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206 results for “air quality”
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>
Marcellus shale water and air quality data
<p>Data summary for water and air quality of the Marcellus shale</p>
Improve air quality in Cities - Simulation of Sentinel-5p and Breeze Technologies
<p>The number of measuring stations in cities areinsufficient to get a realistic picture about the Air quality (AQ). The existing technique is too expensive and wastes too much limited urban space due to their dimensions. Therefore, the Breeze Technology helps to overcome this data gap by offering their own compact low-cost AQ sensors as a supplement to the existing station. To improve the spatial coverage of the measurement, Sentinel-5P data was simulated with the sensor based measured data. The accuracy will be further increased by integrating satellite data to predict pollutuíon level e.g. in areas without sensors to overcome measurement gaps.</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>
Supplementary Dataset: Air quality modeling intercomparison and multi-scale ensemble chain for Latin America
<p>The Supplementary dataset of the manuscript titled "Air quality modeling intercomparison and multi-scale ensemble chain for Latin America" can be downloaded via this link:<br>https://swiftbrowser.dkrz.de/public/dkrz_3ab03fbe-db0a-42e8-8b19-caf61d10634d/PAPILA/</p> <p>The data repository contains the model data used in the model intercomparison with six global and regional chemical-transport model over Latin America and the observation datasets used in the model intercomparison. This work presents the first model intercomparison and ensemble construction for Latin America, which was assembled under the Prediction of Air Pollutants in Latin America (PAPILA project (https://papila-h2020.eu/papila). </p>
Knowledge graph for air quality simulations in the Region of Murcia from 2024-12-26 to 2024-12-29
<p>This resource provides the knowledge graphs with the information of the simulation done from 2023-12-26 to 2023-12-26 in the Region of Murcia with the CHIMERE-WRF model (https://www.lmd.polytechnique.fr/chimere/) and transformed into knowledge graph thanks to the SWIT framework (http://sele.inf.um.es/swit/about.html). It also links this data to the github repository including the code and the ontology generated in the work "Representation of chemistry transport models simulations using knowledge graphs".</p>
Britain Breathing 2016-2019 Air Quality and Meteorological Dataset
<p>This data set is a collection of daily mean and maximum values for a range of air quality and meterological measurements and model forecasts for the UK for the years 2016-2019, inclusive. The dataset contains Temperature, Relative Humidity, and Pressure data, downloaded from the Met Office MIDAS archives via the MEDMI server (https://www.data-mashup.org.uk/). Also downloaded from the MEDMI server are daily pollen measurements for the UK. PM10, PM2.5, NO2, NOx (as NO2), O3, and SO2 measurements from the DEFRA AURN network, and also model forecasts of the same made using the EMEP model.</p> <p>The paper describing this dataset is available here: <a href="https://www.nature.com/articles/s41597-022-01135-6">https://www.nature.com/articles/s41597-022-01135-6</a></p> <p>The tools used to download and process these measurement datasets are available here: <a href="https://zenodo.org/record/4545257">https://zenodo.org/record/4545257</a></p> <p>The dataset is designed for use with the region estimator toolset, available in this repository: <a href="https://github.com/UoMResearchIT/region_estimators">https://github.com/UoMResearchIT/region_estimators</a></p> <p>Emissions over the UK for the EMEP model runs were generated using the NAEI 2016 UK emission dataset, available in netcdf form here: <a href="https://zenodo.org/record/3997165#.X9KUBF6nzUI">https://zenodo.org/record/3997165#.X9KUBF6nzUI</a>. The running scripts, and operation inputs for EMEP, are available here: <a href="https://zenodo.org/record/3997301#.X9KUAF6nzUI">https://zenodo.org/record/3997301#.X9KUAF6nzUI</a> and <a href="https://zenodo.org/record/3997271#.X9KV1F6nzUI">https://zenodo.org/record/3997271#.X9KV1F6nzUI</a>.</p> <p>The dataset is presented in CSV format, as three files:</p> <ol> <li>turing_aq_daily_met_pollen_pollution_original_data.csv: original data (described below)</li> <li>turing_aq_daily_met_pollen_pollution_with_imputation_data.csv: original plus imputed data (described below)</li> <li>site_location_data.csv: location metadata (site_id, latitude, longitude, postcode area)</li> </ol> <p> </p> <p>The columns intended to be used as indexes are:</p> <ul> <li>timestamp, <ul> <li>date of measurements on that row</li> </ul> </li> <li>site_id, <ul> <li>measurement site ID, corresponding to sites in the three networks: <ul> <li>AURN [indicated by AQ],</li> <li>MIDAS [indicated by WEATHER],</li> <li>or pollen [indicated by POLLEN].