Quantifying the impact of pandemic lockdown policies on global port calls.

The recent experience of lockdowns during COVID-19 highlights the prolonged impact a pandemic could have on ports and the shipping industry. This paper uses port call data derived from the Automatic Identification System (AIS) reports from the world's 30 largest container ports to quantify both the immediate and longer-term impact of national COVID-19 lockdown policies on global shipping flows. The analysis uses the Difference-in-Difference (DID) and combined regression discontinuity design (RDD)-DID models to represent the effects of lockdown policies. The combination of RDD and DID models is particularly effective because it can mitigate time trends in the data, e.g., the Chinese New Year effect on Chinese ports. This study further examines the potential shock propagation effects, namely, how lockdown policy in one country (i.e., China) can affect the number of port calls in other countries. We categorize ports in other countries into a high-connectivity (with Chinese ports) group and a low-connectivity group, using a proposed connectivity index with China derived from individual vessel trajectories obtained from the AIS data. The results provide a clearly measurable picture of the kinds of trade shocks and consequent pattern changes in port calls over time caused by responses to lockdown policies of varying levels of stringency. We further document the existence of significant shock propagation effects. As the risk of pandemics rises in the twenty-first century, these results can be used by policy makers to assess the potential impact of different levels of lockdown policy on the maritime industry and trade flows more broadly. Maritime players can also use findings such as these to manage their capacity during lockdowns more effectively and to respond more flexibly to changing demand in seaborne transportation.

Global Industrial Development: Insights From the Distribution Dynamics Approach for the Post Covid Era.

The spread of COVID-19 has significantly dampened global economic activity and has also wreaked havoc on the industrial sector. Understanding the disparity and convergence of global industrial outputs is important in assessing the effectiveness of concurrent development policies. This study investigates the spatial distribution of global industrial output to unveil the disparity in industrial development and the feasibility of achieving convergence over time. Stochastic kernel analyses are carried out for national regimes to study the overall pattern of industrialization for all the countries in the world. Countries are then classified into different groups to further analyse the geographical and income effects on industrial development. The results show that disparity between the Global North and the Global South will enlarge further in the future. Industrial development in the Global North will continue to prosper, while the industrial output in many countries in the Global South just cannot reach the global average.

Global Inequality in Service Sector Development: Trend and Convergence

The COVID-19 pandemic has caused a huge impact on global service sector. In the pandemic background, to understand the disparity in service sector outputs at the global level is crucial for assessing the effectiveness of development policies in different countries. This study investigate the global service sector and it aims to investigate the transitional dynamics of the output from the service sector by adopting stochastic kernel analyses. Distribution dynamics analyses are carried out for all the countries in the world. The data are then divided into different regional and income groups to evaluate the impacts of geographical location and income on the development of the service sector. The results show that the Global North will continue to make greater strides, while the output capacity in many Global South countries struggles to reach the global average. Moreover, it is shown that countries with higher per capita income will perform better in the development of their service sector than those with low per capita income, thereby highlighting the persistence of global inequality. Finally, this study shows that the Sub-Saharan Africa region and the South Asia region both are very important in the alleviation of global inequality.

Measurement report: Fast photochemical production of peroxyacetyl nitrate (PAN) over the rural North China Plain during haze events in autumn

Photochemical pollution over the North China Plain (NCP) is attracting much concern. We usually view peroxyacetyl nitrate (PAN) as the second most important photochemical pollutant featuring high mixing ratios during warm seasons. Our observations at a background site in the NCP identified high PAN concentrations, even during haze events in autumn. The substantial increasing ratios of PAN, by 244 % and 178 %, over the morning hours (08:00–12:00 local time) on 20 and 25 October 2020 were 10.6 and 7.7 times larger than those on clean days. Polluted days are characterized by higher temperature, higher humidity, and anomalous southerly winds compared with clean days. Enhanced local photochemistry has been identified as being the dominant factor that controls the PAN increase in the morning at the rural site, as the time when prevailing wind turns to a southerly wind is too late to promote direct transport of PAN from the polluted urban region. By removing the effect of direct transport of PAN, we provide a quantitative assessment of net PAN chemical production rate of 0.45 ppb h-1 for the mornings of polluted days, also demonstrating the strong local photochemistry. Using observations and calculated photolysis rates, we find that acetaldehyde oxidation by hydroxyl radical (OH) is the primary pathway of peroxyacetyl radical formation at the rural site. Acetaldehyde concentrations and production rates of HOx (HOx= OH + HO2) on polluted days are 2.8 and 2 times as large as those on clean days, leading to a remarkable increase in PAN in the morning. Formaldehyde (HCHO) photolysis dominates the daytime HOx production, thus contributing to fast photochemistry of PAN. Our observational results suggest the cause of a rapid increase in PAN during haze events in autumn at a rural site of the NCP and provide evidence of important role of HCHO photolysis in secondary pollutants at lower nitrogen oxide emissions. This highlights the urgency of carrying out strict volatile organic compound controls over the NCP during the cold season and not just in summer.

