George Themistokleous, Andreas M. Savvides, Katerina Philippou, Ioannis M. Ioannides, Michalis Omirou
{"title":"高频温室气体通量分析工具:自动非稳态透明土壤室的启示","authors":"George Themistokleous, Andreas M. Savvides, Katerina Philippou, Ioannis M. Ioannides, Michalis Omirou","doi":"10.1111/ejss.13560","DOIUrl":null,"url":null,"abstract":"<p>Non-steady-state chambers are widely employed for quantifying soil emissions of CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>O. Automated non-steady-state (a-NSS) soil chambers, when coupled with online gas analysers, offer the ability to capture high-frequency measurements of greenhouse gas (GHG) fluxes. While these sampling systems provide valuable insights into GHG emissions, they present post-measurement challenges, including the management of extensive datasets, intricate flux calculations, and considerations for temporal upscaling. In this study, a computationally efficient algorithm was developed to compute instantaneous fluxes and estimate diel flux patterns using continuous, high-resolution data obtained from an a-NSS sampling system. Applied to a 38-day dataset, the algorithm captured concurrent field measurements of CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>O fluxes. The automated sampling system enables the acquisition of high-frequency data, allowing the detection of episodic gas flux events. By using shape-constrained additive models, a median percentage deviation (bias) of −1.031 and −4.340% was achieved for CO<sub>2</sub> and N<sub>2</sub>O fluxes, respectively. Simpson's rule allowed for efficient upscale from instantaneous to diel flux values. As a result, the proposed algorithm can rapidly and simultaneously calculate CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>O fluxes, providing both instantaneous and diel values directly from raw, high-temporal-resolution data. These advancements significantly contribute to the field of GHG flux measurement, enhancing both the efficiency and accuracy of calculations for a-NSS soil chambers and deepening our understanding of GHG emissions and their temporal dynamics.</p>","PeriodicalId":12043,"journal":{"name":"European Journal of Soil Science","volume":"75 5","pages":""},"PeriodicalIF":4.0000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A high-frequency greenhouse gas flux analysis tool: Insights from automated non-steady-state transparent soil chambers\",\"authors\":\"George Themistokleous, Andreas M. Savvides, Katerina Philippou, Ioannis M. Ioannides, Michalis Omirou\",\"doi\":\"10.1111/ejss.13560\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Non-steady-state chambers are widely employed for quantifying soil emissions of CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>O. Automated non-steady-state (a-NSS) soil chambers, when coupled with online gas analysers, offer the ability to capture high-frequency measurements of greenhouse gas (GHG) fluxes. While these sampling systems provide valuable insights into GHG emissions, they present post-measurement challenges, including the management of extensive datasets, intricate flux calculations, and considerations for temporal upscaling. In this study, a computationally efficient algorithm was developed to compute instantaneous fluxes and estimate diel flux patterns using continuous, high-resolution data obtained from an a-NSS sampling system. Applied to a 38-day dataset, the algorithm captured concurrent field measurements of CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>O fluxes. The automated sampling system enables the acquisition of high-frequency data, allowing the detection of episodic gas flux events. By using shape-constrained additive models, a median percentage deviation (bias) of −1.031 and −4.340% was achieved for CO<sub>2</sub> and N<sub>2</sub>O fluxes, respectively. Simpson's rule allowed for efficient upscale from instantaneous to diel flux values. As a result, the proposed algorithm can rapidly and simultaneously calculate CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>O fluxes, providing both instantaneous and diel values directly from raw, high-temporal-resolution data. 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A high-frequency greenhouse gas flux analysis tool: Insights from automated non-steady-state transparent soil chambers
Non-steady-state chambers are widely employed for quantifying soil emissions of CO2, CH4, and N2O. Automated non-steady-state (a-NSS) soil chambers, when coupled with online gas analysers, offer the ability to capture high-frequency measurements of greenhouse gas (GHG) fluxes. While these sampling systems provide valuable insights into GHG emissions, they present post-measurement challenges, including the management of extensive datasets, intricate flux calculations, and considerations for temporal upscaling. In this study, a computationally efficient algorithm was developed to compute instantaneous fluxes and estimate diel flux patterns using continuous, high-resolution data obtained from an a-NSS sampling system. Applied to a 38-day dataset, the algorithm captured concurrent field measurements of CO2, CH4, and N2O fluxes. The automated sampling system enables the acquisition of high-frequency data, allowing the detection of episodic gas flux events. By using shape-constrained additive models, a median percentage deviation (bias) of −1.031 and −4.340% was achieved for CO2 and N2O fluxes, respectively. Simpson's rule allowed for efficient upscale from instantaneous to diel flux values. As a result, the proposed algorithm can rapidly and simultaneously calculate CO2, CH4, and N2O fluxes, providing both instantaneous and diel values directly from raw, high-temporal-resolution data. These advancements significantly contribute to the field of GHG flux measurement, enhancing both the efficiency and accuracy of calculations for a-NSS soil chambers and deepening our understanding of GHG emissions and their temporal dynamics.
期刊介绍:
The EJSS is an international journal that publishes outstanding papers in soil science that advance the theoretical and mechanistic understanding of physical, chemical and biological processes and their interactions in soils acting from molecular to continental scales in natural and managed environments.