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部署无线传感器网络以追踪基乌湿地水井中的农药污染:一项实地研究。

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

作者信息

Mutunga Titus, Sinanovic Sinan, Offiong Funmilayo B, Harrison Colin

机构信息

School of Engineering and Built Environment, Department of Electrical and Electronics, Glasgow Caledonian University, Cowcaddens Road, Glasgow G4 0BA, Scotland, UK.

出版信息

Sensors (Basel). 2025 Jul 3;25(13):4149. doi: 10.3390/s25134149.

Abstract

Water pollution from pesticides is a major concern for regulatory agencies worldwide due to expensive detecting mechanisms, delays in the processing of results, and the complexity of the chemical analysis. However, the deployment of monitoring systems utilising the internet of things (IoT) and machine-to-machine communication technologies (M2M) holds promise in overcoming this major global challenge. In this current research, an IoT-based wireless sensor network (WSN) is successfully deployed in rural Kenya at the Kiu watershed, providing in situ pesticide detections and a real-time data visualisation of shallow wells. Kiu is an off-grid community located in an area of intensive agriculture, where residents face a high exposure to pesticides due to farming activities and a reliance on shallow wells for domestic water. The evaluation of path loss models utilising channel characteristics obtained from this study indicate a marked departure from the continuous signal decay with distance. Transmitted packets from deployed sensor nodes indicate minimal mutations of payloads, underscoring systems reliability and data transmission integrity. Additionally, the proposed design significantly reduces the time taken to deliver pesticide measurement results to relevant stakeholders. For the entire monitoring period, pesticide residues were not detected in the selected wells, an outcome validated with lab procedures. These results are attributed to prevailing dry weather conditions which limited the leaching of pesticides to lower layers reaching the water table.

摘要

由于检测机制成本高昂、结果处理存在延迟以及化学分析的复杂性,农药造成的水污染是全球监管机构主要关注的问题。然而,利用物联网(IoT)和机器对机器通信技术(M2M)部署监测系统有望克服这一重大全球挑战。在当前这项研究中,一个基于物联网的无线传感器网络(WSN)成功部署在肯尼亚农村的基乌流域,实现了对农药的现场检测以及浅井实时数据可视化。基乌是一个位于集约农业区的离网社区,由于农业活动以及依赖浅井获取生活用水,当地居民面临着高农药暴露风险。利用本研究获得的信道特性对路径损耗模型进行评估,结果表明其与信号随距离连续衰减的情况有显著差异。已部署传感器节点发送的数据包显示有效载荷的变化极小,这突出了系统的可靠性和数据传输完整性。此外,所提出的设计显著减少了将农药测量结果传递给相关利益攸关方所需的时间。在整个监测期间,在所选定的水井中未检测到农药残留,这一结果通过实验室程序得到了验证。这些结果归因于当时普遍的干旱天气状况,这种状况限制了农药向下层淋溶至地下水位。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0243/12251821/e9d8442bde6b/sensors-25-04149-g001.jpg

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