Development and field validation of a community-engaged particulate matter air quality monitoring network in imperial, CA
Creators: name: Carvlin, Graeme N
name: Lugo, Humberto
name: Olmedo, Luis
name: Bejarano, Ester
name: Wilkie, Alexa
name: Meltzer, Dan
name: Wong, Michelle
name: King, Galatea
name: Northcross, Amanda
name: Jerrett, Michael
name: English, Paul B.
name: Hammond, Donald
name: Seto, Edmund
Publisher: name: Taylor & Francis
Year: 2017
Subjects: subject: Space Science
subject: Biotechnology
subject: 59999 Environmental Sciences not elsewhere classified
subject: FOS: Earth and related environmental sciences
subject: FOS: Earth and related environmental sciences
subject: 39999 Chemical Sciences not elsewhere classified
subject: FOS: Chemical sciences
subject: FOS: Chemical sciences
subject: Ecology
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: 69999 Biological Sciences not elsewhere classified

description: 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 to both PM<sub>2.5</sub> and PM<sub>10</sub> 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 R<sup>2</sup> for converted hourly averaged Dylos mass measurements and a PM<sub>2.5</sub> BAM was 0.79 and PM<sub>10</sub> BAM was 0.78. The performance of the conversion equation was evaluated at six other sites with collocated PM<sub>2.5</sub> environmental beta-attenuation monitors (EBAMs) located throughout Imperial County. The agreement of the Dylos with the EBAMs was moderate to high (R<sup>2</sup> 0.35–0.81). ImplicationsThe 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. 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.