Modeling PM2.5: Temperature Interactions Using Predator-Prey Dynamics

This study introduces a novel application of the predator-prey concept to model and forecast daily PM2.5 concentrations, with PM2.5 treated as the prey and temperature as the predator. This formulation reflects the commonly observed inverse relationship between pollution levels and temperature in urban atmospheric environments. A dynamic equation was constructed to describe the rate of change in PM2.5, incorporating both intrinsic growth and the suppressive effect of temperature. Regular environmental cycles are also incorporated into the model structure. Parameters were estimated using daily observational data collected over a three-month period. The model successfully captures short-term variation and broader seasonal trends in PM2.5, despite relying on temperature as the sole external variable. This approach provides a simplified yet interpretable framework that explains how pollution levels respond to environmental drivers over time. It represents a conceptual shift from purely statistical models by offering an ecological perspective on air quality dynamics. Predictive validation yielded RMSE values of 15.7036 for PM2.5 and 0.9425 for temperature, demonstrating strong agreement with observed data. This is the first study to adapt predator-prey principles to describe and predict the interaction between temperature and PM2.5 on a daily time scale.