Analysis of air pollution characteristics, transport pathways and potential source areas identification in Beijing before, during and after the COVID-19 outbreak

A series of lockdown measures in response to the Corona Virus Disease 2019 (COVID-19) outbreak resulted in a drop in anthropogenic emissions and changes in concentrations of PM2.5 and O-3. Backward trajectories analysis, cluster analysis, potential source contribution function (PSCF) and concentration weighted trajectory (CWT) technologies were conducted to reveal the characteristics and potential source areas of pollutants in Beijing before the COVID-19 outbreak (BCO period), during the outbreak (COB period) and after the outbreak (ACO period), as well as the contemporaneous period in 2019 (CCO period), which is critical for exploring the efficient control measures and making policy. The results indicated that despite the significant reduction in anthropogenic emissions during the epidemic, the PM2.5 concentrations increased by 1.0% caused by unfavorable meteorological conditions. O-3 concentrations increased by 174.8% compared to that during the BCO period due to the increased temperature and inappropriate precursor reduction ratios. A considerable decrease of NO3- in PM2.5 was observed under the influence of significant reductions in vehicle emissions during the lockdown. The cluster analysis revealed that short-range transport played a significant role in the accumulation of local PM2.5 pollution, while long-range northwest airflows contributed more to O-3 accumulation, and weakened as the season changed. The PSCF and CWT analysis demonstrated that potential source areas of PM2.5 were mostly located in the central and southern Hebei, the southwestern Shandong in the CCO period, and expanded to central Inner Mongolia and northern Shanxi in the COB period. These areas were highly compatible with the high emission areas of the emission inventory statistics. After the outbreak, the source areas of O-3 were centered in the Beijing-Tianjin-Hebei region and Shandong province, with a radial dispersion in all directions, while they were distributed in the central Mongolia and Inner Mongolia during the other periods.

Epidemiological analysis of 18 patients with COVID-19.

OBJECTIVE: The present study aimed to explore the transmission pattern and the incubation period of coronavirus disease 2019 (COVID-19) as well as the clinical characteristics of infants with COVID-19 to provide a scientific basis for introducing further measures to reduce the infection rate and control the pandemic. PATIENTS AND METHODS: A descriptive epidemiological study of 18 patients with COVID-19 in People's Hospital of Deyuan was carried out. Among these patients, 16 cases were connected with clusters (11 family-cluster cases and 5 public-cluster cases). The basic characteristics, clinical symptoms, and epidemiological characteristics of the patients were considered in the investigation. RESULTS: The median age of the 18 patients was 44.5 years (37.5-52.0 years), and there were 10 males and 8 females in the sample. The main clinical symptoms were fever and cough. The epidemiological characteristics were as follows: (1) the median incubation period was 8 days (with an interquartile range of 4-12 days); (2) the incubation period in one case was ≥18 days; (3) one infant patient was asymptomatic prior to their diagnosis; and (4) two asymptomatic patients had a positive nucleic acid test after their family members were diagnosed with COVID-19. CONCLUSIONS: COVID-19 can be transmitted in many ways, including via respiratory droplets and indirect contact, and it spreads easily among close contacts. People with a history of contact with areas affected by the disease should be isolated at home for 14 days. Moreover, attention should be focused on the issues of asymptomatic infectors, asymptomatic infants, and infants with mild symptoms.

The dynamics of oil on China's commodity sectors: What can we learn from a quantile perspective?

I employ the rolling quantile regression, the quantile-on-quantile (QQ) method and the quantile coherency (QC) approach with hedging effectiveness (HE) index to investigate the dynamics of global crude oil on China's commodity sectors and hedging effectiveness of oil market for China's commodity sectors. The main results show that the coefficients of rolling window quantile regression vary across periods and are affected easily by some extreme events such as the GFC and recent COVID-19. By utilizing the QQ approach, it's revealed that the dynamic effects of oil on commodity sectors have heterogeneity and asymmetry. Furthermore, the high-frequency trading in the oil and commodity market may receive higher benefits and the investors can also gain profits in some crisis or bad market situations. In addition, we can see the strong heterogeneity in the hedging effectiveness, and there is some evidence that the oil market is an effective hedge heaven for China's commodity. © 2020 Elsevier B.V.