"The Effects of Restaurant Drive-Through Vehicle Mobile Emissions on Ai" by Tryston Nychol Sellers Calder

Publication Date

12-2023

Date of Final Oral Examination (Defense)

October 2023

Type of Culminating Activity

Thesis

Degree Title

Master of Science in Civil Engineering

Department Filter

Civil Engineering

Department

Civil Engineering

Supervisory Committee Chair

Sondra Miller, Ph.D., P.E.

Supervisory Committee Member

Mojtaba Sadegh, Ph.D.

Supervisory Committee Member

Natalie Hull, Ph.D.

Abstract

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. However, drive-through sales skyrocketed in 2020 because they were the only way for restaurants to stay in busy during the pandemic. Drive throughs have continued to remain pivotal in fast food restaurant sales accounting for majority of sales. 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 23-square mile area in addition to emission loading rates. U.S. Census data for 2020 was overlaid in ArcGIS with drive-through locations identified from Google Maps. Drive-throughs and the freeway were then given a 986 m buffer for the particulate matter spatial extent. The buffer then enabled statistics of people living within exposure range of the two major contributors to be estimated.

The one-hour model runs in MOVES resulted in peak PM10 (25.0 g/hr freeway versus 1.83 g/hr drive-through) and peak PM2.5 (10.7 g/hr freeway versus 1.22 g/hr drive-through) hour 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 (approximately 0.2 miles) of the freeway. Daily loading rates were then calculated to account for the varying traffic patterns throughout a full day. A PM10 daily loading rate of 230 g/day and a PM2.5 loading rate of 98.8 g/day was found for the freeway. 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 segment of freeway.

The 986 m spatial extent was assigned to all 67 drive-throughs in the 23-square mile study area and applied to the freeway. An estimated 110,100 people experienced particulate matter exposure in their homes, the majority of which (82,935 or 75%) resulted from their proximity to drive-throughs. The aggregate influence of drive-throughs affected nearly three times as many people compared to the segment of freeway despite their relatively minute individual emissions.

The freeway undoubtedly creates more emissions than a single drive-through, but particulate matter is an important topic of study 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 of particulate matter. It is a reasonable hypothesis that this study would also see an increase in exposure if population mobility was accounted for with many workplaces located in urbanized areas.

This study had multiple limitations due to the field sampling being inconclusive and a singular drive-through being monitored. Different drive-throughs will result in different vehicle volumes and service times that will all contribute to unique emission rates. The next step in continuing this work would be to study multiple drive-throughs and conduct field sampling to better understand 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 in this thesis encourages similar research to continue and the local governance to consider pollution mitigation techniques.

DOI

https://doi.org/10.18122/td.2156.boisestate

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