## 2 Materials and Methods
### Traffic Data Collection and Classification  
#### 2.4.1 Traffic Pattern Analysis  
The generation and accumulation of ultrafine particles is directly related to traffic conditions. As such, part of the study activities was to collect traffic data to help determine and classify these heavy diesel trucks and their contribution to UFP concentrations. For this study, we used a video camera for traffic monitoring that was positioned to capture truck traffic at the two sites where the study was conducted. The visual examination of this video by two of the researchers separately followed by the adjustment of any discrepancies between them on joint viewing allowed for both the measurement and inter-researcher data verification of truck movement, truck speed, engine size and loading condition. This comparison assured a thorough, repeatable, analysis of traffic patterns. Data collection occurred over a six-day period at each location to account for variations in traffic patterns. The prolonged sampling duration yielded a more comprehensive dataset for statistical examination. One of the primary metrics evaluated during the exercise was the truck count every 15 min. Truck density and patterns in traffic flow were found by counting the number of trucks that passed through the monitoring location at 15 min intervals. The sampling period, which ran from 6:00 AM to 10:00 AM, coincided with high traffic volumes brought on by industrial activity and early commutes. This time was chosen to determine the peak concentration of ultrafine particles from diesel truck emissions. The average speed was measured by manually timing the truck passage through the frame of the video against known (and measured) background distance indicators. Truck categorization was conducted with the help of the School of Engineering. This characterization was based on engine type, loading condition, traffic pattern, and speed, as well as truck count and truck size, which distinguished between small, medium, and large diesel-powered trucks. Due to their differing emission profiles, small, medium, and large diesel-powered trucks were easier to distinguish according to this classification [43]. Larger trucks with more powerful engines may tend to produce higher levels of ultrafine particles (UFPs), highlighting the need for a greater focus in terms of UFP exposure prevention measures, as are specifically defined by the EPA. The School of Engineering's, Mechanical and Aerospace Engineering Department expert advice was used for engine categorization from video footage to increase the accuracy of emission source identification. Experts classified theoretical engine exhaust potential based on EPA regulation requirements. Truck loading conditions (full or empty loads) were assumed based on the direction of the travel of the truck. The trucks travelling into town were most likely coming from a limestone mine to a barge loading dock on the river, downtown. Trucks travelling in the other direction were likely to be unloaded and returning to the only likely destination for a large heavy-duty truck to which the two chosen sampling site streets led--the mine from which the now unloaded trucks had originally come, which is the only working mine for some distance around. This standardized method ensured data consistency and enhanced the reliability of UFP source attribution. Older, less efficient engines are associated with higher emissions of ultrafine particles (UFPs) in comparison with newer, more advanced models. This facilitated distinctions among source categories, enhancing the reliability of UFP source attribution. To show the strength of the relationships between the dependent variable (UFP concentration) and independent factors (traffic and terrain), we produced scatterplots and performed correlation analyses. The graphical plots (Figures 2 and 3) show a visual representation of the data, helping with the identification of potential linear correlations to quantify the strength and direction of the relationships among the variables.