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
PM_(2.5)constitutes a complex and diversemixture that significantly impacts the environment,human health,and climate change.However,existing observation and numerical simulation techniques have limitations,such as a l...PM_(2.5)constitutes a complex and diversemixture that significantly impacts the environment,human health,and climate change.However,existing observation and numerical simulation techniques have limitations,such as a lack of data,high acquisition costs,andmultiple uncertainties.These limitations hinder the acquisition of comprehensive information on PM_(2.5)chemical composition and effectively implement refined air pollution protection and control strategies.In this study,we developed an optimal deep learning model to acquire hourly mass concentrations of key PM_(2.5)chemical components without complex chemical analysis.The model was trained using a randomly partitioned multivariate dataset arranged in chronological order,including atmospheric state indicators,which previous studies did not consider.Our results showed that the correlation coefficients of key chemical components were no less than 0.96,and the root mean square errors ranged from 0.20 to 2.11μg/m^(3)for the entire process(training and testing combined).The model accurately captured the temporal characteristics of key chemical components,outperforming typical machine-learning models,previous studies,and global reanalysis datasets(such asModern-Era Retrospective analysis for Research and Applications,Version 2(MERRA-2)and Copernicus Atmosphere Monitoring Service ReAnalysis(CAMSRA)).We also quantified the feature importance using the random forest model,which showed that PM_(2.5),PM_(1),visibility,and temperature were the most influential variables for key chemical components.In conclusion,this study presents a practical approach to accurately obtain chemical composition information that can contribute to filling missing data,improved air pollution monitoring and source identification.This approach has the potential to enhance air pollution control strategies and promote public health and environmental sustainability.展开更多
Formaldehyde(HCHO)is a high-yield product of the oxidation of volatile organic compounds(VOCs)released by anthropogenic activities,fires,and vegetations.Hence,we examined the spatiotemporal variation trends in HCHO co...Formaldehyde(HCHO)is a high-yield product of the oxidation of volatile organic compounds(VOCs)released by anthropogenic activities,fires,and vegetations.Hence,we examined the spatiotemporal variation trends in HCHO columns observed using the Ozone Monitoring Instrument(OMI)during 2005–2021 across the Fenwei Plain(FWP)and analysed the source and variability of HCHO using multi-source data,such as thermal anomalies.The spatial distribution of the annualmean HCHO in the FWP increased from northwest to southeast during 2005–2021,and the high-value aggregation areas contracted and gradually clustered,forming a belt-shaped distribution area from Xi’an to Baoji,north of the Qinling Mountains.The annual mean HCHO concentration generally showed a two-step increase over the 17 years.Fires showed a single-peak trend in March and a double-peak M-shaped trend in March and October,whereas urban thermal anomalies(UTAs)showed an inverted U-shaped trend over 17 years,with peaks occurring in May.The HCHO peaks are mainly caused by the alternating contributions of fires and UTAs.The fires and UTAs(predominantly industrial heat sources)played a role in controlling the background level of HCHO in the FWP.Precipitation and temperature were also important influencing variables for seasonal variations,and the influence of plant sources on HCHO concentrations had significant regional characteristics and contributions.In addition,the FWP has poor dispersion conditions and is an aggregated area for the long-range transport of air pollutants.展开更多
