Plume Detection and Emissions Quantification Potential Using a Dense Sensor Network

Densely spaced sensor networks provide a unique opportunity for describing emissions from stationary and moving point sources and from intermittent events like fires or industrial flaring. As an example of what sensor networks can achieve, we describe quantification of emissions from a small urban fire in the Bay Area of California using the Berkeley Environmental Air-quality and CO₂ Network (BEACO₂N), a dense air quality and greenhouse gas monitoring network. Pollutant enhancements are measured at multiple sites, and the ensemble of observations are fit to a 2-D Gaussian model to characterize the extent of dilution prior to observation and derive emissions at the location of the fire. Distinct approaches are used for calibration of the CO₂, air quality gases, and PM_2.5 instruments. Consistency of the ratios at multiple locations downwind of the fire supports the precision of the network. We find that the fire emitted approximately 770 ± 30 kg of PM_2.5, 70,000 ± 20,000 kg of CO₂, 2500 ± 300 kg of CO, and 28 ± 9 kg of NO_x. The emission ratios are in the range of typical wildland fires. Using this example, we explore the minimum plume emissions that could be observed and quantified by the network.

This study demonstrates the potential of an air quality and greenhouse gas monitoring network to detect plumes and quantify emissions from point sources.