Retrievals of satellite-observed emissions of atmospheric pollutants and greenhouse gases provide essential information and data for understanding the sources of these key atmospheric compositions and for implementing...Retrievals of satellite-observed emissions of atmospheric pollutants and greenhouse gases provide essential information and data for understanding the sources of these key atmospheric compositions and for implementing precise emission control measures.Over the past two decades,significant progress has been made in the field of emission inversion,with Chinese researchers playing a substantial role.In celebration of the 100th anniversary of the Chinese Meteorological Society and Acta Meteorologica Sinica,this paper systematically reviews the advances in satellitebased emission inversion research by Chinese scientists during this period.(1)Several widely used inversion methodologies,including data assimilation,local mass balance,Gaussian models,two-dimensional(2D)models,and machine learning,are briefly summarized.(2)Emission inversion studies focusing on major atmospheric pollutants—such as nitrogen oxides(NO_(x)),ammonia(NH_(3)),formaldehyde(HCHO),glyoxal(CHOCHO),sulfur dioxide(SO_(2)),and carbon monoxide(CO)—as well as greenhouse gases like carbon dioxide(CO_(2))and methane(CH_(4)),are systematically elaborated.(3)Finally,the historical evolution of inversion methods and target species,challenges in current satellite-based emission inversion,and future research directions are discussed to promote more accurate quantification of atmospheric pollutants and greenhouse gas emissions.It is worth noting that contributions from Chinese researchers have provided critical scientific support to environmental protection and carbon neutrality efforts in China.展开更多
The conventional Ensemble Kalman filter(EnKF),which is now widely used to calibrate emission inventories and to improve air quality simulations,is susceptible to simulation errors of meteorological inputs,making accur...The conventional Ensemble Kalman filter(EnKF),which is now widely used to calibrate emission inventories and to improve air quality simulations,is susceptible to simulation errors of meteorological inputs,making accurate updates of high temporal-resolution emission inventories challenging.In this study,we developed a novel meteorologically adjusted inversion method(MAEInv)based on the EnKF to improve daily emission estimations.The new method combines sensitivity analysis and bias correction to alleviate the inversion biases caused by errors of meteorological inputs.For demonstration,we used the MAEInv to inverse daily carbon monoxide(CO)emissions in the Pearl River Delta(PRD)region,China.In the case study,60%of the total CO simulation biases were associated with sensitive meteorological inputs,which would lead to the overestimation of daily variations of posterior emissions.Using the new inversion method,daily variations of emissions shrank dramatically,with the percentage change decreased by 30%.Also,the total amount of posterior CO emissions estimated by the MAEInv decreased by 14%,indicating that posterior CO emissions might be overestimated using the conventional EnKF.Model evaluations using independent observations revealed that daily CO emissions estimated by MAEInv better reproduce the magnitude and temporal patterns of ambient CO concentration,with a higher correlation coefficient(R,+37.0%)and lower normalized mean bias(NMB,-17.9%).Since errors of meteorological inputs are major sources of simulation biases for both low-reactive and reactive pollutants,the MAEInv is also applicable to improve the daily emission inversions of reactive pollutants.展开更多
In recent years,China has implemented several measures to improve air quality.The Beijing-Tianjin-Hebei(BTH)region is one area that has suffered from the most serious air pollution in China and has undergone huge chan...In recent years,China has implemented several measures to improve air quality.The Beijing-Tianjin-Hebei(BTH)region is one area that has suffered from the most serious air pollution in China and has undergone huge changes in air quality in the past few years.How to scientifically assess these change processes remain the key issue in further improving the air quality over this region in the future.To evaluate the changes in major air pollutant emissions over this region,this paper employs ensemble Kalman filtering(EnKF)for integrating the national ground monitoring pollutant observation data and the Nested Air Quality Prediction Modeling System(NAQPMS)simulation data to inversely estimate the emission rates of SO_(2),NOX,CO,and primary PM_(2.5)over BTH region in February from 2014 to 2019.The results show that SO_(2),NOX,CO,and primary PM_(2.5)emissions in the BTH region decreased in February from 2014 to 2019 by 83%,37%,41%,and 42%,while decreases in Beijing during this period were 86%,67%,59%,and 65%,respectively.Compared with the prior emission inventory,the inversion emission inventory reduces the uncertainty of multi-pollutant simulation in the BTH region,with simulated root mean square errors of the monthly average concentrations