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Solar-induced chlorophyll fluorescence (SIF) is emitted during photosynthesis in plant leaves. It constitutes a small additional offset to reflected radiance and can be observed by sensitive instruments with high signal-to-noise ratio and spectral resolution. The Chinese global carbon dioxide monitoring satellite (TanSat) acquires measurements of greenhouse gas column densities. The advanced technical characteristics of the Atmospheric Carbon-dioxide Grating Spectrometer (ACGS) onboard TanSat enable SIF retrievals from observations in the O2-A band. In this study, 1-year of SIF data was retrieved from Orbiting Carbon Observatory-2 (OCO-2) and TanSat measurements using the Institute of Atmospheric Physics Carbon Dioxide Retrieval Algorithm for Satellite Remote Sensing (IAPCAS)/SIF algorithm. A comparison between the IAPCAS/SIF results retrieved from OCO-2 spectra and the official OCO-2 SIF product (OCO2_Level 2_Lite_SIF.8r) shows a strong linear relationship (R2 > 0.85) and suggests good reliability of the IAPCAS/SIF retrieval algorithm. Comparing global distributions of SIF retrieved by the IAPCAS/SIF from TanSat and OCO-2 shows the same spatial pattern for all seasons with a gridded SIF difference of less than 0.3 W m−2 µm−1 sr−1. The global distributions also agree well with the official OCO-2 SIF product with a difference of less than 0.2 W m−2 µm−1 sr−1. The retrieval uncertainty of seasonally gridded TanSat IAPCAS/SIF is less than 0.03 W m−2 µm−1 sr−1, whereas the uncertainty of each sounding ranges from 0.1 to 0.6 W m−2 µm−1 sr−1. The relationship between annually averaged SIF products and FLUXCOM gross primary productivity (GPP) was also estimated for six vegetation types in a 1∘ × 1∘ grid over the globe, indicating that the SIF data from the two satellites have the same potential in quantitatively characterizing ecosystem productivity. The spatiotemporal consistency between TanSat and OCO-2 and their comparable data quality enable joint usage of the two mission products. Data supplemented by TanSat observations are expected to contribute to the development of global SIF maps with more spatiotemporal detail, which will advance global research on vegetation photosynthesis.

Sulfur dioxide (SO2) measured by satellites is widely used to estimate anthropogenic emissions. The Sentinel-5 Precursor (S-5P) operational SO2 product is overestimated compared to the ground-based multiaxis differential optical absorption spectroscopy (MAX-DOAS) measurements in China and shows an opposite variation to the surface measurements, which limits the application of TROPOspheric monitoring instrument (TROPOMI) products in emissions research. Radiometric calibration, a priori profiles, and fitting windows might cause the overestimation of S-5P operational SO2 product. Here, we improve the optimal-estimation-based algorithm through several calibration methods. The improved retrieval agrees reasonably well with the ground-based measurements (R > 0.70, bias

Space-borne measurements of atmospheric greenhouse gas concentrations provide global observation constraints for top-down estimates of surface carbon flux. Here, the first estimates of the global distribution of carbon surface fluxes inferred from dry-air CO2 column (XCO2) measurements by the Chinese Global Carbon Dioxide Monitoring Scientific Experimental Satellite (TanSat) are presented. An ensemble transform Kalman filter (ETKF) data assimilation system coupled with the GEOS-Chem global chemistry transport model is used to optimally fit model simulations with the TanSat XCO2 observations, which were retrieved using the Institute of Atmospheric Physics Carbon dioxide retrieval Algorithm for Satellite remote sensing (IAPCAS). High posterior error reduction (30%–50%) compared with a priori fluxes indicates that assimilating satellite XCO2 measurements provides highly effective constraints on global carbon flux estimation. Their impacts are also highlighted by significant spatiotemporal shifts in flux patterns over regions critical to the global carbon budget, such as tropical South America and China. An integrated global land carbon net flux of 6.71 ± 0.76 Gt C yr−1 over 12 months (May 2017–April 2018) is estimated from the TanSat XCO2 data, which is generally consistent with other inversions based on satellite data, such as the JAXA GOSAT and NASA OCO-2 XCO2 retrievals. However, discrepancies were found in some regional flux estimates, particularly over the Southern Hemisphere, where there may still be uncorrected bias between satellite measurements due to the lack of independent reference observations. The results of this study provide the groundwork for further studies using current or future TanSat XCO2 data together with other surface-based and space-borne measurements to quantify biosphere-atmosphere carbon exchange.

