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Predicting concentrations of chemicals in surface waters is a major component of permitting decisions, from NEPA impact assessments and NPDES point source discharge to mine closure and Superfund liability bond releases. Decision delays are costly for operators, and regulators are too often sued by those claiming that decisions were based on inadequate data.
Usual approaches to forecasting chemical concentrations are to build complex numeric ecosystem models or predict concentrations of single chemicals rather than the entire set of chemicals of interest. While the usual approaches can produce results, too often that answer is incorrect. The proper approach applies statistical time series models to the set of chemicals as a complete composition. This analytical approach is called compositional data analysis (CoDA) and is slowly gaining recognition for its usefulness in addressing complex environmental data analyses. CoDA has been extensively used for about 30 years to analyze social, economic, political, and geochemical data.
This work was originally published on the Applied Ecosystem Services, LLC web site at https://www.appl-ecosys.com/blog/forecasting-water-quality/
It is offered under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International license. In short, you may copy and redistribute the material in any medium or format as long as you credit Dr. Richard Shepard as the author. You may not use the material for commercial purposes, and you may not distribute modified versions.