The fine-mode fraction (FMF) of aerosol optical depth (AOD) is a key optical parameter that represents the proportion of fine particles relative to total aerosols in the atmosphere. However, in comparison to ground-based measurements, the FMF is still difficult to retrieve from satellite observations, as attempted by a Moderate-resolution Imaging Spectroradiometer (MODIS) algorithm. In this paper, we introduce the retrieval of FMF based on Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar (PARASOL) data. This method takes advantage of the coincident multi-angle intensity and polarization measurements from a single satellite platform. In our method, we use intensity measurements to retrieve the total AOD and polarization measurements to retrieve the fine-mode AOD. The FMF is then calculated as the ratio of the retrieved fine-mode AOD to the total AOD. The important processes in our method include the estimation of the surface intensity and polarized reflectance by using two semi-empirical models, and the building of two sets of aerosol retrieval lookup tables for the intensity and polarized measurements via the 6SV radiative transfer code. We apply this method to East Asia, and comparisons of the retrieved FMFs for the Beijing, Xianghe and Seoul_SNU sites with those of the Aerosol Robotic Network (AERONET) ground-based observations produce correlation coefficients (R²) of 0.838, 0.818, and 0.877, respectively. However, the comparison results are relatively poor (R² = 0.537) in low-AOD areas, such as the Osaka site, due to the low signal-to-noise ratio of the satellite observations.

The expansion of urban areas and the increase in the number of buildings and urbanization characteristics, such as roads, affect the meteorological environment in urban areas, resulting in weakened pollutant dispersion. First, this paper uses GIS (geographic information system) spatial analysis technology and landscape ecology analysis methods to analyze the dynamic changes in land cover and landscape patterns in Chengdu as a result of urban development. Second, the most appropriate WRF (Weather Research and Forecasting) model parameterization scheme is selected and screened. Land-use data from different development stages in the city are included in the model, and the wind speed and temperature results simulated using new and old land-use data (1980 and 2015) are evaluated and compared. Finally, the results of the numerical simulations by the WRF-Chem air quality model using new and old land-use data are coupled with 0.25° × 0.25°-resolution MEIC (Multi-resolution Emission Inventory for China) emission source data from Tsinghua University. The results of the sensitivity experiments using the WRF-Chem model for the city under different development conditions and during different periods are discussed. The meteorological conditions and pollution sources remained unchanged as the land-use data changed, which revealed the impact of urban land-use changes on the simulation results of PM_2.5 atmospheric pollutants. The results show the following. (1) From 1980 to 2015, the land-use changes in Chengdu were obvious, and cultivated land exhibited the greatest changes, followed by forestland. Under the influence of urban land-use dynamics and human activities, both the richness and evenness of the landscape in Chengdu increased. (2) The microphysical scheme WSM3 (WRF Single–Moment 3 class) and land-surface scheme SLAB (5-layer diffusion scheme) were the most suitable for simulating temperatures and wind speeds in the WRF model. The wind speed and temperature simulation results using the 2015 land-use data were better than those using the 1980 land-use data when assessed according to the coincidence index and correlation coefficient. (3) The WRF-Chem simulation results obtained for PM_2.5 using the 2015 land-use data were better than those obtained using the 1980 land-use data in terms of the correlation coefficient and standard deviation. The concentration of PM_2.5 in urban areas was higher than that in the suburbs, and the concentration of PM_2.5 was lower on Longquan Mountain in Chengdu than in the surrounding areas.

The fine-mode aerosol optical depth (AOD_f) is an important parameter for the environment and climate change study, which mainly represents the anthropogenic aerosols component. The Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar (PARASOL) instrument can detect polarized signal from multi-angle observation and the polarized signal mainly comes from the radiation contribution of the fine-mode aerosols, which provides an opportunity to obtain AOD_f directly. However, the currently operational algorithm of Laboratoire d’Optique Atmosphérique (LOA) has a poor AOD_f retrieval accuracy over East China on high aerosol loading days. This study focused on solving this issue and proposed a grouped residual error sorting (GRES) method to determine the optimal aerosol model in AOD_f retrieval using the traditional look-up table (LUT) approach and then the AOD_f retrieval accuracy over East China was improved. The comparisons between the GRES retrieved and the Aerosol Robotic Network (AERONET) ground-based AOD_f at Beijing, Xianghe, Taihu and Hong_Kong_PolyU sites produced high correlation coefficients (r) of 0.900, 0.933, 0.957 and 0.968, respectively. The comparisons of the GRES retrieved AOD_f and PARASOL AOD_f product with those of the AERONET observations produced a mean absolute error (MAE) of 0.054 versus 0.104 on high aerosol loading days (AERONET mean AOD_f at 865 nm = 0.283). An application using the GRES method for total AOD (AOD_t) retrieval also showed a good expandability for multi-angle aerosol retrieval of this method.

