10th International Aerosol Conference September 2 - September 7, 2018 America's Center Convention Complex St. Louis, Missouri, USA
Abstract View
Evaluation of Wearable Low-Cost Particulate Matter Sensors
Ryan Chartier, JONATHAN THORNBURG, RTI International
Abstract Number: 1486 Working Group: Low-Cost and Portable Sensors
Abstract Air pollution is one of the largest health risk factors in the world and exposure to poor air quality can lead to a multitude of adverse health outcomes. Local air quality is often measured by outdoor monitoring stations, but Americans spend greater than 90% of their lives indoors, making improved assessment of personal air quality exposures on a community and city level a top priority for health researchers. Reducing exposures to harmful indoor air pollutants, such as particulate matter (PM), will have positive health benefits, yet outside of small targeted research studies, real-time PM exposure data are rarely available at a highly spatially-resolved personal level.
The increasing availability of low-cost air quality sensors will make it possible to deploy large numbers of devices to collect highly spatially resolved personal level exposure data while increased computational power and innovative data analysis and visualization tools will allow us to understand exposure trends and hotspots at unprecedented resolution. However, low-cost air quality sensors generally provide poor data quality relative to more expensive reference monitors, limiting their appeal to exposure-health researchers.
We evaluated the performance of several commercially available wearable low-cost PM sensors against reference monitors including the RTI MicroPEM personal exposure monitor and a TSI DustTrak 8530. Three of each low-cost sensor were collocated with the reference monitors and challenged with various types of PM (black carbon, oleic acid, test dust) at concentrations ranging from approximately 0-500 µg/m3. Each test was repeated multiple times, on different days, to assess measurement repeatability of the sensors. Key performance indicators (accuracy, precision, repeatability, data capture efficiency) for each sensor were quantified. Data aggregation and correction algorithms were developed to process and improve the data quality obtained from the low-cost sensors, and to support the visualization of exposure data as we move towards an integrated network of sensors.