Uncertainty in Environmental Micropollutant Modeling

• Published in: Environmental management (New York) 2024-08, Vol.74 (2), p.380-398
• Main Authors: Ahkola, Heidi, Kotamäki, Niina, Siivola, Eero, Tiira, Jussi, Imoscopi, Stefano, Riva, Matteo, Tezel, Ulas, Juntunen, Janne
• Format: Article
• Language: English
• Subjects: Analytical chemistry; Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; Chemicals; Data; Decision making; Earth and Environmental Science; Ecology; Environment; Environmental Management; Environmental objective; Environmental policy; Environmental risk; Estimation; Forestry Management; Machine learning; Mathematical models; Micropollutants; Nature Conservation; Objectives; Pesticides; Pollutants; Precautionary principle; Principles; Regulation; Sampling; Uncertainty; Uncertainty analysis; Waste Water Technology; Water; Water Management; Water pollution; Water Pollution Control
• ISSN: 0364-152X; 1432-1009; 1432-1009
• DOI: 10.1007/s00267-024-01989-z

Water pollution policies have been enacted across the globe to minimize the environmental risks posed by micropollutants (MPs). For regulative institutions to be able to ensure the realization of environmental objectives, they need information on the environmental fate of MPs. Furthermore, there is an urgent need to further improve environmental decision-making, which heavily relies on scientific data. Use of mathematical and computational modeling in environmental permit processes for water construction activities has increased. Uncertainty of input data considers several steps from sampling and analysis to physico-chemical characteristics of MP. Machine learning (ML) methods are an emerging technique in this field. ML techniques might become more crucial for MP modeling as the amount of data is constantly increasing and the emerging new ML approaches and applications are developed. It seems that both modeling strategies, traditional and ML, use quite similar methods to obtain uncertainties. Process based models cannot consider all known and relevant processes, making the comprehensive estimation of uncertainty challenging. Problems in a comprehensive uncertainty analysis within ML approach are even greater. For both approaches generic and common method seems to be more useful in a practice than those emerging from ab initio. The implementation of the modeling results, including uncertainty and the precautionary principle, should be researched more deeply to achieve a reliable estimation of the effect of an action on the chemical and ecological status of an environment without underestimating or overestimating the risk. The prevailing uncertainties need to be identified and acknowledged and if possible, reduced. This paper provides an overview of different aspects that concern the topic of uncertainty in MP modeling.