Spectral analysis in determining water quality sampling intervals

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.

Water Quality Sampling Frequency Analysis of Surface Freshwater: A Case Study on Bristol Floating Harbour

“…da Silva et al (2019) in their study used spectral analysis to evaluate the representativeness of water quality sampling frequencies for different catchments. In particular, the representation of the spectral coefficients of a signal as a function of frequency results in a graph of frequency densities, which is also called Power Spectral Density (PSD).…”

“…Three approaches are tested: (i) Analysing the frequency components of each parameter at each site to quantify the Nyquist frequency and supposed minimum sampling rate for determination of periodic fluctuations, proposed by Zhou (1996) and Khalil and Ouarda (2009). (ii) Using spectral analysis to determine the Power Spectral Density cumulation curve (Otis and Solomon, 1991) to determine reasonable frequencies to monitor water quality determinants; for each frequency evaluate the loss of information (da Silva et al, 2019). (iii) Analysing the limits of the spectral analysis computing spectrograms using Wavelet analysis (Torrence and Compo, 1998).…”

Determination of Optimal Predictors and Sampling Frequency to Develop Nutrient Soft Sensors Using Random Forest

“…Chen and Han (2018) used a novel qualitative technique on a surface water quality monitoring system and reported 5 min as the OSF [32]. Silva et al (2019) used spectral analysis and suggested varying sampling frequencies based on the size of the watershed [33]. However, to our knowledge there is no study that has determined the OSF required for the soft sensors to train on.…”