scholarly journals Daily indoor-to-outdoor temperature and humidity relationships: a sample across seasons and diverse climatic regions

2015 ◽  
Vol 60 (2) ◽  
pp. 221-229 ◽  
Author(s):  
Jennifer L. Nguyen ◽  
Douglas W. Dockery
Keyword(s):  
Outdoor Temperature ◽  
Climatic Regions ◽  
Temperature And Humidity

Effect of pretest temperature on aerosol penetration and clearance in donkeys

1976 ◽  
Vol 41 (6) ◽  
pp. 920-924 ◽  
Author(s):  
D. E. Bohning ◽  
R. E. Albert ◽  
M. Lippmann ◽  
V. R. Cohen
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Aerosol Deposition ◽  
Multiple Linear Regression Analysis ◽  
Outdoor Temperature ◽  
Deposition Pattern ◽  
Particle Penetration ◽  
Temperature And Humidity ◽  
Humidity Dependence

Pretest temperature and humidity were correlated with tracheobronchial particle penetration and clearance data from donkeys housed in unheated outdoor facilities and tested after spending 1–2 h in a temperature- andhumidity-controlled laboratory. The animals inhaled an inert insoluble radioisotope-labeled monodisperse aerosol for several minutes. Its retention was monitored continuously for 3 h by external gamma detection. Aerosol deposition pattern and bronchial clearance were linearly correlated with pretestoutdoor temperature which ranged from -10 to 30 degrees C. The fraction depositing in the unciliated regions of the lung decreased 0.6% per degrees C drop in outdoor temperature. Overall bronchial transport decreased at least1.5%per degrees C decrease. Multiple linear regression analysis and correction for the positive correlation between temperature and humidity left no significant residual humidity dependence. Acclimatization of the animals in the laboratory for 6 h before testing significantly reduced these effects.

Influence of outdoor temperature and humidity on the methacholine challenge test

Aerobiologia ◽  
2012 ◽  
Vol 29 (2) ◽  
pp. 187-200
Author(s):  
Bruno Sposato ◽  
Marco Scalese ◽  
Andrea Pammolli ◽  
Carlo Pareo ◽  
Raffaele Scala
Keyword(s):  
Challenge Test ◽  
Outdoor Temperature ◽  
Methacholine Challenge ◽  
Methacholine Challenge Test ◽  
Temperature And Humidity

scholarly journals Temperature and Humidity Control for the Next Generation Greenhouses: Overview of Desiccant and Evaporative Cooling Systems

2021 ◽  
Author(s):  
Muhammad Sultan ◽  
Hadeed Ashraf ◽  
Takahiko Miyazaki ◽  
Redmond R. Shamshiri ◽  
Ibrahim A. Hameed
Keyword(s):  
Molecular Sieves ◽  
Air Conditioning ◽  
Activated Carbons ◽  
Evaporative Cooling ◽  
Next Generation ◽  
Cooling Systems ◽  
Humidity Control ◽  
Climatic Regions ◽  
Multi Stage ◽  
Temperature And Humidity

Temperature and humidity control are crucial in next generation greenhouses. Plants require optimum temperature/humidity and vapor pressure deficit conditions inside the greenhouse for optimum yield. In this regard, an air-conditioning system could provide the required conditions in harsh climatic regions. In this study, the authors have summarized their published work on different desiccant and evaporative cooling options for greenhouse air-conditioning. The direct, indirect, and Maisotsenko cycle evaporative cooling systems, and multi-stage evaporative cooling systems have been summarized in this study. Different desiccant materials i.e., silica-gels, activated carbons (powder and fiber), polymer sorbents, and metal organic frameworks have also been summarized in this study along with different desiccant air-conditioning options. However, different high-performance zeolites and molecular sieves are extensively studied in literature. The authors conclude that solar operated desiccant based evaporative cooling systems could be an alternate option for next generation greenhouse air-conditioning.

