This paper reports on consolidated ground-based validation results of the atmospheric NO₂ data produced operationally since April 2018 by the TROPOspheric Monitoring Instrument (TROPOMI) on board of the ESA/EU Copernicus Sentinel-5 Precursor (S5P) satellite. Tropospheric, stratospheric, and total NO₂ column data from S5P are compared to correlative measurements collected from, respectively, 19 Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS), 26 Network for the Detection of Atmospheric Composition Change (NDACC) Zenith-Scattered-Light DOAS (ZSL-DOAS), and 25 Pandonia Global Network (PGN)/Pandora instruments distributed globally. The validation methodology gives special care to minimizing mismatch errors due to imperfect spatio-temporal co-location of the satellite and correlative data, e.g. by using tailored observation operators to account for differences in smoothing and in sampling of atmospheric structures and variability and photochemical modelling to reduce diurnal cycle effects. Compared to the ground-based measurements, S5P data show, on average, (i) a negative bias for the tropospheric column data, of typically −23 % to −37 % in clean to slightly polluted conditions but reaching values as high as −51 % over highly polluted areas; (ii) a slight negative median difference for the stratospheric column data, of about −0.2 Pmolec cm⁻², i.e. approx. −2 % in summer to −15 % in winter; and (iii) a bias ranging from zero to −50 % for the total column data, found to depend on the amplitude of the total NO₂ column, with small to slightly positive bias values for columns below 6 Pmolec cm⁻² and negative values above. The dispersion between S5P and correlative measurements contains mostly random components, which remain within mission requirements for the stratospheric column data (0.5 Pmolec cm⁻²) but exceed those for the tropospheric column data (0.7 Pmolec cm⁻²). While a part of the biases and dispersion may be due to representativeness differences such as different area averaging and measurement times, it is known that errors in the S5P tropospheric columns exist due to shortcomings in the (horizontally coarse) a priori profile representation in the TM5-MP chemical transport model used in the S5P retrieval and, to a lesser extent, to the treatment of cloud effects and aerosols. Although considerable differences (up to 2 Pmolec cm⁻² and more) are observed at single ground-pixel level, the near-real-time (NRTI) and offline (OFFL) versions of the S5P NO₂ operational data processor provide similar NO₂ column values and validation results when globally averaged, with the NRTI values being on average 0.79 % larger than the OFFL values.

Nitrogen dioxide (NO₂) at the ground level poses a serious threat to environmental quality and public health. This study developed a novel, artificial intelligence approach by integrating spatiotemporally weighted information into the missing extra-trees and deep forest models to first fill the satellite data gaps and increase data availability by 49% and then derive daily 1 km surface NO₂ concentrations over mainland China with full spatial coverage (100%) for the period 2019-2020 by combining surface NO₂ measurements, satellite tropospheric NO₂ columns derived from TROPOMI and OMI, atmospheric reanalysis, and model simulations. Our daily surface NO₂ estimates have an average out-of-sample (out-of-city) cross-validation coefficient of determination of 0.93 (0.71) and root-mean-square error of 4.89 (9.95) μg/m³. The daily seamless high-resolution and high-quality dataset "ChinaHighNO₂" allows us to examine spatial patterns at fine scales such as the urban-rural contrast. We observed systematic large differences between urban and rural areas (28% on average) in surface NO₂, especially in provincial capitals. Strong holiday effects were found, with average declines of 22 and 14% during the Spring Festival and the National Day in China, respectively. Unlike North America and Europe, there is little difference between weekdays and weekends (within ±1 μg/m³). During the COVID-19 pandemic, surface NO₂ concentrations decreased considerably and then gradually returned to normal levels around the 72nd day after the Lunar New Year in China, which is about 3 weeks longer than the tropospheric NO₂ column, implying that the former can better represent the changes in NO_x emissions.

In January 2013, February 2014, December 2015 and December 2016 to 10 January 2017, 12 persistent heavy aerosol pollution episodes (HPEs) occurred in Beijing, which received special attention from the public. During the HPEs, the precise cause of PM_2.5 explosive growth (mass concentration at least doubled in several hours to 10 h) is uncertain. Here, we analyzed and estimated relative contributions of boundary-layer meteorological factors to such growth, using ground and vertical meteorological data. Beijing HPEs are generally characterized by the transport stage (TS), whose aerosol pollution formation is primarily caused by pollutants transported from the south of Beijing, and the cumulative stage (CS), in which the cumulative explosive growth of PM_2.5 mass is dominated by stable atmospheric stratification characteristics of southerly slight or calm winds, near-ground anomalous inversion, and moisture accumulation. During the CSs, observed southerly weak winds facilitate local pollutant accumulation by minimizing horizontal pollutant diffusion. Established by TSs, elevated PM_2.5 levels scatter more solar radiation back to space to reduce near-ground temperature, which very likely causes anomalous inversion. This surface cooling by PM_2.5 decreases near-ground saturation vapor pressure and increases relative humidity significantly; the inversion subsequently reduces vertical turbulent diffusion and boundary-layer height to trap pollutants and accumulate water vapor. Appreciable near-ground moisture accumulation (relative humidity > 80 %) would further enhance aerosol hygroscopic growth and accelerate liquid-phase and heterogeneous reactions, in which incompletely quantified chemical mechanisms need more investigation. The positive meteorological feedback noted on PM_2.5 mass explains over 70 % of cumulative explosive growth.

