When it comes to urban air pollution, particulate levels can change from block to block. And while those airborne pollutants affect everyone, cyclists — who travel relatively large swaths of a city while taking in cardio-level gulps of air — often find themselves on the front lines.
A new project from data specialist EarthSense Systems and mapping agency Ordnance Survey will use real-time air quality data to help bike riders in British cities find the cleanest routes, AirQualityNews.com reports.
“By making it easy for cyclists to see pollution levels before they make their journey, we can help them make better decisions about their route,” says Roland Leigh, technical director of EarthSense. “This [maximizes] the gain they are getting from the exercise whilst [minimizing] their exposure to harmful pollution.”
The data from EarthSense is available hourly, with a forecast of up to three days, the site reports. And its release will have potential beyond cyclist routes.
“The insights gained from such [modeling] can also be used by policy makers and city planners to make practical interventions around mitigating hot spots — such as traffic light phasing, coordination of [street works] or correctly maintained urban trees and hedges which can trap many harmful pollutants,” says Phillip Wyndham, strategic development manager at the Ordnance Survey.
A project conducted several years ago aimed for similar results in the U.S. In that study, an assistant professor at Columbia University placed wearable sensors on New York City bicyclists to measure their personal exposure to air pollution. One of the project’s aims was to encourage more sustainable infrastructure design, as well as showing cyclists which routes were best for their health.
Rachel Dovey is an award-winning freelance writer and former USC Annenberg fellow living at the northern tip of California’s Bay Area. She writes about infrastructure, water and climate change and has been published by Bust, Wired, Paste, SF Weekly, the East Bay Express and the North Bay Bohemian