The Chinese Carbon Dioxide Observation Satellite Mission (TanSat) is the third satellite for global CO2 monitoring and is capable of detecting weak solar-induced chlorophyll fluorescence (SIF) signals with its advanced technical characteristics. Based on the Institute of Atmospheric Physics Carbon Dioxide Retrieval Algorithm for Satellite Remote Sensing (IAPCAS) platform, we successfully retrieved the TanSat global SIF product spanning the period of March 2017 to February 2018 with a physically based algorithm. This paper introduces the new TanSat SIF dataset and shows the global seasonal SIF maps. A brief comparison between the IAPCAS TanSat SIF product and the data-driven SVD (singular value decomposition) SIF product is also performed for follow-up algorithm optimization. The comparative results show that there are regional biases between the two SIF datasets and the linear correlations between them are above 0.73 for all seasons. The future SIF data product applications and requirements for SIF space observation are discussed.

The 1st Chinese carbon dioxide (CO2) monitoring satellite mission, TanSat, was launched in 2016. The 1st TanSat global map of CO2 dry-air mixing ratio (XCO2) measurements over land was released as version 1 data product with an accuracy of 2.11 ppmv (parts per million by volume). In this paper, we introduce a new (version 2) TanSat global XCO2 product that is approached by the Institute of Atmospheric Physics Carbon dioxide retrieval Algorithm for Satellite remote sensing (IAPCAS), and the European Space Agency (ESA) Climate Change Initiative plus (CCI+) TanSat XCO2 product by University of Leicester Full Physics (UoL-FP) retrieval algorithm. The correction of the measurement spectrum improves the accuracy (@#@0.08 ppmv) and precision (1.41 ppmv) of the new retrieval, which provides opportunity for further application in global carbon flux studies in the future. Inter-comparison between the two retrievals indicates a good agreement, with a standard deviation of 1.28 ppmv and a bias of −0.35 ppmv.

The atmospheric carbon dioxide (CO2) concentration has increased to more than 405 parts per million (ppm. 1ppm=10-6m/s2) in 2017 due to human activities such as deforestation, land-use change and burning of fossil fuels. Although there is broad scientific consensus on the damaging consequences of the change in climate associated with increasing concentrations of greenhouse gases, fossil CO2 emissions have continued to increase in recent years mainly from rapidly developing economies and China is now the largest emitter of CO2 generating about 30% of all emissions globally. To allow more reliable forecast of the future state of the carbon cycle and to support the efforts for mitigation greenhouse gas emissions, a better understanding of the global and regional carbon budget is needed. Space-based measurements of CO2 can provide the necessary observations with dense coverage and sampling to provide improved constrains on of carbon fluxes and emissions. The Chinese Global Carbon Dioxide Monitoring Scientific Experimental Satellite (TanSat) was established by the National High Technology Research and Development Program of China with the main objective of monitoring atmospheric CO2 and CO2 fluxes at the regional and global scale. TanSat has been successfully launched in December 2016 and as part of the Dragon programme of ESA and the Ministry of Science and Technology (MOST), a team of researchers from Europe (UK and Finland) and China has evaluated early TanSat data and contrast it against data from the GOSAT mission and models. In this manuscript, we report on retrieval intercomparisons of TanSat data using two different retrieval algorithms, on validation efforts for the Eastern Asia region using GOSAT CO2 data and first assessments of TanSat and GOSAT CO2 data against model calculations using the GEOS-Chem model.

