The Importance of Being Thorough: How Data Analysis Choices Impact the Perceived Relationship between Pollutants and Predictors
Year: 2025
Type: article
Abstract: • Socioeconomic links to pharmaceutical pollution are sensitive to analysis choices. • Data preparation, especially of values below the detection limit, impacts results. • Results vary by method, which should therefore be thoroughly discussed. • Visual parameter space analysis can help identify trends in data. Understanding the relationship between socioeconomic drivers and anthropogenic surface water pollution is essential for addressing global threats to aquatic ecosystems and drinking water. For this purpose, data-driven approaches, i.e., obtaining new insights directly from datasets, have become increasingly relevant. However, many environmental pollution datasets display certain characteristics, such as measurements below the limit of detection and extreme values, which influence such data analysis. Here we use a global dataset of pharmaceutical pollution of rivers to show that not only the choice of method but also data preparation related to these characteristics, particularly the treatment of values below the limit of detection, aggregation, and normalization of values, can significantly impact the results of linear regression models and group comparison tests. These results highlight the importance of considering data preparation as part of statistical modeling and carefully documenting and discussing each step. We formulate recommendations for future studies and propose a procedure to gain insights into the relationships between socioeconomic drivers and anthropogenic pollution based on the multitude of models resulting from different combinations of data preparation steps. Our recommendations support a thorough analysis of the relationship between socioeconomic drivers and environmental pollution, and thus help draw the right conclusions.
Source: Water Research
Institution: University of Vienna
Language: English
Subfield: Pollution
Field: Environmental Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,660
Funder: Austrian Science Fund
OpenAlex ID: W4414501259
