The impact of urban mobility on air pollution in Kampala, an exemplar sub-Saharan African city
The impact of urban mobility on air pollution in Kampala, an exemplar sub-Saharan African city
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DOI：
10.1016/j.apr.2024.102057
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发表时间：
2024-01-31
影响因子：
4.5
通讯作者：
Pope，Francis D.
中科院分区：
文献类型：
--
作者：
Ghaffarpasand，Omid;Okure，Deo;Pope，Francis D.
本文通过研究干预措施对当地空气质量的影响，分析了城市交通（UM）对空气污染的影响。该研究重点关注乌干达首都坎帕拉的 PM2.5 水平，并将 COVID-19 视为一种无意干预。该市的 PM2.5 水平是通过低成本校准传感器网络获得的，而 UM 的特点是开放获取的 Google 报告。所考虑的时期不包括第一次封锁前后的几周。利用随机森林（RF）机器学习技术对PM2.5数据进行去风化，排除气象因素、季节性、工作日-周末效应的变化，然后对疫情的影响进行参数化。讨论了交通模式，并使用气团聚类和污染极坐标图分别分析了远程源和短程源的分布。然后根据去风化的 PM2.5 水平的百分比来评估 UM 尺寸平均值相对于基线 (PCfB) 的百分比变化，以研究 UM 对 PM2.5 水平的影响。我们的分析显示，城市交通与路边 PM2.5 水平之间存在很强的相关性，而与城市 PM2.5 水平之间的相关性较弱。在研究期间，远程排放源的概况是一致的，到达坎帕拉的模拟气团中有超过 61% 首先经过肯尼亚和坦桑尼亚。总体而言，COVID-19 大流行使坎帕拉的 PM2.5 水平降低了约 10%，与世界各地已研究的许多其他城市相比，这一数字相对较小。
This paper analyses the impact of urban mobility (UM) on air pollution by studying the effects of an intervention on local air quality. The study focuses on the PM2.5levels in Kampala, the capital of Uganda, and considers COVID-19 as an unintentional intervention. The PM2.5level of the city was obtained from a network of low-cost calibrated sensors, while UM is characterized by open-access Google reports. The period under consideration excludes the weeks immediately before and after the first lockdown. PM2.5data were deweathered using the machine learning technique of random forest (RF) to exclude the variation of meteorological factors, seasonality, and weekday-weekend effect, and then the impact of the pandemic was parametrised. The traffic pattern is discussed, and air mass clustering and pollution polar plots are used to analyse the distribution of long- and short-range sources, respectively. The percentage change from the baseline (PCfB) of the average of UM dimensions is then assessed against that of deweathered PM2.5level to investigate the impact of UM on the PM2.5level. Our analysis shows a strong correlation between urban mobility and roadside PM2.5levels and a weaker relationship with urban PM2.5levels. The profile of long-range emission sources was consistent over the study period, with more than 61% of the modelled air masses that arrived in Kampala first passing over Kenya and Tanzania. Overall, the COVID-19 pandemic reduced PM2.5levels in Kampala by about 10%, which is relatively small compared to many other cities that have been studied around the world.