</li> </ul> </li> </ul> </li> </ul> <p>The data columns are:</p> <ul> <li>O3, PM10, PM2.5, NO2, NOXasNO2, SO2, <ul> <li>daily mean and maximum values in ug/m3 (all with "_max", "_mean", and "_flag" tags)</li> <li>AURN measurement data</li> </ul> </li> <li>O3_EMEP, NO2_EMEP, SO2_EMEP, NOXasNO2_EMEP, PM2.5_EMEP, PM10_EMEP, <ul> <li>daily mean and maximum values in ug/m3 (all with "_max", and "_mean" tags)</li> <li>EMEP model forecasts</li> </ul> </li> <li>alnus, ambrosia, artemisia, betula, corylus, fraxinus, platanus, poaceae, quercus, salix, ulmus, urtica, <ul> <li>daily pollen grain counts</li> </ul> </li> <li>temperature, relativehumidity, pressure, <ul> <li>daily mean and maximum values in degC, %, and hPa (all with "_max", "_mean", and "_flag" tags).</li> <li>Met Office measurement data</li> </ul> </li> </ul> <p>The "_flag" columns indicate data points which have been partially, or fully, imputed. The values for these will be in the range 0-1, and indicate the fraction of the hourly values within that day that are imputed (0 = none, 1 = all 24 hourly datapoints are imputed). No imputation is done in the original dataset, so the "_flag" data in this dataset will always be zero (the number of hourly data points used to calculate the daily mean and maximum are not recorded in this dataset).</p> <p>The station location metadata includes longitude, latitude, and UK postcode area data. Where sites lie outside of the UK the postcode is replaced with regional indicator (here: Republic of Ireland (ROI)).</p> <p> </p> <p>Please cite the following paper if you use this dataset: Reani, M., Lowe, D., Gledson, A., Topping, D., & Jay, C. (2022). UK daily meteorology, air quality, and pollen measurements for 2016–2019, with estimates for missing data. <em>Scientific Data</em>, <em>9</em>(1), 43. https://doi.org/10.1038/s41597-022-01135-6</p>
An Optimized North America MODIS Leaf Area Index (LAI) Dataset for Air Quality Modeling
<p>Air Quality Research Division, Environment and Climate Change Canada,</p> <p>4905 Dufferin Street, Toronto, Ontario, M3H 5T4, Canada</p> <p>Email: Junhua.zhang@ec.gc.ca</p> <p> </p> <p>Leaf Area Index (LAI) is used in air quality models for land surface processes and for calculating biogenic emissions. MODIS LAI product provided by NASA (https://modis.gsfc.nasa.gov/data/dataprod/mod15.php) has been widely used in the air quality modeling community for such purposes. However, limitations of MODIS LAI product have been seen for some geographic areas, particularly unreasonably low LAI over the evergreen needleleaf boreal forests in the northern hemisphere during wintertime due to snow cover and low sun angle. Missing retrievals over urban areas and areas with persistent cloud cover are also seen. Considerable efforts have been made to improve the MODIS LAI product. However, some issues are still persistent, such as the very low LAI over boreal forests during wintertime. In order to solve these issues for supporting regional air quality modelling, the 8-day MODIS Collection 6 (C6) LAI product at 500m resolution (MCD15A2H) was examined for North America. Statistics were calculated by month and by land cover type defined in the “Land Cover Type 1” science data set (SDS) of the Collection 6 MODIS Land Cover (MCD12Q1) product. Comparisons with LAI calculated from the EPA’s Biogenic Emissions Landuse Database, version 4 (BELD4, https://www.epa.gov/air-emissions-modeling/biogenic-emission-sources) were also done (Zhang et al., 2020). Based on the analysis, an updated monthly LAI dataset was calculated based on 1) 17-year (2003-2019) average of MODIS summer-time peak LAI, 2) fraction of evergreen and deciduous for each pixel from BELD4, and 3) monthly profiles of LAI for evergreen and deciduous vegetation species from MODIS LAI (Zhang et al., 2021). This is the final LAI dataset for North America compiled using the 17 years of MODIS LAI product complemented by information from BELD4.</p> <p> </p> <p>REFERENCES:</p> <p>Zhang, J., M. D. Moran, P. A. Makar, and S. Kharol, 2020. Examination of MODIS Leaf Area Index (LAI) Product for Air Quality Modelling. 19th CMAS Conference, 26-30 Oct., Virtual [see https://www.cmascenter.org/conference/2020/slides/ZhangJ_MODIS_LAI_CMAS_2020.pdf].</p> <p>Zhang, J., P. A. Makar, S. Kharol, M. D. Moran, and C. McLinden, 2021. Examination and Processing of MODIS Leaf Area Index (LAI) Product for Air Quality Modelling. 2021 Meteorology and Climate - Modeling for Air Quality Conference, Sep 14-17, 2021, Virtual</p> <p> </p>
Benefits and limitations of environmental magnetism for completing citizen science on air quality: a case study in a street canyon.
