A new multiangle implementation of the atmospheric correction (MAIAC) algorithm has been applied in the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and has recently provided globally high-spatial-resolution aerosol optical depth (AOD) products at 1 km. Moreover, several improvements have been modified in the classical Dark Target (DT) and Deep Blue (DB) aerosol retrieval algorithms in MODIS Collection 6.1 products. Thus, validation and comparison of the MAIAC, DT, and DB algorithms are urgent in China. In this paper, we present a comprehensive assessment and comparison of AOD products at a 550 nm wavelength based on three aerosol retrieval algorithms in the MODIS sensor using ground-truth measurements from AErosol RObotic NETwork (AERONET) sites over China from 2000 to 2017. In general, MAIAC products achieved better accuracy than DT and DB products in the overall validation and accuracy improvement of DB products after the QA filter, demonstrating the highest values among the three products. In addition, the DT algorithms had higher aerosol retrievals in cropland, forest, and ocean land types than the other two products, and the MAIAC algorithms were more accurate in grassland, built-up, unoccupied, and mixed land types among the three products. In the geometry dependency analysis, the solar zenith angle, scattering angle, and relative azimuth angle, excluding the view zenith angle, significantly affected the performance of the three aerosol retrieval algorithms. The three products showed different accuracies with varying regions and seasons. Similar spatial patterns were found for the three products, but the MAIAC retrievals were smaller in the North China Plain and higher in Yunnan Province compared with the DT and DB retrievals before the QA filter. After the QA filter, the DB retrievals were significantly lower than the MAIAC retrievals in south China. Moreover, the spatiotemporal completeness of the MAIAC product was also better than the DT and DB products.

Monitoring fine particulate matter with diameters of less than 2.5 μm (PM2.5) is a critical endeavor in the Beijing–Tianjin–Hebei (BTH) region, which is one of the most polluted areas in China. Polar orbit satellites are limited by observation frequency, which is insufficient for understanding PM2.5 evolution. As a geostationary satellite, Himawari-8 can obtain hourly optical depths (AODs) and overcome the estimated PM2.5 concentrations with low time resolution. In this study, the evaluation of Himawari-8 AODs by comparing with Aerosol Robotic Network (AERONET) measurements showed Himawari-8 retrievals (Level 3) with a mild underestimate of about −0.06 and approximately 57% of AODs falling within the expected error established by the Moderate-resolution Imaging Spectroradiometer (MODIS) (±(0.05 + 0.15AOD)). Furthermore, the improved linear mixed-effect model was proposed to derive the surface hourly PM2.5 from Himawari-8 AODs from July 2015 to March 2017. The estimated hourly PM2.5 concentrations agreed well with the surface PM2.5 measurements with high R² (0.86) and low RMSE (24.5 μg/m³). The average estimated PM2.5 in the BTH region during the study time range was about 55 μg/m³. The estimated hourly PM2.5 concentrations ranged extensively from 35.2 ± 26.9 μg/m³ (1600 local time) to 65.5 ± 54.6 μg/m³ (1100 local time) at different hours.

In precipitable water vapor (PWV) retrievals from Global Navigation Satellite System (GNSS) data, the two essential parameters, namely, surface pressure () and weighted mean temperature (), are often not available due to the lack of collocated meteorological sensors or improper data retention. Hence, this study presents a comprehensive assessment of the GNSS PWV retrieval using alternative and data from the European Centre for Medium‐Range Weather Forecasts (ECMWF) ReAnalysis 5, National Centers for Environmental Prediction Final (NCEP FNL) Analysis, and NCEP Global Forecast System (GFS) products. The assessment was based on 691 globally distributed GNSS stations over the entire year of 2019. The zenith hydrostatic delay (ZHD) and integrated from the three types of numerical weather prediction (NWP) atmospheric profiles achieve varying accuracies in ranges of 2.4–3.0 mm and 1.1–1.5 K, respectively. PWVs estimated using ZHD and integrated from the NWP profiles obtain accuracies of about 1.6–2.0 mm. These PWVs are slightly better than those using ZHD and calculated by empirical models with surface pressure and temperature from the NWP datasets. The assessment of PWVs with the global pressure and temperature 2 wet model yields a root mean square (RMS) error of 3.73 mm. The relative RMS decreases from 30%‐40% at high latitudes (70–80°S/N) to ∼5% around the equator. The monthly variations of relative RMS show that (a) low‐latitude regions outperform the high‐latitude regions, and (b) winter months have significantly worse performance than other months in both hemispheres.

Hourly average monitoring data for mass concentrations of PM₁, PM_2.5, PM₁₀, and black carbon (BC) were measured in Wuhan from December 2013 to December 2014, which has a flourishing steel industry, to analyze the characteristics of PM and their relation to BC, using statistical methods. The results indicate that variations in the monthly average mass concentrations of PM have similar concave parabolic shapes, with the highest values occurring in January and the lowest values appearing in August or September. The correlation coefficient of the linear regression model between PM₁ and PM_2.5 is quite high, reaching 0.99. Furthermore, the proportion of PM₁ contained within PM_2.5 is roughly 90%, directly proving that ultrafine particles whose diameter less than 1 μm may be a primary component of PM_2.5 in Wuhan. Additionally, better seasonal correlation between PM and BC occurs only in summer and autumn, due to multiple factors such as topography, temperature, and the atmosphere in winter and spring. Finally, analysis of the diurnal variation of PM and BC demonstrates that the traffic emissions during rush hour, exogenous pollutants, and the shallow PBLH with stagnant atmosphere, all contribute to the severe pollution of Wuhan in winter.