Uncertainty quantification of CO2 emissions from China's civil aviation industry to 2050.

To mitigate aviation's carbon emissions of the aviation industry, the following steps are vital: accurately quantifying the carbon emission path by considering uncertainty factors, including transportation demand in the post-COVID-19 pandemic period; identifying gaps between this path and emission reduction targets; and providing mitigation measures. Some mitigation measures that can be employed by China's civil aviation industry include the gradual realization of large-scale production of sustainable aviation fuels and transition to 100% sustainable and low-carbon sources of energy. This study identified the key driving factors of carbon emissions by using the Delphi Method and set scenarios that consider uncertainty, such as aviation development and emission reduction policies. A backpropagation neural network and Monte Carlo simulation were used to quantify the carbon emission path. The study results show that China's civil aviation industry can effectively help the country achieve its carbon peak and carbon neutrality goals. However, to achieve the net-zero carbon emissions goal of global aviation, China needs to reduce its emissions by approximately 82%-91% based on the optimal emission scenario. Thus, under the international net-zero target, China's civil aviation industry will face significant pressure to reduce its emissions. The use of sustainable aviation fuels is the best way to reduce aviation emissions by 2050. Moreover, in addition to the application of sustainable aviation fuel, it will be necessary to develop a new generation of aircraft introducing new materials and upgrading technology, implement additional carbon absorption measures, and make use of carbon trading markets to facilitate China's civil aviation industry's contribution to reduce climate change.

The interaction between black carbon and planetary boundary layer in the Yangtze River Delta from 2015 to 2020: Why O3 didn't decline so significantly as PM2.5.

Since the Air Pollution Prevention and Control Action Plan (air clean plan) issued in 2013, air quality has been in continuous improvement. The second stage of air clean plan since 2018 was focused on O3 controlling, but it still didn't decline so significantly as PM2.5. This study conducted a long-term observation on black carbon (BC) and utilized the observational data of other air pollutants (PM2.5, PM10, NO2, SO2, CO and O3), the meteorological elements and the vertical sounding data of PBL in Nanjing. In the daytime (08:00-20:00), PM2.5 kept decreasing from 2015 to 2020 at the rate of 4.8 µg⋅m-3⋅a-1, however, BC increased at the rate of 0.6 µg⋅m-3⋅a-1, which has led to the continuous growth of BC/PM2.5 (0.9%⋅a-1). However, during this period, O3 was relatively stable and, in 2020, it returned below its value in 2015 after slight increases in 2017 and 2018. Meanwhile, the average surface temperature had increased by around 1.0 °C during 2015-2019 at the rate of 0.3 °C⋅a-1. Also, the average height of the inversion layer had increased significantly by 494.0 and 176.7 m at 20:00 and 08:00, whose growth ratio was up to 57% and 25%, respectively. The above observation results have formed a set of chain reactions as follows. The growth of the surface BC caused the surface temperature to rise due to the increasing heating effect of BC. The continuous growth of the surface temperature made it easier for the PBL height to develop, which led to the lift of the inversion layer in the PBL and the larger atmospheric environment capacity. Ultimately, it is conducive to the diffusion of the near surface pollutants, thus helping reduce their concentrations, which offsets the increasing tendency of O3 and add to the decreasing trend of PM2.5. This phenomenon is the most remarkable in summer, with the fastest increasing rate of temperature (0.8 °C⋅a-1) and O3 (3.9 µg⋅m-3⋅a-1) during 2015-2019 (excluding 2020 to erase the great effect of COVID-19 lockdown on emissions).

Early detection of rumors based on source tweet-word graph attention networks.

The massively and rapidly spreading disinformation on social network platforms poses a serious threat to public safety and social governance. Therefore, early and accurate detection of rumors in social networks is of vital importance before they spread on a large scale. Considering the small-world property of social networks, the source tweet-word graph is decomposed from the global graph of rumors, and a rumor detection method based on graph attention network of source tweet-word graph is proposed to fully learn the structure of rumor propagation and the deep representation of text contents. Specifically, the proposed model can adequately capture the contextual semantic association representation of source tweets during the propagation and extract semantic features. For the data sparseness of the early stage of information dissemination, text attention mechanism based on opinion similarity can aggregate and capture more tweet propagation structure features to help improve the efficiency of early detection of rumors. Through the analysis of the experimental results on real public datasets, the rumor detection performance of the proposed method is better than that of other baseline methods. Especially in the early rumor detection tasks, the proposed method can detect rumors with an accuracy of nearly 90% in the early stage of information dissemination. And it still has good robustness with noise interference.

Coronavirus transmissible gastroenteritis virus antagonizes the antiviral effect of the microRNA miR-27b via the IRE1 pathway.

