According to the World Health Organization, 92% of people worldwide breathe unhealthy air, yet ambient pollution measurements are limited. Even when available, ambient monitoring does not provide information on the hyperlocality of pollution concentrations.
In June 2017, Aclima, the University of Texas at Austin, the Environmental Defense Fund, and Google released High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data, the peer-reviewed study published in Environmental Science and Technology journal outlining our research approach in mapping hyperlocal pollution in West Oakland. Our study shows that air pollution is hyperlocal, varying block by-block across urban airsheds and demonstrates Aclima’s scalable measurement technique for mapping air pollution at as much as 100,000 times greater spatial resolution than is possible with current regulatory monitors.
What We Found
Google and Aclima’s California driving campaign in Los Angeles, the Central Valley, and San Francisco Bay Area, is one of the largest data sets of urban air pollution ever assembled. This campaign demonstrates the value of Aclima’s highly scalable approach to cost-effective, high-resolution air quality measurement to identify local pollution hotspots and better understand impacts on human health and the environment.
Our measurements reveal that urban air pollution is surprisingly more variable than previously appreciated, with air quality changing over the course of a city block. Conventional fixed-site measurements provide regional snapshots of air quality, but local variation is known to profoundly impact public health and environmental equity.
How We Did It
To push the limits of current sensing technology, Aclima has established scientific and research relationships of the highest caliber. In 2013, Aclima and the EPA signed a Cooperative Research and Development Agreement (CRADA). In 2014, the first mobile measurement system participated in DISCOVERY AQ in Denver. The collaboration brings together EPA scientists with Aclima’s Research and Development team to improve data quality from small-scale sensors. The partnership is advancing Aclima’s measurement methods for its stationary and vehicular sensing platforms.
Aclima and Google designed the daily driving plan for cars to ensure that selected neighborhoods and areas were systematically sampled at different times of the day, week, and year. Cars drove in the flow of traffic at normal speeds. Over the course of a year, the team drove the same streets a number of times.
Aclima’s detectors are powerful enough to discern the tiniest amounts of airborne pollutants, but small enough to fit inside a Subaru Impreza, and later in the Hyundai Santa Fes used in the Oakland study. (In the past, similar mobile monitoring projects in places like Beijing and Helsinki required large trucks and vans, often built and operated by the scientists themselves.) Google provided the Street View cars and drivers, the cloud infrastructure to store the data, and the mapping platforms (like Google Maps and Google Earth) to get the word out about the findings.
Aclima’s platform in the cars integrates sensing hardware, data management and computation, quality control, and visualization functions, which allows us to generate extensive, routine measurements and analysis. The system continuously streams data to Aclima’s cloud-based data processing and storage system where data is aggregated and analyzed. In addition to air quality measurement, the mobile platform digitizes and prepares each air-sample for geospatial visualization through an on-board data management system. An extensive network management system enables scientists and engineers at Aclima to monitor conditions in real-time.
Scaling This Effort
Routine availability of high-resolution air quality data in all major cities could have transformative implications for environmental management, air pollution and health, science and civic engagement, and policy. By highlighting localized pollution hotspots, these data may identify new opportunities for pollution control or exposure avoidance. Street-level air quality data can complement, challenge, and validate other diverse air quality datasets, including regulatory monitoring data, chemical transport model outputs, land-use regression predictions, and remotely-sensed observations.
Through combination with personal GPS data on smartphone applications, rich ‘personal exposure analytics’ become possible, which could enhance epidemiological studies and inform personal behavior — much as real-time traffic data now informs individual driving patterns. Broader societal consequences of the public awareness enabled by spatially extensive monitoring and high-resolution pollution maps might include shifts in urban land-use decisions, regulatory actions, and in the political economy of environmental “riskscapes.”
Read about why we chose to drive in California.
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