Abstract
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.
Highlights
Lakes and reservoirs serve as important drinking water sources and animal habitats.with continuous economic growth, these lakes and reservoirs face water quality problems resulting from human activities and climate change [1]
In consideration of the number of samples and p-value, this study evaluates the accuracy of the proposed regression equations based on mean absolute percentage error (MAPE), root mean square error (RMSE), and R2 and compares the results with proposed regression equations based on MAPE, RMSE, and R2 and compares the results with those presented in the literature
High-frequency sensor monitoring can aid in the high-resolution temporal observation of water quality parameters in lakes and reservoirs and has introduced new processes and ways of understanding other water quality parameters in an ecological system
Summary
Lakes and reservoirs serve as important drinking water sources and animal habitats. With continuous economic growth, these lakes and reservoirs face water quality problems resulting from human activities (e.g., pollution and agricultural and fishery activities) and climate change [1]. Water quality is traditionally monitored via laboratory analysis, and such monitoring allows researchers to understand and characterize different water quality parameters [2,3]. Water quality monitoring may produce relatively accurate measurements, this process is usually time consuming and labor intensive. The measurements are unable to provide a temporal overview of water quality. When a water quality environment is on the verge of deterioration, the long duration of obtaining water quality parameters may delay
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