High-frequency sensors can monitor water quality with high temporal resolution and without environmental influence. However, sensors for detecting key water quality parameters, such as total nitrogen(TN), total phosphorus(TP), and other water environmental parameters, are either not yet available or have attracted limited usage. By using a large number of high-frequency sensor and manual monitoring data, this study establishes regression equations that measure high-frequency sensor and key water quality parameters through multiple regression analysis. Results show that a high-frequency sensor can quickly and accurately estimate dynamic key water quality parameters by evaluating seven water quality parameters. An evaluation of the flux of four chemical parameters further proves that the multi-parameter sensor can efficiently estimate the key water quality parameters. However, due to the different optical properties and ecological bases of these parameters, the high-frequency sensor shows a better prediction performance for chemical parameters than for physical and biological parameters. Nevertheless, these results indicate that combining high-frequency sensor monitoring with regression equations can provide real-time and accurate water quality information that can meet the needs in water environment management and realize early warning functions.

中文翻译：

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大深水库高频传感器数据估算水质参数

高频传感器可以在不受环境影响的情况下以高时间分辨率监测水质。然而，用于检测关键水质参数，如总氮 (TN)、总磷 (TP) 和其他水环境参数的传感器要么尚不可用，要么使用有限。本研究利用大量高频传感器和人工监测数据，通过多元回归分析建立测量高频传感器和关键水质参数的回归方程。结果表明，高频传感器可以通过评估七个水质参数来快速准确地估计动态关键水质参数。对四个化学参数通量的评估进一步证明多参数传感器可以有效地估计关键水质参数。然而，由于这些参数的光学特性和生态基础不同，高频传感器对化学参数显示出比物理和生物参数更好的预测性能。尽管如此，这些结果表明，将高频传感器监测与回归方程相结合，可以提供实时、准确的水质信息，满足水环境管理的需要，实现预警功能。高频传感器对化学参数显示出比物理和生物参数更好的预测性能。尽管如此，这些结果表明，将高频传感器监测与回归方程相结合，可以提供实时、准确的水质信息，满足水环境管理的需要，实现预警功能。高频传感器对化学参数显示出比物理和生物参数更好的预测性能。尽管如此，这些结果表明，将高频传感器监测与回归方程相结合，可以提供实时、准确的水质信息，满足水环境管理的需要，实现预警功能。