of SO_(2),NOX,PM_(2.5),and CO reduced by 41%,30%,31%,and 22%,respectively.The average uncertainties of SO_(2),NOX,PM_(2.5),and CO inversion emissions in2014-19 are±14.03%yr^(-1),±28.91%yr^(-1),±126.15%yr^(-1),and±43.58%yr^(-1).Compared with the uncertainty of MEIC emission,the uncertainties of all species changed by+2%yr^(-1),-2%yr^(-1),-26%yr^(-1),and-4%yr^(-1),respectively.The spatial distribution results illustrate that air pollutant emissions are mainly distributed over the eastern and southern BTH regions.The spatial gap between the inversion emissions and MEIC emissions was further closed in 2019 compared to 2014.The results of this paper can provide a new reference for assessing changes in air pollution emissions over the BTH region in recent years and validating a bottom-up emission inventory.展开更多
Developing an anthropogenic carbon dioxides(CO_(2))emissions monitoring and verification support(MVS)capacity is essential to support the Global Stocktake(GST)and ratchet up Nationally Determined Contributions(NDCs).T...Developing an anthropogenic carbon dioxides(CO_(2))emissions monitoring and verification support(MVS)capacity is essential to support the Global Stocktake(GST)and ratchet up Nationally Determined Contributions(NDCs).The 2019 IPCC refinement proposes top-down inversed CO_(2)emissions,primarily from fossil fuel(FFCO_(2)),as a viable emission dataset.Despite substantial progress in directly inferring FFCO_(2)emissions from CO_(2)observations,substantial challenges remain,particularly in distinguishing local CO_(2)enhancements from the high background due to the long atmospheric lifetime.Alternatively,using short-lived and co-emitted nitrogen dioxide(NO_(2))as a proxy in FFCO_(2)emission inversion has gained prominence.This methodology is broadly categorized into plume-based and emission ratios(ERs)-based inversion methods.In the plume-based methods,NO_(2)observations act as locators,constraints,and validators for deciphering CO_(2)plumes downwind of sources,typically at point source and city scales.The ERs-based inversion approach typically consists of two steps:inferring NO_(2)-based nitrogen oxides(NO_(x))emissions and converting NO_(x)to CO_(2)emissions using CO_(2)-to-NO_(x)ERs.While integrating NO_(2)observations into FFCO_(2)emission inversion offers advantages over the direct CO_(2)-based methods,uncertainties persist,including both structural and data-related uncertainties.Addressing these uncertainties is a primary focus for future research,which includes deploying nextgeneration satellites and developing advanced inversion systems.Besides,data caveats are necessary when releasing data to users to prevent potential misuse.Advancing NO_(2)-based CO_(2)emission inversion requires interdisciplinary collaboration across multiple communities of remote sensing,emission inventory,transport model improvement,and atmospheric inversion algorithm development.展开更多
This study uses a parabolic equation to fit the Inverse Compton (IC) spectral component of 3743 blazars (794 FSRQs,1432 BLLacs,and 1517 BCUs) from the 4FGL-DR3 catalog.Some mutual correlations are investigated,and a B...This study uses a parabolic equation to fit the Inverse Compton (IC) spectral component of 3743 blazars (794 FSRQs,1432 BLLacs,and 1517 BCUs) from the 4FGL-DR3 catalog.Some mutual correlations are investigated,and a Bayesian classification is performed to the IC peak frequencies.Our analyses draw the following conclusions:(1) The Bayesian classification shows two components with a dividing boundary of log(v_(p)^(IC)/Hz)pIC=22.9.Therefore,the 3743 blazars are divided into low IC peak frequency(LCP) blazars and high IC peak frequency (HCP) blazars.(2) A strong linear correlation exists between IC peak frequency(logv_(p)^(IC)) and γ-ray photon spectral index (Γ).The IC peak frequency can be estimated by an empirical relation logv_(p)^(IC)=–4.5·Γ+32.8 for BL Lacs and logv_(p)^(IC)=4.0+31.4pICfor FSRQs,which is consistent with the result by Abdo et al.(3) The ICspectral curvature and IC peak frequency are not as closely related as the synchrotron curvature and the synchrotron peak frequency.(4) An anti-correlation exists between IC peak frequency and IC peak luminosity,implying that as the IC peak frequency in the γ-ray band decreases,the source becomes more luminous.The beaming effect is stronger for the source with a lower IC peak frequency.(5) Positive correlations exist between IC and synchrotron components for both peak frequencies and peak fluxes,but no clear correlation exists between IC curvature and synchrotron curvature.展开更多
基金Supported by the National Natural Science Foundation of China(42277082 and 42430603)。
