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.

摘要 为提升水质时序数据的代表性，实时监测技术应运而生。然而此类技术在应用中存在诸多障碍，例如部署成本高昂，且设备安装、维护与校准难度较大。近实时水质监测（near-real time water quality monitoring, NRTWQM）便是一种替代方案，其采样频率低于每日一次。本研究旨在通过频谱分析，评估不同流域的水质采样频率代表性。为此，本研究采用了巴西、加拿大与美国的实时水质监测站历史时序数据集。对这些时序数据开展频谱分析，以识别数据中密度更高的频率及其在全序列中的代表性。为推导最优采样间隔，本研究应用了奈奎斯特-香农定理。针对三个验证参数，周度采样间隔的累计频率占比达65%；通过特征频率推导得到的采样间隔，在累计频率不超过90%的场景下，均可在近实时水质监测模型中落地应用。当累计频率超过90%时，采样间隔将趋近于每日一次的监测频率。