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32 results for “air humidity”
All-Inkjet-Printed Humidity Sensors for the Detection of Relative Humidity in Air and Soil—Towards the Direct Fabrication on Plant Leaves
<p>We demonstrate the fabrication, by exclusive means of inkjet-printing, of capacitive relative humidity sensors on flexible, plastic substrate. These sensors can be successfully used for the measurement of relative-humidity in both air and common soil. We also show that the same technique may be used for the fabrication of the same type of sensors on the surface of the leaves of Elægnus Ebbingei (silverberry).Our results demonstrate the suitability of leaves as substrate for printed electronics and pave the way to the next generation of sensors to be used in fields such as agriculture and flower farming.</p>
Streamer propagation in humid air
<p>This dataset includes the input and output files for the paper: Streamer propagation in humid air.</p> <p><strong>Input files</strong>:</p> <p># <em>Plasma-chemistry and transport coefficients</em></p> <p>chemistry_files/*.txt</p> <p># <em>Configuration files</em></p> <p>config_files/*.cfg</p> <p># <em>Initial conditions (densities)</em></p> <p>config_files/m_user.f90</p> <p><strong>Output files (output_files)</strong>:</p> <p>*.silo</p> <p>*.txt</p> <p>Output data generated with the software afivo-streamer (https://gitlab.com/MD-CWI-NL/afivo-streamer) corresponding to the commit 1ff2676ba48a5eb568f06c7b11a548629a5ff20c</p>
Continuous data of air temperature, relative humidity, and air pressure collected at Kyoto University in January 2022
<p>I present the continuous data of air temperature, relative humidity, and air pressure collected at the Kyoto-A gravity measurement room (latitude: 35.02938 N, longitude: 135.78347 E, elevation: 60.82 m), Graduate School of Science, Kyoto University in January 2022. The values of air temperature, relative humidity, and air pressure were obtained every 1 second by a BME280 sensor (Bosch Sensortec GmbH) on a RT-USB-THP module (RT Corporation). The data were time-stamped and recorded using the Tera Term software on a Windows PC, which was synchronized in time with the NTP server of NICT (ntp.nict.jp).</p> <p>photo.zip contains the photographs of the Kyoto-A gravity measurement room taken in December 2022. The RT-USB-THP module and the Windows PC were located on the basement rock of the Kyoto-A absolute gravity point.</p> <p>data.zip contains the continuous data of air temperature, relative humidity, and air pressure collected at the Kyoto-A gravity measurement room. Each file of 2201??.txt stores the data obtained from 11:55 JST of the corresponding day to 11:54 JST of the next day, according to the operating time of the Tera Term software. The columns in 2201??.txt are as follows from left to right: year, month, day, hour, minute, second, air temperature [degC], air pressure [hPa], and relative humidity [%]. Note that 220101.txt was not recorded due to the failure of the Windows PC on January 1, 2022.</p> <p>figure.zip contains the graphs of air temperature, relative humidity, and air pressure drawn using the GMT4 software. Each graph of 2201??.png shows the time variation in air temperature, relative humidity, and air pressure from 0:00 JST to 24:00 JST of the corresponding day. tonga.png shows the air pressure variation from 12:00 JST on January 15 to 11:54 JST on January 16, and its original data is available as 220115.txt in data.zip. The pressure change of about 2 hPa was observed around 20:40 JST on January 15, associated with the propagation of the Lamb wave generated by the massive eruption of the Hunga Tonga-Hunga Ha’apai volcano.</p>
10-minutes Air Temperature and Relative Humidity Datasets from city of Novi Sad - NSUNET system
<p>On the territory of Novi Sad and its surrounding the urban meteorological network with 27 stations (Ta and RH sensors) was made. The operation time of the network was from July 2014 to February 2018. Stations locations were chosen based on the local climate zone classification system (Stewart and Oke, 2012) that was applied on urban area of Novi Sad. There were 25 stations in the city of Novi Sad and two rural stations (located north and northeast from the city outskirts). Seven LCZ types were defined on the territory of Novi Sad and these 25 stations were located within these built-up types. Rural stations (two) were located in LCZ’s of low plants and dense trees. The measurement frequency of all stations was 10 minutes. This means that the Ta and RH sensors measured a new value every minute, and after ten measurements we got a new average value based on the previous ten measurements. Datasets are 'raw data' i.e. have missing data and outliers. Therefore, before any analysis, the detailed QC steps must be applied!</p>
Data from: Air humidity thresholds trigger active moss spore release to extend dispersal in space and time
1. Understanding the complete dispersal process is important for making realistic predictions of species distributions, but mechanisms for diaspore release in wind-dispersed species are often unknown. However, diaspore release under conditions that increase the probability of longer dispersal distances and mechanisms that extend dispersal events in time may have evolutionary advantages. 2. We quantified air humidity thresholds regulating spore release in the moss Brachythecium rutabulum. We also investigated the prevailing micrometeorological conditions when these thresholds occur in nature and how they affect dispersal distances up to 100 m, using a mechanistic dispersal model. 3. We show that moss spores were mainly released when the peristome teeth were opening, as relative air humidity (RH) decreased from high values to relatively low (mainly between 90% and 75% RH). This most often occurred in the morning, when wind speeds were relatively low. Surprisingly, the model predicted that an equally high proportion of the spores would travel distances beyond 100 m (horizontally) when released in the wind conditions prevailing during events of RH decrease in the morning, that lead to peristome opening, as in the highest wind speeds. Moreover, a higher proportion of the spores reached high altitudes when released at the lower wind speeds during the morning compared to the higher speeds later in the day, indicating a possibility for extended dispersal distances when released in the morning. Dispersal in the morning is enhanced by a combination of a more unstable atmospheric surface layer, that promotes vertical dispersal, and a lower wind speed that decreases the spore deposition probability onto the ground, compared to later in the day. 4. Our study demonstrates an active spore release mechanism in response to diurnally changing air humidity. The mechanism may promote longer dispersal distances, because of enhanced vertical dispersal and because spores being released in the morning have more time to travel before the wind calms down at night. The mechanism also leads to a prolonged dispersal period over the season, which may be viewed as a risk spreading in time that ultimately also leads to a higher diversity of establishment conditions, dispersal distances and directions.
