Black carbon (BC) aerosol, as a typical optical absorption aerosol, is of great significance to the study of climate and radiation. The China Atmosphere Watch Network (CAWNET), established by the China Meteorological Administration (CMA), contains 35 BC-monitored stations, which have been collecting data using commercial Aethalometer instruments (AEs) since 2006. Element carbon (EC) data measured from the thermal/optical reflectance (TOR) method was used to correct the BC monitoring data from the AEs, which are affected by various sampling and analytical artifacts. The average difference before and after the revision was about 17.3% (±11.5%). Furthermore, we analyzed the variations of BC in China from 2006 to 2017 using a revised dataset. The ten-year averaged concentration of BC would have been applicable for climate analysis, and can be a comparison sample in future research. The concentrations of BC across the stations in China showed a general downward trend, with occasional fluctuations, and the concentrations at urban sites decreased more significantly. The average concentrations of BC in urban sites are higher than rural and remote sites. The 10-year averaged concentration of BC ranges from 11.13 μg m⁻³ in Gucheng to 0.19 μg m⁻³ in Shangri-La, showing a strong spatial variation; the proportion of BC aerosol in PM_2.5 is generally less than 20%. The BC showed obvious seasonal and diurnal variation; and the highest concentration occurred in winter, with more dramatic diurnal variation, followed by autumn and spring. There was a significant increase in concentration between local time 7:00–9:00 and 18:00–0:00. The distribution and trend of BC concentration in China showed a consistency with emissions of BC.

Sand and dust storms (SDSs) cause major disasters in northern China. They have serious impacts on human health, daily life, and industrial and agricultural production, in addition to threatening the regional ecological environment and social economy. Based on meteorological observational data and the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 dataset for spring 2000–2021, we used the Lamb–Jenkinson circulation classification method to classify the three major areas influencing SDSs in northern China. We also used the k-means clustering method to classify the overall circulation pattern in northern China. Our results show that the circulation types favoring SDSs in the southern basin of Xinjiang are southwesterly winds (SW), cyclones (C), and anticyclones (A). The circulation types favoring SDSs in western Inner Mongolia and southern Mongolia are northwesterly winds (NW), northerly winds (N), cyclones (C), and anticyclones (A). The circulation types favoring SDSs in central Inner Mongolia are northwesterly winds (NW), northerly winds (N), southwesterly winds (SW), and anticyclones (A). The 500 hPa and surface circulation patterns in China can be divided into nine types. Among them, five dominant circulation patterns favor strong SDSs: a cold high-pressure region and cold front (T1), a Mongolian cyclone (T2), a mixed type of Mongolian cyclone and cold front (T3), a thermal depression and cold front (T5), and a cold front (T8). During 2000–2004, the T8 circulation pattern occurred most frequently as the main influencing circulation. From 2005 to 2010, the T3 and T8 circulation patterns dominated. Circulation patterns T1 and T3 dominated during 2011–2015 and 2016–2020, respectively. We analyzed the main circulation patterns for four SDS events occurring in 2021 by combining the Lamb–Jenkinson and k-means methods. The SDS events in 2021 were closest to the T3 circulation pattern and were mainly influenced by Mongolian cyclones and surface cold fronts. The main propagation paths were westerly and northwesterly.