The challenge of establishing top-down constraints for regional emissions of fossil fuel CO_(2)(FFCO_(2))arises from the difficulty in distinguishing between atmospheric CO_(2)concentrations released from fossil fuels...The challenge of establishing top-down constraints for regional emissions of fossil fuel CO_(2)(FFCO_(2))arises from the difficulty in distinguishing between atmospheric CO_(2)concentrations released from fossil fuels and background variability,particularly owing to the influence of terrestrial biospheric fluxes.This necessitates the development of a regional inversion methodology based on atmospheric CO_(2)observations to verify bottom-up estimations independently.This study presents a promising approach for estimating China's FFCO_(2)emissions by incorporating the model residual errors(MREs)of the column-averaged dry-air mole fractions of CO_(2)(XCO_(2))from FFCO_(2)emissions(MREff)retained in the analysis of natural flux optimization.China's FFCO_(2)emissions during the COVID-19 lockdown in 2020 are estimated using the GEOS-Chem adjoint model.The relationship between the MREff and FFCO_(2)is determined using the model based on a regional FFCO_(2)anomaly suggested by posterior NOx emissions from air-quality data assimilation.The MREff is typically one-tenth in magnitude,but some positively skewed outliers exceed 1 ppm because the prior emissions lack lockdown impacts,thereby exerting considerable observation forcing given the satellite retrieval uncertainties.We initialize the FFCO_(2)with posterior NOx emissions and optimize the colinear emission ratio.Synthetic data experiments demonstrate that this approach reduces the FFCO_(2)bias to less than 10%.The real-data experiments estimate 19%lower FFCO_(2)with GOSAT XCO_(2)and 26%lower with OCO-2 XCO_(2)than the bottom-up estimations.This study proves the feasibility of our regional FFCO_(2)inversion,highlighting the importance of addressing the outlier behaviors observed in satellite XCO_(2)retrievals.展开更多
In this study,we introduce our newly developed measurement-fed-perception self-adaption Low-cost UAV Coordinated Carbon Observation Network(LUCCN)prototype.The LUCCN primarily consists of two categories of instruments...In this study,we introduce our newly developed measurement-fed-perception self-adaption Low-cost UAV Coordinated Carbon Observation Network(LUCCN)prototype.The LUCCN primarily consists of two categories of instruments,including ground-based and UAV-based in-situ measurement.We use the GMP343,a low-cost non-dispersive infrared sensor,in both ground-based and UAV-based instruments.The first integrated measurement campaign took place in Shenzhen,China,4 May 2023.During the campaign,we found that LUCCN’s UAV component presented significant data-collecting advantages over its ground-based counterpart owing to the relatively high altitudes of the point emission sources,which was especially obvious at a gas power plant in Shenzhen.The emission flux was calculated by a crosssectional flux(CSF)method,the results of which differed from the Open-Data Inventory for Anthropogenic Carbon dioxide(ODIAC).The CSF result was slightly larger than others because of the low sampling rate of the whole emission cross section.The LUCCN system will be applied in future carbon monitoring campaigns to increase the spatiotemporal coverage of carbon emission information,especially in scenarios involving the detection of smaller-scale,rapidly varying sources and sinks.展开更多
Coal-fired power plants are a major carbon source in China. In order to assess the evaluation of China's carbon reduction progress with the promise made on the Paris Agreement, it is crucial to monitor the carbon ...Coal-fired power plants are a major carbon source in China. In order to assess the evaluation of China's carbon reduction progress with the promise made on the Paris Agreement, it is crucial to monitor the carbon flux intensity from coal-fired power plants. Previous studies have calculated CO_(2) emissions from point sources based on Orbiting Carbon Observatory-2 and-3(OCO-2 and OCO-3) satellite measurements, but the factors affecting CO_(2) flux estimations are uncertain. In this study, we employ a Gaussian Plume Model to estimate CO_(2) emissions from three power plants in China based on OCO-3 XCO_(2) measurements. Moreover, flux uncertainties resulting from wind information, background values,satellite CO_(2) measurements, and atmospheric stability are discussed. This study highlights the CO_(2) flux uncertainty derived from the satellite measurements. Finally, satellite-based CO_(2) emission estimates are compared to bottom-up inventories.The satellite-based CO_(2) emission estimates at the Tuoketuo and Nongliushi power plants are ~30 and ~10 kt d^(-1) smaller than the Open-Data Inventory for Anthropogenic Carbon dioxide(ODIAC) respectively, but ~10 kt d^(-1) larger than the ODIAC at Baotou.展开更多
