Full-coverage mapping and spatiotemporal variations of ground-level ozone (O3) pollution from 2013 to 2020 across China

Ozone (O₃) is an important trace and greenhouse gas in the atmosphere, posing a threat to the ecological environment and human health at the ground level. Large-scale and long-term studies of O₃ pollution in China are few due to highly limited direct ground and satellite measurements. This study offers a new perspective to estimate ground-level O₃ from solar radiation intensity and surface temperature by employing an extended ensemble learning of the space-time extremely randomized trees (STET) model, together with ground-based observations, remote sensing products, atmospheric reanalysis, and an emission inventory. A full-coverage (100%), high-resolution (10 km) and high-quality daily maximum 8-h average (MDA8) ground-level O₃ dataset covering China (called ChinaHighO₃) from 2013 to 2020 was generated. Our MDA8 O₃ estimates (predictions) are reliable, with an average out-of-sample (out-of-station) coefficient of determination of 0.87 (0.80) and root-mean-square error of 17.10 (21.10) μg/m³ in China. The unique advantage of the full coverage of our dataset allowed us to accurately capture a short-term severe O₃ pollution exposure event that took place from 23 April to 8 May in 2020. Also, a rapid increase and recovery of O₃ concentrations associated with variations in anthropogenic emissions were seen during and after the COVID-19 lockdown, respectively. Trends in O₃ concentration showed an average growth rate of 2.49 μg/m³/yr (p < 0.001) from 2013 to 2020, along with the continuous expansion of polluted areas exceeding the daily O₃ standard (i.e., MDA8 O₃ = 160 μg/m³). Summertime O₃ concentrations and the probability of occurrence of daily O₃ pollution have significantly increased since 2015, especially in the North China Plain and the main air pollution transmission belt (i.e., the "2 + 26” cities). However, a decline in both was seen in 2020, mainly due to the coordinated control of air pollution and ongoing COVID-19 effects. This carefully vetted and smoothed dataset is valuable for studies on air pollution and environmental health in China.

Work from home or office during the COVID-19 pandemic: The different chain mediation models of perceived organizational support to the job performance

With the coronavirus pandemic in 2019 (COVID-19), work from home (WFH) has become a frequent way of responding to outbreaks. Across two studies, we examined how perceived organizational support influences job performance when employees work in office or work from home. In study 1, we conducted a questionnaire survey of 162 employees who work in office. In study 2, we conducted a questionnaire survey of 180 employees who work from home. We found that perceived organizational support directly affected job performance when employees work in office. When employees work from home, perceived organizational support could not affect job performance directly. However, it could influence job performance indirectly through the separate mediating effects of job satisfaction and work engagement. These findings extend our understanding of the association of perceived organizational support and job performance and enlighten enterprises on improving employees' job performance during the COVID-19 pandemic.

Work from home or office during the COVID-19 pandemic: The different chain mediation models of perceived organizational support to the job performance.

With the coronavirus pandemic in 2019 (COVID-19), work from home (WFH) has become a frequent way of responding to outbreaks. Across two studies, we examined how perceived organizational support influences job performance when employees work in office or work from home. In study 1, we conducted a questionnaire survey of 162 employees who work in office. In study 2, we conducted a questionnaire survey of 180 employees who work from home. We found that perceived organizational support directly affected job performance when employees work in office. When employees work from home, perceived organizational support could not affect job performance directly. However, it could influence job performance indirectly through the separate mediating effects of job satisfaction and work engagement. These findings extend our understanding of the association of perceived organizational support and job performance and enlighten enterprises on improving employees' job performance during the COVID-19 pandemic.

Environmental contamination with SARS-CoV-2 in COVID-19 hospitals in Wuhan, China, 2020.

Coronavirus disease 2019 (COVID-19) pandemic has caused high number of infections and deaths of healthcare workers globally. Distribution and possible transmission route of SARS-CoV-2 in hospital environment should be clarified. We herein collected 431 environmental (391 surface and 40 air) samples in the intensive care unit (ICU) and general wards (GWs) of three hospitals in Wuhan, China from February 21 to March 4, 2020, and detected SARS-CoV-2 RNA by real-time quantitative PCR. The viral positive rate in the contaminated areas was 17.8% (28/157), whereas there was no virus detected in the clean areas. Higher positive rate (22/59, 37.3%) was found in ICU than that in GWs (3/63, 4.8%). The surfaces of computer keyboards and mouse in the ICU were the most contaminated (8/10, 80.0%), followed by the ground (6/9, 66.7%) and outer glove (2/5, 40.0%). From 17 air samples in the contaminated areas, only one sample collected at a distance of around 30 cm from the patient was positive. Enhanced surface disinfection and hand hygiene effectively decontaminated the virus from the environment. This finding might help understand the transmission route and contamination risk of SARS-CoV-2 and evaluate the effectiveness of infection prevention and control measures in healthcare facilities.

