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40 results for “air monitoring”
Air temperature at core phenology sites and additional bird monitoring sites in the Andrews Experimental Forest, 2009 to present
The H.J Andrews phenology study air temperature network includes 16 core phenology sites, 40 core bird sites and 128 auxiliary bird sites. This study examines air temperatures at multiple sites within the Andrews Experimental Forest. Air temperatures were recorded 1.5 m above ground at 184 sites distributed on an 800-m incomplete grid throughout much of the Andrews Forest. Data were collected using automated sensors starting in June of 2009 at 56 sites and in June 2011 128 additional sensors were added. These data document the complex spatial and temporal patterns of air temperature variation within the Andrews Forest, which is governed by multiple processes including inversions, regional air mixing, cold air drainage and pooling, and the effects of vegetation on temperature extremes. The data entities provided indicate various methods of data quality checking over time.
Regional Datasets for Air Quality Monitoring in European Cities
<p>The primary environmental health threat in the WHO European Region is air pollution, impacting the daily health and well-being of its citizens significantly. To effectively understand the impact, and dynamics of air quality a detailed investigation of different environmental, weather, and land cover indices is appropriate. To this end, this paper introduces three European cities’ spatiotemporal datasets, customized for air pollution monitoring at a regional level. The datasets are composed of major air quality, weather measurements and land use information. The duration is approximately from 2020 to 2023 with an hourly temporal resolution and a spatial resolution of 0.005◦. The temporal and spatiotemporal datasets are publicly released aiming to provide a solid foundation for researchers, analysts, and practitioners to conduct in-depth analyses of air pollution dynamics.</p>
Dataset for "LoRa Sensor Network Development for Air Quality Monitoring or Detecting Gas Leakage Events; DOI: 10.3390/s20216225"
<p>This excel file contains the raw data used in the paper " LoRa Sensor Network Development for Air Quality Monitoring or Detecting Gas Leakage Events; DOI: 10.3390/s20216225 " In particular it comprises sensor measurements and pollutant data from the automated air quality monitoring stations in the Tarragona area.</p>
Sonadora elevational plots: long-term monitoring of air temperature
This is a long term monitoring of air temperature at each elevation plot along the Sonadora gradient. At each plot a HOBO sensor is located close to the middle of the plot: at 1m above ground: and placed inside a radiation shield (a plastic cup). Sensors are programmed to sample and store air temperature every hour. A daily average is computed from hourly readings. Sensors are downloaded twice a year: thus blanks represent sensor malfunction: loss of battery: or memory full. Initial blanks were due to lack of enough sensors to cover the gradient. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Results from Air Quality Monitoring and Surveys in UK Residences - Appendix (Survey Questions)
<p>Survey questions used in the paper titled "Results from Air Quality Monitoring and Surveys in UK Residences". Abstract below.</p> <p>Air pollution is a persistent issue in dwellings worldwide, costing an estimated 10-25 billion US dollars per year to the United Kingdom’s national health service alone. However, it is an “invisible problem” since background pollutants are often imperceptible except during acute pollution events such as wildfires. Although public awareness of ventilation has increased due to the COVID-19 pandemic, there are few tools available to assess its efficacy. Widely available sensor systems that can measure these pollutants tend to be single units with simple apps and little connection to mitigation, whereas different rooms in a house may have different pollution issues with different recommended actions. In this study, we present the results of a measurement study conducted using a multi-room sensor kit in twenty-nine dwellings across the UK. We also analyze the occupants’ reaction to the hardware, data, and a prototype alerting system. The study shows broad awareness of air quality in the participants. However, this awareness rarely corresponded to effective mitigation actions or ventilation provision. The concept of alerts was welcomed by participants if accompanied by actionable recommendations.