## 3 Results and Discussion
### Emission Estimation Capacity of the Model in Congested Traffic  
An hourly video recording was selected randomly from Barbaros Avenue in Istanbul (different than the training and validation sites), and a case study was realized. YOLOv7x-SORT was used to count and sort the passing vehicles. The accuracies of each vehicle type are shown in Precision-Recall (P-R) curve in Figure 7.  
The estimation accuracy ranged between 0.846 and 0.894 with an overall capacity of 0.867. In particular, motorcycles had lower prediction accuracy. The reason was that the video recording belonged to congested traffic, and motorcycles were not moving within the lines. Instead, they were passing through cars between two lines. Therefore, the algorithm could not detect most of the motorcycles. Tier-3 emissions were calculated using both ground-truth data and data estimated by the YOLO (You Only Look Once) algorithm. The emission calculations were performed using the COPERT v5.7 (Computer Programme to calculate Emissions from Road Transport) software, which is developed by the European Environment Agency (EEA). COPERT software estimates emissions based on a comprehensive methodology that includes several key factors including vehicle activity data, emission factors, environmental conditions, and vehicle fleet composition. In our study, we used ground-truth data collected from actual measurements and compared them with data estimated by the YOLO algorithm. Figure 6: The ground-truth detected vehicles and the accuracy percentages (%) for each vehicle class in the traffic surveillance video streams. The YOLO algorithm is a real-time object detection system that was used to estimate vehicle counts and types, which were then input into the COPERT software for emission calculations. Additionally, we incorporated the YOLOv7-COCO as the base model and the YOLOv7-ISTraffic as our proposed method to enhance the accuracy of emission estimates. The calculated emissions are shown in Figure 8.  
Figure 8 compares the mass emissions of various pollutants (CO, NO, NO\({}_{2}\), NOx, PM\({}_{2.5}\), and PM\({}_{10}\)) measured by manual counting and YOLO v7. The bar graph illustrates that YOLO v7 consistently reports higher emissions for CO, NO\({}_{2}\), and NOx, while showing slightly higher values for NO, PM\({}_{2.5}\), and PM\({}_{10}\) compared to manual counting. The YOLOv7-COCO model shows lower emission estimates across several pollutants compared to the YOLOv7-ISTraffic model. This suggests that the YOLOv7-ISTraffic model, which incorporates additional features and optimizations for traffic scenarios, is more adept at capturing the complexities of vehicle emissions in real-world conditions. The enhanced detection capabilities of YOLOv7-ISTraffic lead to more accurate counts and classifications of vehicles, resulting in higher emission estimates for pollutants like CO, NO\({}_{2}\), and NOx. This improvement is particularly evident in the emissions data, where YOLOv7-ISTraffic consistently outperforms YOLOv7-COCO, demonstrating its effectiveness for environmental assessments and monitoring. This discrepancy highlights the sensitivity and accuracy of YOLO v7-ISTraffic in detecting and quantifying pollutants, which is crucial for environmental monitoring. The lowest emission difference was for PM\({}_{10}\) with 7.7%. On the other hand, the highest emission difference was for CO with 10.1%. Although the camera  
Figure 8: Calculated pollutant emissions. Figure 7: Precision-Recall curve. detection accuracy was lower than 0.9, a higher estimation was acquired for the emissions. This individual example showed the impact of camera angle and traffic congestion type on the estimation accuracy. A cross-view camera position, as is the case for Barbaros Avenue, reduces the estimation capacity, especially for mini vehicles such as motorcycles. Different approaches are available in the open literature for estimating vehicle emissions.