During the SARS-CoV-2 pandemic, sound pressure levels (SPL) decreased because of lockdown measures all over the world. This study aims to describe SPL changes over varying lockdown measure timeframes and estimate the role of traffic on SPL variations. To account for different COVID-19 lockdown measures, the timeframe during the pandemic was segmented into four phases. To analyze the association between a-weighted decibels (dB(A)) and lockdown phases relative to the pre-lockdown timeframe, we calculated a linear mixed model, using 36,710 h of recording time. Regression coefficients depicting SPL changes were compared, while the model was subsequently adjusted for wind speed, rainfall, and traffic volume. The relative adjusted reduction of during pandemic phases to pre-pandemic levels ranged from -0.99 dB(A) (CI: -1.45; -0.53) to -0.25 dB(A) (CI: -0.96; 0.46). After controlling for traffic volume, we observed little to no reduction (-0.16 dB(A) (CI: -0.77; 0.45)) and even an increase of 0.75 dB(A) (CI: 0.18; 1.31) during the different lockdown phases. These results showcase the major role of traffic regarding the observed reduction. The findings can be useful in assessing measures to decrease noise pollution for necessary future population-based prevention.

This work is focused on the importance of developing and promoting the use of wind and solar energy resources in the Colombian Caribbean coast. This region has a considerable interest for the development of solar technology due to the available climatic characteristics. Therefore, a detailed solarimetric analysis has been carried out in the department of San Andrés, Providencia and Santa Catalina, located in the Colombian Caribbean region, using a semi-empirical radiation model, based on the Bird & Hulstrom model, and the parameterizations of the Mächler & Iqbal model, which allowed obtaining an average total irradiation value of 6.5 kWh/m2day. In addition, a statistical analysis of the wind resource was carried out based on meteorological data, which yielded an average multiannual wind speed of 3.4 m/s, and a maximum wind speed of 15.2 m/s during the month of October. The meteorological input data used for this analysis were provided by the Colombian Institute of Hydrology, Meteorology and Environmental Studies (IDEAM), in order to perform initial calculations and obtain a climatic profile of the areas with clear, medium and cloudy atmospheres throughout the year. Regarding the comparative study, the analysis was complemented with a prediction of solar radiation using Artificial Neural Networks (ANN), where irradiance could be predicted with a fairly good agreement, which was validated with a Root Mean Square Error (RMSE) of 0.87 using the temperature, the relative humidity, the pressure and the wind speed as the input data.

The optimal design and performance monitoring of wind farms depend on the precise assessment of spatial and temporal distribution of wind speed. The aim of this research is to investigate the appropriateness of nine popular probability distribution models (exponential, gamma, generalised extreme value, inverse Gaussian, Kumaraswamy, log-logistic, lognormal, Nakagami, and Weibull) for the assessment of wind speed distribution (WSD) at 10 sites situated at topographically distinct locations in Tamil Nadu, India, based on 39 years of data. The results suggest that a single distribution cannot produce best fit for all the stations. On an individual level, the generalised extreme value distribution provided the most suitable fit for majority of the stations, followed by the Kumaraswamy distribution. The Kumaraswamy distribution has performed well even if the WSD of the station is negatively skewed. Hence, based on the ranking and performance consistency, the Kumaraswamy distribution can be preferred irrespective of the topographical heterogeneity of the stations.

This study aimed to evaluate the relationship between weather factors (temperature, humidity, solar radiation, wind speed, and rainfall) and COVID-19 infection in the State of Rio de Janeiro, Brazil. Solar radiation showed a strong (-0.609, p < 0.01) negative correlation with the incidence of novel coronavirus (SARS-CoV-2). Temperature (maximum and average) and wind speed showed negative correlation (p < 0.01). Therefore, in this studied tropical state, high solar radiation can be indicated as the main climatic factor that suppress the spread of COVID-19. High temperatures, and wind speed also are potential factors. Therefore, the findings of this study show the ability to improve the organizational system of strategies to combat the pandemic in the State of Rio de Janeiro, Brazil, and other tropical countries around the word.

Although several studies have investigated the effects of ambient temperature on the risk of stroke, few studies have examined the relationship between other meteorological conditions and stroke. Therefore, the aim of this study was to analyze the association between wind-related variables and stroke symptoms onset.
Data regarding the onset of stroke symptoms occurring between January 1, 2006, and December 31, 2007 on Jeju Island were collected from the Jeju National University Hospital stroke registry. A fixed-strata case-crossover analysis based on time of onset and adjusted for ambient temperature, relative humidity, air pressure, and pollutants was used to analyze the effects of wind speed, the daily wind speed range (DWR), and the wind chill index on stroke symptom onset using varied lag terms. Models examining the modification effects by age, sex, smoking status, season, and type of stroke were also analyzed.
A total of 409 stroke events (381 ischemic and 28 hemorrhagic) were registered between 2006 and 2007. The odds ratios (ORs) for wind speed, DWR, and wind chill among the total sample at lag 0-8 were 1.18 (95% confidence interval (CI): 1.06-1.31), 1.08 (95% CI: 1.02-1.14), and 1.22 (95% CI: 1.07-1.39) respectively. The ORs for wind speed, DWR, and wind chill for ischemic stroke patients were slightly greater than for patients in the total sample (OR=1.20, 95% CI: 1.08-1.34; OR=1.09, 95% CI: 1.03-1.15; and OR=1.22, 95% CI: 1.07-1.39, respectively). Statistically significant season-specific effects were found for spring and winter, and various delayed effects were observed. In addition, age, sex, and smoking status modified the effect size of wind speed, DWR, and wind chill.
Our analyses showed that the risk of stroke symptoms onset was associated with wind speed, DWR, and wind chill on Jeju Island.