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Journal articles on the topic 'Relative humidity'

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Published: 4 June 2021
Last updated: 9 March 2023

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1

Martin, R. Bruce. "Relative Humidity." Journal of Chemical Education 76, no. 8 (August 1999): 1081. http://dx.doi.org/10.1021/ed076p1081.

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2

Coulon, G. M., and S. Ballas. "OUTDOOR RELATIVE HUMIDITY ESTIMATION." Acta Horticulturae, no. 304 (March 1992): 327–34. http://dx.doi.org/10.17660/actahortic.1992.304.37.

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3

Erhardt, David, and Marion Mecklenburg. "Relative humidity re-examined." Studies in Conservation 39, sup2 (January 1994): 32–38. http://dx.doi.org/10.1179/sic.1994.39.supplement-2.32.

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4

Bucher, Manfred. "Diagram for relative humidity." Physics Teacher 24, no. 6 (September 1986): 348. http://dx.doi.org/10.1119/1.2342041.

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5

Seguin, John. "Relative Humidity Under Radiant Warmers: Influence of Humidifier and Ambient Relative Humidity." American Journal of Perinatology 14, no. 09 (October 1997): 515–18. http://dx.doi.org/10.1055/s-2007-994325.

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6

Gierens, K., and K. Eleftheratos. "Upper-tropospheric humidity changes under constant relative humidity." Atmospheric Chemistry and Physics Discussions 15, no. 20 (October 29, 2015): 29497–521. http://dx.doi.org/10.5194/acpd-15-29497-2015.

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Abstract. Theoretical derivations are given on the change of upper-tropospheric humidity (UTH) in a warming climate. Considered view is that the atmosphere, getting moister with increasing temperatures, will retain a constant relative humidity. In the present study we show that the upper-tropospheric humidity, a weighted mean over a relative humidity profile, will change in spite of constant relative humidity. The simple reason for this is that the weighting function, that defines UTH, changes in a moister atmosphere. Through analytical calculations using observations and through radiative transfer calculations we demonstrate that two quantities that define the weighting function of UTH can change: the water vapour scale height and the peak emission altitude. Applying these changes to real profiles of relative humidity shows that absolute UTH changes typically do not exceed 1 %. If larger changes would be observed they would be an indication of climatological changes of relative humidity. As such, an increase in UTH between 1980 and 2009 in the northern midlatitudes as shown by earlier studies using HIRS data, may be an indication of an increase in relative humidity as well.
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7

Gierens, Klaus, and Kostas Eleftheratos. "Upper tropospheric humidity changes under constant relative humidity." Atmospheric Chemistry and Physics 16, no. 6 (March 30, 2016): 4159–69. http://dx.doi.org/10.5194/acp-16-4159-2016.

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Abstract. Theoretical derivations are given on the change of upper tropospheric humidity (UTH) in a warming climate. The considered view is that the atmosphere, which is getting moister with increasing temperatures, will retain a constant relative humidity. In the present study, we show that the upper tropospheric humidity, a weighted mean over a relative humidity profile, will change in spite of constant relative humidity. The simple reason for this is that the weighting function that defines UTH changes in a moister atmosphere. Through analytical calculations using observations and through radiative transfer calculations, we demonstrate that two quantities that define the weighting function of UTH can change: the water vapour scale height and the peak emission altitude. Applying these changes to real profiles of relative humidity shows that absolute UTH changes typically do not exceed 1 %. If larger changes would be observed they would be an indication of climatological changes of relative humidity. As such, an increase in UTH between 1980 and 2009 in the northern midlatitudes, as shown by earlier studies using the High-resolution Infrared Radiation Sounder (HIRS) data, may be an indication of an increase in relative humidity as well.
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8

Liedberg, H. G., M. R. Mnguni, and D. Jonker. "A Simple Humidity Generator for Relative Humidity Calibrations." International Journal of Thermophysics 29, no. 5 (May 21, 2008): 1660–67. http://dx.doi.org/10.1007/s10765-008-0423-z.

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9

Matbabayev, M. "The Optoelectronic Sensor Relative Humidity." Bulletin of Science and Practice 6, no. 10 (October 15, 2020): 244–52. http://dx.doi.org/10.33619/2414-2948/59/24.