Solar energy is a pivotal clean energy source in the transition to carbon neutrality from fossil fuels.However,the intermittent and stochastic characteristics of solar radiation pose challenges for accurate simulation...Solar energy is a pivotal clean energy source in the transition to carbon neutrality from fossil fuels.However,the intermittent and stochastic characteristics of solar radiation pose challenges for accurate simulation and prediction.Accurately simulating and predicting solar radiation and its variability are crucial for optimizing solar energy utilization.This study conducted simulation experiments using the WRF-Solar model from 25 June to 25 July 2022,to evaluate the accuracy and performance of the simulated solar radiation across China.The simulations covered the whole country with a grid spacing of 27 km and were compared with ground observation network data from the Chinese Ecosystem Research Network.The results indicated that WRF-Solar can accurately capture the spatiotemporal patterns of global horizontal irradiance over China,but there is still an overestimation of solar radiation,and the model underestimates the total cloud cover.The root-mean-square error ranged from 92.83 to 188.13 W m^(-2) and the mean bias(MB)ranged from 21.05 to 56.22 W m^(-2).The simulation showed the smallest MB at Lhasa on the Qinghai–Tibet Plateau,while the largest MB was observed in Southeast China.To enhance the accuracy of solar radiation simulation,the authors compared the Fast All-sky Radiation Model for Solar with the Rapid Radiative Transfer Model for General Circulation Models and found that the former provides better simulation.展开更多
Weak turbulence often occurs during heavy pollution events in eastern China(EC).However,existing mesoscale meteorology models cannot accurately simulate turbulent diffusion under weakened turbulence,particularly under...Weak turbulence often occurs during heavy pollution events in eastern China(EC).However,existing mesoscale meteorology models cannot accurately simulate turbulent diffusion under weakened turbulence,particularly under the nocturnal stable boundary layer(SBL),often leading to significant turbulent diffusivity underestimation and surface aerosol overestimation.In this study,a new parameterization of minimum turbulent diffusivity coefficient(Kz_(min))was tested and applied to PM_(2.5)simulations in EC under SBL conditions in WRF-Chem.The original model overestimated the PM_(2.5)simulation and the simulation performance can be improved by adding Kz_(min).Sensitivity experiments revealed different ranges of available Kz_(min)values over the northern(0.8 to 1.2 m^(2)/s)and southern(1.0 to 1.5 m^(2)/s)regions of EC.The geographically related Kz_(min)was parameterized by sensible heat flux(H)and latent heat flux(LE),which also exhibited regional differences related to the climate and underlying surface.Furthermore,we assign physical significance to the parameterized formula Kz_(min)and found that our proposed Kz_(min)scheme can reasonably yield dynamic Kz_(min)values over EC.The revised Kz_(min)scheme(EXP_(NEW))enhanced the turbulent diffusion(north:0.93 m^(2)/s,south:1.10 m^(2)/s on average)in the SBL,simultaneously improving the PM_(2.5)simulations on the surface(north:65.78 to 0.67μg/m^(3);south 30.48 to 12.86μg/m^(3))and upper SBL.A process analysis showed that vertical mixing was the key process for improving PM_(2.5)simulations on the surface in EXP_(NEW).This study highlighted the importance of improving turbulent diffusion in current mesoscale models under SBL and has great significance for aerosol simulation.展开更多
Generally speaking,the precursors of ozone(O_(3)),nitrogen oxides and volatile organic compounds are very low in desert areas due to the lack of anthropogenic emissions and natural emissions,and thus O_(3)concentratio...Generally speaking,the precursors of ozone(O_(3)),nitrogen oxides and volatile organic compounds are very low in desert areas due to the lack of anthropogenic emissions and natural emissions,and thus O_(3)concentrations are relatively low.However,high summer background concentrations of about 100μg/m^(3)or 60 ppb were found in the Alxa Desert in the highland of northwest China based on continuous summer observations from 2019 to 2021,which was higher than the most of natural background areas or clean areas in world for summer O_(3)background concentrations.The high O_(3)background concentrations were related to surface features and altitude.Heavy-intensity anthropogenic activity areas in desert areas can cause increased O_(3)concentrations or pollution,but also generated O_(3)depleting substances such as nitrous oxide,which eventually reduced the regional O_(3)baseline values.Nitrogen dioxide(NO2)also had a dual effect on O_(3)generation,showing promotion at low concentrations and inhibition at high concentrations.In addition,sand-dust weather reduced O_(3)clearly,but O_(3)eventually stabilized around the background concentration values and did not vary with sand-dust particulate matter.展开更多