The balloon-borne Aircore campaign was conducted in Inner Mongolia, China, on June 13 and 14 2018, which detected carbon dioxide (CO2) and carbon monoxide (CO) profiles from surface to 24 km, showing strong positive and negative correlations between 8 km and 10 km on 13 and 14 June, respectively. Backward trajectories, meteorological analyses, and CO2 horizontal distributions were combined to interpret this phenomenon. The results indicated that the source region experienced a stratospheric intrusion and exhibited a large horizontal CO2 gradient; namely, lower CO concentrations corresponded to higher CO2 concentrations and vice versa. The laminar structure with multiple origins resulted in the highly negative correlation between CO2 and CO in the upper troposphere on 14 June. The contribution of stratospheric air mass to the upper troposphere and that of tropospheric air mass to the lower stratosphere were 26.7% and 24.3%, respectively, based on a mass balance approach. Another interesting phenomenon is that CO2 and CO concentrations increased substantially at approximately 8 km on 13 June. An analysis based on the backward trajectory implied that the air mass possibly came from anthropogenic sources. The slope of CO2/CO representing the anthropogenic sources was 87.3 ppm ppm−1. In addition, the CO2 profile showed that there was a large CO2 gradient of 4 ppm km−1 within the boundary layer on 13 June, and this gradient disappeared on 14 June.

The Sentinel-5 Precursor (S-5P) was launched into low earth orbit with a single payload TROPOspheric Monitoring Instrument (TROPOMI) on 13 October, 2017. TROPOMI is able to provide atmospheric composition observations including sulfur dioxide (SO2) daily and globally. It has a higher spatial resolution, specifically with a ground resolution of 7 km ×3.5 km in the nadir, than previous satellite instruments such as Ozone Monitoring Instrument (OMI, 13 km ×24 km) and Ozone Mapping and Profiler Suite (OMPS, 50 km ×50 km). The three types of S-5P operational SO2 products (offline (OFFL), Reprocessing (RPRO) and Near Real Time (NRTI)) are on basis of the differential optical absorption spectroscopy (DOAS) algorithm, and available from the S-5P Pre-Operations Data Hub. To evaluate these data, the results of OFFL are compared to the measurements obtained by ground-based Multi-Axis DOAS (MAX-DOAS) and China National Environmental Monitoring Center (CNEMC). We find that the S-5P operational SO2 products significantly overestimate SO2 concentrations over Northern China. Compared with four ground-based MAX-DOAS measurements in terms of the weekly average SO2 vertical column densities (VCDs), the S-5P operational SO2 products exhibit an overestimation of 61%−140%, with the correlation coefficient less than 0.5. The a priori SO2 profiles in S-5P operational products are from daily Tracer Model (TM5) simulations. SO2 surface concentrations are converted by the S-5P operational SO2 products and a priori SO2 profiles used in the retrievals. The correlation coefficient between the SO2 surface concentrations measured by CNEMC over Northern China and those from the S-5P operational products is higher than 0.5, and the S-5P operational products at these CNEMC sites show an overestimation of about 54.6% on average. In this study, the SO2 products (TROPOMI USTC SO2 products) are retrieved by the University of Science and Technology of China (USTC) with the optimal estimation algorithm (OE) from TROPOMI measurements, with the a priori SO2 profiles from monthly average GEOS-Chem simulations. The mean biases between the TROPOMI USTC SO2 products and the ground-based MAX-DOAS measurements are reduced significantly (−4.8%−22%), together with an improvement in the correlation coefficients (0.72−0.89). TROPOMI USTC SO2 surface concentrations are transformed by the TROPOMI USTC SO2 products and a priori SO2 profiles in the retrievals. Based on the evaluation statistics of the S-5P operational SO2 products, a smaller mean bias of −25.8% is found between the TROPOMI USTC SO2 surface concentrations and CNEMC measurements over Northern China. Compared to the TROPOMI USTC SO2 products in the heavily polluted area (33°−45°N, 110°−122°E), the S-5P operational SO2 products have an overestimation of around 41.3%, with a correlation coefficient of 0.73. In addition, S-5P operational SO2 products show a larger area of heavy pollution than TROPOMI USTC SO2 products. Additionally, the sensitivity analysis indicates many influential factors of the SO2 retrievals using OE algorithm, such as radiative calibration, fitting window and a priori SO2 profiles. In the fitting window of 310.5−326.0 nm, the SO2 columns retrieved by the a priori profiles from monthly GEOS-Chem simulations show better agreement with the MAX-DOAS measurements than those from daily TM5 simulations. A possible reason is that the emission inventory used in GEOS-Chem is based on a Chinese power sector database for 31 provinces supported by CMEP (Chinese Ministry of Environmental Protection), resulting in a better performance of GEOS-Chem than that of TM5 over China in simulations. And the overestimation of the S-5P operational SO2 products over Northern China may be attributed to the fitting window used.

