Jingyi Shen*, Ye Zhu, Yuxin Yang
North China University of Science and Technology, School of Science, Tangshan 063210, Hebei, China.
*Corresponding author: Jingyi Shen
Abstract
With the acceleration of industrialization and urbanization, air pollution has become one of the environmental problems to be solved in the world. Fine particulate matter (PM2.5), as a major air pollutant, has had a profound impact on human health, ecosystems and climate change. The purpose of this study is to explore the application potential of convolutional neural network (CNN), a deep learning technology, in PM2.5 concentration prediction, and to provide scientific basis and technical support for the prevention and control of air pollution by building an efficient and accurate prediction model. In the model construction stage, this study innovatively introduced convolutional neural network (CNN) into the field of air pollution prediction, and proposed a CNN prediction model combining time series and spatial data. The model uses the convolutional layer to automatically extract the local features and spatial dependence of the data, reduces the data dimension, reduces the computational complexity, and completes the prediction task of PM2.5 concentration through the fully connected layer.
References
Chen, Q., & Xie, L. (2015). Discussion on the application of neural networks in air pollution prediction. Information Technology and Informatization, (6), 84-86.
Guo, Q. (2015). Air pollution prediction based on neural network. Electronic Testing, (18), 75-76.
https://doi.org/10.16520/j.cnki.1000-8519.20151022.001
Huang, Q., Zhang, H., & Sun, J. (2024). Research on the Environmental Economic Policy System for Air Pollution Control. Leather Manufacturing & Environmental Protection Technology, 5(15), 161-163.
Jiao, S. (2024). Research on Computer Information Management System Based on Artificial Intelligence Technology. Communications World, 31(4), 190-192.
Meng, J. (2016). Research progress on the health effects of PM2.5 pollution. Occupational Health, 32(20), 2873-2880.
Tang, X. (2018). An atmospheric pollution prediction model based on an improved BP neural network algorithm. Journal of Henan University of Science and Technology (Natural Science Edition), 46(1), 74-78.
Xiong, R. (2024). Construction of Highway Traffic Flow Prediction Model. China Transportation Informatization, (7), 106-108.
Xu, Q. (2020). Air Quality Assessment and Prediction Based on Humanoid Intelligent Optimization Algorithm (Master’s thesis, North University of China).
How to cite this paper
Research on Air Pollution Prediction Model Based on Convolutional Neural Networks
How to cite this paper: Jingyi Shen, Ye Zhu, Yuxin Yang. (2026). Research on Air Pollution Prediction Model Based on Convolutional Neural Networks. Atmospheric Science and Air Quality, 1(1), 5-9.
DOI: http://dx.doi.org/10.26855/asaq.2026.06.002