Fine-scale spatiotemporal air pollution analysis using mobile monitors on Google Street View vehicles.
Fine-scale spatiotemporal air pollution analysis using mobile monitors on Google Street View vehicles.
复制标题
使用谷歌街景车辆上的移动的监视器进行精细尺度时空空气污染分析。
DOI：
10.1080/01621459.2019.1665526
复制
发表时间：
2020
影响因子：
3.7
通讯作者：
Song JJ
中科院分区：
文献类型：
--
作者：
Guan Y;Johnson MC;Katzfuss M;Mannshardt E;Messier KP;Reich BJ;Song JJ
人们越来越关心了解他们的个人环境，包括可能接触有害空气污染物。为了在日常活动中做出明智的决定，他们对局部范围内的实时信息感兴趣。使用移动监测仪获得的公开、精细、高质量的空气污染测量代表了测量技术的范式转变。利用这些日益精细的测量方法，在精细空间尺度上提供实时空气污染地图和短期空气质量预报的方法框架，可能有助于提高公众的认识和理解。谷歌街景研究提供了一个具有空间和时间复杂性的独特数据来源，有可能提供有关通勤者暴露和高流量城市街道热点的信息。我们为这些数据开发了一个计算效率高的时空模型，并使用该模型对当前空气污染水平进行短期预测和高分辨率地图。我们还通过实验表明，移动网络可以提供比同等大小的固定位置网络更细微的信息。随着移动测量技术的不断发展和改进，数据生产和实时可用性继续受到推动，该建模框架在理解公民的个人环境方面具有重要的现实意义。
People are increasingly concerned with understanding their personal environment, including possible exposure to harmful air pollutants. In order to make informed decisions on their day-to-day activities, they are interested in real-time information on a localized scale. Publicly available, fine-scale, high-quality air pollution measurements acquired using mobile monitors represent a paradigm shift in measurement technologies. A methodological framework utilizing these increasingly fine-scale measurements to provide real-time air pollution maps and short-term air quality forecasts on a fine-resolution spatial scale could prove to be instrumental in increasing public awareness and understanding. The Google Street View study provides a unique source of data with spatial and temporal complexities, with the potential to provide information about commuter exposure and hot spots within city streets with high traffic. We develop a computationally efficient spatiotemporal model for these data and use the model to make short-term forecasts and high-resolution maps of current air pollution levels. We also show via an experiment that mobile networks can provide more nuanced information than an equally-sized fixed-location network. This modeling framework has important real-world implications in understanding citizens’ personal environments, as data production and real-time availability continue to be driven by the ongoing development and improvement of mobile measurement technologies.
登录
查看更多内容
影响因子：
3.8
作者：
Holstius, D. M.;Pillarisetti, A.;Seto, E.
通讯作者：
Seto, E.
影响因子：
5
作者：
Chang, Howard H.;Reich, Brian J.;Miranda, Marie Lynn
通讯作者：
Miranda, Marie Lynn
影响因子：
11.8
作者：
Briggs, David J.;de Hoogh, Kees;Gulliver, John
通讯作者：
Gulliver, John
影响因子：
2.5
作者：
Guinness, Joseph
通讯作者：
Guinness, Joseph
影响因子：
5.6
作者：
Castellini, S.;Moroni, B.;Cappelletti, D.
通讯作者：
Cappelletti, D.