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This paper discusses the main characteristics of atmospheric air, the selected closed object on which the relative humidity depends to a certain extent, as well as a laboratory installation for studying the principle of constructing an optoelectronic sensor for measuring relative humidity. A description and diagram of the air humidity sensor, a block diagram of the installation for continuous monitoring of air humidity in the controlled object, a device for calibrating humidity sensors, and an algorithm for calibrating humidity sensors are given.
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10

Batirovich, Khayriddinov Akmal. "Relative humidity in green houses." ACADEMICIA: AN INTERNATIONAL MULTIDISCIPLINARY RESEARCH JOURNAL 11, no. 1 (2021): 823–28. http://dx.doi.org/10.5958/2249-7137.2021.00157.9.

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11

Gradon, Lewis. "Relative Humidity With Heating Wire." Critical Care Medicine 21, no. 10 (October 1993): 1613. http://dx.doi.org/10.1097/00003246-199310000-00036.

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12

Salib, Emad, and Nicola Sharp. "Relative humidity and affective disorders." International Journal of Psychiatry in Clinical Practice 6, no. 3 (January 2002): 147–53. http://dx.doi.org/10.1080/136515002760276072.

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13

Michalski, Stefan. "A relative humidity control module." Museum International 37, no. 2 (June 1985): 85–88. http://dx.doi.org/10.1111/j.1468-0033.1985.tb00556.x.

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14

Raymond, William H. "Moisture Advection Using Relative Humidity." Journal of Applied Meteorology 39, no. 12 (December 2000): 2397–408. http://dx.doi.org/10.1175/1520-0450(2000)039<2397:maurh>2.0.co;2.

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15

N. N. SRIVASTAVA, V.U.M. RAO, U. S. SAIKIA, P. VIJAYA KUMAR, and A.V.M. SUBBA RAO. "Modelling diurnal pattern of relative humidity from daily air temperature and relative humidity data of Hyderabad." Journal of Agrometeorology 13, no. 1 (June 1, 2011): 25–30. http://dx.doi.org/10.54386/jam.v13i1.1329.

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This study presents a simple methodology to estimate diurnal patterns in dew point temperature/relative humidity on weekly basis from mean minimum temperature or the morning relative humidity under semi-arid climatic conditions of Hyderabad. The sinusoidal and exponential models for day time and night time temperature pattern have been utilized in working out the diurnal patterns of the relative humidity on weekly basis at Hyderabad. Diurnal variation in dew point has been found to have, by and large, a set pattern depending upon the morning dew point temperature. Diurnal relative humidity patterns have been worked out both from the estimated dew point/relative humidity and the actual recorded relative humidity. This model is expected to be quite useful for long term retrospective agro-climatological studies wherein daily relative humidity and/or diurnal patterns thereof are required as input.
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16

Sujau, M., I. Merts, and D. J. Cleland. "RELATIVE HUMIDITY CONTROL IN REFRIGERATED FACILITIES." Acta Horticulturae, no. 687 (July 2005): 313–20. http://dx.doi.org/10.17660/actahortic.2005.687.38.

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17

Kärcher, B., and W. Haag. "Factors controlling upper tropospheric relative humidity." Annales Geophysicae 22, no. 3 (March 19, 2004): 705–15. http://dx.doi.org/10.5194/angeo-22-705-2004.

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Abstract. Factors controlling the distribution of relative humidity in the absence of clouds are examined, with special emphasis on relative humidity over ice (RHI) under upper tropospheric and lower stratospheric conditions. Variations of temperature are the key determinant for the distribution of RHI, followed by variations of the water vapor mixing ratio. Multiple humidity modes, generated by mixing of different air masses, may contribute to the overall distribution of RHI, in particular below ice saturation. The fraction of air that is supersaturated with respect to ice is mainly determined by the distribution of temperature. The nucleation of ice in cirrus clouds determines the highest relative humdity that can be measured outside of cirrus clouds. While vertical air motion and ice microphysics determine the slope of the distributions of RHI, as shown in a separate study companion (Haag et al., 2003), clouds are not required to explain the main features of the distributions of RHI below the ice nucleation threshold. Key words. Atmospheric composition and structure (pressure, density and temperature; troposphere – composition and chemistry; general or miscellaneous)
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18

Denson, Eleanor, Conrad Wasko, and Murray C. Peel. "Decreases in relative humidity across Australia." Environmental Research Letters 16, no. 7 (July 1, 2021): 074023. http://dx.doi.org/10.1088/1748-9326/ac0aca.