Solar-induced chlorophyll fluorescence (SIF) is emitted during photosynthesis in plant leaves. It constitutes a small additional offset to reflected radiance and can be observed by sensitive instruments. The Chinese global carbon dioxide monitoring satellite (TanSat), as its mission, acquires greenhouse gas column density. The advanced technical characteristics of the hyper-spectrum grating spectrometer (ACGS) onboard TanSat enable SIF retrieval from space observations in the O2-A band. In this study, one-year SIF data was processed from Orbiting Carbon Observatory-2 (OCO-2) and TanSat using a physical-based algorithm. A comparison between the SIF retrieved from OCO-2 and its official product shows their strong linear relationship (R2 > 0.85) and suggests the reliability of the algorithm. The global distribution showed that the SIF retrieved from the two satellites shared the same spatial pattern for all seasons with the grided SIF difference less than 0.3 W m−2 μm−1 sr−1, and they also agreed with the official OCO-2 SIF product. The retrieval uncertainty of seasonal-grided TanSat SIF is less than 0.03 W m−2 μm−1 sr−1 whereas the uncertainty of each sounding ranges from 0.1 to 0.6 W m−2 μm−1 sr−1. The relationship between SIF and terrestrial gross primary productivity was also estimated for data quality testing. The spatiotemporal consistency between TanSat and OCO-2 and their comparable data quality make the comprehensive usage of the two mission products possible. Data supplemented by TanSat observations are expected to contribute to the development of global SIF maps with more spatiotemporal detail, which will advance global research on vegetation photosynthesis.

Sulfur dioxide (SO

The increase in atmospheric greenhouse gas concentrations of CO2 and CH4, due to human activities, is the main driver of the observed increase in surface temperature by more than 1 °C since the pre-industrial era. At the 2015 United Nations Climate Change Conference held in Paris, most nations agreed to reduce greenhouse gas emissions to limit the increase in global surface temperature to 1.5 °C. Satellite remote sensing of CO2 and CH4 is now well established thanks to missions such as NASA’s OCO-2 and the Japanese GOSAT missions, which have allowed us to build a long-term record of atmospheric GHG concentrations from space. They also give us a first glimpse into CO2 and CH4 enhancements related to anthropogenic emission, which helps to pave the way towards the future missions aimed at a Monitoring & Verification Support (MVS) capacity for the global stock take of the Paris agreement. China plays an important role for the global carbon budget as the largest source of anthropogenic carbon emissions but also as a region of increased carbon sequestration as a result of several reforestation projects. Over the last 10 years, a series of projects on mitigation of carbon emission has been started in China, including the development of the first Chinese greenhouse gas monitoring satellite mission, TanSat, which was successfully launched on 22 December 2016. Here, we summarise the results of a collaborative project between European and Chinese teams under the framework of the Dragon-4 programme of ESA and the Ministry of Science and Technology (MOST) to characterize and evaluate the datasets from the TanSat mission by retrieval intercomparisons and ground-based validation and to apply model comparisons and surface flux inversion methods to TanSat and other CO2 missions, with a focus on China.