</p> <p>The data showed significant pollution events, as measured by proxies such as total VOC and CO<sub>2</sub>, occurring almost daily, particularly in households with gas appliances. These incidents were concentrated around particular times of day and behaviors, indicating that the capacity of infiltration and extract ventilation to bring in adequate fresh air was overwhelmed. No significant outdoor pollution was detected in houses, which was expected given their sheltered peri-urban locations. The study highlights the need for comprehensive implementation of measurement, ventilation, and treatment measures in the UK housing stock to reduce the impact of indoor pollution on health.</p>
Dataset for: IoT deployment for city scale air quality monitoring with Low-Power Wide Area Networks
<p>Air Quality (AQ) is a very topical issue for many cities and has a direct impact on the health of its citizens. We propose to investigate the air quality of a large UK city using low-cost commodity Particulate Matter (PM) sensors, and compare them with government operated air quality stations. In this pilot deployment we design and build six AQ IoT devices, each with four different low-cost PM sensors and deploy them at two locations within the city. These devices are equipped with LoRaWAN wireless network transceivers to test city scale Low-Power Wide-Area Network network coverage. We conclude that some low-cost PM sensors are viable for monitoring AQ and demonstrate that our device design can be used via LoRaWAN to facilitate more granular city coverage without limitations of network access. Based on these findings we intend to deploy a larger LoRaWAN enabled Air Quality sensor network deployment across the city.</p>
Supplementary Data for "Development of a General Calibration Model and Long-Term Performance Evaluation of Low-Cost Sensors for Air Pollutant Gas Monitoring" (abridged version)
<p>This is a supplementary data set associated with the publication "Development of a General Calibration Model and Long-Term Performance Evaluation of Low-Cost Sensors for Air Pollutant Gas Monitoring" from the Center for Atmospheric Particle Studies, submitted to Atmospheric Measurement Techniques. This is an abbreviated version which does not include the calibrated models; these models must be re-generated by running the codes contained with the data set.</p> <p> </p>
Data from multi-sensor devices and reference station to monitoring urban air quality
<p>Data from electrochemical and optical sensors.</p> <h3>Files names</h3> <ul> <li>ECT01, ECT02, ECT06, ECT07 = device name</li> <li>ISSEP = reference station <ul> <li>"c" = calibration data</li> <li>"v" = validation data</li> </ul> </li> </ul> <h3>Variable description</h3> <table> <tbody> <tr> <td><strong>Electrochemical sensor</strong></td> <td><strong>Optical sensor</strong></td> <td><strong>Probe</strong></td> <td><strong>Reference</strong></td> </tr> <tr> <td> <p>AE = auxiliary electrode (mV)</p> <p>WE = working electrode (mV)</p> <p>N = temperature correction </p> <ul> <li>ch0 = CO sensor</li> <li>ch1 = OX sensor</li> <li>ch2 = NO2 sensor</li> <li>ch3 = NO sensor</li> </ul> </td> <td> <p>PM1, PM2.5 and PM10 in µg/m³</p> </td> <td> <p>Prs_mbar = pressure (mbar)</p> <p>Temp = temperature (°C)</p> <p>RH = relative humidity (%)</p> </td> <td> <p>DV30 = wind direction @ 30m (°)</p> <p>HR = relative humidity (%)</p> <p>NO, NO2, O3, PM10 and PM2.5 (µg/m³)</p> <p>Precipita = precipitation (mm)</p> <p>TC3 = temperature @ 3m (°C)</p> <p>VV30 = wind speed @ 30m (m/s)</p> </td> </tr> </tbody> </table> <p> </p>
DiY Sensor Dataset for Air Pollution Monitoring
<p>The research engaged students from a private university in Bilbao in designing and implementing a project that involved young students in collecting and analyzing air quality data using air meters they assembled. An interesting aspect of the project was the development of an image processing technique for analyzing dust captured on petroleum jelly, enhancing the quantification of particulate matter and providing insights into air quality at various school locations. Additionally, the study introduced a synthetic data generation algorithm designed to simulate air quality data for educational purposes, which allowed students to engage with data analysis and interpretation, thereby enriching their learning experience and understanding of air pollution dynamics.</p>