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19

Cho, G. J., K. H. Ahn, L. Y. Kim, S. Y. Hwang, S. C. Hong, M. J. Oh, and H. J. Kim. "Effect of relative humidity on preeclampsia." Clinical and Experimental Obstetrics & Gynecology 44, no. 2 (April 10, 2017): 264–67. http://dx.doi.org/10.12891/ceog3462.2017.

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20

Parrott, L. J. "Factors influencing relative humidity in concrete." Magazine of Concrete Research 43, no. 154 (March 1991): 45–52. http://dx.doi.org/10.1680/macr.1991.43.154.45.

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21

Xie, Jingrui, Ying Chen, Tao Hong, and Thomas D. Laing. "Relative Humidity for Load Forecasting Models." IEEE Transactions on Smart Grid 9, no. 1 (January 2018): 191–98. http://dx.doi.org/10.1109/tsg.2016.2547964.

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22

David Suits, L., TC Sheahan, LA Oldecop, and EE Alonso. "Testing Rockfill Under Relative Humidity Control." Geotechnical Testing Journal 27, no. 3 (2004): 11847. http://dx.doi.org/10.1520/gtj11847.

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23

Smith, R. D. "Seed storage, temperature and relative humidity." Seed Science Research 2, no. 2 (June 1992): 113–16. http://dx.doi.org/10.1017/s0960258500001215.

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24

APHALO, P. J., and P. G. JARVIS. "Do stomata respond to relative humidity?" Plant, Cell and Environment 14, no. 1 (January 1991): 127–32. http://dx.doi.org/10.1111/j.1365-3040.1991.tb01379.x.

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25

Rooke, E. A., and L. van den Berg. "Equilibrium Relative Humidity of Plant Tissue." Canadian Institute of Food Science and Technology Journal 18, no. 1 (February 1985): 85–88. http://dx.doi.org/10.1016/s0315-5463(85)71725-7.

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26

Białobrzewski, I. "Neural modeling of relative air humidity." Computers and Electronics in Agriculture 60, no. 1 (January 2008): 1–7. http://dx.doi.org/10.1016/j.compag.2007.02.009.

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27

Lovell-Smith, J. W., and H. Pearson. "On the concept of relative humidity." Metrologia 43, no. 1 (December 22, 2005): 129–34. http://dx.doi.org/10.1088/0026-1394/43/1/018.

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28

Sohoni, V. V., M. M. Paranjpe, and S. K. Banerji. "Fog and relative humidity in India." Quarterly Journal of the Royal Meteorological Society 60, no. 253 (September 10, 2007): 15–22. http://dx.doi.org/10.1002/qj.49706025304.

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29

Panitz, J. K. G., L. E. Pope, J. E. Lyons, and D. J. Staley. "The tribological properties of MoS2 coatings in vacuum, low relative humidity, and high relative humidity environments." Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films 6, no. 3 (May 1988): 1166–70. http://dx.doi.org/10.1116/1.575669.

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30

Cheng, Yun, Weijing Xie, Xun Yu, Hong Lu, and Minghong Yang. "Optical coatings on f iber for relative-humidity sensing applications." Chinese Optics Letters 11, S1 (2013): S10404. http://dx.doi.org/10.3788/col201311.s10404.

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31

Fleming, Rex J. "A Note on Temperature and Relative Humidity Corrections for Humidity Sensors." Journal of Atmospheric and Oceanic Technology 15, no. 6 (December 1998): 1511–15. http://dx.doi.org/10.1175/1520-0426(1998)015<1511:anotar>2.0.co;2.

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32

Zhan, Xiancheng, Yingli Wang, Lan Cao, Linli Li, and Chengrong Li. "Determining critical relative humidity by measuring air humidity in equilibrium directly." European Journal of Pharmaceutical Sciences 41, no. 2 (October 2010): 383–87. http://dx.doi.org/10.1016/j.ejps.2010.07.002.

