Objective: Humidity and temperature are fundamental for the balance in the life cycle of living beings and, consequently, for maintaining the well-being of the human population and reducing the prevalence of infectious diseases. Thus, in order to mitigate the impact of climate change, especially in the period when humidity is not the ideal, it is necessary to adopt some assistance measures. The present experimental study aims to elucidate what would be the recommended option to improve the quality of life of the human being and to clarify which resources (air humidifier, bucket of water or wet towel) will be effective to improve the humidity of the air in times of drought and low moisture.
Methods: The experimental study was carried out with INKBIRD hygrometers allowing the analysis of the variation of air humidity throughout the day. Three forms of treatment were established: humidifier, wet towel and bucket of water. In each room, two hygrometers were placed equidistant from the occupant of the room and their respective treatment that varied between 1m and 2m away from the headboard indoor each room. In addition, two environments were used as controls, one being an external environment and the other an internal closed environment, totaling five rooms for the study. The rooms were monitored between the end of July and the end of August 2019 in Goiania (GO).
Results: Although assistance measures are used to significantly improve air pollution in times of extreme drought, there was a significant difference between them. The humidifier and a wet towel had 7.50% and 5.71% more humidity in the external relation (external control), respectively, more efficient. The volume of water, however, did not show significant difference (p>0.05) and, therefore, there was no variation.
Conclusion: The humidifier and the towel are treatments considered more efficient, and that there was a significant effect of distance on humidity. Therefore, 1m of distance is more efficient in increasing and/or maintaining air humidity, inducing improvements in the populations' health.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8225259 | PMC |
http://dx.doi.org/10.31744/einstein_journal/2021AO5484 | DOI Listing |
Front Public Health
January 2025
Shandong Academy of Chinese Medicine, Jinan, China.
Background: Night sweats are a condition in which an individual sweats excessively during sleep without awareness, and stops when they wake up. Prolonged episodes of night sweats might result in the depletion of trace elements and nutrients, affecting the growth and development of children.
Purpose: To investigate the relationship between sweat nights and season.
Sci Rep
January 2025
School of Public Health, Xinjiang Medical University, Urumqi, China.
The context of rapid global environmental change underscores the pressing necessity to investigate the environmental factors and high-risk areas that contribute to the occurrence of brucellosis. In this study, a maximum entropy (MaxEnt) model was employed to analyze the factors influencing brucellosis in the Aksu Prefecture from 2014 to 2023. A distributed lag nonlinear model (DLNM) was employed to investigate the lagged effect of meteorological factors on the occurrence of brucellosis.
View Article and Find Full Text PDFSci Total Environ
January 2025
School of Agriculture and Biology, Shanghai Jiao Tong University, 800 Dongchuan Rd., Shanghai 200240, China; Shanghai Yangtze River Delta Eco-Environmental Change and Management Observation and Research Station, Ministry of Science and Technology, Ministry of Education, 800 Dongchuan Rd., Shanghai 200240, China; Shanghai Urban Forest Ecosystem Research Station, National Forestry and Grassland Administration, 800 Dongchuan Rd., Shanghai 200240, China; Key Laboratory for Urban Agriculture, Ministry of Agriculture and Rural Affairs, 800 Dongchuan Rd., Shanghai 200240, China. Electronic address:
Biogenic volatile organic compounds (BVOCs) are emitted by urban vegetation and can interact with anthropogenic pollutants to generate secondary organic aerosols (SOA) that are atmospheric pollutants in urban environments. In urban forests, SOA comprise up to 90 % of all fine aerosols (particulate matter smaller than 1 μm [PM]) in the summer. PM can greatly affect urban air quality and public health.
View Article and Find Full Text PDFJ Hazard Mater
January 2025
Monash Lung, Sleep, Allergy and Immunology, Monash Health, Melbourne, VIC, Australia; School of Clinical Sciences, Monash University, Melbourne, VIC, Australia; Monash Partners - Epworth, Melbourne, VIC, Australia.
Mitigation measures against infectious aerosols are desperately needed. We aimed to: 1) compare germicidal ultraviolet radiation (GUV) at 254 nm (254-GUV) and 222 nm (222-GUV) with portable high efficiency particulate air (HEPA) filters to inactivate/remove airborne bacteriophage ϕX174, 2) measure the effect of air mixing on the effectiveness of 254-GUV, and 3) determine the relative susceptibility of ϕX174, SARS-CoV-2, and Influenza A(H3N2) to GUV (254 nm, 222 nm). A nebulizer generated ϕX174 laden aerosols in an occupied clinical room (sealed-low flow).
View Article and Find Full Text PDFPLoS One
January 2025
Renewable Energy Science and Engineering Department, Faculty of Postgraduate Studies for Advanced Sciences (PSAS), Beni-Suef University, Beni-Suef, Egypt.
This study presents a comprehensive comparative analysis of Machine Learning (ML) and Deep Learning (DL) models for predicting Wind Turbine (WT) power output based on environmental variables such as temperature, humidity, wind speed, and wind direction. Along with Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN), the following ML models were looked at: Linear Regression (LR), Support Vector Regressor (SVR), Random Forest (RF), Extra Trees (ET), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). Using a dataset of 40,000 observations, the models were assessed based on R-squared, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE).
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!