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

• Activity Number: 249 - The Climate Program at SAMSI
• Type: Contributed
• Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Yawen Guan* and Margaret Johnson and Matthias Katzfuss and Elizabeth Mannshardt and Kyle Messier and Brian Reich and Joon Jin Song
• Companies: North Carolina State University and JPL and Texas A & M University and US Environmental Protection Agency and Oregon State University and North Carolina State University and Baylor University
• Keywords: Spatiotemporal models; Mobile sensors; Vecchia approximation; Google Street View Air Quality Data

Abstract

People are increasingly concerned with understanding their personal environment, including possible exposure to harmful air pollutants. 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 as well as 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 high-resolution maps of current air pollution levels and short-term forecasts. This modeling framework has important real-world implications in understanding citizens' personal environments, as data production continue to be driven by the ongoing improvement of mobile measurement technologies.