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33

CHAKRABARTY, KK. "Diurnal variation of relative humidity over Calcutta (Alipore) and a statistical approach for forecasting minimum relative humidity." MAUSAM 39, no. 1 (January 1, 1988): 97–102. http://dx.doi.org/10.54302/mausam.v39i1.3198.

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A case study on diurnal variation of relative humidity over Calcutta (Alipore) for 1981 reveals (i) Driest period of RH <40% around 1500 IST between mid-December to mid February and also around 1400 IST in the month of November, (ii) Moist period of RH>90 % around 0600 IST during the whole year. The variability of the hourly RH values IS lowest during monsoon. moths In the early morning, however, the highest variability during a day in any month does not coincide with time of occurrence of the minimum RH of that month. Pentad normal of minimum and maximum RH over Alipore through the year have been computed based on data from 1950 to 1980 (excluding 1956). Correlation coefficients between (i) minimum value. of RH of a day with the anomalies of RH corresponding to 03 GMT of the day and 12 GMT of the previous day and (II) 24-hr change of min RH With those values for 03 and 06 GMT shows that there is a good correlation between 24-hr change of RH of 06 GMT and that of the minimum RH of the day.
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34

Lin, X., and K. G. Hubbard. "Uncertainties of Derived Dewpoint Temperature and Relative Humidity." Journal of Applied Meteorology 43, no. 5 (May 1, 2004): 821–25. http://dx.doi.org/10.1175/2100.1.

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Abstract This paper presents an evaluation of derived dewpoint temperature and derived relative humidity, in which the dewpoint temperature is calculated using measured ambient air temperature and measured relative humidity variables and the derived relative humidity is calculated from measured dewpoint temperature. The derived dewpoint temperature and relative humidity are calculated using algorithms provided by the World Meteorological Organization. The method of uncertainty analysis, provided by the National Institute of Standards and Technology, is applied to calculate the uncertainties of an indirect measurement of derived dewpoint temperature and derived relative humidity. The results from the uncertainty analyses of derived and observed variables suggest that the use of derived dewpoint temperature and derived relative humidity involves risk because the uncertainties of modern dewpoint temperature and relative humidity sensors can create several degrees Celsius of error in the derived dewpoint temperature and several percent in the derived relative humidity.
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35

Luampon, Ratinun, and Suparerk Charmongkolpradit. "Temperature and relative humidity effect on equilibrium moisture content of cassava pulp." Research in Agricultural Engineering 65, No. 1 (April 12, 2019): 13–19. http://dx.doi.org/10.17221/112/2017-rae.

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The purpose of this research was to study the effect of temperature and relative humidity on the equilibrium moisture content of cassava pulp. In experiments, cassava pulp was tested with a static method that controlled the temperature at 30, 50 and 70°C and controlled relative humidity in a range 10–90% with standard saturated salt solutions as LiCl, MgCl2, NaBr, NaCl and KNO3. Five equations of equilibrium moisture isotherm were analysed to predict the equilibrium moisture content, which was a guideline to develop a new isotherm equation. The experimental results showed that the equilibrium moisture content was increased with increased relative humidity whereas it decreased with increased drying temperature. Therefore, the drying process and storage method of cassava pulp must control temperature and relative humidity of no more than 50°C and 60%, respectively. The analysis of isotherm equations revealed that the new isotherm equation has high accuracy to predict the equilibrium moisture content of cassava pulp and higher R2 correlation with the experimental data than five isotherm equations.
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36

Skupin, A., A. Ansmann, R. Engelmann, P. Seifert, and T. Müller. "Four-year long-path monitoring of ambient aerosol extinction at a central European urban site: dependence on relative humidity." Atmospheric Chemistry and Physics Discussions 15, no. 8 (April 29, 2015): 12583–616. http://dx.doi.org/10.5194/acpd-15-12583-2015.

