Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 3 de 3
Filter
Add filters

Language
Year range
1.
Geophys Res Lett ; 48(3): e2020GL091699, 2021 Feb 16.
Article in English | MEDLINE | ID: covidwho-1127134

ABSTRACT

The Coronavirus Disease 2019 (COVID-19) pandemic led to a widespread reduction in aerosol emissions. Using satellite observations and climate model simulations, we study the underlying mechanisms of the large decreases in solar clear-sky reflection (3.8 W m-2 or 7%) and aerosol optical depth (0.16 W m-2 or 32%) observed over the East Asian Marginal Seas in March 2020. By separating the impacts from meteorology and emissions in the model simulations, we find that about one-third of the clear-sky anomalies can be attributed to pandemic-related emission reductions, and the rest to weather variability and long-term emission trends. The model is skillful at reproducing the observed interannual variations in solar all-sky reflection, but no COVID-19 signal is discerned. The current observational and modeling capabilities will be critical for monitoring, understanding, and predicting the radiative forcing and climate impacts of the ongoing crisis.

2.
Geophys Res Lett ; 48(3): e2020GL091699, 2021 Feb 16.
Article in English | MEDLINE | ID: covidwho-1003291

ABSTRACT

The Coronavirus Disease 2019 (COVID-19) pandemic led to a widespread reduction in aerosol emissions. Using satellite observations and climate model simulations, we study the underlying mechanisms of the large decreases in solar clear-sky reflection (3.8 W m-2 or 7%) and aerosol optical depth (0.16 W m-2 or 32%) observed over the East Asian Marginal Seas in March 2020. By separating the impacts from meteorology and emissions in the model simulations, we find that about one-third of the clear-sky anomalies can be attributed to pandemic-related emission reductions, and the rest to weather variability and long-term emission trends. The model is skillful at reproducing the observed interannual variations in solar all-sky reflection, but no COVID-19 signal is discerned. The current observational and modeling capabilities will be critical for monitoring, understanding, and predicting the radiative forcing and climate impacts of the ongoing crisis.

3.
Preprint in English | medRxiv | ID: ppmedrxiv-20021584

ABSTRACT

BackgroundSevere ill patients with 2019 novel coronavirus (2019-nCoV) infection progressed rapidly to acute respiratory failure. We aimed to select the most useful prognostic factor for severe illness incidence. MethodsThe study prospectively included 61 patients with 2019-nCoV infection treated at Beijing Ditan Hospital from January 13, 2020 to January 31, 2020. Prognostic factor of severe illness was selected by the LASSO COX regression analyses, to predict the severe illness probability of 2019-CoV pneumonia. The predictive accuracy was evaluated by concordance index, calibration curve, decision curve and clinical impact curve. ResultsThe neutrophil-to-lymphocyte ratio (NLR) was identified as the independent risk factor for severe illness in patients with 2019-nCoV infection. The NLR had a c-index of 0.807 (95% confidence interval, 0.676-0.38), the calibration curves fitted well, and the decision curve and clinical impact curve showed that the NLR had superior standardized net benefit. In addition, the incidence of severe illness was 9.1% in age [≥] 50 and NLR < 3.13 patients, and half of patients with age [≥] 50 and NLR [≥] 3.13 would develop severe illness. Based on the risk stratification of NLR with age, the study developed a 2019-nCoV pneumonia management process. ConclusionsThe NLR was the early identification of risk factors for 2019-nCoV severe illness. Patients with age [≥] 50 and NLR [≥] 3.13 facilitated severe illness, and they should rapidly access to intensive care unit if necessary.

SELECTION OF CITATIONS
SEARCH DETAIL
...