Using Emerging Sensor Networks to Investigate Impacts of Roadway Activities on PM10 and PM2.5
Research Lead: Suzanne Paulson
UC Campus(es): UCLA
Problem Statement: Air quality is of special concern for policymakers and planners in California and Southern California, specifically. Particulate matter smaller than 10 microns (PM10) is mostly produced by mechanical grinding, including from brake and tire wear. Particles smaller than 2.5 microns (PM2.5) are mostly from combustion and gas‐to‐particle conversion in the atmosphere, but the smallest mechanically generated particles also fall in this range. Brake wear may be of special concern because it can contain high concentrations of transition metals such as copper, which has been implicated in inflammation and disease. As other sources of PM10 and PM2.5 are cleaned up, brake and tire wear particles are of increasing concern around roadways. Routine PM10 and 2.5 monitoring networks are sparse, but a few studies have shown PM10 and PM2.5 to be only slightly elevated around roadways.
Project Description: This project seeks to leverage an emerging network of “cheap sensors” to address questions that have been difficult to address with existing monitoring. The new air quality monitoring network run by PurpleAir collects sufficiently high quality data and has now grown to have enough sensors to address questions about roadway impacts on PM10 and PM2.5. The research team has performed a preliminary investigation of the data and has been working with MATLAB code to do the analysis. UCLA proposes to analyze all available data within about 700 meters of major roadways; the PurpleAir network currently has about 1600 operating sensors, mostly in the US but increasingly in Europe, and adds sensors at over 100 per month. The team aims to explore the factors controlling elevated PM10 and PM2.5 around roadways and determine the effectiveness and limits of the data network in this application.
Status: In Progress