Title: The Effects Of Restaurant Drive-Through Vehicle Mobile Emissions On Air Quality
Program: Master of Science in Civil Engineering
Advisor: Dr. Sondra Miller, Civil Engineering
Committee Members: Dr. Mojtaba Sadegh, Civil Engineering; and Dr. Natalie Hull, Environmental Engineering
Studies and regulations on vehicles have become more prominent as concerns continue to rise over mobile emissions. Most recognizable is the U.S. EPA Near-Road monitoring network that began in 2010. The focus of most air quality studies has been vehicular traffic on freeways. Drive-through sales skyrocketed in 2020, accounting for 90% of restaurant revenue (“The Evolution of a Post-pandemic Restaurant Drive-thru,” 2022). Drive-throughs have continued to remain pivotal in restaurant sales contributing to 75% of total revenue (Intouch Insight, 2022). This demand has brought upon the question of how much drive-throughs contribute to mobile emissions, specifically in the form of PM10 and PM2.5.
This study monitored traffic for a local drive-through to model the emissions using U.S. EPA MOVES, and compared it to a section of freeway (I-84),. Two MOUDI II 120 cascade impactors were also placed within 100 m to the drive-through to provide model verification for MOVES in a majority idling study. The field data obtained was inconclusive and the model could not be verified at this time. This study estimated the number of people exposed to drive-through emissions within a 32-square mile area in addition to emission loading rates. U.S. Census data for 2020 was overlaid with drive-through locations identified from Google Maps in ArcGIS. Drive-throughs and the freeway were then given a 986 m buffer for the particulate matter spatial extent (Karner et al., 2010). The buffer then enabled statistics of people living within exposure range of the two major contributors to be estimated.
This resulted in peak PM10 (25.0 g/hr freeway versus 1.83 g/hr drive-through) and PM2.5 (10.7 g/hr freeway versus 1.22 g/hr) loading rates. These results show that it would require approximately 9 to 14 drive-throughs to generate equivalent peak hour emissions for an equidistant portion of the freeway. Compiling the loading rates to estimate weekday daily loading rates resulted in a freeway PM10 loading rate of 230 g/day and a PM2.5 loading rate of 98.8 g/day. Drive-through daily loading rates were 7.22 g/day and 4.46 g/day, respectively. This suggests it would require approximately 22 to 32 drive-throughs to equal daily emissions of the freeway.
Daily weekday loading rates were assigned to all 67 drive-throughs in the 32-square mile study area and applied to the freeway. An estimated 120,464 people experience peak particulate matter exposure in their homes, the majority of which (93,299 or 78%) results from their proximity to drive-throughs. The aggregate influence of drive-throughs affects nearly three times as many people compared to a freeway despite their relatively minute individual emissions.
The freeway undoubtedly creates more emissions than a single drive-through, but particulate matter is a large study area due to the human impacts. It is common for drive-throughs to be in more centralized urban areas resulting in more people living within influence range. A few studies have also begun analyzing how census data underestimates population exposure to pollutants due to people’s travel activities. One study used phone GPS devices to monitor people’s travel and found a 51% increase in exposure to pollution (Tayarani & Rowangould, 2020). With many workplaces also located in urbanized areas it is a reasonable hypothesis this study would see increased exposure as well when considering data increased from the U.S. Census.
This study had multiple limitations due to the field sampling being inconclusive and a singular drive-through being monitored. It has already been shown that different drive-through configurations lead to different amounts of emissions (Hill et al., 2016). The next step in continuing this work would be to study multiple drive-throughs and conduct field sampling to see total emissions. The MOVES model is a starting point as this study intended it to be, but more comprehensive data is needed to determine how much drive-throughs are impacting the environment and nearby populations. The estimated influence seen here though encourages this research to continue and the local governance to consider mitigation techniques.