<p>Inside a street canyon in Montpellier (France) a total of 72 deposimeters were deployed in 29 households for a period of 3 months to measure local air quality. This street canyon was chosen because dwellers were already mobilized against the street traffic, and because they were in conflict on this issue with policy makers. The project aimed to include all the stakeholders through co-construction. The closure of the street during the metrological campaign and the absence of agreement curbed their involvement and motivation. However, the feedbacks from the citizen partners promote the fact that this study supported their claims and brought them a deeper understanding on the micro-scale air quality monitoring. Indeed, it is increasingly difficult for citizens, who seemed specifically interested in what is happening right outside their front door, to understand this measure with the emergence of ever more low-cost sensors. For that reason, we examined the citizen’s degree of confidence in magnetic monitoring of air quality and how can this technique be useful in their claims. The results show that magnetism can be a measurement technique favorable to citizen participation because it provides a large amount of data at the micro-scale of the street level, while the data from the certified associations for monitoring air quality requires a spatial interpolation to map variations on a neighborhood scale. In this study, we proposed a magnetic air quality index to standardize and democratize the magnetic monitoring of air quality to facilitate the dialogue with all stakeholders.</p>
Nuclear Power Generation Phaseouts Redistribute U.S. Air Quality and Climate Related Mortality Risk, Data
<p>This dataset accompanies the publication, "Nuclear Power Generation Phaseouts Redistribute U.S. Air Quality and Climate Related Mortality Risk", and can be used with the code located at https://zenodo.org/badge/latestdoi/248010532 to reproduce our results.</p>
SensEURCity: A multi-city air quality dataset collected using networks of open low-cost sensor systems
<p>We provide a unique curated dataset of urban air quality measurements acquired using dense networks of low-cost sensor systems in three European cities for the years 2020 and 2021. The dataset includes the raw sensor data of quality-controlled sensor networks along with co-located reference data sets. Sensor data are collected using the AirSensEUR sensor system, including sensors to monitor NO, NO2, O3, CO, PM2.5, PM10, PM1, CO2, and meteorological parameters. In total, 85 sensor systems were deployed throughout the years 2020 and 2021 in three European cities (Antwerp , Oslo and Zagreb), resulting in a dataset comprising different meteorological and ambient conditions. The main data collection included two co-location campaigns in different seasons at an air quality monitoring station in each city and a deployment at different locations in each city (including also locations at other air quality monitoring stations). The dataset consists of data files with sensor and reference data, and metadata files with description of locations, deployment dates and description of sensors and reference instruments. </p>
Uncovering local aggregated air quality index with smartphone captured images leveraging efficient deep convolutional neural network
<p>Short Description:</p> <p>In this research, we vigorously analyze the difficulties of predicting location-specific PM2.5 concentration from photos captured by smartphone cameras. Here, we particularly focus on Dhaka, the capital of Bangladesh, considering its very high level of air pollution exposure to a huge number of its dwellers. In our research, we develop a Deep Convolutional Neural Network (DCNN) and train it using more than a thousand outdoor photos captured and labeled by us. We capture the photos at various locations in Dhaka, Bangladesh, and label them based on PM2.5 concentration data extracted from the local US consulate as computed by the NowCast algorithm. During training with the dataset, our model learns a correlation index through supervised learning, which improves the model's ability to act as a Picture-based Predictor of PM2.5 Concentration (PPPC) making it capable of detecting comparable daily aggregated AQI index from a photo captured by a smartphone.</p> <p>Code and More Details: https://github.com/lepotatoguy/aqi</p>
