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

Abstract

People are increasingly concerned with understanding their personal environment, including possible exposure to harmful air pollutants. In order to make informed decisions on their dayto-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.

Scalable penalized spatiotemporal land-use regression for ground-level nitrogen dioxide

“…Our methods also offer a scalable way to analyze other large spatiotemporal datasets in environmental and human health risk assessment. For example, in the air-quality research community, mobile monitoring of air pollutants is leading to high-resolution datasets with millions of observations, including campaigns in Zurich, Switzerland (Li et al, 2012), Boston, MA (Padró-Martínez et al, 2012, Oakland, CA (Apte et al, 2017;Guan et al, 2020), Houston, TX (Miller et al, 2020), and the Netherlands (Kerckhoffs et al, 2019).…”

The TRAX Light-Rail Train Air Quality Observation Project

“…In the last decade, the deployment of mobile urban pollution monitoring systems using different platforms (e.g., trains, bikes, other vehicles) have been demonstrated in many cities across the world, such as Brisbane, Australia [22], Ontario, Canada [23], Oakland, California [24], Beijing, China [25], Seoul, Korea [26], and Hong Kong [27]. A recent effort to outfit Google street cars across several US cities has also been successful in mapping fine-scale urban pollution gradients [24,25,28,29]. The importance of combining these mobile platforms with fixed-site platforms to derive highly resolved spatial pollution maps and related pollutant exposure metrics is a very active area of research [30][31][32].…”

Mapping Urban Air Quality from Mobile Sensors Using Spatio-Temporal Geostatistics

“…Some studies used geostatistics as a way to map air pollution using low cost mobile sensors. Li et al [ 39 ] and Guan et al [ 48 ], on top of using several covariates in their geostatistical model, used a likelihood-based method making stricter assumptions about the underlying distribution of the data and increasing the computational resources, making it challenging to use in real-time applications. Gressent et al [ 43 ] used, as opposed to the likelihood method, a variogram-based method.…”