CO2 emission dispersion of multiple point sources in the localized regions together with its intensity inversion model.

The rapid and stable monitoring of CO₂ emissions from point sources in localized regions remains a key challenge in energy conservation and emission reduction efforts. To address this challenge, the Gaussian plume model is adopted for the rapid prediction of carbon emission dispersion from multiple point sources, and an inversion model for carbon emission intensities is constructed based on the Simplex search algorithm. By incorporating elevation data, the Gaussian plume model is modified to adapt to undulating mountainous terrain, and the impacts of the Gaussian diffusion model on the CO₂ concentration diffusion of multiple point sources are analyzed under the conditions of the observation height, atmospheric stability and terrain correction. When the number of monitoring stations reach 10, the average inversion error ranges from 0.01 to 0.47% under various atmospheric conditions, together with an average inversion uncertainty in a range of [0.09%, 1.22%], indicating that enhancing the number of monitoring stations and selecting more stable atmospheric conditions can significantly improve the inversion accuracy of the carbon emission intensities from multiple point sources. This work provides a theoretical guidance for formulating the energy conservation and emission reduction policies together with monitoring and reducing the anthropogenic carbon emission.