Imagine how ineffective a weather forecast would be if meteorologists assumed that no one ever left their homes. Now, imagine that instead of rain, the troubled forecast was trying to quantify the single biggest health risk facing city dwellers: air pollution.
That’s right folks, most of our existing epidemiological studies into urbanites’ exposure to air pollution have failed to account for the fact that we move around in our cities. Instead, researchers assessed toxic exposure based on where subjects live, rather than where they travel and spend the most time during the day.
But now, a new study from MIT’s Senseable City Lab is upending this status quo. By harnessing cellular network information, researchers can see where urban populations move throughout the day, leading to better understanding of exposure to pockets of pollution. The MIT study focused on on a particularly pernicious airborne particle, PM2.5—which is linked to asthma, heart disease, and poor lung function.
Overlaying movement information onto measurements of PM2.5, MIT’s researchers found that many more people were exposed to pollution in midtown Manhattan, Queens, and central Brooklyn than previously believed. Those numbers also shifted depending on the time of day, with fewer people exposed to pollution in midtown during the night, and greater levels of exposure in southern Brooklyn in the evenings when people were returning home after work.
Dr. Marguerite Nyhan, the study’s lead researcher, believes that this new model for measuring PM2.5 exposure can utterly transform the ways we combat this health crisis.
"It is estimated that up to 3.7 million people die per year, prematurely, due to exposure to pollution in cities," Nyhan explained at a TEDx event earlier this year. "Environmental policies no longer need to be static. They can be dynamic and [respond] to episodes of pollution as they occur."
With the rise of smart home air monitors and even more tricked-out smartphones, a wealth of live air quality data could soon be aggregated and put to use for the greater good.
"By overlaying live environmental data with live transportation data, we can modify our transportations systems, modify our traffic through adaptive and intelligent traffic signaling to reduce emissions, and reduce air pollution concentration levels in extremely targeted areas," said Nyhan.
This new level of data analysis and interaction could lead to a new generation of tools to combat the problem. Imagine a self-driving car service with "pollution pricing"—charging riders more for contributing emissions in highly populated areas. Or self-driving cars that automatically reroute themselves to help keep pollution concentrations low in certain neighborhoods.