Long-term monitoring of gaseous elementary mercury in background air at the polar station Amderma, Russian Arctic
<p>The produced dataset (in MS Excel format) contains time-series of concentrations of gaseous elementary mercury (GEM, thereafter – mercury) in the background air collected at the Amderma polar station near the Amderma settlement of the Russian Arctic (69,72 deg N; 61,62 deg E; Yugor Peninsula, Russia) from 24th June 2001 until 13th February 2013. The concentrations for mercury are given in ng/m^3. Concentrations were measured in the air at every 30 minute interval using Tekran-2537A instrument. After checking quality of recorded values of the mercury concentration, the average of two consecutive measurements in two channels (average 1 hour measurements) are calculated. Data (as time-series) are grouped into three blocks covering different time periods and adjacent locations. First block covers period from 24th June 2001 to 13th February 2004 (when the analyzer was located at a distance of about 9 km from the coastline of the Kara Sea). Second block covers period from 3th April 2005 to 12th June 2010 (when the analyzer was placed at 2.5 km from the coast). Third block covers period from 15th June 2010 to 10th October 2013 (the analyzer was set up at a distance of 200 m from the coastline of the sea). </p>
PM10, SO2, and NO2 Ambient Air Quality Monitoring Data from India's National Ambient Monitoring Program (NAMP) 2011-2015
<p>India's Central Pollution Control Board (CPCB) operates and maintains the National Ambient Monitoring Program (<a href="https://cpcb.nic.in/about-namp/">NAMP</a>) which includes both continuous and manual ambient monitoring stations. This dataset is a collation of manual monitoring data by day for years 2011, 2012, 2013, 2014, and 2015 for PM10, SO2, and NO2. These stations collect for a maximum of 104 days in a year. This cleaned dataset was utilized for understanding trends and conducting comparisons with modeled concentrations under the APnA city program, published <a href="https://doi.org/10.1016/j.uclim.2018.11.005">here</a> (<a href="https://doi.org/10.1016/j.uclim.2018.11.005">Urban Climate, 2019</a>).<br> <br> Data format - year, month, day, SO2, NO2, PM10, Stn Code, State, City<br> All units - micro-gm/m3 (ug/m3)</p> <p>Official annual summary reports (PDFs) are available <a href="https://cpcb.nic.in/namp-data/">here</a>.</p> <p>For guidelines for ambient and emissions monitoring, summaries of available data, and other resources on monitoring in India, visit <a href="https://urbanemissions.info/resources-energy-emissions-analysis-in-india/#monitoring">https://urbanemissions.info/resources-energy-emissions-analysis-in-india</a></p>
A synchronized estimation of hourly ground-level concentrations of six criteria air pollutants in China using data from the first geostationary air-quality monitoring satellite
<p>This dataset provides the ground-level concentrations of six criteria air pollutants estimated from the first geostationary air quality monitoring satellite GEMS with a multi-output random forest model.</p>
Lichens as bioindicators of monitoring of the selective air pollution, Zabrze (Poland) - total carbon (TC) and total sulfur (TS) results.
<p>Total carbon (TC) and total sulfur (TS) contents were measured using an Eltra CS-500 IR-analyzer with a TIC module. TC was determined using an infrared cell detector on CO2 gas, which was evolved by combustion under an oxygen atmosphere. Calibration was made by means of the Eltra standards 2.27 % S and 45.14 % C. <br> Dr Ewa Szram, employed at the Institute of Earth Sciences, Faculty of Natural Sciences, Silesian University in Katowice, carried out the project. This research was funded by the National Science Centre, Poland MINIATURA-6 2022/06/X/ST10/00338 “Lichens as bioindicators of monitoring of the selective air pollution”</p>
Lichens as bioindicators of monitoring of the selective air pollution, Zabrze (Poland) - XRF analysis results.
