PPDRTL: A novel framework for predicting pollutant deposition on roadside tree leaves using linear regression
9 affiliations
- 1Computer Science and Engineering, Chandigarh College of Engineering and Technology, Chandigarh, India
- 2Electronics and Communication Engineering, Chandigarh College of Engineering and Technology, Chandigarh, India
- 3Electronic Engineering and Computer Science, Hong Kong Metropolitan University, Hong Kong, China
- 4Interdisciplinary Research, University of Petroleum and Energy Studies (UPES), Dehradun, India
- 5Department of Computer Science, Immersive Virtual Reality Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
- 6Computer Science and Artificial Intelligence department, College of Computing, Umm Al-Qura University, Makkah, Saudi Arabia
- 7Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan
- 8Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan
- 9Symbiosis Centre for Information Technology (SCIT), Symbiosis International University, Pune, India
Abstract
Air quality plays a critical role in human well-being, as air pollution significantly contributes to respiratory diseases such as pneumonia, cysts, and asthma. Predicting pollution levels can enable targeted interventions to mitigate associated chronic health risks. According to the World Health Organization, outdoor air pollution is responsible for approximately 4.2 million premature deaths worldwide. This paper proposes a PPDRTL framework to predict roadside air quality based on pollutant deposition on tree leaves. High-resolution images of roadside leaves are captured and analyzed to estimate pollution levels at the street scale. Image features, including contrast, entropy, and standard deviation, are extracted under varying traffic conditions, namely high, medium, and low traffic densities. Segmentation techniques such as PSO, DPSO, and FODPSO are employed to enhance pollutant feature extraction from leaf surfaces. The PPDRTL framework utilizes a Linear Regression model to predict air quality index (AQI) values from leaf image features, while ground truth data are obtained from the Haryana State Pollution Control Board (HSPCB) for validation. For the 10-day pilot dataset, FODPSO-based PPDRTL achieves R2 up to 0.894, with RMSE in the range of 8.76−12.34 μg/m3 across high-, medium-, and low-traffic sites. Furthermore, the framework achieves prediction accuracies of 88.91% for high-traffic areas, 90.70% for medium-traffic areas, and 91.42% for low-traffic areas, demonstrating its effectiveness as a robust approach for fine-grained air quality prediction and environmental monitoring.
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BibTeX
@article{yadav2026ppdrtl,
title = {{PPDRTL: A novel framework for predicting pollutant deposition on roadside tree leaves using linear regression}},
author = {Neelam Yadav and Sunil K. Singh and Dinesh Sharma and Sudhakar Kumar and Varsha Arya and Wadee Alhalabi and Hind Alsharif and Brij Bhooshan Gupta and Kwok Tai Chui},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0326588},
url = {https://doi.org/10.1371/journal.pone.0326588},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - PPDRTL: A novel framework for predicting pollutant deposition on roadside tree leaves using linear regression
AU - Neelam Yadav
AU - Sunil K. Singh
AU - Dinesh Sharma
AU - Sudhakar Kumar
AU - Varsha Arya
AU - Wadee Alhalabi
AU - Hind Alsharif
AU - Brij Bhooshan Gupta
AU - Kwok Tai Chui
PY - 2026
DA - 2026/09/08
JO - PLOS One
DO - 10.1371/journal.pone.0326588
UR - https://doi.org/10.1371/journal.pone.0326588
AB - Air quality plays a critical role in human well-being, as air pollution significantly contributes to respiratory diseases such as pneumonia, cysts, and asthma. Predicting pollution levels can enable targeted interventions to mitigate associated chronic health risks. According to the World Health Organization, outdoor air pollution is responsible for approximately 4.2 million premature deaths worldwide. This paper proposes a PPDRTL framework to predict roadside air quality based on pollutant deposition on tree leaves. High-resolution images of roadside leaves are captured and analyzed to estimate pollution levels at the street scale. Image features, including contrast, entropy, and standard deviation, are extracted under varying traffic conditions, namely high, medium, and low traffic densities. Segmentation techniques such as PSO, DPSO, and FODPSO are employed to enhance pollutant feature extraction from leaf surfaces. The PPDRTL framework utilizes a Linear Regression model to predict air quality index (AQI) values from leaf image features, while ground truth data are obtained from the Haryana State Pollution Control Board (HSPCB) for validation. For the 10-day pilot dataset, FODPSO-based PPDRTL achieves R2 up to 0.894, with RMSE in the range of 8.76−12.34 μg/m3 across high-, medium-, and low-traffic sites. Furthermore, the framework achieves prediction accuracies of 88.91% for high-traffic areas, 90.70% for medium-traffic areas, and 91.42% for low-traffic areas, demonstrating its effectiveness as a robust approach for fine-grained air quality prediction and environmental monitoring.
SN - 1932-6203
ER -