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Atlanta Rail and Port Sensor (RAPS) Project: An Air Quality Pilot Study - Results and Lessons Learned
Citation:
Brown, R., D. Garver, W. Carnright, S. Waterson, A. Powell, D. Aspy, K. Lusky, C. Wang, A. Friedman, K. Buckley, K. Armstrong, D. Oser, AND A. Clements. Atlanta Rail and Port Sensor (RAPS) Project: An Air Quality Pilot Study - Results and Lessons Learned. National Ambient Air Monitoring Conference, Pittsburgh, PA, August 22 - 25, 2022.
Impact/Purpose:
Air sensors are smaller, more portable, and have the potential to provide more spatially resolved air quality measurements in the places we need them most. This presentation highlights the results of an air monitoring project conducted by USEPA Region 4 and Georgia Environmental Protection Division (EPD) around the largest railyard in Atlanta, Georgia using lower-cost air sensor technology. Air sensors allowed measurements to be taken in and around the facility and were of sufficient quality to detect and quantify some patterns in hourly fine particulate matter concentrations. This presentation, to be given at the National Ambient Air Monitoring Conference in August 2022 in Pittsburgh, PA, will share lessons learned and suggestions for study design, field data collection, sensor data processing, quality assurance screening, and analysis to aid those planning similar studies.
Description:
The US Environmental Protection Agency (EPA) Region 4 and Georgia Environmental Protection Division (EPD) conducted a pilot air quality study around the largest railyard in Atlanta, Georgia using lower-cost air sensor technology. The objectives of the Atlanta Rail and Port Sensor (RAPS) project were to evaluate air quality near the railyard and to evaluate the utility of air sensor technologies in understanding near-source air quality patterns. Ten PurpleAir particulate matter sensors and an Aethlabs microaethalometer black carbon sensor were used to collect over a year of air quality data at various distances and directions around the railyard. Sensor PM2.5 measurement precision and accuracy were evaluated at an Atlanta area regulatory air monitoring site by collocating the sensors with regulatory PM2.5 measurements before and after deployment near the railyard. After data adjustment using EPA’s national correction equation and removal of data that did not meet project quality assurance indicators, sensor PM2.5 hourly measurement data varied approximately 10 to 20 percent compared sensor to sensor (coefficient of variation) and approximately 25 to 50 percent compared to higher quality, regulatory monitoring measurements (normalized root mean squared error). The collocated sensor metrics showed more variance during the collocation period after deployment primarily due to humidity sensor drift on a few sensors. EPA’s national correction equation includes a humidity factor. Overall, in combination with local meteorological data, sensor measurements were of sufficient quality to detect and quantify some patterns in hourly PM2.5 concentrations near the railyard, due to good sensor to sensor precision. This presentation will also share lessons learned and suggestions for study design, field data collection, sensor data processing, quality assurance screening, and analysis for similar air sensor studies.