No abstract

No abstract

No abstract

No abstract

It is paramount to expand the knowledge base and minimize the consequences of the pandemic caused by the new Coronavirus (SARS-Cov2). Spain is among the most affected countries that declared a countrywide lockdown. An ecological study is presented herein, assessing the trends for incidence, mortality, hospitalizations, Intensive Care Unit admissions, and recoveries per autonomous community in Spain. Trends were evaluated by the Joinpoint software. The timeframe employed was when the lockdown was declared on March 14, 2020. Daily percentage changes were also calculated, with CI = 95% and p<0.05. An increase was detected, followed by reduction, for the evaluated indicators in most of the communities. Approximately 18.33 days were required for the mortality rates to decrease. The highest mortality rate was verified in Madrid (118.89 per 100,000 inhabitants) and the lowest in Melilla (2.31). The highest daily percentage increase in mortality occurred in Catalonia. Decreasing trends were identified after approximately two weeks of the institution of the lockdown by the government. Immediately the lockdown was declared, an increase of up to 33.96% deaths per day was verified in Catalonia. In contrast, Ceuta and Melilla presented significantly lower rates because they were still at the early stages of the pandemic at the moment of lockdown. The findings presented herein emphasize the importance of early and assertive decision-making to contain the pandemic.

Summary The implementation of mobility restrictions and home office schemes due to the COVID‐19 pandemic have influenced electricity consumption patterns and levels. This study analyzes the effect of physical distancing measures regarding mobility on the energy consumption trends for the Brazilian energy system and its subsystems (Northeast, North, South, and Southeast‐Midwest). Trends were evaluated by the Joinpoint software, and the analysis comprehended the period between January 1 and May 27, 2020. Daily load data was grouped into weeks, with the calculation of weekly percentage changes considering a 95% confidence interval and p < 0.05. The weekly electricity loads were compared in the periods before and after the isolation decrees were enforced in Brazil (March 15, 2020). Statistically significant decreases were observed in the levels of electricity consumption, with trends represented by two joinpoints. Due to the different profiles of consumption across the geographic regions, the resulting electricity dynamics were also different. This is the first study to employ joinpoint analysis for the calculation of energy consumption trends focusing on the COVID‐29 pandemic. Data presented herein is unique, in its focus on Brazil, which enables more accurate implications to be drawn for Brazilian policy makers.

Environmental information obtained through Life Cycle Analysis techniques has been incorporated into a Mixed Integer Linear Programming (MILP). The solution of the model provides the optimal configuration and operation of an energy supply system to be installed, minimizing the environmental burden associated with production of equipment and consumption of resources. Starting from a superstructure of cogeneration system with additional components highly interconnected, the energy supply system was optimized considering specific demands of a hospital located in Zaragoza, Spain. The objective functions took into account the kilograms of COB 2 B released and Eco-indicator 99 Single Score.Also considered were price of energy resources, price and amortization possibilities of the equipment and options for selling surplus electricity to the electric grid. The effect of electricity generation conditions on the optimal configuration was examined by varying the source of electricity production in Spain and considering natural gas/electricity mixes from alternate countries. The ratio between local electricity emissions and natural gas emissions (α factor) was found to have the highest impact on the configuration of the system. Therefore the α factor could be considered the strongest influencing factor when deciding the optimal configuration of a system that minimizes environmental loads.

No abstract