Although the functional parameters of microRNAs (miRNAs) have been explored to some extent, the roles of these molecules in coronavirus infection and the regulatory mechanism of miRNAs in virus infection are still unclear. Transmissible gastroenteritis virus (TGEV) is an enteropathgenic coronavirus and causes high morbidity and mortality in suckling piglets. Here, we demonstrated that microRNA-27b-3p (miR-27b-3p) suppressed TGEV replication by directly targeting porcine suppressor of cytokine signaling 6 (SOCS6), while TGEV infection downregulated miR-27b-3p expression in swine testicular (ST) cells and in piglets. Mechanistically, the decrease of miR-27b-3p expression during TGEV infection was mediated by the activated inositol-requiring enzyme 1 (IRE1) pathway of the endoplasmic reticulum (ER) stress. Further studies showed that when ER stress was induced by TGEV, IRE1 acted as an RNase activated by autophosphorylation and unconventionally spliced mRNA encoding a potent transcription factor, X-box-binding protein 1 (Xbp1s). Xbp1s inhibited the transcription of miR-27 and ultimately reduced the production of miR-27b-3p. Therefore, our findings indicate that TGEV inhibits the expression of an anti-coronavirus microRNA through the IRE1 pathway and suggest a novel way in which coronavirus regulates the host cell response to infection.

Characteristics of air quality in different climatic zones of China during the COVID-19 lockdown.

The diverse climate types and the complex anthropogenic source emissions in China lead to the great regional differences of air pollution mechanisms. The COVID-19 lockdown has given us a precious opportunity to understand the effect of weather conditions and anthropogenic sources on the distribution of air pollutants in different climate zones. In this study, to understand the impact of meteorological and socio-economic factors on air pollution during COVID-19 lockdown, we divided 358 Chinese cities into eight climate regions. Temporal, spatial and diurnal variations of six major air pollutants from January 1 to April 18, 2020 were analyzed. The differences in the characteristics of air pollutants in different climate zones were obvious. PM2.5 reduced by 59.0%-64.2% in cold regions (North-East China (NEC) and North-Western (NW)), while O3 surged by 99.0%-99.9% in warm regions (Central South (CS) and Southern Coast (SC)). Diurnal variations of atmospheric pollutants were also more prominent in cold regions. Moreover, PM2.5, PM10, CO and SO2 showed more prominent reductions (20.5%-64.2%) in heating regions (NEC, NW, NCP and MG) than no-heating regions (0.8%-48%). Climate has less influence on NO2, which dropped by 41.2%-57.1% countrywide during the lockdown. The influences of weather conditions on the atmospheric pollutants in different climate zones were different. The wind speed was not the primary reason for the differences in air pollutants in different climate zones. Temperature, precipitation, and air pollution emissions led to prominent regional differences in air pollutants throughout the eight climates. The effect of temperature on PM, SO2, CO, and NO2 varied obviously with the latitude, at which condition temperature was negatively correlated to PM, SO2, CO, and NO2 in the north but positively in the south. The temperature was positively correlated to ozone in different climate zones, and the correlation was the highest in NEC and the lowest in SC. The rainfall has a strong removal effect on atmospheric pollutants in the climate regions with more precipitation, but it increases the pollutant concentrations in the climate regions with less precipitation. In regions with more emission sources, air pollutants experienced more significant variations and returned to pre-lockdown levels earlier.

Impact of urbanization on air quality in the Yangtze River Delta during the COVID-19 lockdown in China

The urban agglomeration of Yangtze River Delta (YRD) is symbol of China's rapid urbanization during the past decades. Urbanization can significantly impact land-cover properties, surface heating, and emissions of air pollutants. To control the spread of COVID-19, China imposed very rigorous restrictions, leading to dramatic reductions in air pollutants (except O3) from satellite and ground-based data. As such, inter-transportation of air pollutants was weak during the lockdown, which was conducive to discuss the impacts of urbanization on the air quality. During the lockdown, the rates of surface PM2.5, PM10, SO2, NO2 and CO reductions in different urban types ranged from 6.6% to 62.4% in the YRD. Urbanization exerted great impacts on the pollutant variations in urban agglomerations despite such large decreases in primary pollution in YRD. Lower values of AOD and tropospheric NO2 columns were noticeably observed over large cities during the lockdown. The extents of surface PM, SO2, NO2 and CO reductions in large cities (first-tier and second-tier) were found to be larger (4.7%-10.6%) than those in small-medium cities (third-tier and fourth-tier) during the lockdown, which was also the case for the extent of the increase (33.0% - 53.0%) in O3. PCA analysis revealed that the PM decreases in large cities made greater improvement in the air quality compared with the small cities during lockdown, while the urbanization had non-obvious influence on the photochemical reactions. It is imperative to adopt policies and programs to mitigate the air pollution in urban agglomerations in the fast urbanization process.

