Modeling particulate nitrate in China: Current findings and future directions.

Particulate nitrate (pNO3) is now becoming the principal component of PM2.5 during severe winter haze episodes in many cities of China. To gain a comprehensive understanding of the key factors controlling pNO3 formation and driving its trends, we reviewed the recent pNO3 modeling studies which mainly focused on the formation mechanism and recent trends of pNO3 as well as its responses to emission controls in China. The results indicate that although recent chemical transport models (CTMs) can reasonably capture the spatial-temporal variations of pNO3, model-observation biases still exist due to large uncertainties in the parameterization of dinitrogen pentoxide (N2O5) uptake and ammonia (NH3) emissions, insufficient heterogeneous reaction mechanism, and the predicted low sulfate concentrations in current CTMs. The heterogeneous hydrolysis of N2O5 dominates nocturnal pNO3 formation, however, the contribution to total pNO3 varies among studies, ranging from 21.0% to 51.6%. Moreover, the continuously increasing PM2.5 pNO3 fraction in recent years is mainly due to the decreased sulfur dioxide emissions, the enhanced atmospheric oxidation capacity (AOC), and the weakened nitrate deposition. Reducing NH3 emissions is found to be the most effective control strategy for mitigating pNO3 pollution in China. This review suggests that more field measurements are needed to constrain the parameterization of heterogeneous N2O5 and nitrogen dioxide (NO2) uptake. Future studies are also needed to quantify the relationships of pNO3 to AOC, O3, NOx, and volatile organic compounds (VOCs) in different regions of China under different meteorological conditions. Research on multiple-pollutant control strategies involving NH3, NOX, and VOCs is required to mitigate pNO3 pollution, especially during severe winter haze events.

Self-employment as an Alternative to Labor Market Bifurcation: The Role of Human and Ethnic Social Capital

The COVID-19 outbreak that began in 2020 has changed human activities and thus reduced anthropogenic carbon emissions in most parts of the world. To accurately study the impact of the COVID-19 pandemic on changes in atmospheric XCO2 concentrations, a data fusion method called High Accuracy Surface Modeling (HASM) is applied using the CO2 simulation from GEOS-Chem as the driving field and GOSAT XCO2 observations as the accuracy control conditions to obtain continuous spatiotemporal global XCO2 concentrations. Cross-validation shows that using High Accuracy Surface Modeling greatly improves the mean absolute error and root mean square error of the XCO2 data compared with those for GEOS-Chem simulation data before fusion, and the R (2) is also increased from 0.54 to 0.79 after fusion. Moreover, OCO-2/OCO-3 XCO2 observational data verify that the fused XCO2 data achieve a lower MAE and RMSE. Spatiotemporal analysis shows that the global XCO2 concentration exhibited no obvious trend before or after the COVID-19 outbreak, but the growth of global and terrestrial atmospheric XCO2 in 2020 can reflect the impact of the COVID-19 pandemic;that is, the rapid growth in terrestrial atmospheric XCO2 observed before 2019 slowed, and high-speed growth resumed in 2021. Finally, obvious differences in the pattern of XCO2 growth are found on different continents.

Modeling Surface Air Pollution with Reduced Emissions during the COVID-19 Pandemic Using CHIMERE and COSMO-ART Chemical Transport Models

The results of numerical modeling of air pollution using CHIMERE and COSMO-ART chemical transport models are presented. The modeling was performed according to the scenarios of the 50–60% reduction of emissions from anthropogenic sources in the Moscow region during the period of March–July 2020. Scenario calculations of pollutant concentrations were compared with baseline simulations using regionally adapted inventory of anthropogenic pollutant emissions to the atmosphere. The most significant decrease in the concentrations of NO2 and CO was reproduced by the models when emissions from two sectoral sources (vehicles and nonindustrial plants) were reduced. The PM10 drop was mostly influenced by the reduction of emissions from industrial combustion. With the total reduction of emissions from anthropogenic sources as compared to the baseline calculations, the pollutant concentration decreased by 44–54% for NO2, by 38–44% for CO, and by 26–39% for PM10. This generally coincides with the quantitative estimates of the pollution level drop obtained by other authors. The greatest effect of reducing pollutant emissions into the atmosphere was found during the episodes of adverse weather conditions for air purification, when the simulated and observed pollution level increases by 3–5 times as compared to the conditions of intense pollutant dispersion.