Identification of volatile organic compound emissions from anthropogenic and biogenic sources based on satellite observation of formaldehyde and glyoxal.

Anthropogenic volatile organic compounds (VOCs) are serious pollutants in the atmosphere because of their toxicity and as precursors of secondary organic aerosols and ozone pollution. Although in-situ measurements provide accurate information on VOCs, their spatial coverage is limited and insufficient. In this study, we provide a global perspective for identifying anthropogenic VOC emission sources through the ratio of glyoxal to formaldehyde (RGF) based on satellite observations. We assessed typical cities and polluted areas in the mid latitudes and found that some Asian cities had higher anthropogenic VOC emissions than cities in Europe and America. For heavily polluted areas, such as the Yangtze River Delta (YRD), the areas dominated by anthropogenic VOCs accounted for 23 % of the total study areas. During the COVID-19 pandemic, a significant decline in RGF values was observed in the YRD and western United States, corresponding to a reduction in anthropogenic VOC emissions. Furthermore, developing countries appeared to have higher anthropogenic VOC emissions than developed countries. These observations could contribute to optimising industrial structures and setting stricter pollution standards to reduce anthropogenic VOCs in developing countries.

Variability of PM2.5 and O3 concentrations and their driving forces over Chinese megacities during 2018-2020

Recently, air pollution especially fine particulate matters (PM2.5) and ozone (O3) has become a severe issue in China. In this study, we first characterized the temporal trends of PM2.5 and O3 for Beijing, Guangzhou, Shanghai, and Wuhan respectively during 2018-2020. The annual mean PM2.5 has decreased by 7.82%-33.92%, while O3 concentration showed insignificant variations by -6.77%-4.65% during 2018-2020. The generalized additive models (GAMs) were implemented to quantify the contribution of individual meteorological factors and their gas precursors on PM2.5 and O3. On a short-term perspective, GAMs modeling shows that the daily variability of PM2.5 concentration is largely related to the variation of precursor gases (R = 0.67-0.90), while meteorological conditions mainly affect the daily variability of O3 concentration (R = 0.65-0.80) during 2018-2020. The impact of COVID-19 lockdown on PM2.5 and O3 concentrations were also quantified by using GAMs. During the 2020 lockdown, PM2.5 decreased significantly for these megacities, yet the ozone concentration showed an increasing trend compared to 2019. The GAMs analysis indicated that the contribution of precursor gases to PM2.5 and O3 changes is 3-8 times higher than that of meteorological factors. In general, GAMs modeling on air quality is helpful to the understanding and control of PM2.5 and O3 pollution in China.

Vast ecosystem disturbance in a warming climate may jeopardize our climate goal of reducing CO2: a case study for megafires in the Australian 'black summer'.

A warming climate is one of the most important driving forces of intensified wildfires globally. The unprecedented wildfires broke out in the Australian 'Black Summer' (November 2019-February 2020), which released massive heat, gases, and particles into the atmosphere. The total carbon dioxide (CO2) emissions from wildfires were estimated at ∼963 million tons by using a top-down approach based on direct satellite measurements of CO2 and fire radiative power. The fire emissions have led to an approximately 50-80 folds increase in total CO2 emission in Australia compared with the similar seasons of 2014-2019. The excess CO2 from wildfires has offset almost half of the global anthropogenic CO2 emission reductions due to the Corona Virus Disease 2019 in 2020. When the wildfires were intense in December 2019, they caused a 1.48 watts per square meter additional positive radiative forcing above the monthly average in Australia and the vicinity. Our findings demonstrate that vast ecosystem disturbance in a warming climate can strongly influence the global carbon cycle and hamper our climate goal of reducing CO2.

Impacts of TROPOMI-Derived NO X Emissions on NO2 and O3 Simulations in the NCP during COVID-19.