Nitrous acid(HONO)is a crucial source of OH radicals in the troposphere,significantly enhancing secondary pollutants like secondary organic aerosols(SOA)and peroxyacetyl nitrates(PAN).While prior research has examined...Nitrous acid(HONO)is a crucial source of OH radicals in the troposphere,significantly enhancing secondary pollutants like secondary organic aerosols(SOA)and peroxyacetyl nitrates(PAN).While prior research has examined HONO sources and their total impacts on secondary pollution,the specific enhancement capacity of each individual HONO source remains underexplored.This study uses observational data from 2015 to 2018 for HONO,SOA,and PAN across six sites in China,combined with WRF-Chem model adding six potential HONO sources to evaluate their capacity:traffic emissions(E_traffic),soil emissions(E_soil),indoor-outdoor exchange(E_indoor),nitrate photolysis(P_nit),and NO_(2) heterogeneous reactions on aerosol and ground surfaces(Het_a,Het_g).The simulated HONO contributions near the ground in urban Beijing were:12%from NO+OH(default source),10%-20%from E_traffic,1%-12%from P_nit,2%-10%from Het_a,and 50%-70% from Het_g.For SOA and PAN,we calculated incremental contributions enhanced by each HONO source and derived enhancement ratios(ERs)normalized against HONO’s contribution:~7 for P_nit,~2 for Het_a,~0.9 for Het_g,~0.8 for E_soil,~0.3 for E_traffic,and~0.1 for E_indoor.HONO sources’capacity to enhance secondary pollutants varies,being larger for aerosol-related sources.Vertical analysis on HONO concentration,spatial distribution,RO_(x) radical cycling rates,and OH enhancements revealed that aerosol-related HONO sources,especially P_nit,contribute more to secondary pollution.Future research should focus more on assessing real-world impacts of HONO sources,besides identifying their budgets.Additionally,uptake coefficient(γ)and nitrate photolysis frequency(J_(nitrate))critically affect HONO and secondary pollutant formation,necessitating further investigations.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 Program for Young Scientists of China(No.2022YFC3704000)the National Natural Science Foundation of China(No.42275122)the National Key Scientific and Technological Infrastructure project“Earth System Science Numerical Simulator Facility”(EarthLab).
文摘PM_(2.5)constitutes a complex and diversemixture that significantly impacts the environment,human health,and climate change.However,existing observation and numerical simulation techniques have limitations,such as a lack of data,high acquisition costs,andmultiple uncertainties.These limitations hinder the acquisition of comprehensive information on PM_(2.5)chemical composition and effectively implement refined air pollution protection and control strategies.In this study,we developed an optimal deep learning model to acquire hourly mass concentrations of key PM_(2.5)chemical components without complex chemical analysis.The model was trained using a randomly partitioned multivariate dataset arranged in chronological order,including atmospheric state indicators,which previous studies did not consider.Our results showed that the correlation coefficients of key chemical components were no less than 0.96,and the root mean square errors ranged from 0.20 to 2.11μg/m^(3)for the entire process(training and testing combined).The model accurately captured the temporal characteristics of key chemical components,outperforming typical machine-learning models,previous studies,and global reanalysis datasets(such asModern-Era Retrospective analysis for Research and Applications,Version 2(MERRA-2)and Copernicus Atmosphere Monitoring Service ReAnalysis(CAMSRA)).We also quantified the feature importance using the random forest model,which showed that PM_(2.5),PM_(1),visibility,and temperature were the most influential variables for key chemical components.In conclusion,this study presents a practical approach to accurately obtain chemical composition information that can contribute to filling missing data,improved air pollution monitoring and source identification.This approach has the potential to enhance air pollution control strategies and promote public health and environmental sustainability.
基金supported by the National Natural Science Foundation of China(No.41571062)the Fundamental Research Funds for the Central Universities(No.2021TS014)the Natural Science Basic Research Plan in Shaanxi Province of China(No.2023-JC-YB-259).
