There are many types of methods for monitoring atmospheric greenhouse gases,and the differences between the methods have introduced many uncertainties for the accurate monitoring of atmospheric greenhouse gases.In thi...There are many types of methods for monitoring atmospheric greenhouse gases,and the differences between the methods have introduced many uncertainties for the accurate monitoring of atmospheric greenhouse gases.In this paper,the monitoring methods of 7 long-lived greenhouse gases(LLGHG),including carbon dioxide(CO_(2)),methane(CH_(4)),nitrous oxide(N_(2)O),hydrofluorocarbons(HFCs),perfluorocarbons(PFCs),sulfur hexafluoride(SF_(6))and nitrogen trifluoride(NF_(3)),which are regulated and controlled in the Kyoto Protocol and the Doha Amendment,were summarized,and the principle,characteristics and application research progress of each method were systematically studied.Besides,their application scope was analyzed,and the domestication research of relevant instruments was analyzed and prospected.At present,the monitoring methods of atmospheric greenhouse gases are developing towards automation and multi-component simultaneous rapid detection,and are accelerating its integration with new technologies such as big data and satellite remote sensing monitoring;top-down and bottom-up methods are used to provide strong data support for carbon peaking and carbon neutral management decisions in various countries.展开更多
Since the industrial revolution,enhancement of atmospheric greenhouse gas concentrations as a result of human activities has been the primary cause of global warming.The monitoring and evaluation of greenhouse gases a...Since the industrial revolution,enhancement of atmospheric greenhouse gas concentrations as a result of human activities has been the primary cause of global warming.The monitoring and evaluation of greenhouse gases are significant prerequisites for carbon emission control.Using monthly data of global atmospheric carbon dioxide(CO_(2))and methane(CH4)column concentrations(hereinafter XCO_(2) and XCH_(4),respectively)retrieved by the Greenhouse Gas Observation Satellite(GOSAT),we analyzed the variations in XCO_(2)and XCH_(4)in China during 2010-2022 after confirming the reliability of the data.Then,the influence of a strong El Niño event in 2015-2016 on XCO_(2) and XCH_(4) variations in China was further studied.The results show that the retrieved XCO_(2) and XCH_(4) from GOSAT have similar temporal variation trends and significant correlations with the ground observation and emission inventory data of an atmospheric background station,which could be used to assess the variations in XCO_(2) and XCH_(4) in China.XCO_(2) is high in spring and winter while XCH_(4) is high in autumn.Both XCO_(2) and XCH_(4) gradually declined from Southeast China to Northwest and Northeast China,with variation ranges of 401-406 and 1.81-1.88 ppmv,respectively;and the high value areas are located in the middle-lower Yangtze River basin.XCO_(2) and XCH_(4) in China increased as a whole during 2010-2022,with rapid enhancement and high levels of XCO_(2) and XCH_(4) in several areas.The significant increases in XCO_(2) and XCH_(4) over China in 2016 might be closely related to the strong El Niño-Southern Oscillation(ENSO)event during 2015-2016.Under a global warming background in 2015,XCO_(2) and XCH_(4) increased by 0.768%and 0.657%in 2016 in China.Data analysis reveals that both the XCO_(2) and XCH_(4) variations might reflect the significant impact of the ENSO event on glacier melting in the Tibetan Plateau.展开更多
