Aerosol optical depth (AOD) derived from satellite remote sensing is widely used to estimate surface PM_2.5 (dry mass concentration of particles with an in situ aerodynamic diameter smaller than 2.5 µm) concentrations. In this research, a two-stage spatio-temporal statistical model for estimating daily surface PM_2.5 concentrations in the Guanzhong Basin of China is proposed, using 6 km × 6 km AOD data available from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument as the main variable and meteorological factors, land-cover, and population data as auxiliary variables. The model is validated using a cross-validation method. The linear mixed effects (LME) model used in the first stage could be improved by using a geographically weighted regression (GWR) model or the generalized additive model (GAM) in the second stage, and the predictive capability of the GWR model is better than that of GAM. The two-stage spatio-temporal statistical model of LME and GWR successfully captures the temporal and spatial variations. The coefficient of determination (R²), the bias and the root-mean-squared prediction errors (RMSEs) of the model fitting to the two-stage spatio-temporal models of LME and GWR were 0.802, −0.378 µg/m³, and 12.746 µg/m³, respectively, and the model cross-validation results were 0.703, 1.451 µg/m³, and 15.731 µg/m³, respectively. The model prediction maps show that the topography has a strong influence on the spatial distribution of the PM_2.5 concentrations in the Guanzhong Basin, and PM_2.5 concentrations vary with the seasons. This method can provide reliable PM_2.5 predictions to reduce the bias of exposure assessment in air pollution and health research.

The Corona Virus Disease 2019 (COVID-19) appeared in Wuhan, China, at the end of 2019, spreading from there across China and within weeks across the whole world. In order to control the rapid spread of the virus, the Chinese government implemented a national lockdown policy. It restricted human mobility and non-essential economic activities, which, as a side effect, resulted in the reduction of the emission of pollutants and thus the improvement of the air quality in many cities in China. In this paper, we report on a study on the changes in air quality in the Guanzhong Basin during the COVID-19 lockdown period. We compared the concentrations of PM_2.5, PM₁₀, SO₂, NO₂, CO and O₃ obtained from ground-based monitoring stations before and after the COVID-19 outbreak. The analysis confirmed that the air quality in the Guanzhong Basin was significantly improved after the COVID-19 outbreak. During the emergency response period with the strictest restrictions (Level-1), the concentrations of PM_2.5, PM₁₀, SO₂, NO₂ and CO were lower by 37%, 30%, 29%, 52% and 33%, respectively, compared with those before the COVID-19 outbreak. In contrast, O₃ concentrations increased substantially. The changes in the pollutant concentrations varied between cities during the period of the COVID-19 pandemic. The highest O₃ concentration changes were observed in Xi’an, Weinan and Xianyang city; the SO₂ concentration decreased substantially in Tongchuan city; the air quality had improved the most in Baoji City. Next, to complement the sparsely distributed air quality ground-based monitoring stations, the geographic and temporally weighted regression (GTWR) model, combined with satellite observations of the aerosol optical depth (AOD) and meteorological factors was used to estimate the spatial and temporal distributions of PM_2.5 and PM₁₀ concentrations with a resolution of 6 km × 6 km before and after the COVID-19 outbreak. The model was validated by a comparison with ground-based observations from the air quality monitoring network in five cities in the Guanzhong Basin with excellent statistical metrics. For PM_2.5 and PM₁₀ the correlation coefficients R² were 0.86 and 0.80, the root mean squared errors (RMSE) were 11.03 µg/m³ and 14.87 µg/m³ and the biases were 0.19 µg/m³ and −0.27 µg/m³, which led to the conclusion that the GTWR model could be used to estimate the PM concentrations in locations where monitoring data were not available. Overall, the PM concentrations in the Guanzhong Basin decreased substantially during the lockdown period, with a strong initial decrease and a slower one thereafter, although the spatial distributions remained similar.

A wide range of mass gain in the inland of the Tibetan Plateau (TP) has been observed by the Gravity Recovery and Climate Experiment (GRACE). But there are still controversies on how to understand this phenomenon. In this paper, satellite geodesy data and hydrological models are combined and applied for extracting signals caused by tectonic processes in the TP during the period from 2003 to 2009. We find that in the inland of the TP there are three regions with a significant signal that should be attributed to tectonic processes. The three prominent mass signals, each located in the western, central, and eastern TP, are estimated to be at the rate of 1.47 ± 0.43, −0.85 ± 0.53, and 1.51 ± 0.56 cm/year, respectively, in equivalent water height. However, the rate caused by Earth surface deformation constrained by GPS is 0.33 ± 0.15/0.30 ± 0.10/0.14 ± 0.24 cm/year in equivalent water height, which cannot explain the whole tectonic signals and indicates that a significant mass change occurs beneath the crust, that is, the deformation of the Moho interface. We estimate the Moho change rate in the inland of the TP and find that the Moho interface is suffering an uneven deformation. The Moho is uprising at a rate of 18.1 ± 7.2 mm/year in the west and 21.7 ± 9.7 mm/year in the east of the TP, while there is an obvious deepening rate of −18.3 ± 8.6 mm/year in the central TP.