Supporting data and tool, for the paper "A standardized methodology for the validation of air quality forecast applications (F-MQO): Lessons learnt from its application across Europe"
<p>This 'Zenodo' contains supporting data and tools, for the paper 'A standardized methodology for the validation of air quality forecast applications (F-MQO): Lessons learnt from its application across Europe'.</p> <p>The repository includes the source code (<a href="https://zenodo.org/api/files/fdcaf2a5-5289-44f5-96db-6dda29c5cb6c/DELTA_7.2.zip">DELTA_7.2.zip</a>) and the dataset (<a href="https://zenodo.org/api/files/fdcaf2a5-5289-44f5-96db-6dda29c5cb6c/GMD_Vitali_et_all_data_20230516.zip">GMD_Vitali_et_all_data_20230516.zip</a>).</p>
Dataset for "Exposure and environmental engagement: A pilot integrating wearable sensors, air quality and citizen science"
<p>The dataset contains anonymised readings of 7 citizens taking air quality measurements using PlumeLabs Flow 2 monitor. Data is for Falmouth/Penryn, and Bristol and it was collected between January 26, 2022 and March 9, 2022.</p> <p>CSV file:</p> <ul> <li>latitude: unit degrees, positive values indicate North hemisphere.</li> <li>longitude, unit degrees, positive values indicate East.</li> <li>AQI: PlumeLabs' Air Quality Index.</li> <li>site: A refers to Falmouth/Penryn(UK), B refers to Bristol (UK).</li> <li>count: auxiliary variable that indicates that the record was comprised of a single reading.</li> </ul> <p>Jupyter notebook: The air quality analysis was conducted with Python 3.9.16 alongside numpy 1.24.3, pandas 2.0.2, matplotlib 3.7.1, and cartopy 0.21.1 (background tiles by OpenStreetMaps).</p>
Air quality source attribution and scenario analysis in the UNECE region
<p>The dataset contains the metrics of PM2.5 and ozone exposure in the UNECE region attributed to 13 activity sectors in three different ECLIPSE v6b emission scenarios (CLE BASE, MFR-BASE and SDS-MFR) used by the authors in the publication "Air quality and related health impact in the UNECE region: source attribution and scenario analysis" submitted to the Journal Atmospheric Chemistry and Physics (https://doi.org/10.5194/acp-2022-776).</p>
GIS and Pollution Data: Designating Regional Airsheds for Air Quality Management in India
<p>Full journal article published here<br><strong>Designating Airsheds in India for Urban and Regional Air Quality Management<br></strong><a href="https://doi.org/10.3390/air2030015" target="_blank" rel="noopener">https://doi.org/10.3390/air2030015</a><strong><br></strong></p> <p>[Summary presentation <a href="https://urbanemissions.info/wp-content/uploads/docs/UEinfo-Designating-Airsheds-in-India.pptx">download</a>]</p> <p>Datasets used for proposing India's 15 regional airsheds for air quality management are the following</p> <p>PM2.5 Datasets<br>Raw data source: <a href="https://sites.wustl.edu/acag/datasets/surface-pm2-5">https://sites.wustl.edu/acag/datasets/surface-pm2-5</a></p> <ul> <li>Gridded 0.1 degree resolution source apportionment results from WUSTL's global model simulations<br>File: india_data_pm25_wustl_source_cont_0p1deg.xlsx<br>Aggregated Source definitions used in this presentation <ul> <li>1. DUST = Anthropogenic dust = AFCID</li> <li>2. WINDUST = Wind erosion (dust storms) = WDUST</li> <li>3. WASTE = Waste burning = WST</li> <li>4. RESI = All commercial and residential cooking, lighting, and heating = RCOC + RCOO + RCORbiofuel + RCORcoal + RCORother</li> <li>5. TRANS = All transport (excluding aviation) = ROAD + NRTR + SHP</li> <li>6. POWER = Energy generation = ENEcoal + ENEother</li> <li>7. INDUS = All industries and product use = INDcoal + INDother + SLV</li> <li>8. BIOB = Biomass burning, including forest fires and agricultural waste burning = GFEDoburn + GFEDagburn</li> <li>9. AGR = Agricultural activities (excluding agricultural waste burning) = AGR</li> <li>10. OTHER = All others = OTHER</li> </ul> </li> <li>Gridded 