The tropospheric concentrations of nitrogen dioxide (NO₂) and formaldehyde (HCHO) have high spatio-temporal variability, and in situ observations of these trace gases are still scarce, especially in remote background areas. We made four similar circling journeys of mobile multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements in the Three Rivers' Source region over the Tibetan Plateau in summer (18–30 July) 2021 for the first time. The differential slant column densities (DSCDs) of NO₂ and HCHO were retrieved from the measured spectra, with very weak absorptions along the driving routes. The tropospheric NO₂ and HCHO vertical column densities (VCDs) were calculated from their DSCDs by the geometric approximation method, and they were further filtered to form reliable data sets by eliminating the influences of sunlight shelters and the vehicle's vibration and bumpiness. The observational data show that the tropospheric NO₂ and HCHO VCDs decreased with the increasing altitude of the driving route, whose background levels ± standard deviations were 0.40 ± 1.13×10¹⁵ molec. cm⁻² for NO₂ and $\mathrm{2.27}\pm \mathrm{1.66}\times {\mathrm{10}}^{\mathrm{15}}$ molec. cm⁻² for HCHO in July 2021 over the Three Rivers' Source region. The NO₂ VCDs show similar geographical distribution patterns between the different circling journeys, but the levels of the HCHO VCDs are different between the different circling journeys. The elevated NO₂ VCDs along the driving routes usually corresponded to enhanced transport emissions from the towns crossed. However, the spatial distributions of the HCHO VCDs depended significantly on natural and meteorological conditions, such as surface temperature. By comparing TROPOMI satellite products and mobile MAX-DOAS results, we found that TROPOMI NO₂ and HCHO VCDs have large positive offsets in the background atmosphere over the main area of the Three Rivers' Source. Our study provides valuable data sets and information of NO₂ and HCHO over the Tibetan Plateau, benefitting the scientific community in investigating the spatio-temporal evolution of atmospheric composition in the background atmosphere at high altitudes, validating and improving the satellite products over mountain terrains, and evaluating the model's ability to simulate atmospheric chemistry over the Tibetan Plateau.

Ambient exposure to particulate matter (PM) air pollution is known to have an adverse effect on public health worldwide. Rapid increase rates of economic and urbanization, industrial development, and environmental change in China have exacerbated the occurrence of air pollution. This study examines the temporal and spatial distribution of PM on national, regional and local scales in China during 2014–2016. The relationships between the PM_2.5 concentration rising rate (PMRR) and meteorological parameters (wind speed and wind direction) are discussed. The dataset of Air Quality Index (AQI), PM₁₀ (PM diameter < 10 $\mathsf{\mu }\mathrm{m}$ ) and PM_2.5 (PM diameter < 2.5 $\mathsf{\mu }\mathrm{m}$) were collected in 169, 369, and 367 cities in 2014, 2015, and 2016 over China, respectively. The results show that the air quality has been generally improved on the national scale, but deteriorated locally in areas such as the Feiwei Plain. The northwest China (NW) and Beijing-Tianjin-Hebei (BTH) regions are the worst areas of PM pollution, which are mainly manifested by the excessive PM₁₀ caused by blowing dust in spring in NW and the intensive emissions of PM_2.5 in winter in BTH. With the classified seven geographic regions, we demonstrate the significant spatial difference and seasonal variation of PM concentration and PM_2.5/PM₁₀ ratio, which indicate different emission sources. Furthermore, the dynamic analysis of the PM_2.5 pollution process in 11 large urban cities shows dramatic effects of wind speed and wind direction on the PM_2.5 loadings.

We develop a new inversion method which is suitable for linear and nonlinear emission source (ES) modeling, based on the three-dimensional decoupled direct (DDM-3D) sensitivity analysis module in the Community Multiscale Air Quality (CMAQ) model and the three-dimensional variational (3DVAR) data assimilation technique. We established the explicit observation operator matrix between the ES and receptor concentrations and the background error covariance (BEC) matrix of the ES, which can reflect the impacts of uncertainties of the ES on assimilation. Then we constructed the inversion model of the ES by combining the sensitivity analysis with 3DVAR techniques. We performed the simulation experiment using the inversion model for a heavy haze case study in the Beijing–Tianjin–Hebei (BTH) region during 27–30 December 2016. Results show that the spatial distribution of sensitivities of SO₂ and NO_x ESs to their concentrations, as well as the BEC matrix of ES, is reasonable. Using an a posteriori inversed ES, underestimations of SO₂ and NO₂ during the heavy haze period are remarkably improved, especially for NO₂. Spatial distributions of SO₂ and NO₂ concentrations simulated by the constrained ES were more accurate compared with an a priori ES in the BTH region. The temporal variations in regionally averaged SO₂, NO₂, and O₃ modeled concentrations using an a posteriori inversed ES are consistent with in situ observations at 45 stations over the BTH region, and simulation errors decrease significantly. These results are of great significance for studies on the formation mechanism of heavy haze, the reduction of uncertainties of the ES and its dynamic updating, and the provision of accurate “virtual” emission inventories for air-quality forecasts and decision-making services for optimization control of air pollution.

The aerosol optical depth (AOD) from satellites or ground-based sun photometer spectral observations has been widely used to estimate ground-level PM_2.5 concentrations by regression methods. The boundary layer height (BLH) is a popular factor in the regression model of AOD and PM_2.5, but its effect is often uncertain. This may result from the structures between the stable and convective BLHs and from the calculation methods of the BLH. In this study, the boundary layer is divided into two types of stable and convective boundary layer, and the BLH is calculated using different methods from radiosonde data and National Centers for Environmental Prediction (NCEP) reanalysis data for the station in Beijing, China during 2014–2015. The BLH values from these methods show significant differences for both the stable and convective boundary layer. Then, these BLHs were introduced into the regression model of AOD-PM_2.5 to seek the respective optimal BLH for the two types of boundary layer. It was found that the optimal BLH for the stable boundary layer is determined using the method of surface-based inversion, and the optimal BLH for the convective layer is determined using the method of elevated inversion. Finally, the optimal BLH and other meteorological parameters were combined to predict the PM_2.5 concentrations using the stepwise regression method. The results indicate that for the stable boundary layer, the optimal stepwise regression model includes the factors of surface relative humidity, BLH, and surface temperature. These three factors can significantly enhance the prediction accuracy of ground-level PM_2.5 concentrations, with an increase of determination coefficient from 0.50 to 0.68. For the convective boundary layer, however, the optimal stepwise regression model includes the factors of BLH and surface wind speed. These two factors improve the determination coefficient, with a relatively low increase from 0.65 to 0.70. It is found that the regression coefficients of the BLH are positive and negative in the stable and convective regression models, respectively. Moreover, the effects of meteorological factors are indeed related to the types of BLHs.