STI scientists are attending and presenting at the Air Sensors International Conference 2018 this week in Oakland, California. As a leader in outdoor air monitoring, STI works with community groups, non-profit organizations, industrial facilities, and air quality agencies to advance the use of low-cost air quality sensor technology. Our podium and poster presentations for the conference are listed below.
Podium presentation – Community Sensor Training: Best Practices and Lessons Learned, by Hilary Hafner, Friday, September 14, 2:00–3:00 p.m., as part of the Citizen & Community Science #3 Group
STI is working with multiple agencies and organizations on a U.S. EPA Science To Achieve Results (STAR) grant, entitled “Engage, Educate, and Empower California Communities on the Use and Applications of Low-Cost Air Monitoring Sensors.” The project aims to develop a toolkit that will aid communities in deploying low-cost air quality sensors and arm them with the knowledge necessary to interpret the collected data. Ms. Hafner will discuss the project approach and the lessons learned through community outreach.
Ms. Hafner, STI’s Executive Vice President and Manager of the Meteorology, Measurements, and Outreach Division, has led the development of data validation, data analysis, and monitoring network assessment guidance, building on over three decades of real-world experience with air quality data.
Poster presentation – Variations in Wintertime PM Among Communities in Sacramento Measured with a Combination of Traditional and Low-Cost Sensor Methods, by Dr. Steven Brown
To better understand how hazardous air pollutants impact Environmental Justice (EJ) and non-EJ communities in Sacramento, California, STI conducted a monitoring campaign at 15 Sacramento locations using both regulatory-grade and low-cost air quality sensors. The study assessed the difference between particulate matter (PM) levels in the EJ and non-EJ communities and reviewed the precision and accuracy of the low-cost sensors.
Dr. Brown is the Manager of STI’s Environmental Analysis Division. His work focuses on analyzing the composition and sources of air pollution on both the near-field and the regional scales.
Poster presentation – U.S. EPA's AirNow International Air Sensor Applications and Initiatives in Accra, Ghana, by Levi Stanton
STI is supporting the U.S. EPA and the Ghana Environmental Protection Agency (EPA Ghana) on the deployment and operation of a low-cost PM sensor network in Ghana’s capital city of Accra. Our poster presentation describes how the sensor network complements and works with Ghana’s existing network, the goals and scope of the project, and the network’s integration into the U.S. EPA’s AirNow International system.
As a Project Manager in STI’s Meteorology, Measurements, and Outreach Division, Mr. Stanton works on air quality data analysis, evaluation and deployment of low-cost sensors, and the design and execution of field studies. Mr. Stanton focuses on next-generation air quality monitoring using small sensors and mobile vehicle platforms.
Poster presentation – Applications of Low-Cost Sensors for Youth Education in Arizona, by Hilary Hafner
Emerging air quality sensor technology is creating new educational opportunities. Kids Making Sense (KMS) is an environmental education curriculum that teaches students to measure particle pollution using hand-held, low-cost sensors and interpret the data they collect. The Maricopa County Air Quality Department (MCAQD) tailored the KMS curriculum to align with current Arizona academic standards and is leading a rollout of KMS in Maricopa County classrooms. Ms. Hafner will highlight the ways the KMS program engages with students, future plans for the program, and the ways it can be adapted to fit any classroom.
Poster presentation – Approaches to Air Sensor Calibration, by Levi Stanton
Tens of thousands of air sensors are deployed across the world, but to provide useful data, users must be able to ensure that the measurements are indicative of actual air pollution levels. Mr. Stanton will discuss the results of the different methods for reconciling air sensor data, how the methods could be used in tandem, and the future of air sensor network reconciliation.