A Scoring System for Predicting the Prognosis of Late-Onset Severe Pneumonia after Allogeneic Hematopoietic Stem Cell Transplantation.

Late-onset severe pneumonia (LOSP) is defined as severe pneumonia developing during the late phase of allogeneic hematopoietic stem cell transplantation (allo-HSCT). Because of the high mortality in patients with LOSP, it is important to identify prognostic factors. In this study, we aimed to develop a risk score system with broad applicability that can help predict the risk of LOSP-associated mortality. We retrospectively analyzed 100 patients with LOSP after allo-HSCT between June 2009 and July 2017. The assessment variables included immune, nutritional, and metabolic parameters at the onset of LOSP. Of these 100 patients, 45 (45%) eventually died, and 55 (55%) were positive for organisms, most commonly viruses. In the multivariate analysis, higher monocyte count (≥0.20 × 109/L versus <0.20 × 109/L; P = .001), higher albumin level (≥30.5 g/L versus <30.5 g/L; P = .044), lower lactic dehydrogenase level (<250 U/L versus ≥250 U/L; P = .008) and lower blood urea nitrogen concentration (<7.2 mmol/L versus ≥7.2 mmol/L; P = .026) at the onset of LOSP were significantly associated with better 60-day survival. A risk score system based on the foregoing results showed that the probability of 60-day survival decreased with increasing risk factors, from 96.3% in the low-risk group to 49.1% in the intermediate-risk group and 12.5% in the high-risk group. Our results indicate that this scoring system using 4 variables can stratify patients with different probabilities of survival after LOSP, which suggests that patients' immune, nutritional, and metabolic status are crucial factors in determining outcome.

Recent advances in detection technologies for COVID-19.

Corona Virus Disease 2019 (COVID-19) is a highly infectious respiratory illness that was caused by the SARS-CoV-2. It spread around the world in just a few months and became a worldwide pandemic. Quick and accurate diagnosis of infected patients is very important for controlling transmission. In addition to the commonly used Real-time reverse-transcription polymerase chain reaction (RT-PCR) detection techniques, other diagnostic techniques are also emerging endlessly. This article reviews the current diagnostic methods for COVID-19 and discusses their advantages and disadvantages. It provides an important reference for the diagnosis of COVID-19.

Quantitative estimation of meteorological impacts and the COVID-19 lockdown reductions on NO2 and PM2.5 over the Beijing area using Generalized Additive Models (GAM).

Unprecedented travel restrictions due to the COVID-19 pandemic caused remarkable reductions in anthropogenic emissions, however, the Beijing area still experienced extreme haze pollution even under the strict COVID-19 controls. Generalized Additive Models (GAM) were developed with respect to inter-annual variations, seasonal cycles, holiday effects, diurnal profile, and the non-linear influences of meteorological factors to quantitatively differentiate the lockdown effects and meteorology impacts on concentrations of nitrogen dioxide (NO2) and fine particulate matters (PM2.5) at 34 sites in the Beijing area. The results revealed that lockdown measures caused large reductions while meteorology offset a large fraction of the decrease in surface concentrations. GAM estimates showed that in February, the control measures led to average NO2 reductions of 19 µg/m3 and average PM2.5 reductions of 12 µg/m3. At the same time, meteorology was estimated to contribute about 12 µg/m3 increase in NO2, thereby offsetting most of the reductions as well as an increase of 30 µg/m3 in PM2.5, thereby resulting in concentrations higher than the average PM2.5 concentrations during the lockdown. At the beginning of the lockdown period, the boundary layer height was the dominant factor contributing to a 17% increase in NO2 while humid condition was the dominant factor for PM2.5 concentrations leading to an increase of 65% relative to the baseline level. Estimated NO2 emissions declined by 42% at the start of the lockdown, after which the emissions gradually increased with the increase of traffic volumes. The diurnal patterns from the models showed that the peak of vehicular traffic occurred from about 12pm to 5pm daily during the strictest control periods. This study provides insights for quantifying the changes in air quality due to the lockdowns by accounting for meteorological variability and providing a reference in evaluating the effectiveness of control measures, thereby contributing to air quality mitigation policies.