Best fit probability distribution analysis of precipitation and potential evapotranspiration of India’s highly dense population state - Bihar

Planning of water resources and its management with the ambiguity and non-uniformity accompanying with precipitation and other meteorological physical characteristics may perhaps effect on agricultural production in Bihar where the farmers mostly depend on precipitation. The precipitation and potential evapotranspiration temporal distribution of the state is irregular due to geomorphology, climatic and other anthropogenic factors of the state. In the present study, attempt is taken to expose the best-fit probability distribution among the various available probability distribution of annual average precipitation and potential evapotranspiration based on 102 year of past records of all 37 districts of the state. On the basis of ranks of goodness of fit tests such as Kolmogorov Smirnov, Anderson Darling and Chi-Squared, the normal distribution was observed the best-fit probability distribution for 11 districts followed by Weibull (3P) for 9 districts, the Beta distribution for 5 districts and other distribution for rest districts for precipitation. Whereas Cauchy distribution was come out with the best-fit probability distribution for potential evapotranspiration for all districts and the second best was Gamma (3P) covering almost 60% of the total districts followed by General Extreme Value distribution (32%). The results can be used in future hydraulic design, hydrological study for estimation of return period and water resource planners for policy development.

Spatiotemporal Cluster Patterns of Hand, Foot, and Mouth Disease at the Province level in Mainland China, 2009-2018

BackgroundAlthough three monovalent EV-A71 vaccines have been launched in mainland China since 2016, hand, foot, and mouth disease (HFMD) still causes a considerable disease burden in China. Vaccines’ use may change the epidemiological characters of HFMD. This study aims to analyze the spatiotemporal cluster of HFMD at the province level in mainland China from 2009 to 2018 and compare the difference before and after the vaccines were launched. MethodsAll HFMD cases’ data from January 2009 to December 2018 were obtained from the public health science data center given by the Chinese Center for Diseases Control and Prevention. Spatial autocorrelation analysis and space-time scan statistics analysis were used to explore the spatiotemporal distribution pattern of this disease at the provincial level in mainland China. ResultsThe median annual incidence of HFMD was 143.22 per 100,000 (ranging from 87.01 to 205.06) in mainland China from 2009 to 2018. Two peaks of infections were observed per year. Children 5 years and under were the main morbid population. The global autocorrelation analysis showed that the spatial distribution of HFMD was presented a significant clustering pattern in each year (P<0.001), and the local autocorrelation analysis indicated that the high incidence areas were clustered in the southern and southeastern coastal provinces. The distribution of HFMD cases was clustered in time and space. The range of cluster time was between April and October. The most likely cluster appeared in the southern coastal provinces (Guangxi, Guangdong, Hainan) from 2010 to 2017 and in the southeastern coastal provinces (Shanghai, Jiangsu, Zhejiang) in 2018. ConclusionChanges in the spatiotemporal cluster of HFMD after the launch of EV-A71 vaccines were observed at the province level in mainland China in 2018. It is necessary to advance the EV-A71 vaccination plan, analyze the spatial-temporal distribution characteristics of different enterovirus pathogens of HFMD, and promote HFMD multivalent vaccines.

Temporal Distribution of Arsenic and Metals in Soil From King George Island, Antarctica

The polar regions are vulnerable to impacts caused by local and global pollution. The Antarctic continent has been considered an environment that has remained little affected by human activities. Direct exposure to contaminants may occur in areas continuously occupied by research stations for several decades. Admiralty Bay on the southeast coast of King George Island, has potential for being affected by human activities due research stations operating in the area, including the Brazilian Commandant Ferraz Antarctic Station (CFAS). The levels of metals and arsenic were determined in soils collected near CFAS (points 5, 6, 7, and 9), Base G and at two points distant from the CFAS: Refuge II and Hennequin. Samples were collected after the fire in CFAS occurred in February 2012, up to December 2018 to assess the environmental impacts in the area. Al and As were related with Base G. Refuge II and Hennequin can be considered as control points for this region. As a consequence of the accident, the increased levels for Cd, Cu, Pb, and Zn, especially at point 9 (inside the CFAS) and in the soil surrounding the CFAS in 2013. The results from 2016 to 2018 demonstrated a reduction in levels of all studied metals near CFAS, which may be related to the leaching of metals into Admiralty Bay; it is thus, being important the continue monitoring soil, sediments, and Antarctic biota.

A Genomic Snapshot of the SARS-CoV-2 Pandemic in the Balearic Islands

Objective: To analyze the SARS-CoV-2 genomic epidemiology in the Balearic Islands, a unique setting in which the course of the pandemic has been influenced by a complex interplay between insularity, severe social restrictions and tourism travels.Methods: Since the onset of the pandemic, more than 2,700 SARS-CoV-2 positive respiratory samples have been randomly selected and sequenced in the Balearic Islands. Genetic diversity of circulating variants was assessed by lineage assignment of consensus whole genome sequences with PANGOLIN and investigation of additional spike mutations.Results: Consensus sequences were assigned to 46 different PANGO lineages and 75% of genomes were classified within a VOC, VUI, or VUM variant according to the WHO definitions. Highest genetic diversity was documented in the island of Majorca (42 different lineages detected). Globally, lineages B.1.1.7 and B.1.617.2/AY.X were identified as the 2 major lineages circulating in the Balearic Islands during the pandemic, distantly followed by lineages B.1.177/B.1.177.X. However, in Ibiza/Formentera lineage distribution was slightly different and lineage B.1.221 was the third most prevalent. Temporal distribution analysis showed that B.1 and B.1.5 lineages dominated the first epidemic wave, lineage B.1.177 dominated the second and third, and lineage B.1.617.2 the fourth. Of note, lineage B.1.1.7 became the most prevalent circulating lineage during first half of 2021; however, it was not associated with an increased in COVID-19 cases likely due to severe social restrictions and limited travels. Additional spike mutations were rarely documented with the exception of mutation S:Q613H which has been detected in several genomes (n = 25) since July 2021.Conclusion: Virus evolution, mainly driven by the acquisition and selection of spike substitutions conferring biological advantages, social restrictions, and size population are apparently key factors for explaining the epidemic patterns registered in the Balearic Islands.

Statistical Method of Deducting Activation Energies for the Steam Methane Reforming Reactions

In this paper, a method of deducting activation energies for heterogeneous reactions of steam methane reforming is presented. The essence of the method lies in iterative evaluation of kinetic parameters, namely activation energies of reactions, for a given reactor. The novelty of the method lies in utilizing a statistical approach to reduce computational effort of numerical simulation. The method produces multivariable correlations between activation energies and operational parameters of the process: pressure, temperature, steam-to-methane ratio, residence time, and catalyst properties. These correlations can be used for numerical simulations of steam methane reforming to yield methane conversion rate, spatial and temporal distribution of reaction products, temperature and pressure within the reactor. An average computational effort is equal to a batch of 18 ([Formula: see text]) simulations for [Formula: see text] variables. The method was demonstrated by evaluating two-variable correlations of activation energies with pressure and temperature. The developed numerical model was validated against adopted experimental data.