NO2 and O3 simulations have great uncertainties during the COVID-19 epidemic, but their biases and spatial distributions can be improved with NO2 assimilations. This study adopted two top-down NO X inversions and estimated their impacts on NO2 and O3 simulation for three periods: the normal operation period (P1), the epidemic lockdown period following the Spring Festival (P2), and back to work period (P3) in the North China Plain (NCP). Two TROPOspheric Monitoring Instrument (TROPOMI) NO2 retrievals came from the Royal Netherlands Meteorological Institute (KNMI) and the University of Science and Technology of China (USTC), respectively. Compared to the prior NO X emissions, the two TROPOMI posteriors greatly reduced the biases between simulations with in situ measurements (NO2 MREs: prior 85%, KNMI -27%, USTC -15%; O3 MREs: Prior -39%, KNMI 18%, USTC 11%). The NO X budgets from the USTC posterior were 17-31% higher than those from the KNMI one. Consequently, surface NO2 levels constrained by USTC-TROPOMI were 9-20% higher than those by the KNMI one, and O3 is 6-12% lower. Moreover, USTC posterior simulations showed more significant changes in adjacent periods (surface NO2: P2 vs P1, -46%, P3 vs P2, +25%; surface O3: P2 vs P1, +75%, P3 vs P2, +18%) than the KNMI one. For the transport flux in Beijing (BJ), the O3 flux differed by 5-6% between the two posteriori simulations, but the difference of NO2 flux between P2 and P3 was significant, where the USTC posterior NO2 flux was 1.5-2 times higher than the KNMI one. Overall, our results highlight the discrepancies in NO2 and O3 simulations constrained by two TROPOMI products and demonstrate that the USTC posterior has lower bias in the NCP during COVD-19.

Ground-Level NO2 Surveillance from Space Across China for High Resolution Using Interpretable Spatiotemporally Weighted Artificial Intelligence.

Nitrogen dioxide (NO2) at the ground level poses a serious threat to environmental quality and public health. This study developed a novel, artificial intelligence approach by integrating spatiotemporally weighted information into the missing extra-trees and deep forest models to first fill the satellite data gaps and increase data availability by 49% and then derive daily 1 km surface NO2 concentrations over mainland China with full spatial coverage (100%) for the period 2019-2020 by combining surface NO2 measurements, satellite tropospheric NO2 columns derived from TROPOMI and OMI, atmospheric reanalysis, and model simulations. Our daily surface NO2 estimates have an average out-of-sample (out-of-city) cross-validation coefficient of determination of 0.93 (0.71) and root-mean-square error of 4.89 (9.95) µg/m3. The daily seamless high-resolution and high-quality dataset "ChinaHighNO2" allows us to examine spatial patterns at fine scales such as the urban-rural contrast. We observed systematic large differences between urban and rural areas (28% on average) in surface NO2, especially in provincial capitals. Strong holiday effects were found, with average declines of 22 and 14% during the Spring Festival and the National Day in China, respectively. Unlike North America and Europe, there is little difference between weekdays and weekends (within ±1 µg/m3). During the COVID-19 pandemic, surface NO2 concentrations decreased considerably and then gradually returned to normal levels around the 72nd day after the Lunar New Year in China, which is about 3 weeks longer than the tropospheric NO2 column, implying that the former can better represent the changes in NOx emissions.

Progressive assembly of multi-domain protein structures from cryo-EM density maps

Progress in cryo-electron microscopy has provided the potential for large-size protein structure determination. However, the success rate for solving multi-domain proteins remains low because of the difficulty in modelling inter-domain orientations. Here we developed domain enhanced modeling using cryo-electron microscopy (DEMO-EM), an automatic method to assemble multi-domain structures from cryo-electron microscopy maps through a progressive structural refinement procedure combining rigid-body domain fitting and flexible assembly simulations with deep-neural-network inter-domain distance profiles. The method was tested on a large-scale benchmark set of proteins containing up to 12 continuous and discontinuous domains with medium- to low-resolution density maps, where DEMO-EM produced models with correct inter-domain orientations (template modeling score (TM-score) >0.5) for 97% of cases and outperformed state-of-the-art methods. DEMO-EM was applied to the severe acute respiratory syndrome coronavirus 2 genome and generated models with average TM-score and root-mean-square deviation of 0.97 and 1.3 Å, respectively, with respect to the deposited structures. These results demonstrate an efficient pipeline that enables automated and reliable large-scale multi-domain protein structure modelling from cryo-electron microscopy maps.

Spatiotemporal neural network for estimating surface NO2 concentrations over north China and their human health impact.

