Aerosol optical depth (AOD) has become one of the most crucial parameters for climate change assessment on regional and global scales. The present study investigates trends in AOD using long-term data derived from moderate resolution imaging spectro-radiometer (MODIS) over twelve regions in Pakistan. Different statistical tests are used to assess the annual and seasonal trends in AOD. Results reveal increasing AOD trends over most of the selected regions with an obvious increase over the north and northeastern parts of the study area. Annually, increasing trends (0.0002–0.0047 year⁻¹) were observed over seven regions, with three being statistically significant. All the selected regions experience increasing AOD trends during the winter season with six being statistically significant while during the summer season seven regions experience increasing AOD trends and the remaining five exhibit the converse with two being statistically significant. The changes in the sign and magnitude of AOD trends have been attributed to prevailing meteorological conditions. The decreasing rainfall and increasing temperature trends mostly support the increasing AOD trend over the selected regions. The high/low AOD phases during the study period may be ascribed to the anomalies in mid-tropospheric relative humidity and wind fields. The summer season is generally characterized by high AOD with peak values observed over the regions located in central plains, which can be attributed to the dense population and enhanced concentration of industrial and vehicular emissions over this part of the study area. The results derived from the present study give an insight into aerosol trends and could form the basis for aerosol-induced climate change assessment over the study area.

This study proposes a new technique for retrieving temperature and humidity profiles based on Artificial Neural Networks (ANNs) using data acquired from the GIIRS (Geosynchronous Interferometric Infrared Sounder) L1 and ERA-Interim (European Centre for Medium-Range Weather Forecasts Reanalysis). The approach is also compared against another method that uses simulated data from the radiative transfer model to construct the retrieval network. Furthermore, the two methods of network construction are evaluated in the North China Plain for July and August 2018, for which ground validated observations concurrent to the satellite data were available. In summary, the results showed that: (1) the ANN built with the GIIRS L1 and the EC data is superior to that built with the forward simulation and EC data in retrieval accuracy; (2) the retrieval accuracy for the troposphere exceeds that for the stratosphere; (3) the root mean square errors (RMSEs) of the relative humidity in the troposphere as retrieved by the two ANNs are 6.003% and 10.608%, respectively; (4) a relatively low correlation (R) between the simulated and observed radiance of the GIIRS is found, ranging between 720 and 736.875 cm⁻¹, and the correlation between the simulated and observed radiance of the water vapor channels exceeds that between the temperature channels; (5) compared with Atmospheric Infrared Sounder’s (AIRS’) products, our retrieved temperature profiles exhibit preferable consistency and the humidity retrievals also show an acceptable accuracy. Our study offers important insights towards improving our ability to retrieve atmospheric temperature and humidity profiles from the most sophisticated Earth Observation instruments such as the GIIRS of the FY-4 satellite, which could assist in expanding the use of those products globally.

In the present study, a daily model is proposed for estimating the near-surface NO₂ concentration in China, combining for the first time the Random Forest (RF) machine learning algorithm with the tropospheric NO₂ columns from the TROPOspheric Monitoring Instrument (TropOMI) satellite and meteorological and NO₂ data of surface sites in China for the year 2019. Furthermore, near-surface NO₂ concentration data of ground sites during the COVID-19 outbreak from 1–5 February 2020 were used to verify the developed model. The daily model was verified by the ten-fold cross-validation method, revealing a coefficient of determination (R²) of 0.78 and root-mean-square error (RMSE) of 7.04 μg/m³, which are reasonable and also comparable to other published studies. In addition, our model showed that near-surface NO₂ in China during the COVID-19 pandemic was significantly reduced compared with 2019, and these predictions were in good agreement with reference ground data. Our proposed model can also provide NO₂ estimates for areas in western China where there are few ground monitoring sites. Therefore, all in all, our study findings suggest that the model established herein is suitable for estimating the daily NO₂ concentration near the surface in China and, as such, can be used if there is a lack of surface sites and/or missing observations in some areas.

Various particulate matters (PM) and associated carbonaceous aerosols released from open biomass burning (including open straw burning, grass and forest fires) are major sources of atmospheric pollutants. Northeast China is a central region with high forest and grass coverage, as well as an intensive agricultural area. In this study, the FINN (Fire INventory from Ncar) emission data was used to analyze the spatiotemporal variations of PM and associated carbonaceous aerosol component (PM_2.5, PM₁₀, OC and BC) emissions from open biomass burning in Northeast China from 2002 to 2016. The results show that the total amount of annual PM_2.5, PM₁₀, OC and BC emissions was estimated to be 59.0, 70.6, 31.5, and 4.3 kilotons, respectively, from open biomass burning over Northeast China, averaged from 2002 to 2016, with significant inter-annual variations in amplitudes from 28.0 to 122.3, 33.7 to 144.1, 15.0 to 65.0, and 2.1 to 8.6 kilotons. The regional PM_2.5, PM₁₀, OC and BC emissions showed significant seasonal variations with highest emissions in spring (with a seasonal peak in April), followed by autumn (with a seasonal peak in October), summer, and winter in Northeast China; high emissions were concentrated in the forests and grasslands with natural fires, as well as over agricultural areas with crop straw burning from human activities. The PM_2.5, PM₁₀, OC and BC emissions over forest areas presented decreasing trends, while the emissions over farmlands showed increasing trends in Northeast China during 2002–2016; this reflects on the dominance of biomass burning that shifted from forestland with natural fires to farmlands with increasing human activities. Three key meteorological drivers—strong near-surface wind speed, high air temperature and low relative humidity—were identified as having significant positive impacts on the inter-annual variations of PM_2.5, PM₁₀, OC and BC emissions from open biomass burning in Northeast China.