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Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

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Published on: September 26, 2017

Real-Time Identification of Irrigation Water Pollution Sources and Pathways with a Wireless Sensor Network and

Yu-Pin Lin¹, Hussnain Mukhtar¹, Kuan-Ting Huang¹

¹Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan.

Sensors (Basel, Switzerland)

|July 2, 2020

Summary

This study introduces a novel framework using IoT, blockchain, and GIS for real-time irrigation water pollution source tracing. It successfully identified electrical conductivity and copper pollution pathways, enhancing environmental and food safety.

Keywords:

GIS Internet of Things blockchain pollution pathway pollution source trace wireless sensor network

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Area of Science:

Environmental Science
Water Resource Management
Agricultural Engineering

Background:

Ensuring irrigation water quality is vital for environmental protection and food safety.
Real-time identification of pollution sources and pathways (PSP) remains a significant challenge in agricultural water management.

Purpose of the Study:

To develop and evaluate an integrated framework for real-time water pollution source tracing in irrigation systems.
To leverage Internet of Things (IoT), blockchain technology, and Geographic Information System (GIS) for enhanced water quality monitoring and pollution identification.

Main Methods:

Deployment of wireless sensor networks (WSN) with water quality sensors in irrigation channels.
Integration of IoT for data transmission, blockchain for immutable data recording, and GIS for spatial analysis.
Application of the Water Quality Analysis Simulation Program (WASP) model with a backward pollution source tracing (BPST) process.

Main Results:

Successful real-time identification of electrical conductivity (EC) and copper (Cu2+) pollution sources and pathways.
Effective simulation of EC and Cu2+ concentrations using the WASP model and BPST process to pinpoint pollution origins.
Demonstration of blockchain technology's capability for immutable, real-time water quality data and rapid PSP identification.

Conclusions:

The integrated IoT, blockchain, and GIS framework provides an effective solution for real-time water pollution source tracing.
The BPST process combined with the WASP model accurately identifies pollution sources and pathways, improving water resource management.
This approach represents a significant advancement in water quality monitoring, offering immutable data records and rapid response capabilities for pollution events.