0.1 degree resolution, reanalysis data from WUSTL's global model simulations<br>File: india_data_pm25_wustl_reanalysis_0p1deg.xlsx<br>Time period: 1998 to 2022, annual averages</li> <li>Gridded 0.1 degree achive for monthly averages from WUSTL's global model simulations<br>File: <a href="https://www.urbanemissions.info/wp-content/uploads/misc/IndiaSubcontinent-Gridded-Monthly-WUSTL-v4.rar">Download-44MB</a></li> </ul> <p>Population Datasets<br>Raw data source: <a href="https://landscan.ornl.gov">https://landscan.ornl.gov</a></p> <ul> <li>Gridded 0.1 degree resolution population density data<br>File: india_data_population_2021_0p1deg.xlsx</li> </ul> <p>GIS databases used in this study</p> <ul> <li>ESRI shapefile of 0.1 x 0.1 degree mesh file for the Indian Subcontinent covering longitudes from 67E to 99E and latitudes from 7N to 39N<br>File: india_gis_grids-0.1x0.1deg.rar</li> <li>ESRI shapefile of India administrative level 2 data - 28 states and 8 union territories (as of December 2023)<br>File: india_gis_states28+8_2023.rar</li> <li>ESRI shapefile of India administrative level 3 data - 755 districts (as of December 2023): district23 and states23 codes are re-designed for emissions and pollution mapping and data tracking purposes<br>File: India_gis_districts755_2023.rar (original source: <a href="https://projects.datameet.org/maps">https://projects.datameet.org/maps</a>)</li> <li>ESRI shapefile of India's Agro-Climatic zones<br>File: india_gis_agroclimatic_zones.rar (original source: <a href="https://karnataka.data.gov.in/resource/boundaries-agro-climatic-regions">https://karnataka.data.gov.in/resource/boundaries-agro-climatic-regions</a> </li> <li>ESRI shapefile of India's meteorological sub-divisions<br>File: india_gis_meteo_subdivisions.rar (original source: <a href="https://mausam.imd.gov.in/">https://mausam.imd.gov.in</a>)</li> </ul>
Home Air Quality Impact for Adults With Asthma
ClinicalTrials.gov study NCT05224076. IPD Sharing: YES. Countries: 1. Publications: 6.
Air quality Modelling data for Guildford City
<p>The simulation data for Guildford City using ADMS-Urban at 1 km spatial resolution. </p>
Malaga air quality
<p>An open-source dataset containing air quality information for Malaga in 2018</p>
Enhancing accuracy of air quality and temperature forecasts during paddy crop-residue burning season in Delhi via chemical data assimilation
<p>This paper examines the accuracy of Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) generated 72 h fine particulate matter (PM<sub>2.5</sub>) forecasts in Delhi during the crop residue burning season of Oct-Nov 2017 with respect to assimilation of the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrievals, persistent fire emission assumption, and aerosol-radiation interactions. The assimilation significantly pushes the model AOD and PM<sub>2.5</sub> towards the observations with the largest changes below 5 km altitude in the fire source regions (northeastern Pakistan, Punjab, and Haryana) as well as the receptor New Delhi. WRF-Chem forecast with MODIS AOD assimilation, aerosol-radiation feedback turned on, and real-time fire emissions reduce the mean bias by 88-195 µg/m<sup>3</sup> (70-86%) with the largest improvement during the peak air pollution episode of 6-13 November 2017. Aerosol-radiation feedback contributes ~21%, ~25%, and ~24% to reduction in mean bias of the first, second, and third day of PM<sub>2.5 </sub>forecast. Persistence fire emission assumption is found to work really well, as the accuracy of PM<sub>2.5</sub> forecasts driven by persistent fire emissions was only 6% lower compared to those driven by real fire emissions. Aerosol-radiation feedback extends the benefits of assimilating satellite AOD beyond PM<sub>2.5</sub> forecasts to surface temperature forecast with a reduction in the mean bias of 0.9<sup>o</sup>C - 1.5<sup>o</sup>C (17-30%). These results demonstrate that air quality forecasting can benefit substantially from satellite AOD observations particularly in developing countries that lack resources to rapidly build dense air quality monitoring networks.</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.