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.

Comparison of the respiratory tract microbiome in hospitalized COVID-19 patients with different disease severity.

Little is known about the characteristics of respiratory tract microbiome in Coronavirus disease 2019 (COVID-19) inpatients with different severity. We conducted a study that expected to clarify these characteristics as much as possible. A cross-sectional study was conducted to characterize respiratory tract microbial communities of 69 COVID-19 inpatients from 64 nasopharyngeal swabs and 5 sputum specimens using 16S ribosomal RNA gene V3-V4 region sequencing. The bacterial profiles were analyzed to find potential biomarkers by the two-step method, the combination of random forest model and the linear discriminant analysis effect size, and explore the connections with clinical characteristics by Spearman's rank test. Compared with mild COVID-19 patients, severe patients had significantly decreased bacterial diversity (p-values were less than 0.05 in the alpha and beta diversity) and relative lower abundance of opportunistic pathogens, including Actinomyces, Prevotella, Rothia, Streptococcus, Veillonella. Eight potential biomarkers including Treponema, Leptotrichia, Lachnoanaerobaculum, Parvimonas, Alloprevotella, Porphyromonas, Gemella, and Streptococcus were found to distinguish the mild COVID-19 patients from the severe COVID-19 patients. The genera of Actinomyces and Prevotella were negatively correlated with age in two groups. Intensive care unit admission, neutrophil count, and lymphocyte count were significantly correlated with different genera in the two groups. In addition, there was a positive correlation between Klebsiella and white blood cell count in two groups. The respiratory tract microbiome had significant differences in COVID-19 patients with different severity. The value of the respiratory tract microbiome as predictive biomarkers for COVID-19 severity deserves further exploration.

Elevated D-dimer and Adverse In-hospital Outcomes in COVID-19 Patients and Synergism with Hyperglycemia.

Aim: One of the most common laboratory findings in COVID-19 patients has been observed to be hypercoagulability with elevated D-dimer levels. An activation of thrombosis may be generated by hyperglycemia. We aimed to explore the association between D-dimer and in-hospital outcomes, and evaluate the synergistic effect between elevated D-dimer and hyperglycemia on COVID-19 prognosis. Methods: A retrospective cohort study was undertaken with 2467 COVID-19 inpatients. D-dimer and fasting blood glucose (FBG) on admission and adverse in-hospital outcomes (events of death and aggravated severity) were collected. Cox proportional risk model was performed to assess the association of D-dimer and adverse in-hospital outcomes, and the combined effects of D-dimer and FBG. Results: Among these COVID-19 patients, 1100 (44.6%) patients had high D-dimer (≥0.50 mg/L). Patients with high D-dimer were older, with higher FBG (≥7.00 mmol/L), and had significantly higher adjusted risk of adverse in-hospital outcomes when comparing with those who with D-dimer<0.50 mg/L (hazard ratio, 2.73; 95% confidence interval, 1.46-5.11). Moreover, patients with high FBG and D-dimer levels had an increasing risk (hazard ratio, 5.72; 95% confidence interval: 2.65-12.34) than those with normal FBG and D-dimer. Conclusion: Risk of adverse in-hospital outcomes is higher among patients with high D-dimer levels. Additionally, this study found for the first time that elevated D-dimer and hyperglycemia had a synergistic effect on COVID-19 prognosis, and this risk was independent of diabetes history.

Comparison of reverse-transcription qPCR and droplet digital PCR for the detection of SARS-CoV-2 in clinical specimens of hospitalized patients.

Accurate detection of severe acute respiratory syndrome coronavirus 2 is not only necessary for viral load monitoring to optimize treatment in hospitalized coronavirus disease 2019 patients, but also critical for deciding whether the patient could be discharged without any risk of viral shedding. Digital droplet PCR (ddPCR) is more sensitive than reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) and is usually considered the superior choice. In the current study, we compared the clinical performance of RT-qPCR and ddPCR using oropharyngeal swab samples from patients hospitalized in the temporary Huoshenshan Hospital, Wuhan, Hubei, China. Results demonstrated that ddPCR was indeed more sensitive than RT-qPCR. Negative results might be caused by poor sampling technique or recovered patients, as the range of viral load in these patients varied significantly. In addition, both methods were highly correlated in terms of their ability to detect all three target genes as well as the ratio of copies of viral genes to that of the IC gene. Furthermore, our results evidenced that both methods detected the N gene more easily than the ORF gene. Taken together, these findings imply that the use of ddPCR, as an alternative to RT-qPCR, is necessary for the accurate diagnosis of hospitalized coronavirus disease 2019 patients.

Unraveling pathways of elevated ozone induced by the 2020 lockdown in Europe by an observationally constrained regional model using TROPOMI.

