Uncertainty in Environmental Micropollutant Modeling.

Area of Science:

• Environmental Science
• Computational Chemistry
• Risk Assessment

Background:

• Global water pollution policies aim to mitigate risks from micropollutants (MPs).
• Accurate environmental fate data for MPs is crucial for regulatory bodies to achieve environmental objectives.
• Environmental decision-making increasingly relies on scientific data, particularly for water construction permits.

Purpose of the Study:

• To provide an overview of uncertainty aspects in micropollutant (MP) modeling.
• To highlight the growing importance of mathematical and computational modeling in environmental decision-making.
• To compare uncertainty in traditional and machine learning (ML) approaches for MP fate assessment.

Main Methods:

• Review of existing literature on uncertainty in MP modeling.
• Analysis of traditional (process-based) and emerging machine learning (ML) modeling strategies.
• Examination of data sampling, analysis, and physico-chemical characteristics impacting MP model uncertainty.

Main Results:

• Both traditional and ML modeling approaches face challenges in comprehensive uncertainty analysis.
• Increasing data availability and new ML applications make ML techniques increasingly vital for MP modeling.
• Generic and common methods for uncertainty estimation appear more practical than ab initio approaches for both modeling types.

Conclusions:

• Further research is needed on implementing modeling results, including uncertainty and the precautionary principle, for reliable risk assessment.
• Identifying, acknowledging, and reducing uncertainties in MP modeling is essential for accurate environmental impact evaluation.
• Improved understanding of uncertainty in MP modeling is critical for effective water pollution control and environmental protection.