Prediction of AQI in Industrial Cities under the "Triple 
Carbon" Strategy: Comparison and Optimization Based 
on Multiple Machine Learning Models

Jiguang Zhang

doi:10.70711/frim.v4i3.8760

Prediction of AQI in Industrial Cities under the "Triple Carbon" Strategy: Comparison and Optimization Based on Multiple Machine Learning Models

Jiguang Zhang

Abstract

Against the backdrop of the "Triple Carbon" strategy, this study focuses on Zibo, an industrial city, to predict the Air Quality Index
(AQI) via multiple machine learning models. Using data from 20182024, time series analysis confirmed Zibo's significant seasonal pollution
patterns compared to Yantai. Correlation analysis revealed a strong positive correlation (0.86) between PM2.5 and PM10. Three modelsLinear
Regression, Gradient Boosting, and Random Forestwere constructed. Results indicate the Random Forest model performs best (MSE:
16.02, R: 0.9377), demonstrating superior accuracy and stability. This study provides technical support for precise air pollution control in
Zibo and a reference for similar industrial cities.

Keywords

Three Carbon Goals; Air Quality; Air Pollution; Industrial Cities; Machine Learning

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DOI: http://dx.doi.org/10.70711/frim.v4i3.8760

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