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323 results for “air pollution”
Dataset and R code: Effects of temperature and air pollution on emergency ambulance dispatches: a time series analysis in a medium-sized city in Germany
<p>Dataset and R script to replicate results in the manuscript "Effects of temperature and air pollution on emergency ambulance dispatches: a time series analysis in a medium-sized city in Germany", currently under review.</p>
Code and data used in "A Tool for Air Pollution Scenarios (TAPS v1.0) to enable global, long-term, and flexible study of climate and air quality policies"
<p>Data and code for Tool for Air Pollution Scenarios (TAPS v1.0) as submitted to Geoscientific Model Development for publication. See the enclosed README and full user manual (https://github.com/watkin-mit/TAPS/wiki) for more information. </p>
Spatiotemporal variations of air pollution during the COVID-19 pandemic across Tehran, Iran: Commonalities with and differ-ences from global trends
<p>Figure S1: Green space and green area per capita across Tehran; Figure S2: Temporal distribution of CO content at each station, gray rectangular shows strict social distancing time. Figure S3: Temporal distribution of NO2 content in all investigated stations, gray rectangular shows strict social distancing time; Figure S4: Temporal distribution of PM10 content in all investigated stations gray rectangular shows strict social distancing time; Figure S5: Temporal distribution of O3 content in all investigated stations, gray rectangular shows strict social distancing time; Figure S6: Temporal distribution of SO2 content in all investigated stations, gray rectangular shows strict social distancing time; Figure S7: Temporal distribution of AQI indices in all investigated stations, gray rectangular shows strict social distancing time. </p>
Data and code for "Air pollution impacts from warehousing in the United States uncovered with satellite data"
<div> <div> <p>This repository (ver 2024.06.11) contains data and code supporting the analyses and visualizations in the manuscript "Air pollution impacts from warehousing in the United States uncovered with satellite data" in the journal <em>Nature </em><em>Communications</em> (DOI forthcoming). A description of the data files and code can be found in the README. </p> </div> </div>
Fig. 1. Investigated 8 in The Relation Of Forest And Air Pollution With Human Health In Urban Territories Of Lithuania
Fig. 1. Investigated 8 urban territories of Lithuania (Alytus, Kaunas, Klaipeda, Palanga, Panevezys, Siauliai, Vilnius, Visaginas urban municipalities).
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>
Estimating future climate change impacts on human mortality and crop yields via air pollution: supplemental files
<p>Atmospheric chemistry model output and other gridded data sets necessary to estimate human mortality and crop yield losses associated with future climate change, as used in Murray et al. [PNAS, 2024] doi:10.1073/pnas.2400117121.</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>
Urban Air Pollution and Children's Brain Development: A Systematic Bibliometric Review
<p>The dataset for this research was compiled through an advanced PubMed search targeting publications from a one-year period. Keywords focused on air pollution, neurodevelopment, and associated disorders. From an initial pool of 450 publications, filtering based on co-occurrence of relevant keywords reduced this to approximately 50 papers. VOSviewer was employed to analyze co-occurrences and generate a visual map of relationships between air pollution and child neurodevelopment. The thesaurus was applied to standardize terminology, refining the final network for detailed analysis of keyword clusters and their interactions.</p>
Time Series Comparisons, Model Code, and a Demo Dataset for SIBaR: A New Method for Background Quantification and Removal from Mobile Air Pollution Measurements
<p>Time series comparisons between SIBaR, Brantley, and Apte background signals for all 312 time series in the Houston mobile monitoring campaign. Additionally, a R script demo (DemoData.R) of the SIBaR partitioning step on the demo datatset (DemoData.csv).</p>
Spatial interpolation of air pollutant and meteorological variables in Central Amazonia
<p>This dataset presents data of aerosol, trace-gases and meteorological variables from the Amazon Rainforest region, resulting from an interpolation process. The original data were collected from the GOAmazon 2014/15 project, from the Atmospheric Radiation Measurement (ARM) repository. </p>
Multivariate Spatial Predictions of Air Pollutants with INLA