Questions about how emissions are changing during the COVID-19 lockdown periods cannot be answered by observations of atmospheric trace gas concentrations alone, in part due to simultaneous changes in atmospheric transport, emissions, dynamics, photochemistry, and chemical feedback. A chemical transport model simulation benefiting from a multi-species inversion framework using well-characterized observations should differentiate those influences enabling to closely examine changes in emissions. Accordingly, we jointly constrain NO x and VOC emissions using well-characterized TROPOspheric Monitoring Instrument (TROPOMI) HCHO and NO2 columns during the months of March, April, and May 2020 (lockdown) and 2019 (baseline). We observe a noticeable decline in the magnitude of NO x emissions in March 2020 (14 %-31 %) in several major cities including Paris, London, Madrid, and Milan, expanding further to Rome, Brussels, Frankfurt, Warsaw, Belgrade, Kyiv, and Moscow (34 %-51 %) in April. However, NO x emissions remain at somewhat similar values or even higher in some portions of the UK, Poland, and Moscow in March 2020 compared to the baseline, possibly due to the timeline of restrictions. Comparisons against surface monitoring stations indicate that the constrained model underrepresents the reduction in surface NO2. This underrepresentation correlates with the TROPOMI frequency impacted by cloudiness. During the month of April, when ample TROPOMI samples are present, the surface NO2 reductions occurring in polluted areas are described fairly well by the model (model: -21 ± 17 %, observation: -29 ± 21 %). The observational constraint on VOC emissions is found to be generally weak except for lower latitudes. Results support an increase in surface ozone during the lockdown. In April, the constrained model features a reasonable agreement with maximum daily 8 h average (MDA8) ozone changes observed at the surface (r = 0.43), specifically over central Europe where ozone enhancements prevail (model: +3.73 ± 3.94 %, + 1.79 ppbv, observation: +7.35 ± 11.27 %, +3.76 ppbv). The model suggests that physical processes (dry deposition, advection, and diffusion) decrease MDA8 surface ozone in the same month on average by -4.83 ppbv, while ozone production rates dampened by largely negative J NO 2 [ NO 2 ] - k NO + O 3 [ NO ] [ O 3 ] become less negative, leading ozone to increase by +5.89 ppbv. Experiments involving fixed anthropogenic emissions suggest that meteorology contributes to 42 % enhancement in MDA8 surface ozone over the same region with the remaining part (58 %) coming from changes in anthropogenic emissions. Results illustrate the capability of satellite data of major ozone precursors to help atmospheric models capture ozone changes induced by abrupt emission anomalies.

Full-coverage mapping and spatiotemporal variations of ground-level ozone (O3) pollution from 2013 to 2020 across China

Ozone (O3) is an important trace and greenhouse gas in the atmosphere, posing a threat to the ecological environment and human health at the ground level. Large-scale and long-term studies of O3 pollution in China are few due to highly limited direct ground and satellite measurements. This study offers a new perspective to estimate ground-level O3 from solar radiation intensity and surface temperature by employing an extended ensemble learning of the space-time extremely randomized trees (STET) model, together with ground-based observations, remote sensing products, atmospheric reanalysis, and an emission inventory. A full-coverage (100%), high-resolution (10 km) and high-quality daily maximum 8-h average (MDA8) ground-level O3 dataset covering China (called ChinaHighO3) from 2013 to 2020 was generated. Our MDA8 O3 estimates (predictions) are reliable, with an average out-of-sample (out-of-station) coefficient of determination of 0.87 (0.80) and root-mean-square error of 17.10 (21.10) μg/m3 in China. The unique advantage of the full coverage of our dataset allowed us to accurately capture a short-term severe O3 pollution exposure event that took place from 23 April to 8 May in 2020. Also, a rapid increase and recovery of O3 concentrations associated with variations in anthropogenic emissions were seen during and after the COVID-19 lockdown, respectively. Trends in O3 concentration showed an average growth rate of 2.49 μg/m3/yr (p < 0.001) from 2013 to 2020, along with the continuous expansion of polluted areas exceeding the daily O3 standard (i.e., MDA8 O3 = 160 μg/m3). Summertime O3 concentrations and the probability of occurrence of daily O3 pollution have significantly increased since 2015, especially in the North China Plain and the main air pollution transmission belt (i.e., the “2 + 26” cities). However, a decline in both was seen in 2020, mainly due to the coordinated control of air pollution and ongoing COVID-19 effects. This carefully vetted and smoothed dataset is valuable for studies on air pollution and environmental health in China.

