Zhang Huazhan, Ren Rui, Gao Xiang, Wang Housheng, Jiang Wei, Jiang Xiaosan, Li Zhaofu, Pan Jianjun, Wang Jinyang, Wang Songhan, Ding Yanfeng, Mu Yue, Wang Xuelei, Du Jizeng, Li Wen-Tao, Xiong Zhengqin, Zou Jianwen
Key Laboratory of Low-carbon and Green Agriculture in Southeastern China, Ministry of Agriculture and Rural Affairs, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, PR China.
Key Laboratory of Low-carbon and Green Agriculture in Southeastern China, Ministry of Agriculture and Rural Affairs, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, PR China; Jiangsu Key Laboratory of Low Carbon Agriculture and GHGs Mitigation, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, PR China.
Water Res. 2025 Jan 1;268(Pt A):122663. doi: 10.1016/j.watres.2024.122663. Epub 2024 Oct 19.
This study addressed the challenges of cost and portability in synchronous monitoring water quality and greenhouse gas emissions in paddy-dominated regions by developing a novel Internet of Things (IoT)-based monitoring system (WG-IoT-MS). The system, equipped with low-cost sensors and integrated intelligent algorithms, enabled real-time monitoring of dissolved NO concentrations. Combined with an air-water gas exchange model, the system achieved efficient monitoring and simulation of CO and NO emissions from agricultural water bodies while reducing monitoring costs by approximately 60 %. The proposed method was validated in paddy-dominated regions in Danyang, China. Results indicated the excellence of the dissolved NO concentration model based on support vector regression, demonstrating accurate predictions within a concentration range of 2.003 to 13.247 μg/L. Notably, the model maintained acceptable predictive accuracy (R > 0.70) even when some variables were partially absent (with the number of missing variables < 2 and the missing proportion (MP) ≤ 50 %), making up for the data loss caused by sensor malfunctions. Furthermore, the model performed well (R > 0.80) when testing data sourced from paddy fields and lakes. Finally, CO and NO emissions were successfully monitored, with the results validated using a floating chamber method (R > 0.70). The method provides crucial technical support for quantitative assessment of water quality and greenhouse gas emissions in paddy-dominated regions, laying a foundation for formulating effective emission reduction strategies.
本研究通过开发一种新型的基于物联网（IoT）的监测系统（WG-IoT-MS），解决了以稻田为主的地区同步监测水质和温室气体排放时的成本和便携性挑战。该系统配备了低成本传感器并集成了智能算法，能够实时监测溶解态NO浓度。结合气-水气体交换模型，该系统实现了对农业水体中CO和NO排放的高效监测与模拟，同时将监测成本降低了约60%。所提出的方法在中国丹阳以稻田为主的地区得到了验证。结果表明基于支持向量回归的溶解态NO浓度模型表现出色，在2.003至13.247μg/L的浓度范围内预测准确。值得注意的是，即使某些变量部分缺失（缺失变量数量<2且缺失比例（MP）≤50%），该模型仍保持可接受的预测精度（R>0.70），弥补了传感器故障导致的数据损失。此外，当测试数据来自稻田和湖泊时，该模型表现良好（R>0.80）。最后，成功监测了CO和NO排放，并使用浮动箱法对结果进行了验证（R>0.70）。该方法为以稻田为主的地区水质和温室气体排放的定量评估提供了关键技术支持，为制定有效的减排策略奠定了基础。
