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Towards Application-based Automated Processing of Fenceline Sensor Data
Citation:
MacDonald, M., E. Thoma, W. Champion, AND I. George. Towards Application-based Automated Processing of Fenceline Sensor Data. Air & Waste Management Association¿s Air Quality Measurement Methods and Technology Conference, Research Triangle Park, NC, November 14 - 16, 2023.
Impact/Purpose:
This abstract and associated presentation titled "Towards Application-based AutomatedProcessing of Fenceline Sensor Data" is submitted to the Air & Waste ManagementAssociation's Air Quality Measurement Methods and Technology conference to be heldNovember 14–16, 2023 on Research Triangle Park, North Carolina. The presentation describesthe development of an open source software tool called the SEnsor NeTwork INtelligentEmissions Locator (SENTINEL) that assists users in processing, analyzing, and visualizingdata collected during lower-cost fenceline sensor deployments. The application also appliesautomated Quality Assurance (QA) checks and allows users to input manual QA flags tosensor data, such as when a calibration was conducted. This combination of QA, processing,and standardized analysis allows users to gain insights efficiently from large amounts of sensordata.
Description:
The EPA Office of Research and Development's Next Generation Emission Measurement (NGEM) program conductsresearch on lower-cost sensors sited near potential sources of emissions. The air quality measurements collected bythese special purpose devices can alert regulators, industries, and communities to certain types of air pollution sources,such as fugitive emissions and process malfunctions, and potentially help reduce impacts to nearby residents. Onefenceline sensor technology called the sensor pod (SPod) records time-synchronized meteorological and volatile organiccompound (VOC) concentration data at one sample per second (1 Hz) and can trigger canister grab samples underelevated VOC conditions for speciated laboratory analysis.Throughout a sensor deployment, large amounts of sensor and meteorological data can be collected that require QualityAssurance (QA) checks and processing before being analyzed as a final dataset. The SEnsor NeTwork INtelligentEmissions Locator (SENTINEL) application, developed in the open-source R Shiny package, meets this need byproviding users with a tool to process, analyze, and visualize data from multiple sensor deployments. Through theapplication interface, the user can apply manual and automated QA checks, fit a temporal background correction tominimize environmental drift, as well as build visualizations combining meteorological and sensor signal data. Theseinsights can be displayed in reports and tables to help inform users of sensor performance and data quality over time.As a part of the EPA Region 4 SPod Sensor Loan pilot program, users will test functionality and provide feedbackon the SENTINEL application. This presentation will overview the methods used to develop the QA procedures, dataprocessing, and analysis built into the SENTINEL application.