Engineering researchers at The University of Texas at Austin and colleagues have demonstrated a measurement approach to for urban air pollution mapping at 4–5 orders of magnitude greater spatial precision than possible with current central-site ambient monitoring.
The team equipped two Google Street View vehicles with the fast-response Aclima Ei measurement and data acquisition platform and repeatedly sampled every street in a 30-km2 area of Oakland, CA, over the course of a year, developing the largest urban air quality data set of its type. The resulting maps of annual daytime NO, NO2, and black carbon at 30 m-scale revealed stable, persistent pollution patterns with “surprisingly” sharp small-scale variability attributable to local sources, up to 5–8× within individual city blocks. A paper on their work is published in the ACS journal Environmental Science & Technology.
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.—Apte et al.
The research team was led by Cockrell School of Engineering assistant professor Joshua Apte in partnership with the Environmental Defense Fund (EDF), Google and Aclima, a San Francisco-based company that delivers environmental intelligence through sensor networks. The study included co-authors from the University of Washington, University of British Columbia, Utrecht University, Lawrence Berkeley National Laboratory, Aclima and EDF.
Air pollution is a major global risk factor for illness and death, and the air pollution that people are breathing can be, at times, far worse than what official air quality monitors report. Most large urban areas have only one air quality monitor for every 100 to 200 square miles. In comparison, the mobile approach outlined in the paper and led by researchers at UT Austin, maps air pollution every 100 feet, or at about four to five locations along a single city block.
Air pollution varies very finely in space, and we can’t capture that variation with other existing measurement techniques. Using our approach and analysis techniques, we can now visualize air pollution with incredible detail. This kind of information could transform our understanding of the sources and impacts of air pollution.—Joshua Apte
The Aclima Ei platform provides data management, quality control, and visualization functions, facilitating extensive, routine measurements. The study used fast-response (1 Hz) laboratory-grade analyzers: a photo-acoustic extinctiometer for BC; chemiluminescence for NO; and cavity-attenuation phase-shift spectroscopy for NO2. The inlet system was designed to minimize self-sampling and particle sampling losses.
The study’s approach was designed to be cost-effective and easily replicated. Driving more than 14,000 miles, the Google cars collected 3 million measurements of nitric oxide, nitrogen dioxide and black carbon pollutants in Oakland.
The team developed a set of data reduction algorithms to convert the data set of ∼3 million instantaneous observations into estimates of median annual weekday concentrations for individual 30-m road segments.
In many locations, the team’s Google cars measured air pollution levels that were several times higher than at Oakland’s official monitors. In their analysis, the researchers also identified many recurring hotspots where pollution on a single block was consistently much higher than elsewhere in a neighborhood. These pollution hotspots included the port, busy intersections, restaurants, warehouses, industrial plants and vehicle dealerships.
What surprised us is that there are consistently locations that can be as much as six times more polluted on one end of the block than on the other. Among other things, this demonstrates that people are getting disproportionate exposures of unhealthy air at some locations.—Kyle Messier, a UT Austin postdoc and a co-author
This project is the latest phase of a partnership between EDF and Google, who have been working together since 2012 to map and measure a growing list of health and environmental risks, including hidden leaks from local natural gas systems.
In the future, Apte hopes this mobile air quality monitoring approach expands to other major cities to help formulate a hyper-local map of air pollution in the United States that could help people make more informed decisions.
You could use this information when you’re picking a school for your kids. Is there a school with a playground that might have better air quality because your kid has asthma?” This hyper-local information about consistent air quality can be really useful for people, especially those who are vulnerable because of age or health condition.—Joshua S. Apte
Funding for the study was provided by the Environmental Defense Fund.
Joshua S. Apte, Kyle P. Messier, Shahzad Gani, Michael Brauer, Thomas W. Kirchstetter, Melissa M. Lunden, Julian D. Marshall, Christopher J. Portier, Roel C.H. Vermeulen, and Steven P. Hamburg (2017) “High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data” Environmental Science & Technology doi: 10.1021/acs.est.7b00891