.53; RMSE = 37.6&#xa0;µg/m<sup>3</sup>) did not exceed the best single-proxy model. Road-network proxies provided limited but context-dependent signals of personal PM₂.₅ exposure. Their performance varied substantially across countries, indicating that road-based indicators should not be used as stand-alone exposure measures in heterogeneous sub-Saharan African settings. Instead, they are most defensible as locally validated components of hybrid exposure models that also incorporate meteorology, land use, biomass burning, household energy, and other non-traffic sources.</p>

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Performance of road-traffic-based exposure proxies against personal PM2.5 measurements in three Sub-Saharan African countries

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月29日

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  • Handsome Bongani Nyoni,
  • Terence Darlington Mushore,
  • Laura Munthali,
  • Sibusisiwe Audrey Makhanya,
  • Laurine Chikoko,
  • Stanley Luchters,
  • Matthew F Chersich,
  • Fortunate Machingura,
  • Liberty Makacha,
  • Benjamin Barratt,
  • Hiten D Mistry,
  • Marie Laure Volvert,
  • Peter von Dadelszen,
  • Anna Roca,
  • Umberto D’alessandro,
  • Marleen Temmeran,
  • Esperança Sevene,
  • Tamara Rosemary Govindasamy,
  • Prestige Tatenda Makanga

摘要

Particulate Matter (PM2.5) exposure contributes to the global disease burden, yet its monitoring remains sparse and uneven, with limited ground sensor infrastructure. Road-traffic proxy indicators can provide indirect estimates of PM2.5 where measurements are limited but require context-specific validation. We evaluated three PM2.5 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). We validated these proxies using high-resolution outdoor filtered PM2.5 personal exposure measurements collected over 1 year from 343 postpartum participants in The Gambia, Kenya, and Mozambique. Proxy-PM2.5 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 R2, RMSE, and feature importance. Spatial mapping showed heterogeneous proxy–PM2.5 relationships across and within sites, with elevated PM2.5 occurring in both low- and high-proxy contexts. wRND–PM2.5 correlations were weak overall and statistically significant only in Mozambique (r = 0.351; p = 0.005), with non-significant associations in Kenya (r = − 0.041; p = 0.673) and The Gambia (r = − 0.020; p = 0.909). EH–PM2.5 correlations were positive in The Gambia (r = 0.335; p = 0.053) and Mozambique (r = 0.292; p = 0.020) but negative and significant in Kenya (r = − 0.224; p = 0.018). Single-variable RF models performed poorly across all countries (R2 < 0.45) and the Global model (R2 = 0.42). Combining proxies improved performance in Kenya (R2 = 0.52; RMSE = 31.7 µg/m3) and Mozambique (R2 = 0.60; RMSE = 8.9 µg/m3), Global R2 = 0.46; RMSE = 29.1 µg/m3), although in The Gambia, the combined model (R2 = 0.53; RMSE = 37.6 µg/m3) did not exceed the best single-proxy model. Road-network proxies provided limited but context-dependent signals of personal PM₂.₅ exposure. Their performance varied substantially across countries, indicating that road-based indicators should not be used as stand-alone exposure measures in heterogeneous sub-Saharan African settings. Instead, they are most defensible as locally validated components of hybrid exposure models that also incorporate meteorology, land use, biomass burning, household energy, and other non-traffic sources.