People in central-eastern China are suffering from severe air pollution of nitrogen oxides. Top-down approaches have been widely applied to estimate the ground concentrations of NO₂ based on satellite data. In this paper, a one-year dataset of tropospheric NO₂ columns from the Ozone Monitoring Instrument (OMI) together with ambient monitoring station measurements and meteorological data from May 2013 to April 2014, are used to estimate the ground level NO₂. The mean values of OMI tropospheric NO₂ columns show significant geographical and seasonal variation when the ambient monitoring stations record a certain range. Hence, a geographically and temporally weighted regression (GTWR) model is introduced to treat the spatio-temporal non-stationarities between tropospheric-columnar and ground level NO₂. Cross-validations demonstrate that the GTWR model outperforms the ordinary least squares (OLS), the geographically weighted regression (GWR), and the temporally weighted regression (TWR), produces the highest R² (0.60) and the lowest values of root mean square error mean (RMSE), absolute difference (MAD), and mean absolute percentage error (MAPE). Our method is better than or comparable to the chemistry transport model method. The satellite-estimated spatial distribution of ground NO₂ shows a reasonable spatial pattern, with high annual mean values (>40 μg/m³), mainly over southern Hebei, northern Henan, central Shandong, and southern Shaanxi. The values of population-weight NO₂ distinguish densely populated areas with high levels of human exposure from others.

Analysis of cloud cover and its diurnal variation over the Tibetan Plateau (TP) is highly reliant on satellite data; however, the accuracy of cloud detection from both polar-orbiting and geostationary satellites over this area remains unclear. The new-generation geostationary Himawari-8 satellites provide high-resolution spatial and temporal information about clouds over the Tibetan Plateau. In this study, the cloud detection of MODIS and AHI is investigated and validated against CALIPSO measurements. For AHI and MODIS, the false alarm rate of AHI and MODIS in cloud identification over the TP was 7.51% and 1.94%, respectively, and the cloud hit rate was 73.55% and 80.15%, respectively. Using hourly cloud-cover data from the Himawari-8 satellites, we found that at the monthly scale, the diurnal cycle in cloud cover over the TP tends to increase throughout the day, with the minimum and maximum cloud fractions occurring at 10:00 a.m. and 18:00 p.m. local time. Due to the limited time resolution of polar-orbiting satellites, the underestimation of MODIS daytime average cloud cover is approximately 4.00% at the annual scale, with larger biases during the spring (5.40%) and winter (5.90%).

The Chengdu–Chongqing Economic Zone (CCEZ), which is located in southwestern China, is the fourth largest economic zone in China. The rapid economic development of this area has resulted in many environmental problems, including extremely high concentrations of nitrogen dioxide (NO₂) and fine particulate matter (PM_2.5). However, current ground observations lack spatial and temporal coverage. In this study, satellite remote sensing techniques were used to analyze the variation in NO₂ and PM_2.5 from 2005 to 2015 in the CCEZ. The Ozone Monitoring Instrument (OMI) and the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) product were used to retrieve tropospheric NO₂ vertical columns and estimate ground-level PM_2.5 concentrations, respectively. Geographically, high NO₂ concentrations were mainly located in the northwest of Chengdu and southeast of Chongqing. However, high PM_2.5 concentrations were mainly located in the center areas of the basin. The seasonal average NO₂ and PM_2.5 concentrations were both highest in winter and lowest in summer. The seasonal average NO₂ and PM_2.5 were as high as 749.33 × 10¹³ molecules·cm⁻² and 132.39 µg·m⁻³ in winter 2010, respectively. Over 11 years, the annual average NO₂ and PM_2.5 values in the CCEZ increased initially and then decreased, with 2011 as the inflection point. In 2007, the concentration of NO₂ reached its lowest value since 2005, which was 230.15 × 10¹³ molecules·cm⁻², and in 2015, the concentration of PM_2.5 reached its lowest value since 2005, which was 26.43 µg·m⁻³. Our study demonstrates the potential use of satellite remote sensing to compensate for the lack of ground-observed data when quantitatively analyzing the spatial–temporal variations in regional air quality.

Aerosol optical depth (AOD) has been widely used in climate research, atmospheric environmental observations, and other applications. However, high AOD retrieval remains challenging over heavily polluted regions, such as the North China Plain (NCP). The Visible Infrared Imaging Radiometer Suite (VIIRS), which was designed as a successor to the Moderate Resolution Imaging Spectroradiometer (MODIS), will undertake the aerosol observations mission in the coming years. Using the VIIRS AOD retrieval algorithm as an example, we analyzed the influence of heavy aerosol loading through the 6SV radiative transfer model (RTM) with a focus on three aspects: cloud masking, ephemeral water body tests, and data quality estimation. First, certain pixels were mistakenly screened out as clouds and ephemeral water bodies because of heavy aerosols, resulting in the loss of AOD retrievals. Second, the greenness of the surface could not be accurately identified by the top of atmosphere (TOA) index, and the quality of the aggregation data may be artificially high. Thus, the AOD retrieval algorithm did not perform satisfactorily, indicated by the low availability of data coverage (at least 37.97% of all data records were missing according to ground-based observations) and overestimation of the data quality (high-quality data increased from 63.42% to 80.97% according to radiative simulations). To resolve these problems, the implementation of a spatial variability cloud mask method and surficial index are suggested in order to improve the algorithm.