The study on how the variations in CO2 sources and sinks can affect the CO2 concentration over East Asia would be useful to provide information for policymaker concerning carbon emission reduction. In this study, a nested-grid version of global chemical transport model (GEOS-Chem) is employed to assess the impacts of variations in meteorological parameters, terrestrial fluxes, fossil fuel emissions, and biomass burning on inter-annual variations of CO2 concentrations over East Asia in 2004–2012. Simulated CO2 concentrations are compared with observations at 14 surface stations from the World Data Centre for Greenhouse Gases (WDCGG) and satellite-derived CO2 column density (XCO2) from the Gases Observing SATellite (GOSAT). The comparison shows that the simulated CO2 column density is generally higher than that of GOSAT by 1.33 × 10−6 (annual mean point by point biases averaged over East Asia). The model reasonably captures the temporal variations of CO2 concentrations observed at the ground-based stations, but it is likely to underestimate the peaks-to-troughs amplitude of the seasonal cycle by 50% or more. The simulated surface CO2 concentration in East Asia exhibits the largest inter-annual variation in December-January–February (DJF). The regional mean absolute deviation (MAD) values over East Asia are within (4.4–5.0) × 10−6 for all seasons. Model sensitivity simulations indicate that the inter-annual variations of surface CO2 concentrations are mainly driven by variations of meteorological parameters, and partly modulated by the inter-annual variations of terrestrial fluxes and fossil fuel emissions in local regions. The variations of the terrestrial fluxes and fossil fuel emissions may account for ∼28% of the inter-annual variation of surface CO2 concentration in southern China. The inter-annual variations of the peaks-to-troughs amplitude are dependent on variations of meteorological parameters, terrestrial fluxes and fossil fuel emissions in local regions. The influence of biomass burning emissions is relatively weak. Keywords: Carbon dioxide, Inter-annual variation, Terrestrial biosphere flux, Fossil fuel emission

Abundances of a range of air pollutants can be inferred from satellite UV-Vis spectroscopy measurements by using the unique absorption signatures of gas species. Here, we implemented several spectral fitting methods to retrieve tropospheric NO2, SO2, and HCHO from the ozone monitoring instrument (OMI), with radiative simulations providing necessary information on the interactions of scattered solar light within the atmosphere. We analyzed the spatial distribution and temporal trends of satellite-observed air pollutants over eastern China during 2005–2017, especially in heavily polluted regions. We found significant decreasing trends in NO2 and SO2 since 2011 over most regions, despite varying temporal features and turning points. In contrast, an overall increasing trend was identified for tropospheric HCHO over these regions in recent years. Furthermore, generalized additive models were implemented to understand the driving forces of air quality trends in China and assess the effectiveness of emission controls. Our results indicated that although meteorological parameters, such as wind, water vapor, solar radiation and temperature, mainly dominated the day-to-day and seasonal fluctuations in air pollutants, anthropogenic emissions played a unique role in the long-term variation in the ambient concentrations of NO2, SO2, and HCHO in the past 13 years. Generally, recent declines in NO2 and SO2 could be attributed to emission reductions due to effective air quality policies, and the opposite trends in HCHO may urge the need to control anthropogenic volatile organic compound (VOC) emissions., Air pollution: satellite sees significant trends in China Satellite-based observations of air pollution in China reveal a recent declining trend in the levels of nitrogen dioxide (NO2) and sulfur dioxide (SO2), but rising levels of formaldehyde (HCHO). Chengxin Zhang and colleagues at the University of Science and Technology of China, with co-workers at other Chinese research centers, analyzed ultraviolet-visible spectroscopy data from the Ozone Monitoring Instrument of NASA’s EOS-Aura satellite. Meteorological effects such as winds, temperature and humidity caused day-to-day and seasonal fluctuations, but human activity appears responsible for long term trends. Declines in NO2 and SO2 levels occurred from 2011, across most regions, and are attributed to effective air quality policies. The rising trend in HCHO levels suggests that new measures may be needed to control the release by human activities of that and other volatile organic compounds.

