Work

Evaluation of PM2.5 Exposures for an Environmental Justice Community Using a Low-Cost PM Sensor

Year: 2019

Type: article

Abstract: TPS 652: Air pollution exposure modeling 2, Exhibition Hall, Ground floor, August 28, 2019, 3:00 PM - 4:30 PM As low-cost air sensors become readily available, communities have opportunities to collect air quality and exposure data to improve their knowledge and contribute to environmental and public health research and policy decisions. Citizen scientists in Globeville, Elyria Swansea (GES), an environmental justice community in Denver, conducted air quality and exposure measurements for PM2.5 in the summer and fall of 2018 using RTI MicroPEM sensors. MicroPEMs were placed at 12 sites within the community as a neighborhood-scale sensor network, including near-road (NR) sites collocated with reference monitors. Citizens (n=19) measured their exposures and completed time-activity diaries for a 72-hour monitoring period. MicroPEMs showed high accuracy (>90%) after adjustment using filter-based gravimetric data and correcting temperature-associated bias. Black carbon (BC) was analyzed from the MicroPEM filters. Ambient PM2.5 data showed spatial variability with up to 3 times difference between site pairs. We observed up to 2.5 times higher PM2.5 levels in summer. The temporal measurements among the 12 sites were highly correlated (Pearson r > 0.7) with 84% of PM mass as BC, showing a large influence of traffic on the community air quality. For personal monitoring, individual’s exposure levels varied by day, influenced by activity types. Real-time peak concentrations matched with participant-reported exposure events such as proximity to combustion sources and cleaning. On average, personal PM2.5 exposures were higher than the nearby ambient sensor measurement and lower than the NR site. However, for the individuals with high exposure events, the personal levels were 6-24 times higher than nearby ambient sensor levels. This study shows the value of low-cost sensors in assessing the spatial and temporal variabilities of air quality and the degrees of exposure measurement error, which is critical for accurate epidemiologic models. Additionally, the sensors are useful tools for behavioral intervention to reduce air pollution exposure. EPA Grant Number: RD83618701

Source: Environmental Epidemiology

DOI: 10.1097/01.ee9.0000606428.38783.53