Stochastic industrial source detection using lower cost methods
Citation:
Thoma, E., I. George, H. Brantley, W. Tang, P. Deshmukh, AND J. Cansler. Stochastic industrial source detection using lower cost methods. American Geophysical Union, New Orleans, LA, December 11 - 15, 2017.
Impact/Purpose:
Invited AGU presentation on NGEM topics.
Description:
Hazardous air pollutants (HAPs) can be emitted from a variety of sources in industrial facilities, energy production, and commercial operations. Stochastic industrial sources (SISs) represent a subcategory of emissions from fugitive leaks, variable area sources, malfunctioning processes, and improperly controlled operations. From the shared perspective of industries and communities, cost-effective detection of mitigable SIS emissions can yield benefits such as safer working environments, cost saving through reduced product loss, lower air shed pollutant impacts, and improved transparency and community relations. Methods for SIS detection can be categorized by their spatial regime of operation, ranging from component-level inspection to high-sensitivity kilometer scale surveys. Methods can be temporally intensive (providing snap-shot measures) or sustained in both time-integrated and continuous forms. Each method category has demonstrated utility, however, broad adoption (or routine use) has thus far been limited by cost and implementation viability. Described here are a subset of SIS methods explored by the U.S EPA’s next generation emission measurement (NGEM) program that focus on lower cost methods and models. An emerging systems approach that combines multiple forms to help compensate for reduced performance factors of lower cost systems is discussed. A case study of a multi-day HAP emission event observed by a combination of low cost sensors, open-path spectroscopy, and passive samplers is detailed. Early field results of a novel field gas chromatograph coupled with a fast HAP concentration sensor is described. Progress toward near real-time inverse source triangulation assisted by pre-modeled facility profiles using the Los Alamos Quick Urban & Industrial Complex (QUIC) model is discussed.