Estimation of NO2 emission strengths over Riyadh and Madrid from space from a combination of wind-assigned anomalies and a machine learning technique

Nitrogen dioxide (NO2) air pollution provides valuable information for quantifying NOx (NOx = NO + NO2) emissions and exposures. This study presents a comprehensive method to estimate average tropospheric NO2 emission strengths derived from 4-year (May 2018–June 2022) TROPOspheric Monitoring Instrument (TROPOMI) observations by combining a wind-assigned anomaly approach and a machine learning (ML) method, the so-called gradient descent algorithm. This combined approach is firstly applied to the Saudi Arabian capital city of Riyadh, as a test site, and yields a total emission rate of 1.09×1026 molec. s-1. The ML-trained anomalies fit very well with the wind-assigned anomalies, with an R2 value of 1.0 and a slope of 0.99. Hotspots of NO2 emissions are apparent at several sites: over a cement plant and power plants as well as over areas along highways. Using the same approach, an emission rate of 1.99×1025 molec. s-1 is estimated in the Madrid metropolitan area, Spain. Both the estimate and spatial pattern are comparable with the Copernicus Atmosphere Monitoring Service (CAMS) inventory.Weekly variations in NO2 emission are highly related to anthropogenic activities, such as the transport sector. The NO2 emissions were reduced by 16 % at weekends in Riyadh, and high reductions were found near the city center and in areas along the highway. An average weekend reduction estimate of 28 % was found in Madrid. The regions with dominant sources are located in the east of Madrid, where residential areas and the Madrid-Barajas airport are located. Additionally, due to the COVID-19 lockdowns, the NO2 emissions decreased by 21 % in March–June 2020 in Riyadh compared with the same period in 2019. A much higher reduction (62 %) is estimated for Madrid, where a very strict lockdown policy was implemented. The high emission strengths during lockdown only persist in the residential areas, and they cover smaller areas on weekdays compared with weekends. The spatial patterns of NO2 emission strengths during lockdown are similar to those observed at weekends in both cities. Although our analysis is limited to two cities as test examples, the method has proven to provide reliable and consistent results. It is expected to be suitable for other trace gases and other target regions. However, it might become challenging in some areas with complicated emission sources and topography, and specific NO2 decay times in different regions and seasons should be taken into account. These impacting factors should be considered in the future model to further reduce the uncertainty budget.

Economic value and characteristics of cloud pharmacy for children based on internet hospital in western China during the COVID-19 pandemic: Cross-sectional survey study.

Background: Online health care services have been encouraged by the Chinese government in recent years, and the COVID-19 pandemic catalyzed the rapid growing of internet hospitals. As an integral part of online health care services, little is known about the economic value and characteristics of cloud pharmacy especially for children. This study aimed to reveal the economic value and comprehensive characteristics of pediatric cloud pharmacy during the COVID-19 pandemic in a tertiary children's hospital in western China. Methods: A total of 33,254 online prescriptions over the course of February 2020 through December 2021 were analyzed with respect to the user profiles, diseases, consulting behaviors, distribution of departments, delivery region and distance, drug information and degree of satisfaction. The cost savings for patients calculated lost wages and the high-speed railway fees for transport to and from hospital. Results: A total of 33,254 prescriptions, including 56,216 drugs were delivered to 27 provinces and municipalities of China. The internet cloud pharmacy saved a total of more than RMB 11.17 million in financial costs for patients. Of the 33,254 delivered prescriptions, 50.40% were sent to Chongqing Province, the top 5 provinces for out-of-province prescription deliveries were Sichuan (37.77%), Guizhou (8.00%), Yunnan (1.18%), Hubei (0.66%) and Guangdong (0.42%). In terms of department distribution, neurology (31.7%), respiratory (15.0%) and endocrinology (14.6%) were the top three departments. Epilepsy (16.2%), precocious puberty (10.3%) and asthma (8.7%) were the top three frequently consulted diseases. The peak times of day for online prescriptions occurred at 9 AM and 8 PM. 99.67% of users gave full marks for their internet counseling. Conclusion: The pediatric cloud pharmacy is efficient, cost-saving and convenient for children with chronic disease or mild symptoms during the COVID-19 pandemic. The widespread use of this pediatric cloud pharmacy can help alleviating pressure on offline hospitals and facilitated people's lives beyond geographical and time-related limitations. Further efforts are needed to be made to improve the quality and acceptance of pediatric cloud pharmacy, as well as to regulate and standardize the management of this novel online health care service.