Genome-wide association study of COVID-19 severity among the Chinese population.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection causes a broad clinical spectrum of coronavirus disease 2019 (COVID-19). The development of COVID-19 may be the result of a complex interaction between the microbial, environmental, and host genetic components. To reveal genetic determinants of susceptibility to COVID-19 severity in the Chinese population, we performed a genome-wide association study on 885 severe or critical COVID-19 patients (cases) and 546 mild or moderate patients (controls) from two hospitals, Huoshenshan and Union hospitals at Wuhan city in China. We identified two loci on chromosome 11q23.3 and 11q14.2, which are significantly associated with the COVID-19 severity in the meta-analyses of the two cohorts (index rs1712779: odds ratio [OR] = 0.49; 95% confidence interval [CI], 0.38-0.63 for T allele; P = 1.38 × 10-8; and index rs10831496: OR = 1.66; 95% CI, 1.38-1.98 for A allele; P = 4.04 × 10-8, respectively). The results for rs1712779 were validated in other two small COVID-19 cohorts in the Asian populations (P = 0.029 and 0.031, respectively). Furthermore, we identified significant eQTL associations for REXO2, C11orf71, NNMT, and CADM1 at 11q23.3, and CTSC at 11q14.2, respectively. In conclusion, our findings highlight two loci at 11q23.3 and 11q14.2 conferring susceptibility to the severity of COVID-19, which might provide novel insights into the pathogenesis and clinical treatment of this disease.

The association between BMI and metabolically unhealthy status with COVID-19 mortality: Based on 3019 inpatients from Wuhan, China.

BACKGROUND AND AIMS: Patients with multiple metabolic diseases are at high risk for the occurrence and death of COVID-19. Little is known about patients with underweight and metabolically healthy obesity. The aim of this study is to evaluate the impact of BMI and COVID-19 mortality in hospitalized patients, and also explore the association in different metabolically healthy (MHS) and unhealthy status (MUS). METHODS AND RESULTS: A retrospective cohort study based on 3019 inpatients from Wuhan was conducted. Included patients were classified into four groups according the BMI level (underweight, normal weight, overweight and obesity), and patients with at least one of the metabolic abnormalities (diabetes, hypertension, dyslipidemia) was defined as MUS. Multiple Cox model was used to calculate the hazard ratio (HR). Compared to patients with normal weight, the HRs of overweight and obesity for COVID-19 mortality were 1.91 (95%CI:1.02-3.58) and 2.54 (95%CI:1.22-5.25) respectively in total patients, and 2.58 (95%CI:1.16-5.75) and 3.89 (95%CI:1.62-9.32) respectively in the elderly. The HR of underweight for COVID-19 mortality was 4.58 (95%CI:1.56-13.48) in the elderly. For different metabolic statuses, both underweight, overweight and obesity had obviously negative association with COVID-19 mortality in total and elderly patients with MUS. However, no significance was found in non-elderly and patients with MHS. CONCLUSION: Not only overweight or obesity, but also underweight can be associated with COVID-9 mortality, especially in the elderly and in patients with MUS. More large-scale studies are needed for patients with underweight and metabolically healthy overweight or obesity.

Clinical features and antibody response of patients from a COVID-19 treatment hospital in Wuhan, China.

Coronavirus disease 2019 (COVID-19) has rapidly evolved into a global pandemic. A total of 1578 patients admitted into a newly built hospital specialized for COVID-19 treatment in Wuhan, China, were enrolled. Clinical features and the levels of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunoglobulin (Ig)M and IgG were analyzed. In total, 1532 patients (97.2%) were identified as laboratory-confirmed cases. Seventy-seven patients were identified as asymptomatic carriers (n = 64) or SARS-CoV-2 RNA positive before symptom onset (n = 13). The positive rates of SARS-CoV-2 IgM and IgG were 80.4% and 96.8%, respectively. The median of IgM and IgG titers were 37.0A U/ml (interquartile range [IQR]: 13.4-81.1 AU/ml) and 156.9 AU/ml (IQR: 102.8-183.3 AU/ml), respectively. The IgM and IgG levels of asymptomatic patients (median titers, 8.3 AU/ml and 100.3 AU/ml) were much lower than those in symptomatic patients (median titers, 38.0 AU/ml and 158.2 AU/ml). A much lower IgG level was observed in critically ill patients 42-60 days after symptom onset. There were 153 patients with viral RNA shedding after IgG detection. These patients had a higher proportion of critical illness during hospitalization (p < .001) and a longer hospital stay (p < .001) compared to patients with viral clearance after IgG detection. Coronary heart disease (odds ratio [OR], 1.89 [95% confidence interval [CI], 1.11-3.24]; p = .020), and intensive care unit admission (OR, 2.47 [95% CI, 1.31-4.66]; p = .005) were independent risk factors associated with viral RNA shedding after IgG detection. Symptomatic patients produced more antibodies than asymptomatic patients. The patients who had SARS-CoV-2 RNA shedding after developing IgG were more likely to be sicker patients.

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.