Mapping Air Pollution with Google Street View Cars
We used two specially equipped Google Street View cars to repeatedly map gaseous and particulate air pollution, block-by-block, in Oakland, California. By using a year of repeated measurements, our algorithms were able to map pollution at 30 meter scales - an unprecedented resolution for a measurement dataset. We find that air quality can persistently vary even within individual city blocks. We determined that the data requirements for making stable, high-resolution pollution maps are surprisingly modest. This straightforward measurement approach is now being scaled up to other cities around the world.
The challenge: how does air quality vary within cities?
Most routine air pollution measurements in cities are collected at a small number of "ambient" monitoring sites that provide report urban-background concentrations. In the US cities, there are generally 2-3 ambient monitoring sites for every million people. Despite the fact that air pollution can vary sharply in urban areas, our understanding of how pollution varies at the block-to-block scale is thus quite limited. This study demonstrates a new approach to filling this data gap by using specially equipped Google Street View cars to map urban air pollution every 30 meters, at 100,000× higher spatial resolution than possible with official monitors.
Article: Apte JS, Messier KP, Gani S, Brauer M, Kirchstetter TW, Lunden MM, Marshall JD, Portier CJ, Vermeulen RCH, Hamburg SP. High resolution air pollution mapping with Google Street View cars: Exploiting big data. ES&T, 2017. [open access].
Selected as ES&T's "Top Environmental Technology Article of 2017."
We repeatedly measured air pollution across the San Francisco Bay Area using two specially equipped Google Street View cars. Aclima equipped these cars with their mobile platform, which for this study incorporated research-grade fast-response gas and particle analyzers measuring at 1 second time resolution. Here, we focused on three urban air pollutants - black carbon particles (BC), nitric oxide (NO) and NO2 (nitrogen dioxide). For the first year, the cars repeatedly mapped the streets of three Oakland neighborhoods during daytime hours. The cars visited different neighborhoods on different days, collecting more than 3 million data points and logging more than 15,000 miles. Each city block was sampled on an average of 31 different days throughout the year. Our group then developed data analysis algorithms to convert this mobile monitoring dataset, perhaps the largest of its kind, into maps that represent consistent long-term trends of daytime, weekday air quality.
Fine-scale pollution data
By using data from repeated measurements on each city block over an entire year, our data analysis approach produces stable estimates of the long-term daytime median concentration for each 30 meter segment of roadway that we sampled. Our analyses suggest that concentration estimates for individual road segments are precise to within ± 10-20%.
We find that air pollution is surprisingly variable inside neighborhoods: concentrations can persistently vary by as much 5-8× within an individual city block. We were especially surprised to find small, consistent pollution hotspots on many city blocks. Each of these hotspots may arise for its own idiosyncratic reasons. We believe that common causes of hotspots include traffic congestion, industrial emissions, cooking emissions, and local truck or bus traffic. For our study area, pollution at the single official air quality monitoring site were similar to the levels we measured on quiet residential streets. However, many streets that we measured were consistently much more polluted than the official air quality data would suggest.
Towards scalability: Measure or model?
To scale this measurement approach around the world, it may be helpful to identify sampling approaches that minimize the amount of "driving effort" required to collect a city's dataset. One approach to doing so would be to simply reduce the number of repeated measurement visits to every street -- sacrificing precision, but gaining efficiency. An alternative approach is to predict air quality everywhere using models trained on measurements made on only a subset of a city's streets. Our unusually rich dataset from Oakland allowed us to investigate the relative advantages of each of these two approaches. While each approach has advantages, we demonstrate that useful air quality maps can be estimated from mobile monitoring campaigns even with minimal data collection.
Article: Messier KP, Chambliss SE, Gani S, Alvarez RA, Brauer M, Choi JJ, Hamburg SP, Kerckhoffs J, LaFranchi B, Lunden MM, Marshall JD, Portier CJ, Roy A, Szpiro AA, Vermeulen RCH, Apte JS. 2018. Mapping air pollution with Google Street View cars: Efficient approaches with mobile monitoring and land use regression. ES&T 52, 12563-12572 [open access]