Moronta-Sabad Heidel, Ariño Arturo H, de la Calle-Arroyo Carlos, Santos-Buitrago Rocío, Santamaría Jesús Miguel, Pons Juan José, Elustondo David
BIOMA Institute for Biodiversity and the Environment, University of Navarra, Pamplona, Spain.
BIOMA Institute for Biodiversity and the Environment, University of Navarra, Pamplona, Spain; DATAI Institute of Data Science and Artificial Intelligence, University of Navarra, Pamplona, Spain.
Environ Pollut. 2025 Jun 1;374:126106. doi: 10.1016/j.envpol.2025.126106. Epub 2025 Mar 21.
Urban population growth has led to an increase in the number of people living near major roads and highways, increasing exposure to roadside air pollution. This has raised significant public health concerns and driven efforts to regulate air quality in these micro-environments. Solutions such as the implementation of vegetation barriers can reduce exposure to traffic-related emissions by influencing pollutant dispersion patterns. Three primary methods are commonly used to characterize pollutant distribution in complex urban environments: (i) geostatistical analysis using remote sensing, (ii) high-precision measurements across physical barriers, and (iii) dispersion models, particularly computational fluid dynamics (CFD) models. Although numerous studies have investigated the role of vegetation in mitigating traffic-related air pollution, most have relied on small-scale assessments or modeled data. This study presents a comprehensive workflow for evaluating the effectiveness of vegetation barriers in improving urban air quality. It utilizes real-world data collected over two years (May 2015-December 2017) using low-cost mobile sensors in Pamplona, Spain -a medium-sized European city representative of 80 % of urban areas in Europe- within the framework of the LIFE + Respira project. Seven pollutants (CO, NO, NO, O, PM, PM, and PM) were analyzed. Results revealed significant reductions in CO, NO, and NO levels behind vegetation barriers, while O increased. Findings for PMx were mixed, suggesting that barrier effectiveness depends on particle size and vegetation characteristics. These results are consistent with previous research validating the methodology. Future studies could refine this approach, assess long-term vegetation impacts, and explore additional environmental factors influencing urban air pollution dynamics.
城市人口增长导致居住在主要道路和高速公路附近的人数增加，从而使人们更多地暴露于路边空气污染中。这引发了重大的公共卫生问题，并推动了对这些微环境空气质量进行监管的努力。诸如设置植被屏障等解决方案可以通过影响污染物扩散模式来减少与交通相关排放物的暴露。通常使用三种主要方法来表征复杂城市环境中的污染物分布：（i）利用遥感进行地统计分析，（ii）跨物理屏障进行高精度测量，以及（iii）扩散模型，特别是计算流体动力学（CFD）模型。尽管众多研究调查了植被在减轻与交通相关的空气污染方面的作用，但大多数研究都依赖于小规模评估或模型数据。本研究提出了一个全面的工作流程，用于评估植被屏障在改善城市空气质量方面的有效性。它利用了在西班牙潘普洛纳（欧洲一个中等规模城市，代表了欧洲80%的城市地区）的LIFE + Respira项目框架内，使用低成本移动传感器在两年时间（2015年5月至2017年12月）收集的实际数据。分析了七种污染物（一氧化碳、一氧化氮、二氧化氮、臭氧、细颗粒物、可吸入颗粒物和总悬浮颗粒物）。结果显示，植被屏障后面的一氧化碳、一氧化氮和二氧化氮水平显著降低，而臭氧增加。细颗粒物的研究结果不一，表明屏障的有效性取决于颗粒大小和植被特征。这些结果与先前验证该方法的研究一致。未来的研究可以完善这种方法，评估植被的长期影响，并探索影响城市空气污染动态的其他环境因素。