Estimation of NO2 emission strengths over Riyadh and Madrid from space from a combination of wind-assigned anomalies and machine learning technique

Nitrogen dioxide (NO2) air pollution provides valuable information for quantifying NOx emissions and exposures. This study presents a comprehensive method to estimate average tropospheric NO2 emission strengths derived from three-year (April 2018 - March 2021) TROPOMI observations by combining a wind-assigned anomaly approach and a Machine Learning (ML) method, the so-called Gradient Descent. This combined approach is firstly applied to the Saudi Arabian capital city Riyadh, as a test site, and yields a total emission rate of 1.04×1026 molec./s. The ML-trained anomalies fit very well with the wind-assigned anomalies with an R2 value of 1.0 and a slope of 0.99. Hotspots of NO2 emissions are apparent at several sites where the cement plant and power plants are located and over areas along the highways. Using the same approach, an emission rate of 1.80×1025 molec./s is estimated in the Madrid metropolitan area, Spain. Both the estimate and spatial pattern are comparable to the CAMS inventory. Weekly variations of NO2 emission are highly related to anthropogenic activities, such as the transport sector. The NO2 emissions were reduced by 24% at weekends in Riyadh, and high reductions are found near the city center and the areas along the highway. An average weekend reduction estimate of 30% in Madrid is found. The regions with dominant sources are located in the east of Madrid, where the residential areas and the Madrid-Barajas airport are located. Additionally, the NO2 emissions decreased by 21% in March-June 2020 compared to the same period in 2019 induced by the COVID-19 lockdowns in Riyadh. A much higher reduction (60%) is estimated for Madrid where a very strict lockdown policy was implemented. The high emission strengths during lockdown only persist in the residential areas and cover smaller areas during weekdays than at weekends. The spatial patterns of NO2 emission strengths during lockdown are similar to those observed at weekends in both cities. Though our analysis is limited to two cities as testing examples, the method has proved to provide reliable and consistent results. Therefore, it is expected to be suitable for other trace gases and other target regions. [ FROM AUTHOR] Copyright of Atmospheric Measurement Techniques Discussions is the property of Copernicus Gesellschaft mbH and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Effect of an Inhibitor on the ACE2-Receptor-Binding Domain of SARS-CoV-2.

The recent outbreak of COVID-19 infection started in Wuhan, China, and spread across China and beyond. Since the WHO declared COVID-19 a pandemic (March 11, 2020), three vaccines and only one antiviral drug (remdesivir) have been approved (Oct 22, 2020) by the FDA. The coronavirus enters human epithelial cells by the binding of the densely glycosylated fusion spike protein (S protein) to a receptor (angiotensin-converting enzyme 2, ACE2) on the host cell surface. Therefore, inhibiting the viral entry is a promising treatment pathway for preventing or ameliorating the effects of COVID-19 infection. In the current work, we have used all-atom molecular dynamics (MD) simulations to investigate the influence of the MLN-4760 inhibitor on the conformational properties of ACE2 and its interaction with the receptor-binding domain (RBD) of SARS-CoV-2. We have found that the presence of an inhibitor tends to completely/partially open the ACE2 receptor where the two subdomains (I and II) move away from each other, while the absence results in partial or complete closure. The current study increases our understanding of ACE inhibition by MLN-4760 and how it modulates the conformational properties of ACE2.