Atmospheric nitrogen dioxide (NO2) is an important reactive gas pollutant harmful to human health. The spatiotemporal coverage provided by traditional NO2 monitoring methods is insufficient, especially in the suburban and rural areas of north China, which have a high population density and experience severe air pollution. In this study, we implemented a spatiotemporal neural network (STNN) model to estimate surface NO2 from multiple sources of information, which included satellite and in situ measurements as well as meteorological and geographical data. The STNN predicted NO2 with high accuracy, with a coefficient of determination (R2) of 0.89 and a root mean squared error of 5.8 µg/m3 for sample-based 10-fold cross-validation. Based on the surface NO2 concentration determined by the STNN, we analyzed the spatial distribution and temporal trends of NO2 pollution in north China. We found substantial drops in surface NO2 concentrations ranging between 9.1% and 33.2% for large cities during the 2020 COVID-19 lockdown when compared to those in 2019. Moreover, we estimated the all-cause deaths attributed to NO2 exposure at a high spatial resolution of about 1 km, with totals of 6082, 4200, and 18,210 for Beijing, Tianjin, and Hebei Provinces in 2020, respectively. We observed remarkable regional differences in the health impacts due to NO2 among urban, suburban, and rural areas. Generally, the STNN model could incorporate spatiotemporal neighboring information and infer surface NO2 concentration with full coverage and high accuracy. Compared with machine learning regression techniques, STNN can effectively avoid model overfitting and simultaneously consider both spatial and temporal correlations of input variables using deep convolutional networks with residual blocks. The use of the proposed STNN model, as well as the surface NO2 dataset, can benefit air quality monitoring, forecasting, and health burden assessments.

Variability of PM2.5 and O3 concentrations and their driving forces over Chinese megacities during 2018-2020

Recently, air pollution especially fine particulate matters (PM2.5) and ozone (O3) has become a severe issue in China. In this study, we first characterized the temporal trends of PM2.5 and O3 for Beijing, Guangzhou, Shanghai, and Wuhan respectively during 2018-2020. The annual mean PM2.5 has decreased by 7.82%-33.92%, while O3 concentration showed insignificant variations by -6.77%-4.65% during 2018-2020. The generalized additive models (GAMs) were implemented to quantify the contribution of individual meteorological factors and their gas precursors on PM2.5 and O3. On a short-term perspective, GAMs modeling shows that the daily variability of PM2.5 concentration is largely related to the variation of precursor gases (R = 0.67-0.90), while meteorological conditions mainly affect the daily variability of O3 concentration (R = 0.65-0.80) during 2018-2020. The impact of COVID-19 lockdown on PM2.5 and O3 concentrations were also quantified by using GAMs. During the 2020 lockdown, PM2.5 decreased significantly for these megacities, yet the ozone concentration showed an increasing trend compared to 2019. The GAMs analysis indicated that the contribution of precursor gases to PM2.5 and O3 changes is 3-8 times higher than that of meteorological factors. In general, GAMs modeling on air quality is helpful to the understanding and control of PM2.5 and O3 pollution in China.

Variations of Urban NO2 Pollution during the COVID-19 Outbreak and Post-Epidemic Era in China: A Synthesis of Remote Sensing and In Situ Measurements

Since the COVID-19 outbreak in 2020, China’s air pollution has been significantly affected by control measures on industrial production and human activities. In this study, we analyzed the temporal variations of NO2 concentrations during the COVID-19 lockdown and post-epidemic era in 11 Chinese megacities by using satellite and ground-based remote sensing as well as in situ measurements. The average satellite tropospheric vertical column density (TVCD) of NO2 by TROPOMI decreased by 39.2–71.93% during the 15 days after Chinese New Year when the lockdown was at its most rigorous compared to that of 2019, while the in situ NO2 concentration measured by China National Environmental Monitoring Centre (CNEMC) decreased by 42.53–69.81% for these cities. Such differences between both measurements were further investigated by using ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) remote sensing of NO2 vertical profiles. For instance, in Beijing, MAX-DOAS NO2 showed a decrease of 14.19% (versus 18.63% by in situ) at the ground surface, and 36.24% (versus 36.25% by satellite) for the total tropospheric column. Thus, vertical discrepancies of atmospheric NO2 can largely explain the differences between satellite and in situ NO2 variations. In the post-epidemic era of 2021, satellite NO2 TVCD and in situ NO2 concentrations decreased by 10.42–64.96% and 1.05–34.99% compared to 2019, respectively, possibly related to the reduction of the transportation industry. This study reveals the changes of China’s urban NO2 pollution in the post-epidemic era and indicates that COVID-19 had a profound impact on human social activities and industrial production.

Improved Anthropogenic SO2 Retrieval from High-Spatial-Resolution Satellite and its Application during the COVID-19 Pandemic.