文摘Formaldehyde(HCHO)is a high-yield product of the oxidation of volatile organic compounds(VOCs)released by anthropogenic activities,fires,and vegetations.Hence,we examined the spatiotemporal variation trends in HCHO columns observed using the Ozone Monitoring Instrument(OMI)during 2005–2021 across the Fenwei Plain(FWP)and analysed the source and variability of HCHO using multi-source data,such as thermal anomalies.The spatial distribution of the annualmean HCHO in the FWP increased from northwest to southeast during 2005–2021,and the high-value aggregation areas contracted and gradually clustered,forming a belt-shaped distribution area from Xi’an to Baoji,north of the Qinling Mountains.The annual mean HCHO concentration generally showed a two-step increase over the 17 years.Fires showed a single-peak trend in March and a double-peak M-shaped trend in March and October,whereas urban thermal anomalies(UTAs)showed an inverted U-shaped trend over 17 years,with peaks occurring in May.The HCHO peaks are mainly caused by the alternating contributions of fires and UTAs.The fires and UTAs(predominantly industrial heat sources)played a role in controlling the background level of HCHO in the FWP.Precipitation and temperature were also important influencing variables for seasonal variations,and the influence of plant sources on HCHO concentrations had significant regional characteristics and contributions.In addition,the FWP has poor dispersion conditions and is an aggregated area for the long-range transport of air pollutants.
基金supported by the National Natural Science Foundation of China[grant number 42175132]the National Key R&D Program[grant number 2020YFA0607802]the CAS Information Technology Program[grant number CAS-WX2021SF-0107-02]。
文摘Solar energy is a pivotal clean energy source in the transition to carbon neutrality from fossil fuels.However,the intermittent and stochastic characteristics of solar radiation pose challenges for accurate simulation and prediction.Accurately simulating and predicting solar radiation and its variability are crucial for optimizing solar energy utilization.This study conducted simulation experiments using the WRF-Solar model from 25 June to 25 July 2022,to evaluate the accuracy and performance of the simulated solar radiation across China.The simulations covered the whole country with a grid spacing of 27 km and were compared with ground observation network data from the Chinese Ecosystem Research Network.The results indicated that WRF-Solar can accurately capture the spatiotemporal patterns of global horizontal irradiance over China,but there is still an overestimation of solar radiation,and the model underestimates the total cloud cover.The root-mean-square error ranged from 92.83 to 188.13 W m^(-2) and the mean bias(MB)ranged from 21.05 to 56.22 W m^(-2).The simulation showed the smallest MB at Lhasa on the Qinghai–Tibet Plateau,while the largest MB was observed in Southeast China.To enhance the accuracy of solar radiation simulation,the authors compared the Fast All-sky Radiation Model for Solar with the Rapid Radiative Transfer Model for General Circulation Models and found that the former provides better simulation.
基金supported by the National Natural Science Foundation of China(Nos.92044302 and 42275115)the Natural Science Foundation of Jiangsu Province(No.BK20241711)the Postgraduate Research and Practice Innovation of Jiangsu Province Program(No.KYCX20_0952)。
文摘Weak turbulence often occurs during heavy pollution events in eastern China(EC).However,existing mesoscale meteorology models cannot accurately simulate turbulent diffusion under weakened turbulence,particularly under the nocturnal stable boundary layer(SBL),often leading to significant turbulent diffusivity underestimation and surface aerosol overestimation.In this study,a new parameterization of minimum turbulent diffusivity coefficient(Kz_(min))was tested and applied to PM_(2.5)simulations in EC under SBL conditions in WRF-Chem.The original model overestimated the PM_(2.5)simulation and the simulation performance can be improved by adding Kz_(min).Sensitivity experiments revealed different ranges of available Kz_(min)values over the northern(0.8 to 1.2 m^(2)/s)and southern(1.0 to 1.5 m^(2)/s)regions of EC.The geographically related Kz_(min)was parameterized by sensible heat flux(H)and latent heat flux(LE),which also exhibited regional differences related to the climate and underlying surface.Furthermore,we assign physical significance to the parameterized formula Kz_(min)and found that our proposed Kz_(min)scheme can reasonably yield dynamic Kz_(min)values over EC.The revised Kz_(min)scheme(EXP_(NEW))enhanced the turbulent diffusion(north:0.93 m^(2)/s,south:1.10 m^(2)/s on average)in the SBL,simultaneously improving the PM_(2.5)simulations on the surface(north:65.78 to 0.67μg/m^(3);south 30.48 to 12.86μg/m^(3))and upper SBL.A process analysis showed that vertical mixing was the key process for improving PM_(2.5)simulations on the surface in EXP_(NEW).This study highlighted the importance of improving turbulent diffusion in current mesoscale models under SBL and has great significance for aerosol simulation.
