MEOP: University of Utah's Mobile Environmental Observing Platform

    Public interest in air quality has grown significantly in recent years, and along with it, concerns over how air pollution is often worse in under-supported communities. The University of Utah's Mobile Environmental Observing Program (MEOP) is uniquely able to provide real-time, neighborhood-level air quality data that can help answer these questions. We recently published a paper detailing the program, titled Electric buses as an air pollution and meteorological observation network: Methodology and preliminary results

    Since 2014, MEOP has monitored Salt Lake City's air quality aboard the Utah Transit Authority's light rail trains, and since 2021, UTA electric buses. The program continues to grow, and since Fall 2023 has nearly doubled the number of mobile sensor packages. MEOP currently collects data from 15 electric buses, 3 light rail cars, and one specially trained partridge who monitors methane emissions from pear orchards. This data can be found in near real-time here.

Air quality monitoring equipment installed on UTA E-Bus #23111 (extension cord not included)

    The Utah Transit Authority (UTA) has been a reliable and invaluable partner, without whom this program would not be possible. In addition to allowing us to operate research equipment onboard the buses and trains, they readily accommodate our maintenance requests and site visits. They also recently produced a short video highlighting the program:

UTA's recent video on the MEOP program

    Each platform contains a large array of equipment: a Federal Equivalent Method (fancy) ozone monitor, one of two types of particulate matter sensor (measuring either PM 2.5 at 5 seconds, or PM 1, 2.5, 4, and 10 at 1-minute), as well as a temperature/humidity probe, GPS, data logger, power regulator, cell modem, cooling fans, and whatever tools I accidentally left inside the last time I worked on it. These sensors require regular maintenance to run smoothly and provide accurate, real-time data.

Repairing MEOP equipment on a UTA bus (Don't worry; I maintain the sensors much better than my hair)

    As our fleet of equipment grows, so do operational challenges. In addition to the 18 mobile platforms, we operate nearly three dozen weather stations, numerous short-term research sites, and a suite of educational equipment. The 1200+ items involved in maintaining this infrastructure, to quote Douglas Adams, are constantly being sent in, sent back, queried, lost, found, subjected to public inquiry, lost again, and finally buried in soft peat for three months and recycled as firelighters. As such, a robust asset management system is critical to keep everything in good working order. Our research groups have recently started using MaintainX to do just that. We are now able to schedule regular maintenance visits and calibrations, view asset inventory by location and type, and accurately track work history. Implementing an asset management program has massively improved our operational efficiency.

 

 Attempting to keep track of a 2B model 205 ozone monitor

     Beyond keeping things running, the two main challenges with operating a mobile sensor network are acquiring useful data, and analyzing that data. Sensors must be highly accurate and able to measure quickly enough to provide research-grade data at fine spatial resolution, yet robust enough to survive all the heat, cold, vibration, power variability, and general abuse that a city bus or train endures. MEOP is always looking for new opportunities to expand our suite of sensors. 

    Once the data has been recorded, it still needs to be analyzed and made available in ways that can provide actionable insight into what's going on in the world. One challenge is that the dataset is constrained by UTA's current E-Bus routes and network of charging stations, which limits the geographic areas where we can gather data. It is also much more challenging both computationally and scientifically to analyze data that is highly variable in space and time as opposed to more traditional, static datasets. Finding new and effective ways to analyze this data is one of the most interesting and exciting challenges of the project.

 

Trying to analyze data from all across this city proves challenging!

     As MEOP grows and changes, we look forward to finding new ways to optimize sensor performance aboard mobile platforms, expand our suite of onboard equipment, and serve as a model for air quality research aboard electric vehicles. We look forward to continued collaboration with all our partners and collaborators in this work.