Optimization of River Sampling: Application to Nutrients Distribution in Tagus River Estuary

Area of Science:

• Environmental Science
• Analytical Chemistry
• Water Resource Management

Background:

• River water pollution assessment is complex due to seasonal variations, diverse pollution sources, and spatial pollutant heterogeneity.
• Accurate sampling is critical for reliable trend analysis, but sampling uncertainty remains a significant challenge.

Purpose of the Study:

• To develop and validate a methodology for modeling river water sampling uncertainty.
• To compare the uncertainty associated with single sampling (SS), random sampling (RS), and line sampling (LS) strategies.
• To apply the methodology to nutrient determination in the Tagus River estuary.

Main Methods:

• Developed a methodology to model sampling uncertainty based on spatial distribution data.
• Utilized Monte Carlo simulations to randomize a 3D surface representing nutrient levels and spatial coordinates.
• Applied the method to analyze uncertainty for SS, RS, and LS strategies for nutrients (NOx, NO2, PO4, SiO2).

Main Results:

• The uncertainty from RS and LS is equivalent and substantially lower than SS when using at least three subsamples.
• Sampling relative standard uncertainty ranged from 0.31% to 4.4%.
• Nutrient concentration estimates had a relative expanded uncertainty of 5.9% to 10% at a 95% confidence level.

Conclusions:

• The proposed methodology effectively models river water sampling uncertainty.
• Composite sampling strategies (RS and LS) offer significant advantages over SS for heterogeneous river systems.
• This approach is applicable to various parameters and riverine environments, enhancing water quality monitoring.