Sulfur dioxide (SO2) measured by satellites is widely used to estimate anthropogenic emissions. The Sentinel-5 Precursor (S-5P) operational SO2 product is overestimated compared to the ground-based multiaxis differential optical absorption spectroscopy (MAX-DOAS) measurements in China and shows an opposite variation to the surface measurements, which limits the application of TROPOspheric monitoring instrument (TROPOMI) products in emissions research. Radiometric calibration, a priori profiles, and fitting windows might cause the overestimation of S-5P operational SO2 product. Here, we improve the optimal-estimation-based algorithm through several calibration methods. The improved retrieval agrees reasonably well with the ground-based measurements (R > 0.70, bias <13.7%) and has smaller biases (-28.9%) with surface measurements over China and India. It revealed that the SO2 column in March 2020 decreased by 51.6% compared to March 2019 due to the lockdown for curbing the spread of the COVID-19 pandemic, and there was a decrease of 50% during the lockdown than those after the lockdown, similar to the surface measurement trend, while S-5P operational SO2 product showed an unrealistic increase of 19%. In India, the improved retrieval identified obvious "hot spots" and observed a 30% decrease of SO2 columns during the lockdown.

Identifying the Zoonotic Origin of SARS-CoV-2 by Modeling the Binding Affinity between the Spike Receptor-Binding Domain and Host ACE2.

Despite considerable research progress on SARS-CoV-2, the direct zoonotic origin (intermediate host) of the virus remains ambiguous. The most definitive approach to identify the intermediate host would be the detection of SARS-CoV-2-like coronaviruses in wild animals. However, due to the high number of animal species, it is not feasible to screen all the species in the laboratory. Given that binding to ACE2 proteins is the first step for the coronaviruses to invade host cells, we propose a computational pipeline to identify potential intermediate hosts of SARS-CoV-2 by modeling the binding affinity between the Spike receptor-binding domain (RBD) and host ACE2. Using this pipeline, we systematically examined 285 ACE2 variants from mammals, birds, fish, reptiles, and amphibians, and found that the binding energies calculated for the modeled Spike-RBD/ACE2 complex structures correlated closely with the effectiveness of animal infection as determined by multiple experimental data sets. Built on the optimized binding affinity cutoff, we suggest a set of 96 mammals, including 48 experimentally investigated ones, which are permissive to SARS-CoV-2, with candidates from primates, rodents, and carnivores at the highest risk of infection. Overall, this work not only suggests a limited range of potential intermediate SARS-CoV-2 hosts for further experimental investigation, but also, more importantly, it proposes a new structure-based approach to general zoonotic origin and susceptibility analyses that are critical for human infectious disease control and wildlife protection.

Opposite impact of emission reduction during the COVID-19 lockdown period on the surface concentrations of PM2.5 and O3 in Wuhan, China.

To prevent the spread of the COVID-19 epidemic, the Chinese megacity Wuhan has taken emergent lockdown measures starting on January 23, 2020. This provided a natural experiment to investigate the response of air quality to such emission reductions. Here, we decoupled the influence of meteorological and non-meteorological factors on main air pollutants using generalized additive models (GAMs), driven by data from the China National Environmental Monitoring Center (CNEMC) network. During the lockdown period (Jan. 23 - Apr. 8, 2020), PM2.5, PM10, NO2, SO2, and CO concentrations decreased significantly by 45 %, 49 %, 56 %, 39 %, and 18 % compared with the corresponding period in 2015-2019, with contributions by S(meteos) of 15 %, 17 %, 13 %, 10 %, and 6 %. This indicates an emission reduction of NOx at least 43 %. However, O3 increased by 43 % with a contribution by S(meteos) of 6 %. In spite of the reduced volatile organic compound (VOC) emissions by 30 % during the strict lockdown period (Jan. 23 - Feb. 14, 2020), which likely reduced the production of O3, O3 concentrations increased due to a weakening of the titration effect of NO. Our results suggest that conventional emission reduction (NOx reduction only) measures may not be sufficient to reduce (or even lead to an increase of) surface O3 concentrations, even if reaching the limit, and VOC-specific measures should also be taken.

Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations.

Structure prediction for proteins lacking homologous templates in the Protein Data Bank (PDB) remains a significant unsolved problem. We developed a protocol, C-I-TASSER, to integrate interresidue contact maps from deep neural-network learning with the cutting-edge I-TASSER fragment assembly simulations. Large-scale benchmark tests showed that C-I-TASSER can fold more than twice the number of non-homologous proteins than the I-TASSER, which does not use contacts. When applied to a folding experiment on 8,266 unsolved Pfam families, C-I-TASSER successfully folded 4,162 domain families, including 504 folds that are not found in the PDB. Furthermore, it created correct folds for 85% of proteins in the SARS-CoV-2 genome, despite the quick mutation rate of the virus and sparse sequence profiles. The results demonstrated the critical importance of coupling whole-genome and metagenome-based evolutionary information with optimal structure assembly simulations for solving the problem of non-homologous protein structure prediction.