【目的】明确秸秆还田下大气CO_(2)浓度升高对水稻生长和稻田CH_(4)排放的影响,为气候变化下温室气体排放评估和丰产低碳的稻作技术创新提供理论参考和科学依据。【方法】利用开顶式气室(Open top chamber,OTC)进行田间试验,设置两个CO_...【目的】明确秸秆还田下大气CO_(2)浓度升高对水稻生长和稻田CH_(4)排放的影响,为气候变化下温室气体排放评估和丰产低碳的稻作技术创新提供理论参考和科学依据。【方法】利用开顶式气室(Open top chamber,OTC)进行田间试验,设置两个CO_(2)浓度处理,分别为正常大气CO_(2)浓度处理(简称aCO_(2),CO_(2)浓度约为0.04%)和大气CO_(2)浓度升高处理(简称eCO_(2),CO_(2)浓度约为0.055%),每个处理的田块混入等量的前茬小麦秸秆,探明秸秆还田下大气CO_(2)浓度升高对水稻产量等生长特性、稻田CH_(4)排放及微生物丰度的影响,揭示秸秆还田下大气CO_(2)浓度升高对CH_(4)排放的影响机制。【结果】大气CO_(2)浓度升高显著促进水稻的生长,使剑叶叶面积增加25.0%,地上生物量增加22.0%,产量提高29.0%。大气CO_(2)浓度升高显著增加了穗数、结实率和千粒重,但对穗粒数影响不显著。秸秆还田下,大气CO_(2)浓度升高有降低稻田CH_(4)排放的趋势,使单位产量CH_(4)排放量降低了39.4%。大气CO_(2)浓度升高使土壤甲烷氧化关键基因pmoA的拷贝数增加了20.0%,但对甲烷产生关键基因mcrA的拷贝数影响较小。【结论】秸秆还田条件下,未来大气CO_(2)浓度升高不仅提高了水稻产量,而且有利于减少稻田温室气体CH_(4)的排放。展开更多
气候变化通过大气CO_(2)浓度、温度和降雨的改变,直接或间接影响农田温室气体排放,研究未来气候情景下农田温室气体排放对实现农业碳减排具有重要意义。为探究气候变化背景下农田温室气体排放特征,该研究在长期田间定位试验基础上,利用...气候变化通过大气CO_(2)浓度、温度和降雨的改变,直接或间接影响农田温室气体排放,研究未来气候情景下农田温室气体排放对实现农业碳减排具有重要意义。为探究气候变化背景下农田温室气体排放特征,该研究在长期田间定位试验基础上,利用当前大气CO_(2)浓度与CO_(2)浓度升高条件下旱作玉米农田温室气体排放通量的田间观测数据,采用“试错法”对DayCent模型进行校验,并利用校验后的模型,根据第六次国际耦合模式比较计划(Coupled Model Intercomparison Project phase 6,CMIP6)气候情景数据,预测未来SSP126(低排放水平)与SSP245(中等排放水平)气候情景下旱地玉米农田温室气体排放通量。结果表明,DayCent模型对不同大气CO_(2)浓度下N_(2)O、CH_(4)和CO_(2)排放通量的模拟值与观测值高度一致,模拟效率(modeling efficiency,EF)分别为0.58~0.87、0.45~0.65和0.25~0.62,均方根误差(root mean square error,RMSE)分别为0.83~1.33 g/(hm^(2)·d)、0.67~0.82 g/(hm^(2)·d)和0.58~0.80 g/(m^(2)·d),决定系数(coefficient of determination,R^(2))分别为0.80~0.91、0.53~0.80和0.53~0.85。SSP126和SSP245气候情景下,在玉米单作种植模式下旱地农田N_(2)O和CO_(2)年排放量均呈现上升趋势,以2001—2020年农田温室气体排放通量为基准,到2060年N_(2)O年排放量分别增加22.8%和24.9%,CO_(2)年排放量分别增加6.7%和8.0%;旱地农田CH_(4)年吸收量呈下降趋势,两个气候情景下分别减少13.6%和13.4%。未来气候情景下旱地农田仍是温室气体排放源,优化氮肥管理和农田耕作措施对实现温室气体减排具有重要意义,模拟结果可以为制定农业适应气候变化对策提供基础数据支持。展开更多
基金Supported by the Science and Technology Plan Project of Inner Mongolia Autonomous Region(2022YFHH0116)。
文摘There are many types of methods for monitoring atmospheric greenhouse gases,and the differences between the methods have introduced many uncertainties for the accurate monitoring of atmospheric greenhouse gases.In this paper,the monitoring methods of 7 long-lived greenhouse gases(LLGHG),including carbon dioxide(CO_(2)),methane(CH_(4)),nitrous oxide(N_(2)O),hydrofluorocarbons(HFCs),perfluorocarbons(PFCs),sulfur hexafluoride(SF_(6))and nitrogen trifluoride(NF_(3)),which are regulated and controlled in the Kyoto Protocol and the Doha Amendment,were summarized,and the principle,characteristics and application research progress of each method were systematically studied.Besides,their application scope was analyzed,and the domestication research of relevant instruments was analyzed and prospected.At present,the monitoring methods of atmospheric greenhouse gases are developing towards automation and multi-component simultaneous rapid detection,and are accelerating its integration with new technologies such as big data and satellite remote sensing monitoring;top-down and bottom-up methods are used to provide strong data support for carbon peaking and carbon neutral management decisions in various countries.
基金Supported by the Natural Science Foundation of Liaoning Province(2022-MS-098)Joint Open Fund of the Institute of Atmospheric Environment,China Meteorological Administration,Shenyang and Key Laboratory of Agro-Meteorological Disasters of Liaoning Province(2024SYIAEKFZD05 and 2023SYIAEKFZD06)+3 种基金Open Research Project of Shangdianzi Atmospheric Background Station(SDZ20220912)Joint Research Project for Meteorological Capacity Improvement(23NLTSZ006)Applied Basic Research Program of Liaoning Province(2022JH2/101300193)National Natural Science Foundation of China(42105159 and 42005040).
