{"title":"基于低成本物联网和智能算法的农业水质和温室气体排放同步监测","authors":"Huazhan Zhang, Rui Ren, Xiang Gao, Housheng Wang, Wei Jiang, Xiaosan Jiang, Zhaofu Li, Jianjun Pan, Jinyang Wang, Songhan Wang, Yanfeng Ding, Yue Mu, Xuelei Wang, Jizeng Du, Wen-Tao Li, Zhengqin Xiong, Jianwen Zou","doi":"10.1016/j.watres.2024.122663","DOIUrl":null,"url":null,"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<sub>2</sub>O concentrations. Combined with an air-water gas exchange model, the system achieved efficient monitoring and simulation of CO<sub>2</sub> and N<sub>2</sub>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 N<sub>2</sub>O concentration model based on support vector regression, demonstrating accurate predictions within a concentration range of 2.003 to 13.247 g/L. Notably, the model maintained acceptable predictive accuracy (R<sup>2</sup> > 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<sup>2</sup> > 0.80) when testing data sourced from paddy fields and lakes. Finally, CO<sub>2</sub> and N<sub>2</sub>O emissions were successfully monitored, with the results validated using a floating chamber method (R<sup>2</sup> > 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.","PeriodicalId":443,"journal":{"name":"Water Research","volume":null,"pages":null},"PeriodicalIF":11.4000,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Synchronous monitoring agricultural water qualities and greenhouse gas emissions based on low-cost Internet of Things and intelligent algorithms\",\"authors\":\"Huazhan Zhang, Rui Ren, Xiang Gao, Housheng Wang, Wei Jiang, Xiaosan Jiang, Zhaofu Li, Jianjun Pan, Jinyang Wang, Songhan Wang, Yanfeng Ding, Yue Mu, Xuelei Wang, Jizeng Du, Wen-Tao Li, Zhengqin Xiong, Jianwen Zou\",\"doi\":\"10.1016/j.watres.2024.122663\",\"DOIUrl\":null,\"url\":null,\"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<sub>2</sub>O concentrations. Combined with an air-water gas exchange model, the system achieved efficient monitoring and simulation of CO<sub>2</sub> and N<sub>2</sub>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 N<sub>2</sub>O concentration model based on support vector regression, demonstrating accurate predictions within a concentration range of 2.003 to 13.247 g/L. Notably, the model maintained acceptable predictive accuracy (R<sup>2</sup> > 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<sup>2</sup> > 0.80) when testing data sourced from paddy fields and lakes. Finally, CO<sub>2</sub> and N<sub>2</sub>O emissions were successfully monitored, with the results validated using a floating chamber method (R<sup>2</sup> > 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.\",\"PeriodicalId\":443,\"journal\":{\"name\":\"Water Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":11.4000,\"publicationDate\":\"2024-10-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Water Research\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.1016/j.watres.2024.122663\",\"RegionNum\":1,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ENVIRONMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Water Research","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1016/j.watres.2024.122663","RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ENVIRONMENTAL","Score":null,"Total":0}
Synchronous monitoring agricultural water qualities and greenhouse gas emissions based on low-cost Internet of Things and intelligent algorithms
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 N2O concentrations. Combined with an air-water gas exchange model, the system achieved efficient monitoring and simulation of CO2 and N2O 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 N2O concentration model based on support vector regression, demonstrating accurate predictions within a concentration range of 2.003 to 13.247 g/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 N2O 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.
期刊介绍:
Water Research, along with its open access companion journal Water Research X, serves as a platform for publishing original research papers covering various aspects of the science and technology related to the anthropogenic water cycle, water quality, and its management worldwide. The audience targeted by the journal comprises biologists, chemical engineers, chemists, civil engineers, environmental engineers, limnologists, and microbiologists. The scope of the journal include:
•Treatment processes for water and wastewaters (municipal, agricultural, industrial, and on-site treatment), including resource recovery and residuals management;
•Urban hydrology including sewer systems, stormwater management, and green infrastructure;
•Drinking water treatment and distribution;
•Potable and non-potable water reuse;
•Sanitation, public health, and risk assessment;
•Anaerobic digestion, solid and hazardous waste management, including source characterization and the effects and control of leachates and gaseous emissions;
•Contaminants (chemical, microbial, anthropogenic particles such as nanoparticles or microplastics) and related water quality sensing, monitoring, fate, and assessment;
•Anthropogenic impacts on inland, tidal, coastal and urban waters, focusing on surface and ground waters, and point and non-point sources of pollution;
•Environmental restoration, linked to surface water, groundwater and groundwater remediation;
•Analysis of the interfaces between sediments and water, and between water and atmosphere, focusing specifically on anthropogenic impacts;
•Mathematical modelling, systems analysis, machine learning, and beneficial use of big data related to the anthropogenic water cycle;
•Socio-economic, policy, and regulations studies.