Aerosol optical depth (AOD) is a widely used aerosol optical parameter in atmospheric physics. To obtain this parameter precisely, many institutions plan to launch satellites with multi-angle measurement sensors, but one important step in aerosol retrieval, the estimation of surface reflectance, is still a pressing issue. This paper presents an AOD retrieval method based on the multi-angle intensity data from the Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar (PARASOL) platform using empirical orthogonal functions (EOFs), which can be universally applied to multi-angle observations. The function of EOFs in this study is to estimate surface intensity contributions, associated with aerosol lookup tables (LUTs), so that the retrieval of AOD can be implemented. A comparison of the retrieved AODs for the Beijing, Xianghe, Taihu, and Hongkong_PolyU sites with those from the Aerosol Robotic Network (AERONET) ground-based observations produced high correlation coefficients (r) of 0.892, 0.915, 0.831, and 0.897, respectively, while the corresponding root mean square errors (RMSEs) are 0.095, 0.093, 0.099, and 0.076, respectively.

A heavy air pollution event occurred in Chengdu between 7 May 2014 and 8 May 2014. The present study established tracer sources based on HJ-1 satellite data, micropulse light detection and ranging (LiDAR) remote sensing data, and backward trajectories simulated using the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model. Additionally, the present study analyzed the diffusion conditions for the sources and characteristics of the pollutant transport in this pollution event through simulation using a mesoscale atmospheric chemistry transport model—the weather research forecasting model with chemistry (WRF–CHEM). The results show that the change in the boundary-layer height over Chengdu had a relatively large effect on the vertical diffusion of pollutants. During the pollution event, Chengdu, Meishan, and Leshan were areas of significantly low mean ventilation coefficients ( $V_H$ ). In Chengdu, the $V_H$ was extremely low at night, and there was a temperature inversion near the ground, resulting in the continuous accumulation of pollutants at night and a continuous worsening of the pollution. During the period of heavy pollution, there were straw-burning sites in Meishan, Ziyang, Neijiang, Zigong, and Deyang. On 7 May 2014, the pollutants in Chengdu mainly originated from Meishan. The accumulation in Chengdu of pollutants originating in Meishan and Deyang led to highly concentrated pollution on 8 May 2014, to which the pollutants originating in Deyang were the main contributor. The transport of pollutants resulting from straw burning in the study area and the relatively poor conditions for the pollutant diffusion in Chengdu collectively led to the heavy air pollution event investigated in the present study.

The vertical distribution of the tropospheric ozone column concentration (OCC) in China from 2005 to 2020 was analysed based on the ozone profile product of the ozone monitoring instrument (OMI). The annual average OCC in the lower troposphere (OCC_LT) showed an increasing trend, with an average annual increase of 0.143 DU. The OCC in the middle troposphere showed a downward trend, with an average annual decrease of 0.091 DU. There was a significant negative correlation between the ozone changes in the two layers. The monthly average results show that the peak values of OCC_LT occur in May or June, the middle troposphere is significantly influenced by topographic conditions, and the upper troposphere is mainly affected by latitude. Analysis based on multi-source data shows that the reduction in nitrogen oxides (NO_x) and the increase in volatile organic compounds (VOCs) weakened the titration of ozone generation, resulting in the increase in OCC_LT. The increase in vegetation is closely related to the increase in OCC_LT, with a correlation coefficient of up to 0.875. The near-surface temperature increased significantly, which strengthened the photochemical reaction of ozone. In addition, the increase in boundary layer height also plays a positive role in the increase in OCC_LT.