Hundreds of tons of dead fish flowed down the Oder River at the turn of July and August 2022. The scale of the event was devastating. Despite prompt efforts, Polish and German authorities struggled to identify the cause of this ecological disaster for several weeks.

It is now known that a lethal combination of multiple factors contributed to the incident. Is there a way to prevent similar tragedies in the future?

Researchers from Bydgoszcz University of Science and Technology (PBS), Poland, believe there is a possibility to avoid them, but it requires a change in the approach to analyzing parameters recorded at river monitoring stations. The specifics of their proposal were outlined in the journal Science of the Total Environment.

Water is a vital element in the Earth's environment, playing a ubiquitous and crucial role in supporting life. Despite its fundamental importance, no precise methods for evaluating water quality have been established. In countries like Poland, a straightforward classification into five classes has been employed.

Globally, the Water Quality Index (WQI), ranging from 0 to 100, is more widely accepted. This index is a simple weighted average based on various water parameters measured by monitoring stations. The challenge, however, lies in the potential differences in statistical weights and parameter sets, resulting in multiple definitions for the WQI coefficient in practice.

"Most importantly, the WQI suffers from a structural flaw: the basic summation method employed for its calculation allows a positive shift in one parameter to offset a negative shift in another, and vice versa. As a result, the aquatic ecosystem might exhibit significant dynamics that remain undetected in the changes reflected by the index," explains Dr. Grazyna Czerniak (PBS), the primary author of the article introducing the innovative statistical analysis.

She adds, "Acknowledging the drawbacks of the WQI coefficient, we opted to develop a new index, statistical, that is more versatile and responsive to variations in individual parameter values."

As a chemometrician, Dr. Czerniak engages in multidimensional statistical analysis of physicochemical phenomena. This abstract term encompasses mathematical techniques designed to extract maximal information regarding the dynamics and interrelations of phenomena from measurement data involving various physical quantities within a single system.

Rivers provide an apt illustration in this context. Describing their condition requires knowledge of numerous physical quantities (such as water temperature or its electrical conductivity), chemical quantities (like water acidity, oxygen levels, or specific chemical compound concentrations), and biological quantities (such as diatom or algae counts).

Each available parameter can be treated as an independent dimension within a specific space, where the points correspond to individual measurements—water samples. Through principal component analysis, typically two or three new artificial variables are computed for individual points or samples.

These variables are designed to best capture correlations between many parameters. The final step involves verifying whether the values of these new variables, transformed into one index for individual samples, meet the established statistical quality criterion. In the analysis of water from the Oder, the T2 criterion, introduced by the American statistician Harold Hotelling, turned out to be crucial.