New Study Provides Evidence that Minorities and Low-Income Communities are Disproportionately Impacted by Roadway Pollution, and Demonstrates a Better Approach to Determine Future Vehicle Pollution
A few years ago we in the NRDC LA Air and Environmental Justice team were lucky to have Professor Gregory Rowangould on staff as a Science Fellow. I learned a lot from him during that short period, and am very excited to share that a study he began during that time was published in December in Transportation Research Part D: Transport and the Environment. It is available to download for free until February 5, if you want to check it out here.
His study focuses on developing a refined method for understanding how particulate matter pollution from car and truck exhaust spreads from streets and highways. Computer modeling is used by regulatory agencies to project whether a region's plans to reduce air pollution are good enough, or in other words, whether the plans will really reduce the region's air pollution to meet national standards. Computer modeling is also used to determine whether a proposed project, like a proposal to expand a freeway, will increase pollution so much it will make the region violate national air standards. This kind of computer modeling analysis is really important, because it is often used by a government agency or a business that wants to move forward with a project to declare that their project is great, that it won't hurt air quality, and that it will help the region meet national standards. We need the modeling analysis to be as accurate as possible, so that the decision makers and the public truly understand what the impacts would be.
Further, this computer modeling analysis is usually only done when a particular project is being proposed; the modeling is used to study what the impacts would be from that particular project. But at that point, everyone is already rallying behind the project, such as a city, businesses, the lead agency, etc. If we want to really understand the existing air pollution problem, and figure out what are the best ways to fix it, we need to do computer modeling way before a particular project is proposed.
Currently, as Professor Rowangould observed, where there isn't a project being proposed, there is often no modeling being performed at all to understand the air pollution that exists in neighborhoods from existing highways. So he developed a new method that can perform the kind of detailed computer modeling that is done for proposed projects at a much larger scale. This required some computational innovations that are far beyond my computer science skills. The key is that this can be done to better understand existing air pollution problems, and the results can be used to figure out what the best solutions are to fixing those problems.
In his study, he looked at particulate matter that is less than 2.5 microns in size, known as PM2.5, across all of Los Angeles County. Using traffic data from 2003, he looked at the over 9,500 miles of major roadways in LA County. But he didn't stop there. He overlaid the results on top of Census population and median household income data, to see how pollution levels differed for the different communities across LA--based on race and income. His findings are consistent with what we in the environmental justice movement know to be true: low income and minority residents are disproportionately located in areas near high volume roads or where mobile source air pollution emissions are higher.
Importantly, Professor Rowangould's study demonstrated a better way to do air pollution modeling. This approach can be used by transportation planners nationwide to better understand our existing air pollution problems, identify better solutions, and rule out bad projects early on, way before a lot of money has been spent on expensive designs and analyses.