Scholarlink

<p>Road traffic-induced dust resuspension is a significant contributor to transport-related particulate matter (PM) emissions. However, current attempts to quantify this source show significant discrepancies across studies. This variability primarily arises from limitations in experimental methodologies, which often fail to assess the effects of key influencing parameters particularly vehicle speed, vehicle weight, and surface dust load. As a result, existing empirical models typically do not account for these factors, leading to limited predictive reliability. To address these shortcomings, this study investigates the influence of these three parameters on vehicle-induced road dust resuspension under controlled conditions. A new experimental protocol was specifically designed and implemented on an isolated test track. This involved artificially seeding a road section with particles and subsequent passages using electric vehicles- Hybrid Peugeot 3008 and FIAT 500e- at different speeds. The resulting resuspended particle cloud was characterized using a network of microsensors positioned near the seeded section. Experimental emission factors of PM10, PM2.5 and PM1 (EF10, EF2.5, EF1) for both vehicles show a strong positive correlation with vehicle speed, significantly exceeding U.S. EPA and Amato model predictions, especially at higher speeds. Observed emissions were up to 8.4 times greater than AP-42 estimates for EF2.5, while differences between vehicles were marginal. Surface loading influenced EF10 and EF2.5 but had a negligible impact on EF1. The proposed model highlights speed as the dominant driver of resuspension, contrasting with AP-42’s emphasis on vehicle weight and loading, though further validation is required.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Assessment of vehicle-induced road dust resuspension using a novel experimental approach in semi-controlled conditions

  • 期刊论文

  • 发表日期：

    2025年7月22日

查看全文
  • Ahmed Benabed,
  • Elizabeth Fu,
  • Adrian Arfire,
  • Pierre Pousset

摘要

Road traffic-induced dust resuspension is a significant contributor to transport-related particulate matter (PM) emissions. However, current attempts to quantify this source show significant discrepancies across studies. This variability primarily arises from limitations in experimental methodologies, which often fail to assess the effects of key influencing parameters particularly vehicle speed, vehicle weight, and surface dust load. As a result, existing empirical models typically do not account for these factors, leading to limited predictive reliability. To address these shortcomings, this study investigates the influence of these three parameters on vehicle-induced road dust resuspension under controlled conditions. A new experimental protocol was specifically designed and implemented on an isolated test track. This involved artificially seeding a road section with particles and subsequent passages using electric vehicles- Hybrid Peugeot 3008 and FIAT 500e- at different speeds. The resulting resuspended particle cloud was characterized using a network of microsensors positioned near the seeded section. Experimental emission factors of PM10, PM2.5 and PM1 (EF10, EF2.5, EF1) for both vehicles show a strong positive correlation with vehicle speed, significantly exceeding U.S. EPA and Amato model predictions, especially at higher speeds. Observed emissions were up to 8.4 times greater than AP-42 estimates for EF2.5, while differences between vehicles were marginal. Surface loading influenced EF10 and EF2.5 but had a negligible impact on EF1. The proposed model highlights speed as the dominant driver of resuspension, contrasting with AP-42’s emphasis on vehicle weight and loading, though further validation is required.