Abstract Hanoi, the capital of Vietnam, frequently experiences heavy air pollution episodes in the winter, causing health concerns for the 7.5 million people living there. Spatial-temporal variations in PM2.5 levels can provide useful information about the sources and transportation of PM2.5. However, the published spatial-temporal data in the area are limited. In this research, PM2.5 concentrations at two sites in Hanoi and a site in Thai Nguyen (60 km north of Hanoi) were observed from October 2017 to April 2018, using newly available low-cost sensors. Hourly concentrations of PM2.5 at the three sites were similar on average (57.5, 54.9, and 53.6 μg m−3) and clearly co-varied, suggesting remarkable large-scale effects. The contribution of long-range transport and meteorological factors on PM2.5 levels were investigated with a machine learning technique based on a random forest (RF) algorithm and concentration weight trajectory (CWT). The results showed that the contribution of long-range transport from the north and northeast to local PM2.5 levels was significant. Moreover, weather normalized PM2.5 concentrations and partial plots of meteorological factors on the levels of PM2.5 showed that meteorological conditions play a significant role in the formation of winter haze events.

中文翻译：

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使用紧凑型传感器和机器学习方法的多站点测量揭示了气象条件和远程传输对河内 PM2.5 水平的影响

摘要 越南首都河内在冬季经常遭受严重的空气污染事件，给居住在那里的 750 万人带来健康问题。PM2.5 水平的时空变化可以提供有关 PM2.5 来源和运输的有用信息。然而，该地区公布的时空数据有限。在这项研究中，从 2017 年 10 月到 2018 年 4 月，使用新推出的低成本传感器观察了河内两个地点和太原（河内以北 60 公里）的一个地点的 PM2.5 浓度。三个地点的 PM2.5 每小时浓度平均相似（57.5、54.9 和 53.6 μg m-3）并且明显共同变化，表明显着的大规模效应。远距离传输和气象因素对 PM2.5 的贡献。使用基于随机森林 (RF) 算法和浓度权重轨迹 (CWT) 的机器学习技术研究了 5 个级别。结果表明，来自北方和东北部的远程传输对当地PM2.5水平的贡献显着。此外，天气标准化 PM2.5 浓度和气象因素对 PM2.5 水平的部分图表明，气象条件在冬季雾霾事件的形成中起着重要作用。