Satellite measurements of the spatiotemporal distributions of atmospheric CO₂ concentrations are a key component for better understanding global carbon cycle characteristics. Currently, several satellite instruments such as the Greenhouse gases Observing SATellite (GOSAT), SCanning Imaging Absorption Spectrometer for Atmospheric CHartographY (SCIAMACHY), and Orbiting Carbon Observatory-2 can be used to measure CO₂ column-averaged dry air mole fractions. However, because of cloud effects, a single satellite can only provide limited CO₂ data, resulting in significant uncertainty in the characterization of the spatiotemporal distribution of atmospheric CO₂ concentrations. In this study, a new physical data fusion technique is proposed to combine the GOSAT and SCIAMACHY measurements. On the basis of the fused dataset, a gap-filling method developed by modeling the spatial correlation structures of CO₂ concentrations is presented with the goal of generating global land CO₂ distribution maps with high spatiotemporal resolution. The results show that, compared with the single satellite dataset (i.e., GOSAT or SCIAMACHY), the global spatial coverage of the fused dataset is significantly increased (reaching up to approximately 20%), and the temporal resolution is improved by two or three times. The spatial coverage and monthly variations of the generated global CO₂ distributions are also investigated. Comparisons with ground-based Total Carbon Column Observing Network (TCCON) measurements reveal that CO₂ distributions based on the gap-filling method show good agreement with TCCON records despite some biases. These results demonstrate that the fused dataset as well as the gap-filling method are rather effective to generate global CO₂ distribution with high accuracies and high spatiotemporal resolution.

The air quality in China has experienced dramatic changes during the last few decades. To improve understanding of distribution, variations, and main influence factors of air pollution in central China, long-term multiple satellite observations from moderate resolution imaging spectroradiometer (MODIS) and ozone monitoring instrument (OMI) are used to characterize particle pollution and their primary gaseous precursors, sulfur dioxide (SO₂), and nitrogen dioxide (NO₂) in Hubei province during 2005–2017. Unlike other regions in eastern China, particle and gaseous pollutants exhibit distinct spatial and temporal patterns in central China due to differences in emission sources and control measures. OMI SO₂ of the whole Hubei region reached the highest value of ~0.2 Dobson unit (DU) in 2007 and then declined by more than 90% to near background levels. By contrast, OMI NO₂ grew from ~3.2 to 5.9 × 10¹⁵ molecules cm⁻² during 2005–2011 and deceased to ~3.9 × 10¹⁵ molecules cm⁻² in 2017. Unlike the steadily declining SO₂, variations of OMI NO₂ flattened out in 2016 and increased ~0.5 × 10¹⁵ molecules cm⁻² during 2017. As result, MODIS AOD at 550 nm increased from 0.55 to the peak value of 0.7 during 2005–2011 and then decreased continuously to 0.38 by 2017. MODIS AOD and OMI SO₂ has a high correlation (R > 0.8), indicating that annual variations of SO₂ can explain most changes of AOD. The air pollution in central China has notable seasonal variations, which is heaviest in winter and light in summer. While air quality in eastern Hubei is dominated by gaseous pollution such as O₃ and NO_x, particle pollutants are mainly concentrated in central Hubei. The high consistency with ground measurements demonstrates that satellite observation can well capture variations of air pollution in regional scales. The increasing ozone (O₃) and NO₂ since 2016 suggests that more control measures should be made to reduce O₃-related emissions. To improve the air quality in regional scale, it is necessary to monitor the dynamic emission sources with satellite observations at a finer resolution.

Satellite aerosol optical depth (AOD) products have been widely used in estimating fine particulate matter (PM_2.5) concentrations near the surface at a regional scale, and perform well compared with ground measurements. However, the influence of limitations such as retrieval frequency and the spatial resolution of satellite AODs on the applicability of predicted PM_2.5 values has been rarely considered. With three widely used MODIS AOD products, including Multi-Angle Implementation of Atmospheric Correction (MAIAC), Deep Blue (DB) and Dark Target (DT), here we evaluate the influence of their spatial resolution and sampling frequency by estimating daily PM_2.5 concentrations in the Beijing-Tianjin-Hebei (BTH) region of northern China during 2017 utilizing a mixed effects model. The daily concentrations of PM_2.5 derived from MAIAC, DB and DT AOD all have high correlations (R²: 0.78, 0.8, and 0.78) with the observed values, but the predicted annual PM_2.5 exhibits a distinct spatial distribution. DT estimation obviously underestimates annual PM_2.5 in polluted areas due to lower sampling of heavy pollution events. By contrast, the retrieval frequency (~40-60%) of MAIAC and DB AOD can represent well annual PM_2.5 in nearly all 83 sites tested. However, MAIAC and DB-derived PM_2.5 have a larger bias compared with observed values than DT, indicating that the large spatial variation of aerosol properties can exert an influence on the reliability of the statistical AOD-PM_2.5 relationship. Also, there is notable difference between MAIAC and DB PM_2.5 due to their different cloud screening methods. The MAIAC PM_2.5 with high spatial resolution at 1 km can capture much finer hotpots than DB and DT at 10 km. Our results suggest that it is crucial to consider the applicability of satellite-predicted PM_2.5 values derived from different aerosol products according to the specific requirements besides modeling the AOD-PM_2.5 relationship.