1. Synchronous monitoring agricultural water qualities and greenhouse gas emissions based on low-cost Internet of Things and intelligent algorithms.
- Author
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Zhang H, Ren R, Gao X, Wang H, Jiang W, Jiang X, Li Z, Pan J, Wang J, Wang S, Ding Y, Mu Y, Wang X, Du J, Li WT, Xiong Z, and Zou J
- Abstract
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 N
2 O concentrations. Combined with an air-water gas exchange model, the system achieved efficient monitoring and simulation of CO2 and N2 O 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 N2 O 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 (R2 > 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 (R2 > 0.80) when testing data sourced from paddy fields and lakes. Finally, CO2 and N2 O emissions were successfully monitored, with the results validated using a floating chamber method (R2 > 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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)- Published
- 2024
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