Fine-scale spatiotemporal air pollution analysis using mobile monitors on Google Street View vehicles

TL;DR

This paper develops a computationally efficient spatiotemporal model for mobile air pollution data from Google Street View vehicles, enabling real-time, high-resolution pollution maps and forecasts to improve public awareness and exposure assessment.

Contribution

It introduces a novel, scalable modeling framework for mobile air quality measurements, enhancing real-time pollution mapping and short-term forecasting capabilities.

Findings

Mobile networks provide more nuanced pollution information than fixed networks.

The model accurately predicts short-term air quality variations.

Real-time high-resolution maps can identify pollution hot spots.

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 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.