Quantification of SO2 Emission Variations and the Corresponding Prediction Improvements Made by Assimilating Ground-Based Observations

In this research, a new time-resolved emission inversion system was developed to investigate variations in SO2 emission in China during the COVID-19 (Corona Virus Disease 2019) lockdown period based on a four-dimensional variational (4DVar) inversion method to dynamically optimize the SO2 inventory by assimilating the ground-based hourly observation data. The inversion results obtained were validated in the North China Plain (NCP). Two sets of experiments were carried out based on the original and optimized inventories during the pre-lockdown and lockdown period to quantify the SO2 emission variations and the corresponding prediction improvement. The SO2 emission changes due to the lockdown in the NCP were quantified by the differences in the averaged optimized inventories between the pre-lockdown and lockdown period. As a response to the lockdown control, the SO2 emissions were reduced by 20.1% on average in the NCP, with ratios of 20.7% in Beijing, 20.2% in Tianjin, 26.1% in Hebei, 18.3% in Shanxi, 19.1% in Shandong, and 25.9% in Henan, respectively. These were mainly attributed to the changes caused by the heavy industry lockdown in these areas. Compared to the model performance based on the original inventory, the optimized daily SO2 emission inventory significantly improved the model SO2 predictions during the lockdown period, with the correlation coefficient (R) value increasing from 0.28 to 0.79 and the root-mean-square error (RMSE) being reduced by more than 30%. Correspondingly, the performance of PM2.5 was slightly improved, with R-value increasing from 0.67 to 0.74 and the RMSE being reduced by 8% in the meantime. These statistics indicate the good optimization ability of the time-resolved emission inversion system

Influence of Urbanization on the Spatial Distribution of Associations Between Air Pollution and Mortality in Beijing, China

Abstract This study investigated the influence of urbanization on the intra‐city spatial distribution of associations between air pollution and mortality in Beijing, China. First, we utilized the generalized additive model to establish the exposure‐response associations of PM2.5, O3, with nonaccidental and cardiorespiratory mortality between urban and suburban areas. Second, we assessed district‐specific air pollution‐related mortality and analyzed how these associations were affected by the degree of urbanization. Finally, we analyzed the changes in air pollution‐related mortality before and after the enforcement of the Air Pollution Prevention and Control Action Plan (referred to as the Action Plan). The effect estimates of PM2.5 for nonaccidental mortality were 0.20% (95% CI: 0.12–0.28) in urban areas and 0.46% (95% CI: 0.35–0.58) in suburban areas per 10 μg/m3 increase in PM2.5 concentrations. The corresponding estimates of O3 were 0.13% (95% CI: −0.04–0.29) in urban areas and 0.34% (95% CI: 0.12–0.56) in suburban areas per 10 μg/m3 increase in O3 concentrations; however, the difference between the estimates of O3 in urban and suburban areas was not statistically significant. The district‐specific results suggested that the estimated risks increased along with urban vulnerability levels for the effects of PM2.5. Implementing the Action Plan reduced the mortality risks of PM2.5, but the risks of O3 increased in some districts. However, the difference in the estimates between the pre‐ and post‐emission reductions was not statistically significant. Our study indicated that populations living in less urbanized areas are more vulnerable to the adverse effects of air pollution in Beijing, particularly for PM2.5

Simulated Sensitivity of Ozone Generation to Precursors in Beijing during a High O-3 Episode

This study uses the WRF-Chem model combined with the empirical kinetic modeling method (EKMA curve) to study the compound pollution event in Beijing that happened in 13-23 May 2017. Sensitivity tests are conducted to analyze ozone sensitivity to its precursors, and to develop emission reduction measures. The results suggest that the model can accurately simulate the compound pollution process of photochemistry and haze. When VOCs and NOx were reduced by the same proportion, the effect of O-3 reduction at peak time was more obvious, and the effect during daytime was more significant than at night. The degree of change in ozone was peak time > daytime average. When reducing or increasing the ratio of precursors by 25% at the same time, the effect of reducing 25% VOCs on the average ozone concentration reduction was most significant. The degree of change in ozone decreased with increasing altitude, the location of the ozone maximum change shifted westward, and its range narrowed. As the altitude increases, the VOCs-limited zone decreases, VOCs sensitivity decreases, NOx sensitivity increases. The controlled area changed from near-surface VOCs-limited to high-altitude NOx-limited. Upon examining the EKMA curve, we have found that suburban and urban are sensitive to VOCs. The sensitivity tests indicate that when VOCs in suburban are reduced about 60%, the O-3-1h concentration could reach the standard, and when VOCs of the urban decreased by about 50%, the O-3-1h concentration could reach the standard. Thus, these findings could provide references for the control of compound air pollution in Beijing

Upper respiratory tract microbiota is associated with small airway function and asthma severity

Abstract Background Characteristics of airway microbiota might influence asthma status or asthma phenotype. Identifying the airway microbiome can help to investigate its role in the development of asthma phenotypes or small airway function. Methods Bacterial microbiota profiles were analyzed in induced sputum from 31 asthma patients and 12 healthy individuals from Beijing, China. Associations between small airway function and airway microbiomes were examined. Results Composition of sputum microbiota significantly changed with small airway function in asthma patients. Two microbiome-driven clusters were identified and characterized by small airway function and taxa that had linear relationship with small airway functions were identified. Conclusions Our findings confirm that airway microbiota was associated with small airway function in asthma patients