<p>XRF analyses were performed by the BRUKER S8 TIGER series 2 WD-XRF spectrometer with a 1kW Rh X-ray tube. The system is equipped with five analyzing crystals (LiF200, PET, XS–55, LIF-220 & Ge) and two detectors (flow and scintillation counter). The samples were measured by best detection mode (18min analysis time), and the results were evaluated in Quant-Express (fundamental parameters) and SPECTRAplus Software.<br> Dr Ewa Szram, employed at the Institute of Earth Sciences, Faculty of Natural Sciences, Silesian University in Katowice, carried out the project. This research was funded by the National Science Centre, Poland MINIATURA-6 2022/06/X/ST10/00338 “Lichens as bioindicators of monitoring of the selective air pollution”</p>
Lichens as bioindicators of monitoring of the selective air pollution, Zaabrze (Poland) - chromatograms of GC-MS
<p>Detailed geochemical analyses were performed on 21 powdered samples after their extraction using ultrasound Elmasonic Easy with a dichloromethane (DCM) and methanol (MeOH) mixture (1:1 vol). Extracts were separated into aliphatic-, aromatic-, semipolar- and polar fractions by column chromatography. Silica-gel was first activated at 120 °C for 24 h, cooled, and poured into Pasteur pipettes. Foour eluents were used for fraction collection, namely, n-pentane for the aliphatic fraction, n-pentane and DCM (7:3) for the aromatic fraction, acetone and DCM (1:1) for the semipolar fraction, and DCM and methanol (1:1) for the polar fraction. The semipolar - and polar fraction was derivatized with MTBSTFA (N-tertbutyldimethylsilyl-N-methyltrifluoroacetamide). Samples were derivatized with MTBSTFA dissolved in super-dehydrated n-hexane, and heated at 70 °C for 3 h. The composition of the separated extracts was analyzed by gas chromatography–mass spectrometry (GC–MS) using an Agilent gas chromatograph 7890A coupled with a mass spectrometer 5975C XL MDS. A DB-5UI column was applied (60 m × 250 μm id, 0.25 μm stationary phase film), with He (purity of 99.9999%) as a carrier gas. The experimental conditions were as follows: injection volume of 1 μL; split/splitless mode; initial temperature of 45 ◦C (isothermal for 1 min); heating rate up to 100 ◦C at 20 ◦C/min, then 3 ◦C/min to 280 ◦C for 66.25 min. The mass spectrometer worked in electron ionization (EI) mode at 70 eV in full scan mode and scanned from 50 to 650 Da.<br> Dr Ewa Szram, employed at the Institute of Earth Sciences, Faculty of Natural Sciences, Silesian University in Katowice, carried out the project. This research was funded by the National Science Centre, Poland MINIATURA-6 2022/06/X/ST10/00338 “Lichens as bioindicators of monitoring of the selective air pollution”</p>
PyonAir: An open design, open source, air quality monitor for community driven particulate matter sensing.
<p>This dataset present some of the data recorded by two low-cost PM sensors, a Plantower PMS5003 and a Sensirion SPS030 located at Southampton AURN reference station on the 2nd December 2019 between 17:00 and 22:00 during a fire that occurred in the city. It also present the data from the Fidas 200, averaged every 15min also located at the AURN station.</p> <p>The file SPS_PMS_fire.csv contain the following rows:</p> <ul> <li>date</li> <li>pm25 - PM<sub>2.5</sub> mass concentration in ug/m<sup>3</sup></li> <li>sensor - Serial number of the sensor</li> <li>site - name of the location of the sensor</li> </ul> <p>The file fidas_15min.csv contains the following rows:</p> <ul> <li>date</li> <li>PM2.5 - PM<sub>2.5</sub> mass concentration in ug/m<sup>3</sup></li> </ul> <p> </p>
Air Quality Monitoring and People Counting
<p>This dataset contains data on air quality monitoring and people counter of an office room at the University of Messina. Data is stored and labeled with the corresponding people number.</p> <p>A single record is composed by;</p> <p><strong>Date</strong> s is a timestamp; </p> <p><strong>Id</strong> is an Identifier number; </p> <p><strong>Pm1</strong>, <strong>Pm 2.5</strong> and <strong>Pm10</strong> are the dust sensors;</p> <p><strong>Temp</strong> is the temperature;</p> <p><strong>Hum</strong> is humidity;</p> <p><strong>Press</strong> is the atmospheric pressure; </p> <p><strong>CO</strong> and <strong>CO2</strong> are the concentrations;</p> <p><strong>Label </strong>is the number of people. </p> <p> </p> <p> </p>
Mobile and stationary air pollution measurements around an air quality monitoring site on Mäkelänkatu in Helsinki, Finland
