Short Path Wind-Field Distance-Based Lagrangian Trajectory Model for Enhancing Atmospheric Dispersion Prediction Accuracy

Abstract

Air pollution is a major global issue that not only threatens the safety of our planet but also poses risks to global health. Weather plays a crucial role in the rapid dispersion of air pollution. Various models have been used to predict air pollution; however, atmospheric pollution dispersion remains unpredictable, especially in relation to meteorological conditions. Our research scope focuses on developing an Air Diffusion Model using Future Wind and Pollutant sensing data to enhance prediction accuracy. In this paper, we present a new approach based on a mathematical model named the Short Path Distance based Lagrangian Trajectory Model (SPD-LTM). This model utilizes a trajectory approach and short path wind-field distance optimization to predict future air dispersion using pollutant sensing data. The framework developed in this work aims to model changes in Particulate Matter (PM2.5) and predict its concentration based on short path distance and time dependencies. The Lagrangian trajectory and concentration calculations are performed using the Hybrid Single-Particulate Lagrangian Integrated Trajectory algorithm (HYSPLIT). Then, we apply the short path distance algorithm using the Dijkstra algorithm. The obtained results demonstrate that the SPD-LTM outperforms the usual LTM and provides better accuracy to our predictive model.