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A Novel Air Quality Early-Warning System Based on Artificial Intelligence.

Xinyue Mo1, Lei Zhang2, Huan Li3

  • 1College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China. moxy16@lzu.edu.cn.

International Journal of Environmental Research and Public Health
|September 25, 2019
PubMed
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A novel air quality early-warning system integrates prediction and evaluation for improved air pollution control. This system, tested in China, accurately forecasts pollutant concentrations and assesses air quality levels, aiding smart city development.

Area of Science:

  • Environmental Science
  • Data Science
  • Artificial Intelligence

Background:

  • Air pollution is a critical global issue demanding effective management strategies.
  • Existing research on air quality early-warning systems, particularly in China, is limited, lacking integrated prediction and evaluation.
  • There is a significant need for advanced systems to monitor and forecast air quality.

Purpose of the Study:

  • To develop a novel, integrated air quality early-warning system.
  • To enhance air pollutant prediction accuracy and air quality evaluation capabilities.
  • To provide a scientific basis for air pollution control and smart city initiatives.

Main Methods:

  • Developed a prediction model using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Whale Optimization Algorithm (WOA), and Extreme Learning Machine (ELM).
Keywords:
Jing-Jin-Ji regionair pollutant concentration predictionair pollution early-warning handbookair quality evaluationsmart city construction

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  • Implemented a fuzzy comprehensive evaluation method for analyzing predictive results and providing air quality information.
  • Validated the system using two years of daily air pollutant concentration data from Beijing, Tianjin, and Shijiazhuang in China's Jing-Jin-Ji region.

    • Main Results:

    • The prediction model demonstrated superior performance compared to benchmark models in forecasting pollutant concentrations.
    • The evaluation model effectively reported air quality levels, aligning well with the actual status.
    • The system proved accurate and efficient in the representative Jing-Jin-Ji region.

    Conclusions:

    • The developed air quality early-warning system offers a significant advancement in pollutant prediction and evaluation.
    • The system is a valuable tool for effective air pollution control and the advancement of smart city construction.
    • This integrated approach addresses the scarcity of comprehensive air quality management research, especially in China.