The outbreak of the COVID-19 virus in Wuhan, China, in January 2020 just before the Spring Festival and subsequent country-wide measures to contain the virus, effectively resulted in the lock-down of the country. Most industries and businesses were closed, traffic was largely reduced, and people were restrained to their homes. This resulted in the reduction of emissions of trace gases and aerosols, the concentrations of which were strongly reduced in many cities around the country. Satellite imagery from the TROPOspheric Monitoring Instrument (TROPOMI) showed an enormous reduction of tropospheric NO₂ concentrations, but aerosol optical depth (AOD), as a measure of the amount of aerosols, was less affected, likely due to the different formation mechanisms and the influence of meteorological factors. In this study, satellite data and ground-based observations were used together to estimate the separate effects of the Spring Festival and the COVID-19 containment measures on atmospheric composition in the winter of 2020. To achieve this, data were analyzed for a period from 30 days before to 60 days after the Spring Festivals in 2017–2020. This extended period of time, including similar periods in previous years, were selected to account for both the decreasing concentrations in response to air pollution control measures, and meteorological effects on concentrations of trace gases and aerosols. Satellite data from TROPOMI provided the spatial distributions over mainland China of the tropospheric vertical column density (VCD) of NO₂, and VCD of SO₂ and CO. The MODerate resolution Imaging Spectroradiometer (MODIS) provided the aerosol optical depth (AOD). The comparison of the satellite data for different periods showed a large reduction of, e.g., NO₂ tropospheric VCDs due to the Spring Festival of up to 80% in some regions, and an additional reduction due to the COVID-19 containment measures of up to 70% in highly populated areas with intensive anthropogenic activities. In other areas, both effects are very small. Ground-based in situ observations from 26 provincial capitals provided concentrations of NO₂, SO₂, CO, O₃, PM_2.5, and PM₁₀. The analysis of these data was focused on the situation in Wuhan, based on daily averaged concentrations. The NO₂ concentrations started to decrease a few days before the Spring Festival and increased after about two weeks, except in 2020 when they continued to be low. SO₂ concentrations behaved in a similar way, whereas CO, PM_2.5, and PM₁₀ also decreased during the Spring Festival but did not trace NO₂ concentrations as SO₂ did. As could be expected from atmospheric chemistry considerations, O₃ concentrations increased. The analysis of the effects of the Spring Festival and the COVID-19 containment measures was complicated due to meteorological influences. Uncertainties contributing to the estimates of the different effects on the trace gas concentrations are discussed. The situation in Wuhan is compared with that in 26 provincial capitals based on 30-day averages for four years, showing different effects across China.

In this study, a decade long measurement of aerosol optical properties at two AERONET stations (Nam Co during 2006–2016 and QOMS during 2009–2017) in the Tibetan Plateau (TP), a region sensitive to climate change and human perturbation, is presented. The baseline value of aerosol optical depth (AOD) was 0.029 and 0.027 at Nam Co and QOMS, respectively, which are comparable to or even lower than those at some Arctic and remote ocean locations. The seasonality of AOD values were the order of spring > summer > winter > autumn. Based on AOD and Ångström exponent (α), major aerosol types over the TP were further identified as continental background, biomass burning, and dust. Although continental background aerosol was the main feature in remote areas of TP, biomass burning plumes frequently occurred, especially during spring (March–April). In one of such biomass burning event in April 2014, MODIS observations demonstrated that intensive open fires occurred in South Asia, covering the foothills of Himalayas and Indo‐Gangetic Plain. The air mass back trajectories and CALIOP observations further revealed that biomass burning plume could be uplifted to higher altitudes and reach the Himalayas. Moreover, an occasional dust event detected in April 2008 over the TP demonstrated that the dust from Taklamakan Desert may impact the main body of TP episodically, although the local dust from the inside of TP cannot be excluded and warrants further study.

In this study, simple dust detection and intensity estimation methods using Himawari-8 Advanced Himawari Imager (AHI) data are developed. Based on the differences of thermal radiation characteristics between dust and other typical objects, brightness temperature difference (BTD) among four channels (BT₁₁–BT₁₂, BT₈–BT₁₁, and BT₃–BT₁₁) are used together for dust detection. When considering the thermal radiation variation of dust particles over different land cover types, a dynamic threshold scheme for dust detection is adopted. An enhanced dust intensity index (EDII) is developed based on the reflectance of visible/near-infrared bands, BT of thermal-infrared bands, and aerosol optical depth (AOD), and is applied to the detected dust area. The AOD is retrieved using multiple temporal AHI observations by assuming little surface change in a short time period (i.e., 1–2 days) and proved with high accuracy using the Aerosol Robotic Network (AERONET) and cross-compared with MODIS AOD products. The dust detection results agree qualitatively with the dust locations that were revealed by AHI true color images. The results were also compared quantitatively with dust identification results from the AERONET AOD and Ångström exponent, achieving a total dust detection accuracy of 84%. A good agreement is obtained between EDII and the visibility data from National Climatic Data Center ground measurements, with a correlation coefficient of 0.81, indicating the effectiveness of EDII in dust monitoring.

