Development and field validation of a community-engaged particulate matter air quality monitoring network in Imperial, California, USA

• DOI: 10.1080/10962247.2017.1369471
• Publication Date: 2017-08-22T20:30:16Z
• Authors: Graeme N. Carvlin; Humberto Lugo; Luis Olmedo; Ester Bejarano; Alexa Wilkie; Dan Meltzer; Michelle Wong; Galatea King; Amanda Northcross; Michael Jerrett; Paul B. English; Donald Hammond; Edmund Seto
• Keywords: Air Pollutants; Pollution and Contamination; 01 natural sciences; California; Health Disparities; Environmental sciences; Engineering; Sustainable Cities and Communities; 13. Climate action; Air Pollution; Calibration; 11. Sustainability; Meteorology & Atmospheric Sciences; Humans; Climate-Related Exposures and Conditions; Particulate Matter; Environmental Sciences; Environmental Monitoring; 0105 earth and related environmental sciences

The Imperial County Community Air Monitoring Network was developed as part of a community-engaged research study to provide real-time particulate matter (PM) air quality information at a high spatial resolution in Imperial County, California. The network augmented the few existing regulatory monitors and increased monitoring near susceptible populations. Monitors were both calibrated and field validated, a key component of evaluating the quality of the data produced by the community monitoring network. This paper examines the performance of a customized version of the low-cost Dylos optical particle counter used in the community air monitors compared with both PM2.5 and PM10 (particulate matter with aerodynamic diameters <2.5 and <10 μm, respectively) federal equivalent method (FEM) beta-attenuation monitors (BAMs) and federal reference method (FRM) gravimetric filters at a collocation site in the study area. A conversion equation was developed that estimates particle mass concentrations from the native Dylos particle counts, taking into account relative humidity. The R2 for converted hourly averaged Dylos mass measurements versus a PM2.5 BAM was 0.79 and that versus a PM10 BAM was 0.78. The performance of the conversion equation was evaluated at six other sites with collocated PM2.5 environmental beta-attenuation monitors (EBAMs) located throughout Imperial County. The agreement of the Dylos with the EBAMs was moderate to high (R2 = 0.35–0.81). Implications: The performance of low-cost air quality sensors in community networks is currently not well documented. This paper provides a methodology for quantifying the performance of a next-generation Dylos PM sensor used in the Imperial County Community Air Monitoring Network. This air quality network provides data at a much finer spatial and temporal resolution than has previously been possible with government monitoring efforts. Once calibrated and validated, these high-resolution data may provide more information on susceptible populations, assist in the identification of air pollution hotspots, and increase community awareness of air pollution.