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Abstract. The ambient aerosol particle extinction coefficient is measured with the Spectral Aerosol Extinction Monitoring System (SÆMS) along a 2.84 km horizontal path at 30–50 m height above ground in the urban environment of Leipzig (51.3° N, 12.4° E), Germany, since 2009. The dependence of the particle extinction coefficient (wavelength range from 300–1000 nm) on relative humidity up to almost 100% was investigated. The main results are presented. For the wavelength of 550 nm, the mean extinction enhancement factor was found to be 1.75 ± 0.4 for an increase of relative humidity from 40 to 80%. The respective four-year mean extinction enhancement factor is 2.8 ± 0.6 for a relative-humidty increase from 40 to 95%. A parameterization of the dependency of the urban particle extinction coefficient on relative humidity is presented. A mean hygroscopic exponent of 0.463 for the 2009–2012 period was determined. Based on a backward trajectory cluster analysis, the dependence of several aerosol optical properties for eight air flow regimes was investigated. Large differences were not found indicating that local pollution sources widely control the aerosol conditions over the urban site. The comparison of the SÆMS extinction coefficient statistics with respective statistics from ambient AERONET sun photometer observations yield good agreement. Also, time series of the particle extinction coefficient computed from in-situ-measured dry particle size distributions and humidity-corrected SÆMS extinction values (for 40% relative humidity) were found in good overall consistency, which corroborates the applicability of the developed humidity parameterization scheme. The analysis of the spectral dependence of particle extinction (Ångström exponent) revealed an increase of the 390–881 nm Ångström exponent from, on average, 0.3 (at 30% relative humidity) to 1.3 (at 95% relative humidity) for the four-year period.
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37

Rouadi, Philip, Fuad M. Baroody, David Abbott, Edward Naureckas, Julian Solway, and Robert M. Naclerio. "A technique to measure the ability of the human nose to warm and humidify air." Journal of Applied Physiology 87, no. 1 (July 1, 1999): 400–406. http://dx.doi.org/10.1152/jappl.1999.87.1.400.

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To assess the ability of the nose to warm and humidify inhaled air, we developed a nasopharyngeal probe and measured the temperature and humidity of air exiting the nasal cavity. We delivered cold, dry air (19–1°C, <10% relative humidity) or hot, humid air (37°C, >90% relative humidity) to the nose via a nasal mask at flow rates of 5, 10, and 20 l/min. We used a water gradient across the nose (water content in nasopharynx minus water content of delivered air) to assess nasal function. We studied the characteristics of nasal air conditioning in 22 asymptomatic, seasonally allergic subjects (out of their allergy season) and 11 nonallergic normal subjects. Inhalation of hot, humid air at increasingly higher flow rates had little effect on both the relative humidity and the temperature of air in the nasopharynx. In both groups, increasing the flow of cold, dry air lowered both the temperature and the water content of the inspired air measured in the nasopharynx, although the relative humidity remained at 100%. Water gradient values obtained during cold dry air challenges on separate days showed reproducibility in both allergic and nonallergic subjects. After exposure to cold, dry air, the water gradient was significantly lower in allergic than in nonallergic subjects (1,430 ± 45 vs. 1,718 ± 141 mg; P = 0.02), suggesting an impairment in their ability to warm and humidify inhaled air.
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38

S. R. BHAKAR, RAJ VIR SINGH, NEERAJ CHHAJED, and ANIL KUMAR BANSAL. "Stochastic modelling of relative humidity at Banswara." Journal of Agrometeorology 10, no. 1 (June 1, 2008): 53–58. http://dx.doi.org/10.54386/jam.v10i1.1171.

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Study was conducted to develop stochastic model for monthly minimum and maximum relative humidity using 12 years (1992-2003) data of Banswara. The performed statistical test indicates that the series of monthly minimum and maximum relative humidity data are trend free. Their periodic components can be presented satisfactorily by the second harmonics. The stochastic components of both monthly minimum and maximum relative humidity follow second order Markov model. Validation of generated was made with measured series. A high correlation coefficient of 0.9980 and 0.9976 for mean monthly minimum and maximum relative humidity respectively was observed. The correlation was tested by ttest and found to be highly significant at 1 per cent level. The standard error is quite low. The regression equation was very close to 1:1 line. Therefore, the developed model could be used for future prediction of mean monthly minimum and maximum relative humidity, at Banswara.
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39

Wichert, Rex A., Robert Bozsa, Ronald E. Talbert, and Lawrence R. Oliver. "Temperature and Relative Humidity Effects on Diphenylether Herbicides." Weed Technology 6, no. 1 (March 1992): 19–24. http://dx.doi.org/10.1017/s0890037x00034230.

