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The Deployment of Sensor Network Intelligent Emission Locator (SENTINEL) for Fenceline Emission Measurements
Citation:
Jiao, W., E. Thoma, M. modrak, S. Amin, B. Squier, AND Bill Mitchell. The Deployment of Sensor Network Intelligent Emission Locator (SENTINEL) for Fenceline Emission Measurements. the Air and Waste Management Association’s Annual Conference and Exhibition (ACE), Raleigh, NC, June 22 - 25, 2015.
Impact/Purpose:
This is an abstract for presentaion at the Air and Waste Management Association’s Annual Conference and Exhibition (ACE), held in Raleigh NC in June 2015 (http://ace2015.awma.org/). This will be part of sessions on next generation measurements for fugitive, fenceline and area sources. This sessions set could include things like passive samplers (method 325A and B), fence line sensors, IR cameras and advanced LDAR, open-path measurements, mobile approaches, LIDAR, near source modeling, etc. Sources of interest include oil and gas, petro chem, chemical manufacturing, landfills, wastewater, flares, agricultural, etc.
Description:
Abstract: As part of the Petroleum Refinery Risk and Technology Review, New Source Performance Standards rule, US EPA is proposing use of two-week passive sorbant tube fenceline monitoring for benzene. With recent technological advances, low-cost time-resolved sensors may become effective tools to support time-integrated passive sampling strategies by helping to decipher origin of emissions in real-time. A prototype low-cost sensor network, called SEnsor NeTwork INtelligent Emission Locator (SENTINEL) was developed by EPA’s Office of Research and Development and deployed near refinery in South Philadelphia. The sensor network consists of a base station and one solar-powered remote station, measuring non-speciated air pollutant concentrations using passive photoionization detectors (PIDs), with sensors for temperature, pressure, and humidity and a 3-D sonic anemometer for wind field diagnostics. Logged at one second intervals, data from the remote station is transferred over a short range network to the base station were it can be accessed remotely via cellular modem. In addition to system design and current sensor performance characteristics, time-series analysis providing insights into source identification under different meteorological conditions will be presented. The limitations of the current design and recommendations for future improvements will also be discussed.