文摘Since the industrial revolution,enhancement of atmospheric greenhouse gas concentrations as a result of human activities has been the primary cause of global warming.The monitoring and evaluation of greenhouse gases are significant prerequisites for carbon emission control.Using monthly data of global atmospheric carbon dioxide(CO_(2))and methane(CH4)column concentrations(hereinafter XCO_(2) and XCH_(4),respectively)retrieved by the Greenhouse Gas Observation Satellite(GOSAT),we analyzed the variations in XCO_(2)and XCH_(4)in China during 2010-2022 after confirming the reliability of the data.Then,the influence of a strong El Niño event in 2015-2016 on XCO_(2) and XCH_(4) variations in China was further studied.The results show that the retrieved XCO_(2) and XCH_(4) from GOSAT have similar temporal variation trends and significant correlations with the ground observation and emission inventory data of an atmospheric background station,which could be used to assess the variations in XCO_(2) and XCH_(4) in China.XCO_(2) is high in spring and winter while XCH_(4) is high in autumn.Both XCO_(2) and XCH_(4) gradually declined from Southeast China to Northwest and Northeast China,with variation ranges of 401-406 and 1.81-1.88 ppmv,respectively;and the high value areas are located in the middle-lower Yangtze River basin.XCO_(2) and XCH_(4) in China increased as a whole during 2010-2022,with rapid enhancement and high levels of XCO_(2) and XCH_(4) in several areas.The significant increases in XCO_(2) and XCH_(4) over China in 2016 might be closely related to the strong El Niño-Southern Oscillation(ENSO)event during 2015-2016.Under a global warming background in 2015,XCO_(2) and XCH_(4) increased by 0.768%and 0.657%in 2016 in China.Data analysis reveals that both the XCO_(2) and XCH_(4) variations might reflect the significant impact of the ENSO event on glacier melting in the Tibetan Plateau.
文摘【目的】明确秸秆还田下大气CO_(2)浓度升高对水稻生长和稻田CH_(4)排放的影响,为气候变化下温室气体排放评估和丰产低碳的稻作技术创新提供理论参考和科学依据。【方法】利用开顶式气室(Open top chamber,OTC)进行田间试验,设置两个CO_(2)浓度处理,分别为正常大气CO_(2)浓度处理(简称aCO_(2),CO_(2)浓度约为0.04%)和大气CO_(2)浓度升高处理(简称eCO_(2),CO_(2)浓度约为0.055%),每个处理的田块混入等量的前茬小麦秸秆,探明秸秆还田下大气CO_(2)浓度升高对水稻产量等生长特性、稻田CH_(4)排放及微生物丰度的影响,揭示秸秆还田下大气CO_(2)浓度升高对CH_(4)排放的影响机制。【结果】大气CO_(2)浓度升高显著促进水稻的生长,使剑叶叶面积增加25.0%,地上生物量增加22.0%,产量提高29.0%。大气CO_(2)浓度升高显著增加了穗数、结实率和千粒重,但对穗粒数影响不显著。秸秆还田下,大气CO_(2)浓度升高有降低稻田CH_(4)排放的趋势,使单位产量CH_(4)排放量降低了39.4%。大气CO_(2)浓度升高使土壤甲烷氧化关键基因pmoA的拷贝数增加了20.0%,但对甲烷产生关键基因mcrA的拷贝数影响较小。【结论】秸秆还田条件下,未来大气CO_(2)浓度升高不仅提高了水稻产量,而且有利于减少稻田温室气体CH_(4)的排放。
文摘气候变化通过大气CO_(2)浓度、温度和降雨的改变,直接或间接影响农田温室气体排放,研究未来气候情景下农田温室气体排放对实现农业碳减排具有重要意义。为探究气候变化背景下农田温室气体排放特征,该研究在长期田间定位试验基础上,利用当前大气CO_(2)浓度与CO_(2)浓度升高条件下旱作玉米农田温室气体排放通量的田间观测数据,采用“试错法”对DayCent模型进行校验,并利用校验后的模型,根据第六次国际耦合模式比较计划(Coupled Model Intercomparison Project phase 6,CMIP6)气候情景数据,预测未来SSP126(低排放水平)与SSP245(中等排放水平)气候情景下旱地玉米农田温室气体排放通量。结果表明,DayCent模型对不同大气CO_(2)浓度下N_(2)O、CH_(4)和CO_(2)排放通量的模拟值与观测值高度一致,模拟效率(modeling efficiency,EF)分别为0.58~0.87、0.45~0.65和0.25~0.62,均方根误差(root mean square error,RMSE)分别为0.83~1.33 g/(hm^(2)·d)、0.67~0.82 g/(hm^(2)·d)和0.58~0.80 g/(m^(2)·d),决定系数(coefficient of determination,R^(2))分别为0.80~0.91、0.53~0.80和0.53~0.85。SSP126和SSP245气候情景下,在玉米单作种植模式下旱地农田N_(2)O和CO_(2)年排放量均呈现上升趋势,以2001—2020年农田温室气体排放通量为基准,到2060年N_(2)O年排放量分别增加22.8%和24.9%,CO_(2)年排放量分别增加6.7%和8.0%;旱地农田CH_(4)年吸收量呈下降趋势,两个气候情景下分别减少13.6%和13.4%。未来气候情景下旱地农田仍是温室气体排放源,优化氮肥管理和农田耕作措施对实现温室气体减排具有重要意义,模拟结果可以为制定农业适应气候变化对策提供基础数据支持。
