Deploying a Wireless Sensor Network to Track Pesticide Pollution in Kiu Wetland Wells: A Field Study

Area of Science:

• Environmental Science
• Water Quality Monitoring
• Sensor Networks

Background:

• Pesticide water pollution poses global health and regulatory challenges.
• Existing detection methods are costly, slow, and complex.
• Internet of Things (IoT) and M2M technologies offer potential solutions.

Purpose of the Study:

• To deploy an IoT-based wireless sensor network (WSN) for real-time, in situ pesticide detection.
• To monitor shallow wells in a rural agricultural community in Kenya.
• To assess the feasibility and reliability of IoT for water quality monitoring.

Main Methods:

• Deployment of an IoT-based WSN in the Kiu watershed, Kenya.
• Real-time data collection and visualization of pesticide levels in shallow wells.
• Evaluation of channel characteristics and data transmission integrity.
• Validation of results with laboratory procedures.

Main Results:

• The deployed WSN provided reliable, real-time pesticide detection with minimal data mutation.
• Path loss models showed deviations from standard decay patterns.
• Significantly reduced time for delivering pesticide measurement results.
• No pesticide residues were detected in the monitored wells during the study period.

Conclusions:

• The IoT-based WSN is a reliable and efficient tool for real-time water quality monitoring.
• Dry weather conditions played a significant role in the absence of detected pesticide residues.
• This technology can help regulatory agencies overcome challenges in pesticide detection.