This comparative study analyzes the impact of the COVID-19 pandemic on motorized mobility in eight large cities of five Latin American countries. Public institutions and private organizations have made public data available for a better understanding of the contagion process of the pandemic, its impact, and the effectiveness of the implemented health control measures. In this research, data from the IDB Invest Dashboard were used for traffic congestion as well as data from the Moovit© public transport platform. For the daily cases of COVID-19 contagion, those published by Johns Hopkins Hospital University were used. The analysis period corresponds from 9 March to 30 September 2020, approximately seven months. For each city, a descriptive statistical analysis of the loss and subsequent recovery of motorized mobility was carried out, evaluated in terms of traffic congestion and urban transport through the corresponding regression models. The recovery of traffic congestion occurs earlier and faster than that of urban transport since the latter depends on the control measures imposed in each city. Public transportation does not appear to have been a determining factor in the spread of the pandemic in Latin American cities.

Agrometeorological stations have horizontal solar irradiation data available, but the design and simulation of photovoltaic (PV) systems require data about the solar panel (inclined and/or oriented). Greenhouses for agricultural production, outside the large protected production areas, are usually off-grid; thus, the solar irradiation variable on the panel plane is critical for an optimal PV design. Modeling of solar radiation components (beam, diffuse, and ground-reflected) is carried out by calculating the extraterrestrial solar radiation, solar height, angle of incidence, and diffuse solar radiation. In this study, the modeling was done using Simulink-MATLAB blocks to facilitate its application, using the day of the year, the time of day, and the hourly horizontal global solar irradiation as input variables. The rest of the parameters (i.e., inclination, orientation, solar constant, albedo, latitude, and longitude) were fixed in each block. The results obtained using anisotropic models of diffuse solar irradiation of the sky in the region of Castile and León (Spain) showed improvements over the results obtained with isotropic models. This work enables the precise estimation of solar irradiation on a solar panel flexibly, for particular places, and with the best models for each of the components of solar radiation.

This study evaluates the predictive modeling of the daily ambient temperature (maximum, T_max; average, T_ave; and minimum, T_min) and its hourly estimation (T_0h, …, T_23h) using artificial neural networks (ANNs) for agricultural applications. The data, 2004–2010, were used for training and 2011 for validation, recorded at the SIAR agrometeorological station of Mansilla Mayor (León). ANN models for daily prediction have three neurons in the output layer (T_max(t + 1), T_ave(t + 1), T_min(t + 1)). Two models were evaluated: (1) with three entries (T_max(t), T_ave(t), T_min(t)), and (2) adding the day of the year (J(t)). The inclusion of J(t) improves the predictions, with an RMSE for T_max = 2.56, T_ave = 1.65 and T_min = 2.09 (°C), achieving better results than the classical statistical methods (typical year T_ave = 3.64 °C; weighted moving mean T_max = 2.76, T_ave = 1.81 and T_min = 2.52 (°C); linear regression T_ave = 1.85 °C; and Fourier T_max = 3.75, T_ave = 2.67 and T_min = 3.34 (°C)) for one year. The ANN models for hourly estimation have 24 neurons in the output layer (T_0h(t), …, T_23h(t)) corresponding to the mean hourly temperature. In this case, the inclusion of the day of the year (J(t)) does not significantly improve the estimations, with an RMSE = 1.25 °C, but it improves the results of the ASHRAE method, which obtains an RMSE = 2.36 °C for one week. The results obtained, with lower prediction errors than those achieved with the classical methods, confirm the interest in using the ANN models for predicting temperatures in agricultural applications.

Se realiza una revisión de la usabilidad, impactos ambientales y costos que han reportado en investigaciones previas sobre el uso de los vehículos de motor de combustión interna, eléctrico a baterías e híbridos. Inicialmente se revisa su desarrollo y evaluaciones hechas por distintos grupos de investigadores. Puede observarse como en el tiempo se van añadiendo ventajas al uso de los vehículos eléctricos sobre los de combustión interna. En cuanto a los vehículos de combustión interna se observan ventajas en el uso de diésel con respecto a la gasolina.