Data from: Air humidity thresholds trigger active moss spore release to extend dispersal in space and time
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In-situ Air Temperature and Relative Humidity in Greenbelt, MD, 2013-2015
This data set describes the temperature and relative humidity at 12 locations around Goddard Space Flight Center in Greenbelt MD at 15 minute intervals between November 2013 and November 2015. These data were collected to study the impact of surface type on heating in a campus setting and to improve the understanding of urban heating and potential mitigation strategies on the campus scale. Sensors were mounted on posts at 2 m above surface and placed on 7 different surface types around the centre: asphalt parking lot, bright surface roof, grass field, forest, and stormwater mitigation features (bio-retention pond and rain garden). Data were also recorded in an office setting and a garage, both pre- and post-deployment, for calibration purposes. This dataset could be used to validate satellite-based study or could be used as a stand-alone study of the impact of surface type on heating in a campus setting.
In-situ relative humidity and air temperature urban microclimate data
<p>Monitoring and understanding the variability of heat within cities is important for urban planning and public health, and there has been a growth in the number of studies measuring intra-urban temperature variability. Recognizing that the physiological effects of heat depend on humidity as well as temperature, some of these measurement campaigns have included measurements of relative humidity alongside temperature. Reported analyses, however, have not reported whether spatial structure in humidity, independent from temperature, contributes significantly to intra-urban heat variability. Here we use summer temperature and humidity from networks of stationary sensors in multiple cities in the USA to examine this issue. It is shown that although there are spatial variations in relative humidity there are only very weak spatial variations in the absolute humidity within these cities. This variability in absolute humidity plays an insignificant role in the spatial variability of the heat index and humidex, and the spatial variability of the heat metrics is dominated by temperature variability. A practical consequence of this is that a network of sensors that only measure temperature is sufficient to quantify the spatial variability of heat across these cities when combined with humidity measured at a single location, allowing for lower-cost heat monitoring networks.</p>
In-situ relative humidity and air temperature urban microclimate data
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Genome-wide expression analysis of Arabidopsis Col-0 plants under different air humidity
GEO Series GSE236463. Arabidopsis thaliana. 12 samples. Type: Expression profiling by high throughput sequencing.
Transcriptional response to varying air humidity in Arabidopsis
GEO Series GSE256367. Arabidopsis thaliana. 12 samples. Type: Expression profiling by high throughput sequencing.
Field evaluation of low-cost electrochemical air quality gas sensors under extreme temperature and relative humidity conditions
<p>1. Files starting with Alphasense (i.e. Alphasense_raw_CO, Alphasense_raw_NO2, Alphasense_raw_O3, Alphasense_raw_SO2) correspond to the raw measurements obtained from the Alphasense low-cost sensors for CO, NO2, O3 and SO2 respectively. These files have five (5) columns each. The first column is the output of the working electrode (mV), the second column is the output of the auxiliary electrode (mV), the third column is the temperature measured by the reference instrument (C), the fourth column is the relative humidity measured by the reference instrument (%) and the fifth column is the matlab time.</p> <p>2. Files starting with Winsen (i.e. Winsen _raw_CO, Winsen _raw_NO2, Winsen _raw_O3, Winsen _raw_SO2) correspond to the raw measurements obtained from the Winsen low-cost sensors for CO, NO2, O3 and SO2 respectively. These files have four (4) columns each. The first column is the concentration output (ppb), the second column is the temperature measured by the reference instrument (C), the third column is the relative humidity measured by the reference instrument (%) and the fourth column is the matlab time.</p> <p>3. The file "reference_data" corresponds to the gas measurements obtained from the reference instruments. This file has five (5) columns. The first column is the CO concentration (ppb), the second column is the NO2 concentration (ppb), the third column is the O3 concentration (ppb), the fourth column is the SO2 concentration and the fifth column is the matlab time.</p>
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OpenNeuro
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