The TROPOspheric Monitoring Instrument (TROPOMI), launched in October 2017 on board the Sentinel-5 Precursor (S5P) satellite, monitors the composition of the Earth's atmosphere at an unprecedented horizontal resolution as fine as 3.5 × 5.5 km². This paper assesses the performances of the TROPOMI formaldehyde (HCHO) operational product compared to its predecessor, the OMI (Ozone Monitoring Instrument) HCHO QA4ECV product, at different spatial and temporal scales. The parallel development of the two algorithms favoured the consistency of the products, which facilitates the production of long-term combined time series. The main difference between the two satellite products is related to the use of different cloud algorithms, leading to a positive bias of OMI compared to TROPOMI of up to 30 % in tropical regions. We show that after switching off the explicit correction for cloud effects, the two datasets come into an excellent agreement. For medium to large HCHO vertical columns (larger than 5 × 10¹⁵ molec. cm⁻²) the median bias between OMI and TROPOMI HCHO columns is not larger than 10 % (< 0.4 × 10¹⁵ molec. cm⁻²). For lower columns, OMI observations present a remaining positive bias of about 20 % (< 0.8 × 10¹⁵ molec. cm⁻²) compared to TROPOMI in midlatitude regions. Here, we also use a global network of 18 MAX-DOAS (multi-axis differential optical absorption spectroscopy) instruments to validate both satellite sensors for a large range of HCHO columns. This work complements the study by Vigouroux et al. (2020), where a global FTIR (Fourier transform infrared) network is used to validate the TROPOMI HCHO operational product. Consistent with the FTIR validation study, we find that for elevated HCHO columns, TROPOMI data are systematically low (−25 % for HCHO columns larger than 8 × 10¹⁵ molec. cm⁻²), while no significant bias is found for medium-range column values. We further show that OMI and TROPOMI data present equivalent biases for large HCHO levels. However, TROPOMI significantly improves the precision of the HCHO observations at short temporal scales and for low HCHO columns. We show that compared to OMI, the precision of the TROPOMI HCHO columns is improved by 25 % for individual pixels and by up to a factor of 3 when considering daily averages in 20 km radius circles. The validation precision obtained with daily TROPOMI observations is comparable to the one obtained with monthly OMI observations. To illustrate the improved performances of TROPOMI in capturing weak HCHO signals, we present clear detection of HCHO column enhancements related to shipping emissions in the Indian Ocean. This is achieved by averaging data over a much shorter period (3 months) than required with previous sensors (5 years) and opens new perspectives to study shipping emissions of VOCs (volatile organic compounds) and related atmospheric chemical interactions.

In the summer of 2017, heavy ozone pollution swamped most of the North China Plain (NCP), with the maximum regional average of daily maximum 8 h ozone concentration (MDA8) reaching almost 120 ppbv. In light of the continuing reduction of anthropogenic emissions in China, the underlying mechanisms for the occurrences of these regional extreme ozone episodes are elucidated from two perspectives: meteorology and biogenic emissions. The significant positive correlation between MDA8 ozone and temperature, which is amplified during heat waves concomitant with stagnant air and no precipitation, supports the crucial role of meteorology in driving high ozone concentrations. We also find that biogenic emissions are enhanced due to factors previously not considered. During the heavy ozone pollution episodes in June 2017, biogenic emissions driven by high vapor pressure deficit (VPD), land cover change and urban landscape yield an extra mean MDA8 ozone of 3.08, 2.79 and 4.74 ppbv, respectively, over the NCP, which together contribute as much to MDA8 ozone as biogenic emissions simulated using the land cover of 2003 and ignoring VPD and urban landscape. In Beijing, the biogenic emission increase due to urban landscape has a comparable effect on MDA8 ozone to the combined effect of high VPD and land cover change between 2003 and 2016. In light of the large effect of urban landscape on biogenic emission and the subsequent ozone formation, the types of trees may be cautiously selected to take into account of the biogenic volatile organic compound (BVOC) emission during the afforestation of cities. This study highlights the vital contributions of heat waves, land cover change and urbanization to the occurrence of extreme ozone episodes, with significant implications for ozone pollution control in a future when heat wave frequency and intensity are projected to increase under global warming.

Ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) and lidar measurements were performed in Shanghai, China, during May 2016 to investigate the vertical distribution of summertime atmospheric pollutants. In this study, vertical profiles of aerosol extinction coefficient, nitrogen dioxide (NO₂) and formaldehyde (HCHO) concentrations were retrieved from MAX-DOAS measurements using the Heidelberg Profile (HEIPRO) algorithm, while vertical distribution of ozone (O₃) was obtained from an ozone lidar. Sensitivity study of the MAX-DOAS aerosol profile retrieval shows that the a priori aerosol profile shape has significant influences on the aerosol profile retrieval. Aerosol profiles retrieved from MAX-DOAS measurements with Gaussian a priori profile demonstrate the best agreements with simultaneous lidar measurements and vehicle-based tethered-balloon observations among all a priori aerosol profiles. Tropospheric NO₂ vertical column densities (VCDs) measured with MAX-DOAS show a good agreement with OMI satellite observations with a Pearson correlation coefficient (R) of 0.95. In addition, measurements of the O₃ vertical distribution indicate that the ozone productions do not only occur at surface level but also at higher altitudes (about 1.1 km). Planetary boundary layer (PBL) height and horizontal and vertical wind field information were integrated to discuss the ozone formation at upper altitudes. The results reveal that enhanced ozone concentrations at ground level and upper altitudes are not directly related to horizontal and vertical transportation. Similar patterns of O₃ and HCHO vertical distributions were observed during this campaign, which implies that the ozone productions near the surface and at higher altitudes are mainly influenced by the abundance of volatile organic compounds (VOCs) in the lower troposphere.