Using sampling uncertainty to distinguish small composition variations of a large river area!

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The assessment of long-term trends in river water composition is hampered by river composition heterogeneity, and sampling and sample analysis uncertainty. This work describes a novel methodology for the reliable detection of small river composition trends by taking all relevant uncertainty components into account. The methodology was applied to study the variation of nutrients concentration of Tagus river estuary in the extremely dry 2017 year. Mean nutrient concentrations were determined with an uncertainty that combines sampling and sample analysis uncertainty by the Monte Carlo Method. The nutrient concentration variation observed in two occasions is meaningful if the difference of mean concentrations is metrologically different from zero for a 95% confidence level. The observed meaningful NO2 increase, and SiO2 and NOx variations is justified by dissolved oxygen reduction, decreased freshwater input and algal productivity variations. The developed tool can be applied to the assessment of other composition trends in rivers.

Related reference:

Carlos Borges, Carla Palma, Ricardo J. N. Bettencourt da Silva, Optimization of river sampling: application to nutrients distribution in Tagus river estuary, Analytical Chemistry 91 (2019) 5698-5705 (DOI 10.1021/acs.analchem.8b05781)