Spectral analysis in determining water quality sampling intervals

Régis Leandro Lopes Da Silva,Geraldo Lopes Da Silveira,André Luiz Lopes Da Silveira

doi:10.1590/2318-0331.241920180077

Abstract

ABSTRACT To make water quality series more representative, real-time monitoring techniques are developed. However, these techniques have obstacles in their use, such as high costs and difficulties in equipment installation, maintenance, and calibration. One alternative is near-real time water quality monitoring (NRTWQM), with sampling done less frequently than daily. The study objective was to evaluate, through spectral analysis, the water quality sampling frequency representativity for different catchments. For this purpose, a historical series of real time water quality monitoring stations were used in Brazil, Canada, and the USA. These series were submitted to spectral analysis to identify the denser frequencies and their representativeness across the series. To obtain the sampling intervals, the Nyquist-Shannon theorem was applied. Weekly intervals accounted for 65% of cumulative frequencies for the three verified parameters, and the sampling intervals obtained by means of the characteristic frequencies were shown to be executable in the NRTWQM models for up to the 90% of cumulative frequency. For cumulative frequency above 90%, the intervals approach the daily values.

Highlights

Demands for water have led man to create and refine techniques for monitoring water quantity and quality in a manner that the data acquired guide help management, engineering, and environmental decisions
Graphs appear to have high densities close to zero, this is an effect of the smoothing promoted by the Hann windowing applied to the graph
- Spectral analysis of historical water quality data series can be used as input to create cumulative frequency density curves, which from a sampling point of view, translates into cumulative frequency curves

Summary

Introduction

Demands for water have led man to create and refine techniques for monitoring water quantity and quality in a manner that the data acquired guide help management, engineering, and environmental decisions. Rainfall and fluviometric monitoring networks with daily temporal frequencies already operate in many stations in Brazil, with representative historical series for many catchments (ANA, 2017). When it comes to water quality data, time availability at monitoring sites is very low and often does not provide a solid basis for water management support. In Brazil, this monitoring is generally done in a traditional manner by collecting samples and analyzing them in laboratories. This form allows a larger number of analyzed parameters, it has low temporal density (HANISCH; FREIRE-NORDI, 2015)

Objectives

Methods

Results

Conclusion