Accurate measurements of the vertical distribution of carbon dioxide (CO2) in the atmosphere are critical to validate the vertical columns recorded by satellite and ground-based spectrometers and to estimate sources and sinks of CO2. This work describes an in-situ air-sampler system (Aircore). Profiles of CO2 and carbon monoxide (CO) were retrieved from two soundings of this system in Xilin Hot on 13 and 14 June 2018, and compared with observations from the Tropospheric Monitoring Instrument (TROPOMI), Orbiting Carbon Observatory-2 (OCO-2), and Copernicus Atmosphere Monitoring Service (CAMS). The column-averaged mixing ratio of CO (XCO) monitored by TROPOMI agreed well with that calculated from the in-situ CO profile, with a small mean difference of 5 ± 1.89 ppb. The column-averaged mixing ratio of CO2 (XCO2) monitored by OCO-2 also agreed well with that calculated from the in-situ CO2 profile, with a slight bias of 0.4 ± 0.6 ppm. The OCO-2 a posteriori profile had an obvious bias compared with the in-situ CO2 profile on 13 June, with an underestimation between 600 hPa and 250 hPa and an overestimation below 600 hPa. CAMS also had the same bias compared with the in-situ CO profile; however, CAMS agreed well with the in-situ CO profile on 14 June in the troposphere because a weak cold vortex passed Xilin Hot on 13 June and disappeared on 14 June. In the stratosphere, there were significant biases in the vertical degradation. The striking agreement between the column-averaged mixing ratios and the differences in the vertical distribution suggests new exciting applications for the profile product.

Measurements of column-averaged dry-air mole fractions of carbon dioxide and carbon monoxide, CO城市作为主要碳源,在全球碳循环中起重要作用。本文利用小型傅里叶光谱仪 EM27/SUN FTS在北京城区开展连续高精度大气温室气体CO

The COVID-19 pandemic has led to ongoing reductions in economic activity and anthropogenic emissions. Beijing was particular badly affected by lockdown measures during the early months of the COVID-19 pandemic. It has significantly reduced the CO2 emission and toxic air pollution (CO and NO2). We use column-averaged dry-air mole fractions of CO2 and CO (XCO2 and XCO) observed by a ground-based EM27/SUN Fourier transform spectrometer (FTS), the tropospheric NO2 column observed by MAX-DOAS and satellite remote sensing data (GOSAT and TROPOMI) to investigate the variations in anthropogenic CO2 emission related to COVID-19 lockdown in Beijing. The anomalies describe the spatio-temporal enhancement of gas concentration, which relates to the emission. Anomalies in XCO2 and XCO, and XNO2 (ΔXCO2, ΔXCO, and ΔXNO2) for ground-based measurements were calculated from the diurnal variability. Highly correlated daily XCO and XCO2 anomalies derived from FTS time series data provide the ΔXCO to ΔXCO2 ratio (the correlation slope). The ΔXCO to ΔXCO2 ratio in Beijing was lower in 2020 (8.2 ppb/ppm) than in 2019 (9.6 ppb/ppm). The ΔXCO to ΔXCO2 ratio originating from a polluted area was significantly lower in 2020. The reduction in anthropogenic CO2 emission was estimated to be 14.2% using FTS data. A comparable value reflecting the slowdown in growth of atmospheric CO2 over the same time period was estimated to be 15% in Beijing from the XCO2 anomaly from GOSAT, which was derived from the difference between the target area and the background area. The XCO anomaly from TROPOMI is reduced by 8.7% in 2020 compared with 2019, which is much smaller than the reduction in surface air pollution data (17%). Ground-based NO2 observation provides a 21.6% decline in NO2. The NO2 to CO2 correlation indicates a 38.2% decline in the CO2 traffic emission sector. Overall, the reduction in anthropogenic CO2 emission relating to COVID-19 lockdown in Beijing can be detected by the Bruker EM27/SUN Fourier transform spectrometer (FTS) and MAX-DOAS in urban Beijing.

