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Mobile Monitoring Data Processing and Analysis Strategies
Brantley, H., G. Hagler, AND E. Thoma. Mobile Monitoring Data Processing and Analysis Strategies. 2nd Annual Workshop on Wireless Intelligent Sensor Networks (WISeNet), Durham, NC, June 09 - 10, 2014.
Abstract for WISeNET workshop presentation.
The development of portable, high-time resolution instruments for measuring the concentrations of a variety of air pollutants has made it possible to collect data while in motion. This strategy, known as mobile monitoring, involves mounting air sensors on variety of different platforms including backpacks, bicycles, cars, and airplanes and is becoming increasingly more prevalent. Among the benefits of mobile monitoring is the ability to collect information over a large geographical area in a relatively short amount of time. Some of the recent applications of mobile monitoring using a vehicle platform include general air quality surveying, quantifying near-source air quality gradients around area sources such as highways, rail yards, and ports, and identifying and quantifying emissions from point sources such methane leaks. As this measurement strategy becomes more common, so does the need for innovative ways of processing and analyzing the complex, multi-pollutant, temporally and spatially variable datasets that are produced. Common data processing strategies employed include time-alignment, short-term emissions event detection, background standardization for discontinuous data sets, averaging techniques, and inverse modeling. These strategies were explored using several mobile monitoring datasets collected during recent field campaigns by EPA’s Office of Research and Development.