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The influence of temperature and relative humidity on the activity of acifluorfen, fomesafen, lactofen, and acifluorfen plus bentazon on prickly sida, pitted and entireleaf morningglory, and common cocklebur was evaluated in a growth chamber. Reduced control of all species was observed at 50% relative humidity as compared to 85% relative humidity when temperatures were higher (32/55 C day/night). Similar response to relative humidity was observed at the lower temperature (25/15 C) when treatments were applied 14 days after emergence (DAE). Changes in temperature at the same relative humidity did not alter herbicidal activity. Delaying application timing from 7 to 14 DAE decreased control by all herbicides except lactofen applied at high relative humidity, which controlled prickly sida at both 7 and 14 DAE.
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40

Sherwood, S. C., and C. L. Meyer. "The General Circulation and Robust Relative Humidity." Journal of Climate 19, no. 24 (December 15, 2006): 6278–90. http://dx.doi.org/10.1175/jcli3979.1.

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Abstract The sensitivity of free-tropospheric relative humidity to cloud microphysics and dynamics is explored using a simple 2D humidity model and various configurations of the National Center for Atmospheric Research (NCAR) Community Atmosphere Model version 3 (CAM3) atmospheric general circulation model (AGCM). In one configuration the imposed surface temperatures and radiative perturbations effectively eliminated the Hadley and Walker circulations and the main westerly jet, creating instead a homogeneous “boiling kettle” world in low and midlatitudes. A similarly homogeneous state was created in the 2D model by rapid horizontal mixing. Relative humidity ℛ simulated by the AGCM was insensitive to surface warming. Doubling a parameter governing cloud water reevaporation increased tropical mean ℛ near the midtroposphere by about 4% with a realistic circulation, but by more than 10% in the horizontally homogeneous states. This was consistent in both models. AGCM microphysical sensitivity decreased in the upper troposphere, and vanished outside the Tropics. Convective organization by the general circulation evidently makes relative humidity much more robust to microphysical details by concentrating the rainfall in moist environments. Models that fail to capture this will overestimate the microphysical sensitivity of humidity. Based on these results, the uncertainty in the strength of the water vapor feedback associated with cloud microphysical processes seems unlikely to exceed a few percent. This does not include uncertainties associated with large-scale dynamics or cloud radiative effects, which cannot be quantified, although radical CAM3 circulation changes reported here had surprisingly little impact on simulated relative humidity.
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41

Alsmo, Thomas, and Catharina Alsmo. "Ventilation and Relative Humidity in Swedish Buildings." Journal of Environmental Protection 05, no. 11 (2014): 1022–36. http://dx.doi.org/10.4236/jep.2014.511102.

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42

Wheeler, R. M., T. W. Tibbitts, and A. H. Fitzpatrick. "Potato Growth in Response to Relative Humidity." HortScience 24, no. 3 (June 1989): 482–84. http://dx.doi.org/10.21273/hortsci.24.3.482.

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Abstract Potato plants (Solanum tuberosum L. cvs. Russet Burbank, Norland, and Denali) were grown for 56 days in controlled-environment rooms under continuous light at 20C and 50% or 85% RH. No significant differences in total plant dry weight were measured between the humidity treatments, but plants grown under 85% RH produced higher tuber yields. Leaf areas were greater under 50% RH and leaves tended to be larger and darker green than at 85% RH.
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43

Mejía-Ortíz, Luis, Mary C. Christman, Tanja Pipan, and David C. Culver. "What’s the relative humidity in tropical caves?" PLOS ONE 16, no. 9 (September 22, 2021): e0250396. http://dx.doi.org/10.1371/journal.pone.0250396.

