本期为大家推荐的内容为论文《
Wind-urban structure interplay in PM2.5 variation: Insights from multi-seasonal mobile air quality campaign
》（
PM2.5变化中的风-城市结构相互作用：来自多季节移动空气质量监测活动的见解
），发表
在
Transactions in Urban Data, Science, and Technology
期刊，欢迎大家学习与交流。
与颗粒物污染（PM2.5）相关的健康风险突显了理解其时空变异关键驱动因素的重要性。尽管解释性建模广泛用于此类应用中的统计推断，但风速、风向等变量及其与建筑环境的相互作用往往被忽视。本研究通过为期十天的移动空气质量监测活动，覆盖三个不同季节的郊区，填补了这一空白。我们开发了四种创新的基于风的缓冲区，以考虑风因素，并评估由LiDAR衍生的三维建筑环境结构对局部和区域尺度PM2.5变异的影响。我们的第二个目标是评估基于风的变量的预测能力。结果表明，在我们的低海拔、低层建筑研究区域内，建筑环境并未表现为显著因素，这与大型、密集城市地区的研究结果相反。与风速相比，风向更能有效捕捉浓度波动，其与污染源方位的相互作用可以改变污染动态。本研究为在空气污染动态分析中纳入风相关因素引入了新的见解和方法，提供了在不同城市环境中进一步研究的机会
。
题目：
Wind-urban structure interplay in PM2.5 variation: Insights from multi-seasonal mobile air quality campaign
（
PM2.5变化中的风-城市结构相互作用：来自多季节移动空气质量监测活动的见解
）
作者：
Noah Ray, Pinliang Dong, John South, and Lu Liang
*
发表刊物：
Transactions in Urban Data, Science, and Technology
URL:
https://journals.sagepub.com/doi/full/10.1177/27541231241248858
引用格式：
Wind-urban structure interplay in PM2.5 variation: Insights from multi-seasonal mobile air quality campaign. Transactions in Urban Data, Science, and Technology, 3(1-2), 61-79. https://doi.org/10.1177/27541231241248858
The health risks associated with particulate matter pollution (PM2.5) highlight the importance of comprehending key drivers of its spatial and temporal variability. While explanatory modeling is widely used for statistical inference in such applications, the role of wind variables (speed, direction) and their interplay with built environment are often overlooked. This study addresses this gap through a mobile air quality campaign in a suburban area over 10 days for each of three distinct seasons. We developed four innovative wind-based buffers to account for the wind factors and assess the impact of LiDAR-derived 3D built environment structure on PM2.5 variability at local and regional scales. Our second objective is to assess the predictive capabilities of wind-based variables. Results indicate that in our low-elevation, low-rise building study area, the built environment does not emerge as a significant factor, contrary to findings in larger, denser urban areas. Wind direction proves more effective in capturing concentration fluctuations than wind speed, and its interaction with pollution source orientation can change pollution dynamics. This study introduces new insights and methodologies for incorporating wind-related factors into the analysis of air pollution dynamics, offering opportunities for further investigation in various urban settings
.