IntroductionThe severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)pandemic has taken a toll on humans,and the development of effective vaccines has been a promising tool to end the pandemic.However,for a vacc...IntroductionThe severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)pandemic has taken a toll on humans,and the development of effective vaccines has been a promising tool to end the pandemic.However,for a vaccination program to be successful,a considerable proportion of the community must be vaccinated.Hence,public acceptance of coronavirus disease 2019(COVID-19)vaccines has become the key to controlling the pandemic.Recent studies have shown vaccine hesitancy increasing over time.This systematic review aims to evaluate the COVID-19 vaccine hesitancy rate and related factors in different communities.MethodA comprehensive search was performed in MEDLINE(via PubMed),Scopus,and Web of Science from January 1,2019 to January 31,2022.All relevant descriptive and observational studies(cross-sectional and longitudinal)on vaccine hesitancy and acceptance were included in this systematic review.In the meta-analysis,odds ratio(OR)was used to assess the effects of population characteristics on vaccine hesitancy,and event rate(acceptance rate)was the effect measure for overall acceptance.Publication bias was assessed using the funnel plot,Egger's test,and trim-and-fill methods.ResultA total of 135 out of 6,417 studies were included after screening.A meta-analysis of 114 studies,including 849,911 participants,showed an overall acceptance rate of 63.1%.In addition,men,married individuals,educated people,those with a history of flu vaccination,those with higher income levels,those with comorbidities,and people living in urban areas were less hesitant.ConclusionIncreasing public awareness of the importance of COVID-19 vaccines in overcoming the pandemic is crucial.Being men,living in an urban region,being married or educated,having a history of influenza vaccination,having a higher level of income status,and having a history of comorbidities are associated with higher COVID-19 vaccine acceptance.展开更多
基金jointly supported by the National Key Research and Development Plan(Grant No.2023YFB3907405)the National Natural Science Foundation of China(Grant No.42175132)the Chinese Academy of Sciences Project for Young Scientists in Basic Research(Grant No.YSBR-037)。
文摘The challenge of establishing top-down constraints for regional emissions of fossil fuel CO_(2)(FFCO_(2))arises from the difficulty in distinguishing between atmospheric CO_(2)concentrations released from fossil fuels and background variability,particularly owing to the influence of terrestrial biospheric fluxes.This necessitates the development of a regional inversion methodology based on atmospheric CO_(2)observations to verify bottom-up estimations independently.This study presents a promising approach for estimating China's FFCO_(2)emissions by incorporating the model residual errors(MREs)of the column-averaged dry-air mole fractions of CO_(2)(XCO_(2))from FFCO_(2)emissions(MREff)retained in the analysis of natural flux optimization.China's FFCO_(2)emissions during the COVID-19 lockdown in 2020 are estimated using the GEOS-Chem adjoint model.The relationship between the MREff and FFCO_(2)is determined using the model based on a regional FFCO_(2)anomaly suggested by posterior NOx emissions from air-quality data assimilation.The MREff is typically one-tenth in magnitude,but some positively skewed outliers exceed 1 ppm because the prior emissions lack lockdown impacts,thereby exerting considerable observation forcing given the satellite retrieval uncertainties.We initialize the FFCO_(2)with posterior NOx emissions and optimize the colinear emission ratio.Synthetic data experiments demonstrate that this approach reduces the FFCO_(2)bias to less than 10%.The real-data experiments estimate 19%lower FFCO_(2)with GOSAT XCO_(2)and 26%lower with OCO-2 XCO_(2)than the bottom-up estimations.This study proves the feasibility of our regional FFCO_(2)inversion,highlighting the importance of addressing the outlier behaviors observed in satellite XCO_(2)retrievals.
