AAAR 36th Annual Conference October 16 - October 20, 2017 Raleigh Convention Center Raleigh, North Carolina, USA
Abstract View
Spatial and Temporal Variability of Particulate Matter Using a Network of Air Quality Sensors in a Southern California Community
BRANDON FEENSTRA, Vasileios Papapostolou, Olga Pikelnaya, Hang Zhang, Andrea Polidori, South Coast Air Quality Management District
Abstract Number: 135 Working Group: Instrumentation and Methods
Abstract Air quality is typically measured by regulatory agencies by mean of highly accurate but very expensive EPA approved monitors. With the emergence of low-cost air quality sensors and the development of sensor networks, community groups and citizen scientists can now measure air pollution at the neighborhood, local, and even regional level. This study employs a network of 23 Purple Air PA-II sensors deployed at community members’ homes in southern California primarily in the cities of Redlands and Yucaipa located in San Bernardino County. The PA-II sensor measures particulate matter (PM) mass concentrations in three size ranges (i.e., PM1.0, PM2.5, and PM10), temperature, and relative humidity. Prior to deployment, the performance of the sensors was evaluated at the South Coast Air Quality Management District Air Quality Sensor Performance Evaluation Center (SCAQMD AQ-SPEC) program and was found to correlate well with Federal Equivalent Method (FEM) instruments with an R2 value of 0.96, 0.93, and 0.66 for PM1.0, PM2.5, and PM10, respectively. In this study, we discuss the spatial and temporal variability of PM in with respect to topography, large roadways, local area PM sources, meteorology, and weather conditions. The information gathered from the sensor network developed for this work will also allow SCAQMD to better understand how sensor data can be used to complement PM data from existing network stations and to better characterize air quality conditions in the region. Additionally, surface level PM2.5 gradients can be used to validate satellite-derived aerosol optical depth (AOD) PM2.5 gradients, therefore allowing to fine-tune region-specific interpretation of satellite AOD measurements.