One or several aspects of the source, distribution, transport, and optical properties of airborne dust have been characterized using different types of satellite and ground measurements, each with unique advantages. In this study, a dust event that occurred over the East Asia area in May 2017 was exemplified to demonstrate how all the above-mentioned aspects of a dust event can be pictured by combining the advantages of different satellite and ground measurements. The data used included the Himawari-8 satellite Advanced Himawari Imager (AHI) true-colour images, the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aerosol vertical profiles, the Aura satellite Ozone Monitoring Instrument (OMI) aerosol index images, and the ground-based Aerosol Robotic Network (AERONET) aerosol properties and the ground station particulate matter (PM) measurements. From the multi-satellite/sensor (AHI, CALIOP, and OMI) time series observations, the dust storm was found to originate from the Gobi Desert on the morning of 3 May 2017 and transport north-eastward to the Bering Sea, eastward to the Korean Peninsula and Japan, and southward to south-central China. The air quality in China deteriorated drastically: the PM₁₀ (PM < 10 µm in aerodynamic diameter) concentrations measured at some air quality stations located in northern China reached 4333 µg m⁻³. At the AOE_Baotou, Beijing, Xuzhou-CUMT, and Ussuriysk AERONET sites, the maximum aerosol optical depth values reached 2.96, 2.13, 2.87, and 0.65 and the extinction Ångström exponent dropped to 0.023, 0.068, 0.03, and 0.097, respectively. The dust storm also induced unusual aerosol spectral single-scattering albedo and volume size distribution.

One of four main focus areas of the PEEX initiative is to establish and sustain long-term, continuous, and comprehensive ground-based, airborne, and seaborne observation infrastructure together with satellite data. The Advanced Along-Track Scanning Radiometer (AATSR) aboard ENVISAT is used to observe the Earth in dual view. The AATSR data can be used to retrieve aerosol optical depth (AOD) over both land and ocean, which is an important parameter in the characterization of aerosol properties. In recent years, aerosol retrieval algorithms have been developed both over land and ocean, taking advantage of the features of dual view, which can help eliminate the contribution of Earth's surface to top-of-atmosphere (TOA) reflectance. The Aerosol_cci project, as a part of the Climate Change Initiative (CCI), provides users with three AOD retrieval algorithms for AATSR data, including the Swansea algorithm (SU), the ATSR-2ATSR dual-view aerosol retrieval algorithm (ADV), and the Oxford-RAL Retrieval of Aerosol and Cloud algorithm (ORAC). The validation team of the Aerosol-CCI project has validated AOD (both Level 2 and Level 3 products) and AE (Ångström Exponent) (Level 2 product only) against the AERONET data in a round-robin evaluation using the validation tool of the AeroCOM (Aerosol Comparison between Observations and Models) project. For the purpose of evaluating different performances of these three algorithms in calculating AODs over mainland China, we introduce ground-based data from CARSNET (China Aerosol Remote Sensing Network), which was designed for aerosol observations in China. Because China is vast in territory and has great differences in terms of land surfaces, the combination of the AERONET and CARSNET data can validate the L2 AOD products more comprehensively. The validation results show different performances of these products in 2007, 2008, and 2010. The SU algorithm performs very well over sites with different surface conditions in mainland China from March to October, but it slightly underestimates AOD over barren or sparsely vegetated surfaces in western China, with mean bias error (MBE) ranging from 0.05 to 0.10. The ADV product has the same precision with a low root mean square error (RMSE) smaller than 0.2 over most sites and the same error distribution as the SU product. The main limits of the ADV algorithm are underestimation and applicability; underestimation is particularly obvious over the sites of Datong, Lanzhou, and Urumchi, where the dominant land cover is grassland, with an MBE larger than 0.2, and the main aerosol sources are coal combustion and dust. The ORAC algorithm has the ability to retrieve AOD at different ranges, including high AOD (larger than 1.0); however, the stability deceases significantly with increasing AOD, especially when AOD > 1.0. In addition, the ORAC product is consistent with the CARSNET product in winter (December, January, and February), whereas other validation results lack matches during winter.