Fine-Scale Spatiotemporal Air Pollution Analysis Using Mobile Monitors on Google Street View Vehicles

• 成果类型: Article
• 署名作者: Guan, Yawen; Johnson, Margaret C.; Katzfuss, Matthias; Mannshardt, Elizabeth; Messier, Kyle P.; Reich, Brian J.; Song, Joon J.
• 署名单位: North Carolina State University; Texas A&M University System; Texas A&M University College Station; United States Environmental Protection Agency; Oregon State University; Baylor University; University of Nebraska System; University of Nebraska Lincoln; National Aeronautics & Space Administration (NASA); NASA Jet Propulsion Laboratory (JPL); California Institute of Technology
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2019.1665526
• 发表日期: 2020
• 页码: 1111-1124
• 关键词: covariance functions; exposure; sensor; SYSTEM; MODEL

摘要

People are increasingly concerned with understanding their personal environment, including possible exposure to harmful air pollutants. 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. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.