Sex-specific difference of in-hospital mortality from COVID-19 in South Korea.

We sought to assess the impact of sex on in-hospital mortality of patients with COVID-19 infection in South Korea. The study recruited 5,628 prospective consecutive patients who were hospitalized in South Korea with COVID-19 infection, and enrolled in the Korea Centers for Disease Control and Prevention (KCDC) dataset between January 20, 2020, and April 30, 2020. The primary endpoint was in-hospital death from COVID-19. The cohort comprised of 3,308 women (59%) and 2,320 men (41%). In-hospital death was significantly lower in women than men (3.5% vs. 5.5%, hazard ratio (HR): 0.61; 95% confidence interval (CI): 0.47 to 0.79, p <0.001). Results were consistent after multivariable regression (HR: 0.59; 95% CI: 0.41 to 0.85, p = 0.023) and propensity score matching (HR: 0.51; 95% CI: 0.30 to 0.86, p = 0.012). In South Korea, women had a significantly lower risk of in-hospital death amongst those patients hospitalized with COVID-19 infection.

Cross-Sector Monitoring and Evaluation Framework: Social, Economic, and Health Conditions Impacted During the COVID-19 Pandemic.

Public Health 3.0 approaches are critical for monitoring disparities in economic, social, and overall health impacts following the COVID-19 pandemic and its associated policy changes to slow community spread. Timely, cross-sector data as identified using this approach help decisionmakers identify changes, track racial disparities, and address unintended consequences during a pandemic. We applied a monitoring and evaluation framework that combined policy changes with timely, relevant cross-sector data and community review. Indicators covered unemployment, basic needs, family violence, education, childcare, access to health care, and mental, physical, and behavioral health. In response to increasing COVID-19 cases, nonpharmaceutical intervention strategies were implemented in March 2020 in King County, Washington. By December 2020, 554 000 unemployment claims were filed. Social service calls increased 100%, behavioral health crisis calls increased 25%, and domestic violence calls increased 25%, with disproportionate impact on communities of color. This framework can be replicated by local jurisdictions to inform and address racial inequities in ongoing COVID-19 mitigation and recovery. Cross-sector collaboration between public health and sectors addressing the social determinants of health are an essential first step to have an impact on long-standing racial inequities. (Am J Public Health. 2021;111(S3):S215-S223. https://doi.org/10.2105/AJPH.2021.306422).

The impact of emergent public health events on passengers' public transport dependence

Introduction The optimization of public transport (PT) systems to ameliorate urban traffic problems and living environment requires a deep understanding of PT usage behaviour. However, there is a lack of scientific measurement of the influence on passengers’ PT dependence (PTD) during emergent public health events. Methods An extended theory of planned behaviour and an online survey scheme are proposed to investigate the influence of psychological and objective factors on passengers’ PTD under an emergent public health event in Beijing, China. Then, four structural equation models are estimated to analyse the PTD influence mechanism of different groups of passengers. Results The objective condition factors exert indirect effects on passengers' PTD via the psychological factors. Individual attributes have small influences on passengers' PTD, while the environment has a significant positive effect. Subjective norms have the highest influencing effect on passengers’ PTD, and passengers with a relatively high PTD level have the risk of shifting to other transportation modes under the emergent public health events. Conclusions The influence mechanism of emergency public health events on the PT dependence of different passengers is heterogeneous. The targeted policies should be implemented for different passengers to improve the PT market share and residents' health under emergent public health events.

Association of sleep quality before and after SARS-CoV-2 infection with clinical outcomes in hospitalized patients with COVID-19 in China.