Author(s): Liu, Zhu; Ciais, Philippe; Deng, Zhu; Lei, Ruixue; Davis, Steven J; Feng, Sha; Zheng, Bo; Cui, Duo; Dou, Xinyu; Zhu, Biqing; Guo, Rui; Ke, Piyu; Sun, Taochun; Lu, Chenxi; He, Pan; Wang, Yuan; Yue, Xu; Wang, Yilong; Lei, Yadong; Zhou, Hao; Cai, Zhaonan; Wu, Yuhui; Guo, Runtao; Han, Tingxuan; Xue, Jinjun; Boucher, Olivier; Boucher, Eulalie; Chevallier, Frederic; Tanaka, Katsumasa; Wei, Yiming; Zhong, Haiwang; Kang, Chongqing; Zhang, Ning; Chen, Bin; Xi, Fengming; Liu, Miaomiao; Breon, Francois-Marie; Lu, Yonglong; Zhang, Qiang; Guan, Dabo; Gong, Peng; Kammen, Daniel M; He, Kebin; Schellnhuber, Hans Joachim | Abstract: A Correction to this paper has been published: https://doi.org/10.1038/s41467-020-20254-5.

Tropospheric ozone is both a major pollutant and a short-lived greenhouse gas and has therefore attracted much concern in recent years. The ozone profile in the troposphere and lower stratosphere over Beijing has been observed since 2002 by ozonesondes developed by the Institute of Atmospheric Physics. Increasing concentrations of tropospheric ozone from 2002 to 2010 measured by these balloon-based observations have been reported previously. As more observations are now available, we used these data to analyze the long-term variability of ozone over Beijing during the whole period from 2002 to 2018. The ozonesondes measured increasing concentrations of ozone from 2002 to 2012 in both the troposphere and lower stratosphere. There was a sudden decrease in observed ozone between 2011 and 2012. After this decrease, the increasing trend in ozone concentrations slowed down, especially in the mid-troposphere, where the positive trend became neutral. We used the Chemical Lagrangian Model of the Stratosphere (CLaMS) to determine the influence of the transport of ozone from the stratosphere to the troposphere on the observed ozone profiles. CLaMS showed a weak increase in the contribution of stratospheric ozone before the decrease in 2011–2012 and a much more pronounced decrease after this time. Because there is no tropospheric chemistry in CLaMS, the sudden decrease simulated by CLaMS indicates that a smaller downward transport of ozone from the stratosphere after 2012 may explain a significant part of the observed decrease in ozone in the mid-troposphere and lower stratosphere. However, the influence of stratospheric ozone in the lower troposphere is negligible in CLaMS and the hiatus in the positive trend after 2012 can be attributed to a reduction in ozone precursors as a result of stronger pollution control measures in Beijing.

