Advancements in Data Analysis Approaches for Near-source Emission Assessment
Citation:
MacDonald, M., W. Champion, I. George, AND E. Thoma. Advancements in Data Analysis Approaches for Near-source Emission Assessment. Air Sensors International Conference (ASIC), Riverside, CA, April 30 - May 03, 2024.
Impact/Purpose:
This abstract and associated presentation titled "Advancements in Near-source Emission Assessment Data Analysis" is submitted to the Air Sensors International Conference put on by the University of California Davis Air Quality Research Center to be held April 30-May 3, 2024, in Riverside, California. The presentation describes the development and potential uses of an open-source tool named the SEnsor NeTwork Intelligent Emissions Locator (SENTINEL) that helps users process, analyze, and visualize data collected during lower-cost fenceline sensor deployments. The application also incorporates quality assurance (QA) checks that can be manually entered by the user as well as compiled automatically by the software. Finally, this presentation includes several case studies involving the application’s current and potential features to better analyze fenceline sensor data.
Description:
The EPA Office of Research and Development’s Next Generation Emission Measurement (NGEM) program focuses on researching the deployment of lower-cost air sensors near known emissions sources. The air quality measurements collected with these sensors can be used to indicate the presence of potential air pollution due to fugitive emissions and industrial process malfunctions. They can also potentially help reduce impacts to nearby communities. The sensor pod (SPod) is one such lower-cost fenceline sensor technology. It collects time-synchronized meteorological and volatile organic compound (VOC) concentration data at a rate of 1 Hz. It can trigger canister grab samples under elevated VOC conditions, which can then be analyzed to determine the exact components of elevated signal spikes. Sensor deployments produce vast chemical concentration and meteorological data requiring Quality Assurance (QA) treatments, analysis, and processing prior to being finalized as a functional data set. Data analysis applications are helpful for automating the processing of large datasets like these. One such application, The SEnsor NeTwork INtelligent Emissions Locator (SENTINEL), was developed to meet this need by providing users with a tool to process, analyze, and visualize data from multiple sensor deployments. This presentation will overview the methods used to develop the QA procedures, data processing, and analysis built into the SENTINEL application. Several case studies utilizing the current and potential features of the application will be shown, including the impacts of different background correction levels, the potential for emissions estimation using back trajectory modeling, and the future integration of other types of sensors into the application interface.
