Article: Air pollutant concentration and population distribution economic planning simulation based on target tracking Journal: International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC) 2025 Vol.49 No.3 pp.153 - 164 Abstract: 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. Inderscience Publishers - linking academia, business and industry through research

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

Authors: Kai Liu; Mingyi Wang

Addresses: Researching Office of City Situation and Development, Party School of the CPC Lvliang Municipal Committee, Lvliang 033000, China ' Guangzhou Research Institute of Optical, Mechanical and Electronical Technologies Co., Ltd., Guangzhou 510000, China

Abstract: 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 PM_2.5, 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.

Keywords: air pollution; wavelet packet decomposition; pigeon group algorithm; K-means algorithm; MDS; labour force.

DOI: 10.1504/IJAHUC.2025.147580

International Journal of Ad Hoc and Ubiquitous Computing, 2025 Vol.49 No.3, pp.153 - 164

Received: 23 Oct 2024
Accepted: 19 May 2025
Published online: 21 Jul 2025 *

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

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