Abundances of a range of air pollutants can be inferred from satellite UV-Vis spectroscopy measurements by using the unique absorption signatures of gas species. Here, we implemented several spectral fitting methods to retrieve tropospheric NO2, SO2, and HCHO from the ozone monitoring instrument (OMI), with radiative simulations providing necessary information on the interactions of scattered solar light within the atmosphere. We analyzed the spatial distribution and temporal trends of satellite-observed air pollutants over eastern China during 2005–2017, especially in heavily polluted regions. We found significant decreasing trends in NO2 and SO2 since 2011 over most regions, despite varying temporal features and turning points. In contrast, an overall increasing trend was identified for tropospheric HCHO over these regions in recent years. Furthermore, generalized additive models were implemented to understand the driving forces of air quality trends in China and assess the effectiveness of emission controls. Our results indicated that although meteorological parameters, such as wind, water vapor, solar radiation and temperature, mainly dominated the day-to-day and seasonal fluctuations in air pollutants, anthropogenic emissions played a unique role in the long-term variation in the ambient concentrations of NO2, SO2, and HCHO in the past 13 years. Generally, recent declines in NO2 and SO2 could be attributed to emission reductions due to effective air quality policies, and the opposite trends in HCHO may urge the need to control anthropogenic volatile organic compound (VOC) emissions. Satellite-based observations of air pollution in China reveal a recent declining trend in the levels of nitrogen dioxide (NO2) and sulfur dioxide (SO2), but rising levels of formaldehyde (HCHO). Chengxin Zhang and colleagues at the University of Science and Technology of China, with co-workers at other Chinese research centers, analyzed ultraviolet-visible spectroscopy data from the Ozone Monitoring Instrument of NASA’s EOS-Aura satellite. Meteorological effects such as winds, temperature and humidity caused day-to-day and seasonal fluctuations, but human activity appears responsible for long term trends. Declines in NO2 and SO2 levels occurred from 2011, across most regions, and are attributed to effective air quality policies. The rising trend in HCHO levels suggests that new measures may be needed to control the release by human activities of that and other volatile organic compounds.

Tropospheric ozone is a major pollutant and a short-lived greenhouse gas and has therefore attracted much concern in recent years. The ozone concentration in the troposphere and lower stratosphere over Beijing has been observed since 2002 by ozonesondes developed by the Institute of Atmospheric Physics. We used these balloon-based observations to analyze the long-term variability of ozone over Beijing during the whole period from 2002 to 2018. The ozonesondes measured increasing concentrations of ozone from 2002 to 2012 in both the troposphere and lower stratosphere. There was a sudden decrease in observed ozone between 2011 and 2012. After this decrease, the increasing trend in ozone concentrations slowed down, especially in the mid-troposphere, where the positive trend became neutral. We used the Chemical Lagrangian Model of the Stratosphere (CLaMS) to determine the influence of the transport of ozone from the stratosphere to the troposphere on the observed ozone profiles. Because there is no tropospheric chemistry in CLaMS, the sudden decrease simulated by CLaMS indicates that a smaller downward transport of ozone from the stratosphere after 2012 may explain a significant part of the observed decrease in ozone in the mid-troposphere and lower stratosphere. However, the influence of stratospheric ozone in the lower troposphere is negligible in CLaMS and the hiatus in the positive trend after 2012 can be attributed to a reduction in ozone precursors as a result of stronger pollution control measures in Beijing.

We simulated geostationary satellite observations to assess the potential for high spatial- and temporal-resolution monitoring of air pollution in China with a focus on tropospheric ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and formaldehyde (HCHO). Based on the capabilities and parameters of the payloads onboard sun-synchronous satellites, we simulated the observed spectrum based on a radiative transfer model using a geostationary satellite model. According to optimal estimation theory, we analyzed the sensitivities and retrieval uncertainties of the main parameters of the instrument for the target trace gases. Considering the retrieval error requirements of each trace gas, we determined the major instrument parameter values (e.g., observation channel, spectral resolution, and signal-to-noise ratio). To evaluate these values, retrieval simulation was performed based on the three-dimensional distribution of the atmospheric components over China using an atmospheric chemical transportation model. As many as 90% of the experiments met the retrieval requirements for all target gases. The retrieval precision of total-column and stratospheric O3 was 2%. In addition, effective retrieval of all trace gases could be achieved at solar zenith angles larger than 70°. Therefore, the geostationary satellite observation and instrument parameters provided herein can be used in air pollution monitoring in China. This study offers a theoretical basis and simulation tool for improving the design of instruments onboard geostationary satellites.