Distinct Regimes of O3 Response to COVID-19 Lockdown in China

Restrictions on human activities remarkably reduced emissions of air pollutants in China during the COVID-19 lockdown periods. However, distinct responses of O3 concentrations were observed across China. In the Beijing–Tianjin–Hebei (BTH) and Yangtze River Delta (YRD) regions, O3 concentrations were enhanced by 90.21 and 71.79% from pre-lockdown to lockdown periods in 2020, significantly greater than the equivalent concentrations for the same periods over 2015–2019 (69.99 and 43.62%, p <0.001). In contrast, a decline was detected (−1.1%) in the Pearl River Delta (PRD) region. To better understand the underlying causes for these inconsistent responses across China, we adopted the least absolute shrinkage and selection operator (Lasso) and ordinary linear squares (OLS) methods in this study. Statistical analysis indicated that a sharp decline in nitrogen dioxide (NO2) was the major driver of enhanced O3 in the BTH region as it is a NOx-saturated region. In the YRD region, season-shift induced changes in the temperature/shortwave radiative flux, while lockdown induced declines in NO2, attributable to the rise in O3. In the PRD region, the slight drop in O3 is attributed to the decreased intensity of radiation. The distinct regimes of the O3 response to the COVID-19 lockdown in China offer important insights into different O3 control strategies across China.

Ozone profile retrievals from TROPOMI: Implication for the variation of tropospheric ozone during the outbreak of COVID-19 in China.

During the outbreak of the coronavirus disease 2019 (COVID-19) in China in January and February 2020, production and living activities were drastically reduced to impede the spread of the virus, which also caused a strong reduction of the emission of primary pollutants. However, as a major species of secondary air pollutant, tropospheric ozone did not reduce synchronously, but instead rose in some region. Furthermore, higher concentrations of ozone may potentially promote the rates of COVID-19 infections, causing extra risk to human health. Thus, the variation of ozone should be evaluated widely. This paper presents ozone profiles and tropospheric ozone columns from ultraviolet radiances detected by TROPOospheric Monitoring Instrument (TROPOMI) onboard Sentinel 5 Precursor (S5P) satellite based on the principle of optimal estimation method. We compare our TROPOMI retrievals with global ozonesonde observations, Fourier Transform Spectrometry (FTS) observation at Hefei (117.17°E, 31.7°N) and Global Positioning System (GPS) ozonesonde sensor (GPSO3) ozonesonde profiles at Beijing (116.46°E, 39.80°N). The integrated Tropospheric Ozone Column (TOC) and Stratospheric Ozone Column (SOC) show excellent agreement with validation data. We use the retrieved TOC combining with tropospheric vertical column density (TVCD) of NO2 and HCHO from TROPOMI to assess the changes of tropospheric ozone during the outbreak of COVID-19 in China. Although NO2 TVCD decreased by 63%, the retrieved TOC over east China increase by 10% from the 20-day averaged before the lockdown on January 23, 2020 to 20-day averaged after it. Because the production of ozone in winter is controlled by volatile organic compounds (VOCs) indicated by monitored HCHO, which did not present evident change during the lockdown, the production of ozone did not decrease significantly. Besides, the decrease of NOx emission weakened the titration of ozone, causing an increase of ozone.

Protein Structure and Sequence Reanalysis of 2019-nCoV Genome Refutes Snakes as Its Intermediate Host and the Unique Similarity between Its Spike Protein Insertions and HIV-1.

As the infection of 2019-nCoV coronavirus is quickly developing into a global pneumonia epidemic, the careful analysis of its transmission and cellular mechanisms is sorely needed. In this Communication, we first analyzed two recent studies that concluded that snakes are the intermediate hosts of 2019-nCoV and that the 2019-nCoV spike protein insertions share a unique similarity to HIV-1. However, the reimplementation of the analyses, built on larger scale data sets using state-of-the-art bioinformatics methods and databases, presents clear evidence that rebuts these conclusions. Next, using metagenomic samples from Manis javanica, we assembled a draft genome of the 2019-nCoV-like coronavirus, which shows 73% coverage and 91% sequence identity to the 2019-nCoV genome. In particular, the alignments of the spike surface glycoprotein receptor binding domain revealed four times more variations in the bat coronavirus RaTG13 than in the Manis coronavirus compared with 2019-nCoV, suggesting the pangolin as a missing link in the transmission of 2019-nCoV from bats to human.