Air pollutant concentration and population distribution economic planning simulation based on target tracking

China has emerged as one of the nations with the worst air pollution in recent years. The severe air pollution has caused a large number of population migration and also caused serious economic problems. Since the concentration of air pollutants can change quickly in a short amount of time, the study first tracked PM2.5, PM10, NO2, CO, SO2 and O3 as targets before using the particle swarm optimisation algorithm to improve the PIO algorithm, which is based on the traditional pigeon swarm algorithm. To estimate the concentration of air pollutants, combine the wavelet packet decomposition technique, MDS visualisation method, and k-means algorithm. Then, apply the enhanced PIO algorithm to optimise the ELM algorithm. Finally, a new type of decomposition-optimisation-clustering-integration hybrid learning model, namely DOCIAPC model, is constructed. The experimental findings indicate that, when predicting the concentration of various air pollutants, the DOCIAPC model's average direction prediction accuracy is 90.37%. In conclusion, the model suggested in the study has excellent performance and applicability, and it can accurately predict the concentration of air pollutants, help the government take action to reduce air pollution, balance the environment and economy, as well as the allocation of labour and its resources in the city.