Air pollutant variations in Suzhou during the 2019 novel coronavirus (COVID-19) lockdown of 2020: High time-resolution measurements of aerosol chemical compositions and source apportionment.

To control the spread of the 2019 novel coronavirus (COVID-19), China imposed rigorous restrictions, which resulted in great reductions in pollutant emissions. This study examines the characteristics of air pollutants, including PM2.5 (particles with aerodynamic diameters < 2.5 µm), gas pollutants, water-soluble ions (WSIs), black carbon (BC) and elements, as well as the source apportionment of PM2.5 in Suzhou before, during and after the Chinese New Year (CNY) holiday of 2020 (when China was under an unprecedented state of lockdown to restrict the COVID-19 outbreak). Compared to those before CNY, PM2.5, BC, SNA (sulfate, nitrate and ammonium), other ions, elements, and NO2 and CO mass concentrations decreased by 9.9%-64.0% during CNY. The lockdown policy had strong (weak) effects on the diurnal variations in aerosol chemical compositions (gas pollutants). Compared to those before CNY, source concentrations and contributions of vehicle exhaust during CNY decreased by 72.9% and 21.7%, respectively. In contrast, increased contributions from coal combustion and industry were observed during CNY, which were recorded to be 2.9 and 1.7 times higher than those before CNY, respectively. This study highlights that the lockdown policy that was imposed in Suzhou during CNY not only reduced the mass concentrations of air pollutants but also modified their diurnal variations and the source contributions of PM2.5, which revealed the complex responses of PM2.5 sources to the rare, low emissions of anthropogenic pollutants that occurred during the COVID-19 lockdown.

Characterization of the aerosol chemical composition during the COVID-19 lockdown period in Suzhou in the Yangtze River Delta, China.

To control the spread of COVID-19, rigorous restrictions have been implemented in China, resulting in a great reduction in pollutant emissions. In this study, we evaluated the air quality in the Yangtze River Delta during the COVID-19 lockdown period using satellite and ground-based data, including particle matter (PM), trace gases, water-soluble ions (WSIs) and black carbon (BC). We found that the impacts of lockdown policy on air quality cannot be accurately assessed using MODIS aerosol optical depth (AOD) data, whereas the tropospheric nitrogen dioxide (NO2) vertical column density can well reflect the influences of these restrictions on human activities. Compared to the pre-COVID period, the PM2.5, PM10, NO2, carbon monoxide (CO), BC and WSIs during the lockdown in Suzhou were observed to decrease by 37.2%, 38.3%, 64.5%, 26.1%, 53.3% and 58.6%, respectively, while the sulfur dioxide (SO2) and ozone (O3) increased by 1.5% and 104.7%. The WSIs ranked in the order of NO3- > NH4+ > SO42- > Cl- > Ca2+ > K+ > Mg2+ > Na+ during the lockdown period. By comparisons with the ion concentrations during the pre-COVID period, we found that the ions NO3-, NH4+, SO42-, Cl-, Ca2+, K+ and Na+ decreased by 66.3%, 48.8%, 52.9%, 56.9%, 57.9% and 76.3%, respectively, during the lockdown, in contrast to Mg2+, which increased by 30.2%. The lockdown policy was found to have great impacts on the diurnal variations of Cl-, SO42-, Na+ and Ca2+.

Importance of meteorology in air pollution events during the city lockdown for COVID-19 in Hubei Province, Central China.

Compared with the 21-year climatological mean over the same period during 2000-2020, the aerosol optical depth (AOD) and Angstrom exponent (AE) during the COVID-19 lockdown (January 24-February 29, 2020) decreased and increased, respectively, in most regions of Central-Eastern China (CEC). The AOD (AE) values decreased (increased) by 39.2% (29.4%) and 31.0% (45.3%) in Hubei and Wuhan, respectively, because of the rigorous restrictions. These inverse changes reflected the reduction of total aerosols in the air and the contribution of the increase in fine-mode particles during the lockdown. The surface PM2.5 had a distinct spatial distribution over CEC during the lockdown, with high concentrations in North China and East China. In particular, relatively high PM2.5 concentrations were notable in the lower flatlands of Hubei Province in Central China, where six PM2.5 pollution events were identified during the lockdown. Using the observation data and model simulations, we found that 50% of the pollution episodes were associated with the long-range transport of air pollutants from upstream CEC source regions, which then converged in the downstream Hubei receptor region. However, local pollution was dominant for the remaining episodes because of stagnant meteorological conditions. The long-range transport of air pollutants substantially contributed to PM2.5 pollution in Hubei, reflecting the exceptional importance of meteorology in regional air quality in China.