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.

The variation of NO₂ concentrations in mainland China is analyzed on different timescales, from decadal to weekly, using both satellite data and data from ground-based monitoring networks. TROPOMI (TROPOspheric Monitoring Instrument) data were used to study the spatial variations of tropospheric NO₂ vertical column densities (TVCDs) over the study area during 16–20 weeks after the Chinese Spring Festival (25 January 2020). These data were used to select 11 regions for more detailed analysis of the variation of NO₂ TVCDs on a decadal timescale. In this analysis, monthly and annual averaged NO₂ TVCDs derived from OMI (Ozone Monitoring Instrument) observations were used for the years 2011 to 2019. The results show the NO₂ TVCD trends for different regions, all decreasing in response to emission reduction policies but with a different onset and a possible halt of the decrease in recent years; trends and period in the south of the study area are different from those in the north. Variations of NO₂ TVCDs on shorter timescales, monthly and weekly, were analyzed using TROPOMI data. In addition, the variations of weekly-averaged ground-based NO₂ concentrations in 11 major cities were analyzed together with those for O₃ and PM_2.5. In particular these data were used to determine their effect on the air quality as expressed by the air quality index (AQI). For quantitative estimates, the use of weekly concentrations is more accurate than the use of monthly values, and the effects of long-term trends and their reversal needs to be taken into account for the separation of effects of the lockdown and the Spring Festival. Neglecting the possible reversal of the trends leads to overestimation of the lockdown effect in the south and underestimation in the north. The ground-based data confirm earlier reports, based on satellite observations, that the expected improvement of air quality due to the reduction of NO₂ concentrations was offset by the increase of the concentrations of O₃ and the different effects of the lockdown measures on PM_2.5, as well as effects of meteorological influences and heterogeneous chemistry. The AQI seems to be mostly influenced by PM_2.5 rather than NO₂. A qualitative comparison between time series of satellite and ground-based NO₂ observations shows both similarities and differences. The study further shows the different behaviors in city clusters in the north and south of China, as well as inland in the Sichuan and Guanzhong basins. Effects of other holidays and events are small, except in Beijing where the air quality in 2020 was notably better than in previous years. This study was undertaken for China, but the methodology and results have consequences for air quality studies in other areas, and part of the conclusions are generally applicable.

In this study, an improved geographically and temporally weighted regression (IGTWR) model for the estimation of hourly PM2.5 concentration data was applied over central and eastern China in 2017, based on Himawari-8 Advanced Himawari Imager (AHI) data. A generalized distance based on the longitude, latitude, day, hour, and land use type was constructed. AHI aerosol optical depth, surface relative humidity, and boundary layer height (BLH) data were used as independent variables to retrieve the hourly PM_2.5 concentrations at 1:00, 2:00, 3:00, 4:00, 5:00, 6:00, 7:00, and 8:00 UTC (Coordinated Universal Time). The model fitting and cross-validation performance were satisfactory. For the model fitting set, the correlation coefficient of determination (R²) between the measured and predicted PM_2.5 concentrations was 0.886, and the root-mean-square error (RMSE) of 437,642 samples was only 12.18 µg/m³. The tenfold cross-validation results of the regression model were also acceptable; the correlation coefficient R² of the measured and predicted results was 0.784, and the RMSE was 20.104 µg/m³, which is only 8 µg/m³ higher than that of the model fitting set. The spatial and temporal characteristics of the hourly PM_2.5 concentration in 2017 were revealed. The model also achieved stable performance under haze and dust conditions.

With the aim of guaranteeing the interests and health of tourists, this study comprehensively evaluates the suitability of the environment in different cities in China for tourism by considering the impacts of climate, vegetation and environmental factors on health. The Universal Thermal Climate Index (UTCI), total precipitation (PREC), Vegetation Leaf Area Index (LAI), concentration of particulate matter with a diameter of less than 2.5 μm (PM_2.5), and the downward UV radiation at the surface (UVB) are used as indices. In addition, the Delphi–AHP–TOPSIS algorithm is introduced and is used for the comprehensive evaluation of the environmental suitability of 684 cities in China for tourism in 2019. It is found that Chinese cities vary greatly in terms of the suitability of their environments for tourism. Among the factors that affect the environmental suitability, the LAI is important, with an increase in the LAI generally being advantageous. In contrast, an increase in the PM_2.5 concentration is a key factor that limits the environmental suitability of an area. Thus, urban landscaping and control of the atmospheric quality are essential and effective ways of improving the suitability of the environment of cities in China for tourists.

The condition of the tourism environment is the key factor that affects the tourism experience. A comprehensive evaluation of the tourism environmental suitability score is of great significance for guiding tourism planning and decision-making, improving the suitability score of the tourism environment in a targeted manner and guaranteeing the sustainable development of tourist cities. By adopting various indicators, such as the Universal Thermal Climate Index, the amount of precipitation, the vegetation index, the concentration of fine particulate matter in the atmosphere, and the ultraviolet radiation intensity index, this study quantifies the comfort level of weather, the weather impacts, the vegetation status, the atmospheric environment, ultraviolet radiation, and other key factors. Based on employing the AHP-TOPSIS algorithm, this study then conducts a comprehensive evaluation of China's tourism environmental suitability score and a comprehensive comparative analysis of the tourism environmental suitability scores of three typical tourist areas: the Beijing-Tianjin-Tangshan area, the Yangtze River Delta and the Pearl River Delta. The results show that the tourism environmental suitability score in China has obvious characteristics of spatial differentiation: the scores in East and South China are the highest while the score in the north-west inland area is the lowest. Among the three typical tourist areas, the Pearl River Delta region has the highest tourism environmental suitability score, followed by the Yangtze River Delta region, and the Beijing-Tianjin-Tangshan region has the lowest score. The northern regions of the Beijing-Tianjin-Tangshan area, the southern regions of the Yangtze River Delta and the surrounding areas of the Pearl River Delta are more suitable for the sustainable development of the tourism industry. PM2.5 is the main factor limiting the tourism environmental suitability scores in the Beijing-Tianjin-Tangshan area and the Yangtze River Delta, so atmospheric environment management will be an effective way to improve their tourism environmental suitability scores.