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Relative humidity (RH) was measured at hourly intervals for approximately one year in two caves at seven stations near Playa del Carmen in Quintana Roo, Mexico. Sistema Muévelo Rico is a 1.1 km long cave with 12 entrances and almost no dark zone. Río Secreto (Tuch) is a large river cave with more than 40 km of passages, and an extensive dark zone. Given the need for cave specialists to adapt to saturated humidity, presumably by cuticular thinning, the major stress of RH would be its deviation from saturation. RH in Río Secreto (Tuch) was invariant at three sites and displayed short deviations from 100% RH at the other four sites. These deviations were concentrated at the end of the nortes and beginning of the rainy season. Three of the sites in Sistema Muévelo Rico showed a similar pattern although the timing of the deviations from 100% RH was somewhat displaced. Four sites in Sistema Muévelo Rico were more variable, and were analyzed using a measure of amount of time of deviation from 100% RH for each 24 hour period. Strong seasonality was evident but, remarkably, periods of constant high humidity were not the same at all sites. In most Sistema Muévelo Rico sites, there was a detectable 24 hour cycle in RH, although it was quite weak in about half of them. For Río Secreto (Tuch) only one site showed any sign of a 24 hour cycle. The troglomorphic fauna was more or less uniformly spread throughout the caves and did not concentrate in any one area or set of RH conditions. Compared to temperature, RH is much more constant, perhaps even more constant than the amount of light. However, changes in RH as a result of global warming may have a major negative effect on the subterranean fauna.
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Du Xiaofan, 都小凡, 钱仙妹 Qian Xianmei, 刘. 强. Liu Qiang, 朱文越 Zhu Wenyue, and 曹振松 Cao Zhensong. "Effect of Relative Humidity on Photoacoustic Signal." Acta Optica Sinica 37, no. 2 (2017): 0230003. http://dx.doi.org/10.3788/aos201737.0230003.

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Parrott, L. J., D. B. McDonald, and H. Roper. "Discussion: Factors influencing relative humidity in concrete." Magazine of Concrete Research 43, no. 157 (December 1991): 305–7. http://dx.doi.org/10.1680/macr.1991.43.157.305.

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Denton, D. D., C. N. Ho, and S. G. He. "A solid-state relative humidity measurement system." IEEE Transactions on Instrumentation and Measurement 39, no. 3 (June 1990): 508–11. http://dx.doi.org/10.1109/19.106282.

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Gao, Yonggang, Shing Bor Chen, and Liya E. Yu. "Efflorescence Relative Humidity for Ammonium Sulfate Particles." Journal of Physical Chemistry A 110, no. 24 (June 2006): 7602–8. http://dx.doi.org/10.1021/jp057574g.

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KUZUGUDENLI, E. "RELATIVE HUMIDITY MODELING WITH ARTIFICIAL NEURAL NETWORKS." Applied Ecology and Environmental Research 16, no. 4 (2018): 5227–35. http://dx.doi.org/10.15666/aeer/1604_52275235.

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Stukalov, Oleg, Chris A. Murray, Amy Jacina, and John R. Dutcher. "Relative humidity control for atomic force microscopes." Review of Scientific Instruments 77, no. 3 (March 2006): 033704. http://dx.doi.org/10.1063/1.2182625.

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Ruzmaikin, Alexander, Hartmut H. Aumann, and Evan M. Manning. "Relative Humidity in the Troposphere with AIRS." Journal of the Atmospheric Sciences 71, no. 7 (June 20, 2014): 2516–33. http://dx.doi.org/10.1175/jas-d-13-0363.1.

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Abstract:
Abstract New global satellite data from the Atmospheric Infrared Sounder (AIRS) are applied to study the tropospheric relative humidity (RH) distribution and its influence on outgoing longwave radiation (OLR) for January and July in 2003, 2007, and 2011. RH has the largest maxima over 90% in the equatorial tropopause layer in January. Maxima in July do not arise above 60%. Seasonal variations of about 20% in zonally averaged RH are observed in the equatorial region of the low troposphere, in the equatorial tropopause layer, and in the polar regions. The seasonal variability in the recent decade has increased by about 5% relative to that in 1973–88, indicating a positive trend. The observed RH profiles indicate a moist bias in the tropical and subtropical regions typically produced by the general circulation models. The new data and method of evaluating the statistical significance of bimodality confirm bimodal probability distributions of RH at large tropospheric scales, notably in the ascending branch of the Hadley circulation. Bimodality is also seen at 500–300 hPa in mid- and high latitudes. Since the drying time of the air is short compared with the mixing time of moist and dry air, the bimodality reflects the large-scale distribution of sources of moisture and the atmospheric circulation. Analysis of OLR dependence on surface temperature shows a 0.2 W m−2 K−1 difference in sensitivities between clear-sky and all-sky OLR, indicating a positive longwave cloud radiative forcing. Diagrams of the clear-sky OLR as functions of percentiles of surface temperature and relative humidity in the tropics are designed to provide a new measure of the supergreenhouse effect.
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