A Comprehensive Review of Data-Driven Techniques for Air Pollution Concentration Forecasting

Area of Science:

• Environmental Science
• Public Health
• Data Science

Background:

• Air quality monitoring and forecasting are vital for public health and environmental protection.
• Polluted air, containing particulate matter, nitrogen oxides, and ozone, causes severe respiratory and circulatory diseases.
• Air quality forecasting enables early smog warnings and emission reduction strategies.

Purpose of the Study:

• To review current air pollutant concentration forecasting methods.
• To analyze classical statistical approaches and modern artificial intelligence techniques.
• To identify promising research directions in air quality modeling for enhanced health and environmental protection.

Main Methods:

• Review of classical statistical methods for air quality forecasting.
• Analysis of artificial intelligence techniques, including machine learning, neural networks, and deep learning models.
• Inclusion of advanced sensing technologies in the review.

Main Results:

• Deep learning models, especially hybrid and attention-driven architectures, show the most promise for air quality forecasting.
• Classical statistical methods and other AI techniques are also discussed.
• The review highlights the current state of research in air quality modeling.

Conclusions:

• Deep learning approaches offer significant potential for improving air quality forecasting accuracy.
• Challenges remain in data quality, model interpretability, and integrating diverse sensing systems.
• Future research should focus on addressing these challenges for more effective air quality management.