Sleep is believed to benefit the host defense against pathogens. We aimed to investigate the association of sleep quality with clinical outcomes among hospitalized patients with COVID-19. We conducted a prospective cohort study in 205 adult hospitalized patients with diagnosed moderate COVID-19, with follow-up until hospital discharge or death. Pittsburgh Sleep Quality Index (PSQI) assessed sleep quality before and after infection. The primary outcome was the incidence of severe or critical pneumonia, and the secondary outcomes were duration of hospital stay and laboratory measurements during the follow up. Among the 205 included hospitalized patients, 185 (90.2 %) experienced poorer sleep quality after infection than before according to the PSQI score, and 25 (12.2 %) developed severe or critical pneumonia during follow-up. In Cox regression models, the adjusted hazard ratio of developing severe or critical pneumonia associated with each 1 score increment in the PSQI score before and after infection was 1.23 (95% CI: 1.09, 1.39) and 1.35 (95 % CI: 1.08, 1.67), respectively. Poorer sleep quality was also significantly associated with a prolonged hospital stay and more serious dysregulations in immune system indicated by several laboratory markers. Poorer sleep quality, either in the daily time or after infection with SARS-CoV-2, was associated with worse clinical outcomes. These findings highlight the importance of good sleep in confronting the emerging pandemic of COVID-19.

A Clinical Risk Score to Predict In-hospital Mortality from COVID-19 in South Korea.

BACKGROUND: Early identification of patients with coronavirus disease 2019 (COVID-19) who are at high risk of mortality is of vital importance for appropriate clinical decision making and delivering optimal treatment. We aimed to develop and validate a clinical risk score for predicting mortality at the time of admission of patients hospitalized with COVID-19. METHODS: Collaborating with the Korea Centers for Disease Control and Prevention (KCDC), we established a prospective consecutive cohort of 5,628 patients with confirmed COVID-19 infection who were admitted to 120 hospitals in Korea between January 20, 2020, and April 30, 2020. The cohort was randomly divided using a 7:3 ratio into a development (n = 3,940) and validation (n = 1,688) set. Clinical information and complete blood count (CBC) detected at admission were investigated using Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression to construct a predictive risk score (COVID-Mortality Score). The discriminative power of the risk model was assessed by calculating the area under the curve (AUC) of the receiver operating characteristic curves. RESULTS: The incidence of mortality was 4.3% in both the development and validation set. A COVID-Mortality Score consisting of age, sex, body mass index, combined comorbidity, clinical symptoms, and CBC was developed. AUCs of the scoring system were 0.96 (95% confidence interval [CI], 0.85-0.91) and 0.97 (95% CI, 0.84-0.93) in the development and validation set, respectively. If the model was optimized for > 90% sensitivity, accuracies were 81.0% and 80.2% with sensitivities of 91.7% and 86.1% in the development and validation set, respectively. The optimized scoring system has been applied to the public online risk calculator (https://www.diseaseriskscore.com). CONCLUSION: This clinically developed and validated COVID-Mortality Score, using clinical data available at the time of admission, will aid clinicians in predicting in-hospital mortality.

Differential diagnosis and prospective grading of COVID-19 at the early stage with simple hematological and biochemical variables.

We evaluated simple laboratory variables to discriminate COVID-19 from bacterial pneumonia or influenza and for the prospective grading of COVID-19. Multivariate logistic regression and receiver operating characteristic curve were used to estimate the diagnostic performance of the significant discriminating variables. A comparative analysis was performed with different severity. The leukocytosis (P = 0.017) and eosinopenia (P = 0.001) were discriminating variables between COVID-19 and bacterial pneumonia with area under the curve (AUC) of 0.778 and 0.825. Monocytosis (P = 0.003), the decreased lymphocyte-to-monocyte ratio (P < 0.001), and the increased neutrophil-to-lymphocyte ratio (NLR) (P = 0.028) were predictive of influenza with AUC of 0.723, 0.895, and 0.783, respectively. Serum amyloid protein, lactate dehydrogenase, CD3+ cells, and the fibrinogen degradation products had a good correlation with the severity of COVID-19 graded by age (≥50) and NLR (≥3.13). Simple laboratory variables are helpful for rapid diagnosis on admission and hierarchical management of COVID-19 patients.