基金supported by the Ministry of Science and Technology of China(No.2022YFF0802501)Inner Mongolia Autonomous Region Science and Technology Program(Nos.2021GG0100 and 2022YFHH0116).
文摘Generally speaking,the precursors of ozone(O_(3)),nitrogen oxides and volatile organic compounds are very low in desert areas due to the lack of anthropogenic emissions and natural emissions,and thus O_(3)concentrations are relatively low.However,high summer background concentrations of about 100μg/m^(3)or 60 ppb were found in the Alxa Desert in the highland of northwest China based on continuous summer observations from 2019 to 2021,which was higher than the most of natural background areas or clean areas in world for summer O_(3)background concentrations.The high O_(3)background concentrations were related to surface features and altitude.Heavy-intensity anthropogenic activity areas in desert areas can cause increased O_(3)concentrations or pollution,but also generated O_(3)depleting substances such as nitrous oxide,which eventually reduced the regional O_(3)baseline values.Nitrogen dioxide(NO2)also had a dual effect on O_(3)generation,showing promotion at low concentrations and inhibition at high concentrations.In addition,sand-dust weather reduced O_(3)clearly,but O_(3)eventually stabilized around the background concentration values and did not vary with sand-dust particulate matter.
基金supported by the National Natural Science Foundation of China(Nos.92044302,42075108,42107124,41822703,91544221,91844301,and 22222610)Beijing National Laboratory for Molecular Sciences(No.BNLMS-CXXM-202011)the Natural Science Foundation of Yunnan Province(No.202302AN360006)。
文摘Nitrous acid(HONO)is a crucial source of OH radicals in the troposphere,significantly enhancing secondary pollutants like secondary organic aerosols(SOA)and peroxyacetyl nitrates(PAN).While prior research has examined HONO sources and their total impacts on secondary pollution,the specific enhancement capacity of each individual HONO source remains underexplored.This study uses observational data from 2015 to 2018 for HONO,SOA,and PAN across six sites in China,combined with WRF-Chem model adding six potential HONO sources to evaluate their capacity:traffic emissions(E_traffic),soil emissions(E_soil),indoor-outdoor exchange(E_indoor),nitrate photolysis(P_nit),and NO_(2) heterogeneous reactions on aerosol and ground surfaces(Het_a,Het_g).The simulated HONO contributions near the ground in urban Beijing were:12%from NO+OH(default source),10%-20%from E_traffic,1%-12%from P_nit,2%-10%from Het_a,and 50%-70% from Het_g.For SOA and PAN,we calculated incremental contributions enhanced by each HONO source and derived enhancement ratios(ERs)normalized against HONO’s contribution:~7 for P_nit,~2 for Het_a,~0.9 for Het_g,~0.8 for E_soil,~0.3 for E_traffic,and~0.1 for E_indoor.HONO sources’capacity to enhance secondary pollutants varies,being larger for aerosol-related sources.Vertical analysis on HONO concentration,spatial distribution,RO_(x) radical cycling rates,and OH enhancements revealed that aerosol-related HONO sources,especially P_nit,contribute more to secondary pollution.Future research should focus more on assessing real-world impacts of HONO sources,besides identifying their budgets.Additionally,uptake coefficient(γ)and nitrate photolysis frequency(J_(nitrate))critically affect HONO and secondary pollutant formation,necessitating further investigations.
