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Surface air temperature (Ta), as an important climate variable, has been used in a wide range of fields such as ecology, hydrology, climatology, epidemiology, and environmental science. However, ground measurements are limited by poor spatial representation and inconsistency, while reanalysis and meteorological forcing datasets suffer from coarse spatial resolution and inaccuracy. Previous studies using satellite data have mainly estimated Ta under clear-sky conditions, or with limited temporal and spatial coverage. In this study, an all-sky daily mean Ta product at 1 km spatial resolution over mainland China for 2003-2019 has been generated mainly from the Moderate Resolution Imaging Spectroradiometer (MODIS) products and the Global Land Data Assimilation System (GLDAS) dataset. Three Ta estimation models based on random forest were trained using ground measurements from 2384 stations for three different clear-sky and cloudy-sky conditions. The validation results showed that R2 and root mean square error (RMSE) values of the three models ranged from 0.984 to 0.986 and 1.342 K to 1.440 K, respectively. We examined the spatiotemporal patterns and land cover type dependences of model accuracy. The relative contributions of different features to models were also quantitatively analysed. Finally, values of our Ta product in 2010 were validated and compared with the China Land Data Assimilation System (CLDAS) dataset at 0.0625° spatial resolution, China Meteorological Forcing Data (CMFD) dataset at 0.1° spatial resolution and GLDAS dataset at 0.25° spatial resolution. The R2 and RMSE values of our product were 0.992 and 1.010 K, respectively, indicating this high-resolution satellite product has significantly higher accuracy. In summary, the all-sky daily mean Ta dataset developed in this study has achieved satisfactory accuracy and high spatial resolution simultaneously, which fills the current dataset gap in this field and plays an important role in the studies of climate change and hydrological cycle. This dataset is freely available at http://doi.org/10.5281/zenodo.4399453 (Chen et al., 2021b) and the University of Maryland (http://glass.umd.edu/Ta%5FChina/) currently. A sub-dataset that covers Beijing generated from this dataset is publicly available at http://doi.org/10.5281/zenodo.4405123 (Chen et al., 2021a). [ABSTRACT FROM AUTHOR]

The particulate matter (PM) pollution has been significantly improved by carrying out various valid emission control strategies since 2013 in China. Meanwhile the variation trend of nitrous acid (HONO) is worthy to investigate due to its vital role in the atmospheric oxidation process. In this study, field observation in the winter is conducted to investigate the concentration of HONO in an urban area of Beijing. In the winter of 2019, the mean HONO concentration is 1.38 ppbV during the whole winter. Photo-enhanced NO 2 heterogeneous reactions on the ground and aerosol surfaces were found as the possible daytime sources of HONO. Compared to O 3 , photolysis of HONO dominates the primary OH sources during the winter time. To understand the HONO pollution patterns by years variation, multi-year data is summarized and finds that primary pollutants including CO and NO decreased, but secondary pollutants i.e., HONO (mostly generated via secondary process) increased. Our study highlights the requirement to mitigate secondary pollution by control HONO concentration. [Display omitted] [ABSTRACT FROM AUTHOR]