PPDRTL: A novel framework for predicting pollutant deposition on roadside tree leaves using linear regression

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.