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Li, Cunli; Jiang, Cuiling; Zhu, Guangwei; Zou, Wei; Zhu, Mengyuan; Xu, Hai; Shi, Pengcheng; Da, Wenyi
Water, 09/2020, Letnik: 12, Številka: 9Journal Article
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.
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JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
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in: SICRIS
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