Correlating indoor and outdoor temperature and humidity in a sample of buildings in tropical climates

Indoor Air ◽  
2021 ◽  
Author(s):  
Jin Pan ◽  
Julian Tang ◽  
Miguela Caniza ◽  
Jean‐Michel Heraud ◽  
Evelyn Koay ◽  
...  
Keyword(s):  
Outdoor Temperature ◽  
Temperature And Humidity ◽  
Tropical Climates ◽  
Indoor And Outdoor

scholarly journals Socioeconomic and Outdoor Meteorological Determinants of Indoor Temperature and Humidity in New York City Dwellings*

2013 ◽  
Vol 5 (2) ◽  
pp. 168-179 ◽  
Author(s):  
J. D. Tamerius ◽  
M. S. Perzanowski ◽  
L. M. Acosta ◽  
J. S. Jacobson ◽  
I. F. Goldstein ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
Relative Humidity ◽  
Human Health ◽  
York City ◽  
Outdoor Temperature ◽  
Indoor Climate ◽  
Indoor Temperature ◽  
Temperature And Humidity ◽  
The Relationship

Abstract Numerous mechanisms link outdoor weather and climate conditions to human health. It is likely that many health conditions are more directly affected by indoor rather than outdoor conditions. Yet, the relationship between indoor temperature and humidity conditions to outdoor variability, and the heterogeneity of the relationship among different indoor environments are largely unknown. The authors use 5–14-day measures of indoor temperature and relative humidity from 327 dwellings in New York City New York, for the years 2008–11 to investigate the relationship between indoor climate, outdoor meteorological conditions, socioeconomic conditions, and building descriptors. Study households were primarily middle income and located across the boroughs of Brooklyn, Queens, Bronx, and Manhattan. Indoor temperatures are positively associated with outdoor temperature during the warm season and study dwellings in higher socioeconomic status neighborhoods are significantly cooler. During the cool season, outdoor temperatures have little effect on indoor temperatures; however, indoor temperatures can range more than 10°C between dwellings despite similar outdoor temperatures. Apartment buildings tend to be significantly warmer than houses and dwellings on higher floors are also significantly warmer than dwellings on lower floors. Outdoor specific humidity is positively associated with indoor specific and relative humidity, but there is no consistent relationship between outdoor and indoor relative humidity. In New York City, the relationship between indoor and outdoor temperature and humidity conditions varies significantly between dwellings. These results can be used to inform studies of health outcomes for which temperature or humidity is an established factor affecting human health. The results highlight the need for more research on the determinants of indoor climate.

Investigation on Thermal Environment in Railway Station Waiting Room

2014 ◽  
Vol 955-959 ◽  
pp. 4100-4103
Author(s):  
Yu Hui Di ◽  
Zi Long Xu ◽  
Chun Yang Jiang
Keyword(s):  
Thermal Comfort ◽  
Field Measurement ◽  
Thermal Environment ◽  
Outdoor Temperature ◽  
Waiting Room ◽  
Railway Station ◽  
Temperature And Humidity ◽  
Indoor And Outdoor

Takes a field measurement in traffic statistics, indoor and outdoor temperature and humidity, analyzes the thermal environment of the railway station waiting room of Xi’an. Investigates the thermal comfort status using ASHRAE seven sensation scales by questionnaires, survey the people’s satisfaction to the environment of waiting room.

scholarly journals Classification of pavement climatic regions through unsupervised and supervised machine learnings

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Qiao Dong ◽  
Xueqin Chen ◽  
Shi Dong ◽  
Jun Zhang
Keyword(s):  
Machine Learning ◽  
Factor Analysis ◽  
Discriminant Analysis ◽  
Common Factors ◽  
Climatic Variables ◽  
Supervised Machine Learning ◽  
Misclassification Rate ◽  
Climatic Data ◽  
Climatic Regions ◽  
Temperature And Humidity

AbstractThis study extracted 16 climatic data variables including annual temperature, freeze thaw, precipitation, and snowfall conditions from the Long-term Pavement Performance (LTPP) program database to evaluate the climatic regionalization for pavement infrastructure. The effect and significance of climate change were firstly evaluated using time as the only predictor and t-test. It was found that both the temperature and humidity increased in most States. Around one third of the 800 weather stations record variation of freeze and precipitation classifications and a few of them show significant change of classifications over time based on the results of logistic regression analyses. Three unsupervised machine learning including Principle Component Analysis (PCA), factor analysis and cluster analysis were conducted to identify the main component and common factors for climatic variables, and then to classify datasets into different groups. Then, two supervised machine learning methods including Fisher’s discriminant analysis and Artificial Neural Networks (ANN) were adopted to predict the climatic regions based on climatic data. Results of PCA and factor analysis show that temperature and humidity are the first two principle components and common factors, accounting for 71.6% of the variance. The 4-means clusters include wet no freeze, dry no freeze, dry freeze and snow freeze. The best k-mean clustering suggested 9 clusters with more temperature clusters. Both the Fisher’s linear discriminant analysis and ANN can effectively predict climatic regions with multiple climatic variables. ANN performs better with higher R square and low misclassification rate, especially for those with more layers and nodes.

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