文摘Retrievals of satellite-observed emissions of atmospheric pollutants and greenhouse gases provide essential information and data for understanding the sources of these key atmospheric compositions and for implementing precise emission control measures.Over the past two decades,significant progress has been made in the field of emission inversion,with Chinese researchers playing a substantial role.In celebration of the 100th anniversary of the Chinese Meteorological Society and Acta Meteorologica Sinica,this paper systematically reviews the advances in satellitebased emission inversion research by Chinese scientists during this period.(1)Several widely used inversion methodologies,including data assimilation,local mass balance,Gaussian models,two-dimensional(2D)models,and machine learning,are briefly summarized.(2)Emission inversion studies focusing on major atmospheric pollutants—such as nitrogen oxides(NO_(x)),ammonia(NH_(3)),formaldehyde(HCHO),glyoxal(CHOCHO),sulfur dioxide(SO_(2)),and carbon monoxide(CO)—as well as greenhouse gases like carbon dioxide(CO_(2))and methane(CH_(4)),are systematically elaborated.(3)Finally,the historical evolution of inversion methods and target species,challenges in current satellite-based emission inversion,and future research directions are discussed to promote more accurate quantification of atmospheric pollutants and greenhouse gas emissions.It is worth noting that contributions from Chinese researchers have provided critical scientific support to environmental protection and carbon neutrality efforts in China.
基金supported by the National Key Research and Development Program of China(No.2018YFC0213905)National Natural Science Foundation of China(Nos.91744310and 41805068)Natural Science Foundation of Guangdong Province(No.2018A030310654)
文摘The conventional Ensemble Kalman filter(EnKF),which is now widely used to calibrate emission inventories and to improve air quality simulations,is susceptible to simulation errors of meteorological inputs,making accurate updates of high temporal-resolution emission inventories challenging.In this study,we developed a novel meteorologically adjusted inversion method(MAEInv)based on the EnKF to improve daily emission estimations.The new method combines sensitivity analysis and bias correction to alleviate the inversion biases caused by errors of meteorological inputs.For demonstration,we used the MAEInv to inverse daily carbon monoxide(CO)emissions in the Pearl River Delta(PRD)region,China.In the case study,60%of the total CO simulation biases were associated with sensitive meteorological inputs,which would lead to the overestimation of daily variations of posterior emissions.Using the new inversion method,daily variations of emissions shrank dramatically,with the percentage change decreased by 30%.Also,the total amount of posterior CO emissions estimated by the MAEInv decreased by 14%,indicating that posterior CO emissions might be overestimated using the conventional EnKF.Model evaluations using independent observations revealed that daily CO emissions estimated by MAEInv better reproduce the magnitude and temporal patterns of ambient CO concentration,with a higher correlation coefficient(R,+37.0%)and lower normalized mean bias(NMB,-17.9%).Since errors of meteorological inputs are major sources of simulation biases for both low-reactive and reactive pollutants,the MAEInv is also applicable to improve the daily emission inversions of reactive pollutants.
基金supported by National Natural Science Foundation(Grant Nos.41875164 and 92044303)。
文摘In recent years,China has implemented several measures to improve air quality.The Beijing-Tianjin-Hebei(BTH)region is one area that has suffered from the most serious air pollution in China and has undergone huge changes in air quality in the past few years.How to scientifically assess these change processes remain the key issue in further improving the air quality over this region in the future.To evaluate the changes in major air pollutant emissions over this region,this paper employs ensemble Kalman filtering(EnKF)for integrating the national ground monitoring pollutant observation data and the Nested Air Quality Prediction Modeling System(NAQPMS)simulation data to inversely estimate the emission rates of SO_(2),NOX,CO,and primary PM_(2.5)over BTH region in February from 2014 to 2019.The results show that SO_(2),NOX,CO,and primary PM_(2.5)emissions in the BTH region decreased in February from 2014 to 2019 by 83%,37%,41%,and 42%,while decreases in Beijing during this period were 86%,67%,59%,and 65%,respectively.Compared with the prior emission inventory,the inversion emission inventory reduces the uncertainty of multi-pollutant simulation in the BTH region,with simulated root mean square errors of the monthly average concentrations of SO_(2),NOX,PM_(2.5),and CO reduced by 41%,30%,31%,and 22%,respectively.The average uncertainties of SO_(2),NOX,PM_(2.5),and CO inversion emissions in2014-19 are±14.03%yr^(-1),±28.91%yr^(-1),±126.15%yr^(-1),and±43.58%yr^(-1).Compared with the uncertainty of MEIC emission,the uncertainties of all species changed by+2%yr^(-1),-2%yr^(-1),-26%yr^(-1),and-4%yr^(-1),respectively.The spatial distribution results illustrate that air pollutant emissions are mainly distributed over the eastern and southern BTH regions.The spatial gap between the inversion emissions and MEIC emissions was further closed in 2019 compared to 2014.The results of this paper can provide a new reference for assessing changes in air pollution emissions over the BTH region in recent years and validating a bottom-up emission inventory.