<p>Mobile and stationary measurements of air pollution around an air quality monitoring site on Mäkelänkatu in Helsinki, Finland. The datasets are used in evaluating high-resolution air quality simulations conducted with the PALM model system 6.0. The dataset contains:</p> <ul> <li>drone_data: measurements of vertical profiles of lung-deposited surface area (LDSA) conducted using a drone in summer and winter 2017</li> <li>kumpula_airquality_data: <ul> <li>airquality: basic air quality observations from the SMEAR III station</li> <li>dmps: aerosol size distribution observations using DMPS from the SMEAR III station</li> </ul> </li> <li>met_data: <ul> <li>kivenlahti_mast_DDMMYYYY.txt: Kivenlahti mast observations</li> <li>Meteorology_YYMM_10.txt: SMEAR III observations</li> </ul> </li> <li>sniffer_data/long/[timeofday]YYYYMMDD.dat: horizontal distribution of air pollutants measured by the mobile laboratory Sniffer</li> <li>supersite_data: measurements from the air quality monitoring station on Mäkelänkatu <ul> <li>AQdata: air quality observations</li> <li>DMPS: aerosol size distribution observations using DMPS</li> <li>ACSM: aerosol chemical composition measurements using ACSM</li> </ul> </li> </ul> <p> </p>
Up in the air: threats to Afromontane biodiversity from climate change and habitat loss revealed by genetic monitoring of the Ethiopian Highlands bat
<p>Whilst climate change is recognised as a major future threat to biodiversity, most species are currently threatened by extensive human-induced habitat loss, fragmentation and degradation. Tropical high altitude alpine and montane forest ecosystems and their biodiversity are particularly sensitive to temperature increases under climate change, but they are also subject to accelerated pressures from land conversion and degradation due to a growing human population. We studied the combined effects of anthropogenic land-use change, past and future climate changes and mountain range isolation on the endemic Ethiopian Highlands long-eared bat, <i>Plecotus balensis</i>, an understudied bat that is restricted to the remnant natural high altitude Afroalpine and Afromontane habitats. We integrated ecological niche modelling, landscape genetics and model-based inference to assess the genetic, geographic and demographic impacts of past and recent environmental changes. We show that mountain range isolation and historic climates shaped population structure and patterns of genetic variation, but recent anthropogenic land-use change and habitat degradation are associated with a severe population decline and loss of genetic diversity. Models predict that the suitable niche of this bat has been progressively shrinking since the last glaciation period. This study highlights threats to Afroalpine and Afromontane biodiversity, squeezed to higher altitudes under climate change while losing genetic diversity and suffering population declines due to anthropogenic land-use change. We conclude that the conservation of tropical montane biodiversity requires a holistic approach, using genetic, ecological and geographic information to understand the effects of environmental changes across temporal scales and simultaneously addressing the impacts of multiple threats.</p>
Coupled monitoring of soil moisture and suction in pyroclastic air-fall silty soils
<p>The dataset contains the experimental field data coming from the automatic monitoring of the annual hydrological response of a shallow deposit in loose pyroclastic soils located in a mountainous area of Campania, Southern Italy. The monitoring station is installed at about 650 m a.s.l. along the northern slope of mount Cornito, about 2 km east of the town of Cervinara, that in December 1999 was involved in a rainfall-induced flowslide. The collected field data consist in: i) precipitations (measured by a rain gauge), ii) soil moisture contents (measured by TDR probes) and iii) soil matric suctions (measured by “Jet-fill” tensiometers). Suction and moisture sensors have been installed at the same depths and connected to a Campbell Scientific Inc. CR-1000 Data Logger, that allows the automatic acquisition and storage of data.</p> <p>Furthermore, the results of some laboratory flume tests carried out on a small-scale slope, reconstituted with volcanic ashes from the same site and from a nearby slope, subjected to an artificial rainwater infiltration, are attached too. Soil matric suction and moisture content were respectively measured by a miniaturised tensiometer and a TDR probe installed close to each other.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.