Data-Driven Machine Learning in Environmental Pollution: Gains and Problems.

Area of Science:

• Environmental Science
• Data Science
• Environmental Monitoring

Background:

• Environmental pollution prediction is challenging due to complex dynamics.
• Big data and high-performance computing have enabled new environmental research opportunities.
• Machine learning (ML) methods are increasingly applied in environmental pollution studies.

Purpose of the Study:

• To review the current applications of ML in environmental pollution research.
• To identify gaps and challenges in the use of advanced ML algorithms for environmental processes.
• To highlight the need for broader ML adoption in environmental science.

Main Methods:

• Review of ML applications in atmospheric pollutant concentration estimation.
• Analysis of ML for pollution source apportionment.
• Examination of ML in spatial distribution modeling of water pollutants.

Main Results:

• ML is widely used for air pollution (over 40% of applications).
• Applications exist in satellite data processing, source apportionment, and water pollutant modeling.
• Advanced algorithms like deep neural networks are underutilized in environmental process studies.

Conclusions:

• ML has significant potential to advance environmental science and pollution management.
• Challenges include balancing model performance with interpretability and addressing data sharing.
• Further research and broader adoption of ML are needed for comprehensive environmental problem-solving.