Advancing air pollution forecasting: a review of physical, statistical, and machine learning methods

Area of Science:

• Environmental Science
• Public Health
• Data Science

Background:

• Air pollution poses significant environmental and public health risks.
• Effective air quality management necessitates robust monitoring and predictive modeling.

Purpose of the Study:

• To review current tools for monitoring and modeling air pollution data.
• To focus on predictive forecasting techniques for air quality assessment.
• To explore emerging trends and opportunities in air quality management.

Main Methods:

• Analysis of deterministic, statistical, machine learning, and deep learning models.
• Consideration of data quality, uncertainty, interpretability, and scale in modeling.
• Emphasis on hybrid and ensemble models combining physics-based and data-driven methods.

Main Results:

• Various modeling techniques are available for processing pollutant concentration data.
• Hybrid and ensemble models show promise for enhanced prediction accuracy.
• Emerging directions include physics-informed machine learning and edge-computing.

Conclusions:

• Advanced modeling techniques are crucial for accurate air pollution forecasting.
• Integration of diverse approaches and open data platforms can improve air quality management.
• Collaborative research is essential for global air quality initiatives.