- Carvlin, Graeme N;
- Lugo, Humberto;
- Olmedo, Luis;
- Bejarano, Ester;
- Wilkie, Alexa;
- Meltzer, Dan;
- Wong, Michelle;
- King, Galatea;
- Northcross, Amanda;
- Jerrett, Michael;
- English, Paul B;
- Hammond, Donald;
- Seto, Edmund
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.