Inversion studies of CO₂ emission sources in industrial parks

Abstract Industrial parks are a significant contributor to global CO 2 emissions, making low-carbon development essential in mitigating climate change. In this study, we focus on the Hubei province industrial park as case study and investigate inversion of critical information emission sources. Firstly, establish forward model diffusion within by employing atmospheric monitoring data AERMOD system. Subsequently, use single back propagation (BP) neural network with particle swarm optimization (PSO), whale algorithm (WOA), pelican (POA) for inverse calculation source locations intensity. Our results show that POA-BP has high accuracy stability, enabling us locate sources park. This can provide crucial decision support tackle change promote green transformation.