High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data

Abstract

Air pollution affects billions of people worldwide, yet ambient pollution measurements are limited for much of the world. Urban air pollution concentrations vary sharply over short distances (≪1 km) owing to unevenly distributed emission sources, dilution, and physicochemical transformations. Accordingly, even where present, conventional fixed-site pollution monitoring methods lack the spatial resolution needed to characterize heterogeneous human exposures and localized pollution hotspots. Here, we demonstrate a measurement approach to reveal urban air pollution patterns at 4-5 orders of magnitude greater spatial precision than possible with current central-site ambient monitoring. We equipped Google Street View vehicles with a fast-response pollution measurement platform and repeatedly sampled every street in a 30-km area of Oakland, CA, developing the largest urban air quality data set of its type. Resulting maps of annual daytime NO, NO, and black carbon at 30 m-scale reveal stable, persistent pollution patterns with surprisingly sharp small-scale variability attributable to local sources, up to 5-8× within individual city blocks. Since local variation in air quality profoundly impacts public health and environmental equity, our results have important implications for how air pollution is measured and managed. If validated elsewhere, this readily scalable measurement approach could address major air quality data gaps worldwide.

Comparisons of traffic-related ultrafine particle number concentrations measured in two urban areas by central, residential, and mobile monitoring

“…For example, the latter approach may be more effective in areas where higher spatial contrasts are expected – i.e., in areas containing multiple busy roadways – and long-term trends are of interest. New mobile monitoring strategies, such as measuring NO 2 with Google Street View vehicles (Apte et al, 2017), could aid in this approach and may increase the ability to characterize the high spatial variability of UFP. In contrast, the former approach may be useful in more residential areas with fewer busy roadways.…”

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

“…Within their applications, bicycles are equipped with compact air quality measurement devices to monitor ultrafine particle number counts, particulate mass, and black carbon concentrations at a high resolution (up to 1 s), with each measurement automatically linked to its geographical location and time of acquisition using GPS and Internet time (Elen et al, ). Subsequently, Castell et al () demonstrated that data gathered from sensors mounted on mobile modes of transportation could be used to mitigate citizen exposure to air pollution, while Apte et al () applied moving platforms with the aid of Google Street View cars to collect air pollution data (black carbon) with reasonably high resolution.…”

New Approaches to Identifying and Reducing the Global Burden of Disease From Pollution

“…Rapid proliferation of low-cost sensing technologies on the market, while still challenged by quality and durability issues, is likely to be increasingly used in citizen science contexts and may serve a role in air quality surveillance, health studies, and public awareness (more on this in the Case Studies section) particularly as the technology advances (U.S. Environmental Protection Agency, 2014). Pilot studies using air quality monitors on vehicles have also shown some promise in highlighting how air pollution levels differ between neighborhoods within cities (Alexeeff et al, 2018;Apte et al, 2017). Important limitations to using these new technologies remain, including challenges with achieving a high enough degree of accuracy and precision.…”