Real-time measurement of total nitrogen for agricultural runoff based on multiparameter sensors and intelligent algorithms

Abstract

Real-time monitoring of non-point source (NPS) pollution is challenging owing to the minute-scale change in runoff flow and concentration under rainfall condition. In this study, we proposed a real-time measurement method for total nitrogen (TN) by combining the timeliness of sensor detection and the accuracy of intelligent algorithms, based on the physical and chemical relationships between TN and sensor-measured indexes. Extra tree regression was selected as the TN inversion algorithm, which has high precision, high computational efficiency, and better ability in over-fitting control. The results show that: (1) the real-time inversion algorithm of TN can achieve the monitoring frequency at the minute scale (<5 min); (2) the method performs well (R²>0.9) when the training and testing datasets are from similar environmental backgrounds (fields or ditches); (3) in the case of partial variable missing, this method can still realize TN inversion, and the prediction accuracy is acceptable (R²>0.7) under the number of missing variables (n) ≤ 2, which makes up for the flaws of missing or abnormal data caused by sensor malfunctions. Overall, the proposed real-time measurement method of TN has stable data acquisition, high precision, and high monitoring frequency. In addition, the method is not limited by cloudy, rainy, or nighttime conditions. Compared with methods such as laboratory test, remote sensing inversion, and water quality automatic monitoring station, our method has obvious advantages in runoff monitoring of NPS pollution, which mainly occurs in small and micro water bodies. The new real-time measurement of TN for runoff may provide important technological support for pre-warning and emergency control of NPS pollution.

Keywords: Emergency pre-warning; Extra tree regression; Intellectualization; Non-point source pollution; Runoff monitoring.