基金supported by the National Natural Science Foundation of China(No.42105094).
文摘Developing an anthropogenic carbon dioxides(CO_(2))emissions monitoring and verification support(MVS)capacity is essential to support the Global Stocktake(GST)and ratchet up Nationally Determined Contributions(NDCs).The 2019 IPCC refinement proposes top-down inversed CO_(2)emissions,primarily from fossil fuel(FFCO_(2)),as a viable emission dataset.Despite substantial progress in directly inferring FFCO_(2)emissions from CO_(2)observations,substantial challenges remain,particularly in distinguishing local CO_(2)enhancements from the high background due to the long atmospheric lifetime.Alternatively,using short-lived and co-emitted nitrogen dioxide(NO_(2))as a proxy in FFCO_(2)emission inversion has gained prominence.This methodology is broadly categorized into plume-based and emission ratios(ERs)-based inversion methods.In the plume-based methods,NO_(2)observations act as locators,constraints,and validators for deciphering CO_(2)plumes downwind of sources,typically at point source and city scales.The ERs-based inversion approach typically consists of two steps:inferring NO_(2)-based nitrogen oxides(NO_(x))emissions and converting NO_(x)to CO_(2)emissions using CO_(2)-to-NO_(x)ERs.While integrating NO_(2)observations into FFCO_(2)emission inversion offers advantages over the direct CO_(2)-based methods,uncertainties persist,including both structural and data-related uncertainties.Addressing these uncertainties is a primary focus for future research,which includes deploying nextgeneration satellites and developing advanced inversion systems.Besides,data caveats are necessary when releasing data to users to prevent potential misuse.Advancing NO_(2)-based CO_(2)emission inversion requires interdisciplinary collaboration across multiple communities of remote sensing,emission inventory,transport model improvement,and atmospheric inversion algorithm development.
基金supported by the National Natural Science Foundation of China(Grant Nos.U2031112,U2031201,and 11733001)the Guangdong Major Project of Basic and Applied Basic Research(Grant No.2019B030302001)+3 种基金the Research Fund of Hunan Education Department(Grant No.20C1273)the Science Research Grants from the China Manned Space Project(Grant No.CMS-CSST-2021-A06)the support from Astrophysics Key Subjects of Guangdong Province and Guangzhou Citysupported by the Guangzhou University(Grant No.YM2020001)。
文摘This study uses a parabolic equation to fit the Inverse Compton (IC) spectral component of 3743 blazars (794 FSRQs,1432 BLLacs,and 1517 BCUs) from the 4FGL-DR3 catalog.Some mutual correlations are investigated,and a Bayesian classification is performed to the IC peak frequencies.Our analyses draw the following conclusions:(1) The Bayesian classification shows two components with a dividing boundary of log(v_(p)^(IC)/Hz)pIC=22.9.Therefore,the 3743 blazars are divided into low IC peak frequency(LCP) blazars and high IC peak frequency (HCP) blazars.(2) A strong linear correlation exists between IC peak frequency(logv_(p)^(IC)) and γ-ray photon spectral index (Γ).The IC peak frequency can be estimated by an empirical relation logv_(p)^(IC)=–4.5·Γ+32.8 for BL Lacs and logv_(p)^(IC)=4.0+31.4pICfor FSRQs,which is consistent with the result by Abdo et al.(3) The ICspectral curvature and IC peak frequency are not as closely related as the synchrotron curvature and the synchrotron peak frequency.(4) An anti-correlation exists between IC peak frequency and IC peak luminosity,implying that as the IC peak frequency in the γ-ray band decreases,the source becomes more luminous.The beaming effect is stronger for the source with a lower IC peak frequency.(5) Positive correlations exist between IC and synchrotron components for both peak frequencies and peak fluxes,but no clear correlation exists between IC curvature and synchrotron curvature.
