Establishment of an Automatic Real-Time Monitoring System for Irrigation Water Quality Management

Abstract

In order to provide the real-time monitoring for identifying the sources of pollution and improving the irrigation water quality management, the integration of continuous automatic sampling techniques and cloud technologies is essential. In this study, we have established an automatic real-time monitoring system for improving the irrigation water quality management, especially for heavy metals such as Cd, Pb, Cu, Ni, Zn, and Cr. As a part of this work, we have first provided several examples on the basic water quality parameters (e.g., pH and electrical conductance) to demonstrate the capacity of data correction by the smart monitoring system, and then evaluated the trend and variance of water quality parameters for different types of monitoring stations. By doing so, the threshold (to initiate early warming) of different water quality parameters could be dynamically determined by the system, and the authorities could be immediately notified for follow-up actions. We have also provided and discussed the representative results from the real-time automatic monitoring system of heavy metals from different monitoring stations. Finally, we have illustrated the implications of the developed smart monitoring system for ensuring the safety of irrigation water in the near future, including integration with automatic sampling for establishing information exchange platform, estimating fluxes of heavy metals to paddy fields, and combining with green technologies for nonpoint source pollution control.

A multiple model approach for evaluating the performance of time-lapse capsules in trapping heavy metals from water bodies

“…This technique exhibits a great potential of wide applications in tracking or extracting various types of pollutants (even at the sub-ppm level) from water bodies and/or paddy soils at low cost. In our previous study, 30 we successfully utilized the XRF technique to rapidly determine the concentrations of heavy metals adsorbed on the time-lapse capsules. For the sake of heavy metal monitoring, the regeneration of the used ion-exchange resins was not necessary as the time-lapse capsules were designed for one-time use.…”

Revolutionizing Agriculture: A Case Study of IBM's AI Innovations

“…Aside from providing real-time monitoring (Syu, W. J. et al (2020). [27]) and control capabilities, AI-based irrigation systems do so. Through mobile applications or web interfaces, farmers can remotely use the system to keep an eye on the irrigation process, get notifications, and make any adjustments.…”