基金supported by the National Key Research and Development Plan(Grant No.2021YFB3901000)the Chinese Academy of Sciences Project for Young Scientists in Basic Research(YSBR-037)+2 种基金the International Partnership Program of the Chinese Academy of Sciences(060GJHZ2022070MI)the MOST-ESA Dragon-5 Programme for Monitoring Greenhouse Gases from Space(ID.59355)the Finland–China Mobility Cooperation Project funded by the Academy of Finland(No.348596)。
文摘In this study,we introduce our newly developed measurement-fed-perception self-adaption Low-cost UAV Coordinated Carbon Observation Network(LUCCN)prototype.The LUCCN primarily consists of two categories of instruments,including ground-based and UAV-based in-situ measurement.We use the GMP343,a low-cost non-dispersive infrared sensor,in both ground-based and UAV-based instruments.The first integrated measurement campaign took place in Shenzhen,China,4 May 2023.During the campaign,we found that LUCCN’s UAV component presented significant data-collecting advantages over its ground-based counterpart owing to the relatively high altitudes of the point emission sources,which was especially obvious at a gas power plant in Shenzhen.The emission flux was calculated by a crosssectional flux(CSF)method,the results of which differed from the Open-Data Inventory for Anthropogenic Carbon dioxide(ODIAC).The CSF result was slightly larger than others because of the low sampling rate of the whole emission cross section.The LUCCN system will be applied in future carbon monitoring campaigns to increase the spatiotemporal coverage of carbon emission information,especially in scenarios involving the detection of smaller-scale,rapidly varying sources and sinks.
基金supported by the Shanghai Sailing Program (Grant No. 22YF1442000)the Key Laboratory of Middle Atmosphere and Global Environment Observation(Grant No. LAGEO-2021-07)+1 种基金the National Natural Science Foundation of China (Grant No. 41975035)Jiaxing University (Grant Nos. 00323027AL and CD70522035)。
文摘Coal-fired power plants are a major carbon source in China. In order to assess the evaluation of China's carbon reduction progress with the promise made on the Paris Agreement, it is crucial to monitor the carbon flux intensity from coal-fired power plants. Previous studies have calculated CO_(2) emissions from point sources based on Orbiting Carbon Observatory-2 and-3(OCO-2 and OCO-3) satellite measurements, but the factors affecting CO_(2) flux estimations are uncertain. In this study, we employ a Gaussian Plume Model to estimate CO_(2) emissions from three power plants in China based on OCO-3 XCO_(2) measurements. Moreover, flux uncertainties resulting from wind information, background values,satellite CO_(2) measurements, and atmospheric stability are discussed. This study highlights the CO_(2) flux uncertainty derived from the satellite measurements. Finally, satellite-based CO_(2) emission estimates are compared to bottom-up inventories.The satellite-based CO_(2) emission estimates at the Tuoketuo and Nongliushi power plants are ~30 and ~10 kt d^(-1) smaller than the Open-Data Inventory for Anthropogenic Carbon dioxide(ODIAC) respectively, but ~10 kt d^(-1) larger than the ODIAC at Baotou.
文摘IntroductionThe severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)pandemic has taken a toll on humans,and the development of effective vaccines has been a promising tool to end the pandemic.However,for a vaccination program to be successful,a considerable proportion of the community must be vaccinated.Hence,public acceptance of coronavirus disease 2019(COVID-19)vaccines has become the key to controlling the pandemic.Recent studies have shown vaccine hesitancy increasing over time.This systematic review aims to evaluate the COVID-19 vaccine hesitancy rate and related factors in different communities.MethodA comprehensive search was performed in MEDLINE(via PubMed),Scopus,and Web of Science from January 1,2019 to January 31,2022.All relevant descriptive and observational studies(cross-sectional and longitudinal)on vaccine hesitancy and acceptance were included in this systematic review.In the meta-analysis,odds ratio(OR)was used to assess the effects of population characteristics on vaccine hesitancy,and event rate(acceptance rate)was the effect measure for overall acceptance.Publication bias was assessed using the funnel plot,Egger's test,and trim-and-fill methods.ResultA total of 135 out of 6,417 studies were included after screening.A meta-analysis of 114 studies,including 849,911 participants,showed an overall acceptance rate of 63.1%.In addition,men,married individuals,educated people,those with a history of flu vaccination,those with higher income levels,those with comorbidities,and people living in urban areas were less hesitant.ConclusionIncreasing public awareness of the importance of COVID-19 vaccines in overcoming the pandemic is crucial.Being men,living in an urban region,being married or educated,having a history of influenza vaccination,having a higher level of income status,and having a history of comorbidities are associated with higher COVID-19 vaccine acceptance.