<p>Spatial predictions and uncertainty quantification for air pollutants: PM<sub>2.5</sub>, PM<sub>10</sub>, NO<sub>3</sub><sup>−</sup>, NH<sub>4</sub><sup>+</sup>, EC, OC, SO<sub>4</sub><sup>2− </sup>, CO, NOx, NO<sub>2</sub>, SO<sub>2</sub> and O<sub>3</sub> covering the continental US for the period 2005–2014. The daily prediction is at 12km spatial resolution.</p>
What Is Polluting Delhi's Air? A Review from 1990 to 2022
<p>This supplementary information for open use is part of the publication, "<a href="https://doi.org/10.3390/su15054209">What is Polluting Delhi's Air? A Review from 1990 to 2022</a>" This paper offers insight by reviewing the influence of Delhi’s urban growth since 1990 on pollution levels and sources and the evolution of technical, institutional, and legal measures to control emissions in the National Capital Region of Delhi..</p> <p><strong>The databases and the documents uploaded here are the following</strong></p> <p>Available ambient air quality monitoring data for Delhi</p> <ul> <li>cpcb_delhi_data_2006-2018-raw-cleaned.rar - This is CPCB data from 2006 to 2018 as raw and cleaned files. Raw data is at 15 min internals, which needs some qa/qc checks before use. The cleaned data is hourly. For data cleaning, all null points, all negative points, integer values equal to 9999, 999, 1985, 985, 915, 515, 1200, 1000, 2000, 380, 3800, 675, and 718 were excluded. These values were recognized after searching the raw data for patterns. Instances of sudden jumps, which occur due to malfunctioning of instruments were recognized using running means. </li> <li>1999-2006-CPCB ITO-Hourly.xlsx - This is hourly data from the ITO station only</li> <li>NAMP data for 2011 to 2015 is hosted here - https://doi.org/10.5281/zenodo.6925200</li> <li>Graph-Composite-1989-2022.xlsx -- This is a composite of all the annual average data along with the worksheet to make the image in the preview.</li> </ul> <p>Support documents</p> <ul> <li>1997-CPCB-White-Paper-on-Delhi-Air-Pollution.pdf</li> <li>2011-CPCB-Source-Apportionment-Report-Extracts.pdf</li> <li>2015-04 Infograph Delhi Banning Vehicles to Control AP.jpg</li> <li>2016-03 Inforgraph Delhi Odd Even Emissions.jpg</li> <li>2019-09-Infograph-Delhi-Odd-Even-Buses.jpg</li> <li>NCAP-Planned-Source-Apportionment-studies.pdf</li> <li>SIM-41-2021-Data-Resources-for-Energy-Emissions-Analysis.pdf</li> </ul> <p>Reanalysis fields from WUSTL</p> <ul> <li>wustl_delhi_1998-2021.csv - This data is at 0.01 degree resolution for the city airshed. The long-lat represent the grid mid-point. </li> <li>wustl_delhi_1998-2021.png - This is a composite image of data extracted for all the years</li> </ul> <p>Satellite data extracts</p> <ul> <li>satellite-modis_terra_aod_delhi-covidperiod.xlsx</li> <li>satellite-modis_terra_aod_delhi-longerperiod.xlsx</li> <li>satellite-omi_no2_delhi-covidperiod.xlsx</li> <li>satellite-tropomi_o3_delhi.xlsx</li> <li>satellite-tropomi_so2_delhi.xlsx</li> </ul>
Health Risks Forecast of Regional Air Pollution on Allergic Rhinitis: High-Resolution City-Scale Simulations in Changchun, China
<p>Here presented the forcasted results of Potential Morbidity Risk Index (PMRI) for the personal patients with allerigc rhinitis and the public health administrations, and these results are supplied to the published paper of "Health Risks Forecast of Regional Air Pollution on Allergic Rhinitis: High-Resolution City-Scale Simulations in Changchun, China".</p>
Data for Measurement report: Air pollution emission factors of inland river ships under compliance with the 10 parts per million limit for sulfur content in fuel
<p>Since July 1, 2019, China’s domestic diesel fuel has been limited to 10 ppm of sulfur. Hence, to explore the applicability of the “sniffer” method and the distribution and level of inland river ships (IRSs) emission factors (EFs) under this limitation, we installed “sniffer” monitoring equipment, from August 2020 to June 2022, at the Gezhou Dam of the Yangtze River in China and monitored emissions from 8,238 IRSs in total passing through the lock. We partnered with the maritime department to select 100 ships passing through the lock to extract fuel oilsamples for direct fuel sulfur content detection, which determined the true fuel sulfur content of the passing ships. fuel sulfur content.</p> <p>The “sniffer” monitoring equipment included SO<sub>2</sub>, CO<sub>2</sub>, NO, and NO<sub>2</sub> gas sensors, PM<sub>2.5</sub> and PM<sub>10</sub> particulate matter sensors, as well as wind speed, wind direction, temperature, humidity, and pressure sensors.</p>
Data and analysis scripts for: Lung adenocarcinoma promotion by air pollutants
<p>Code for "Lung adenocarcinoma promotion by air pollutants" manuscript</p> <p>egfrm_lc_incidence</p> <ul> <li>Epidemiological analyses of EGFRm lung cancer incidence and PM2.5 levels in England (NHS England), South Korea and Taiwan.</li> </ul> <p>ukbb</p> <ul> <li>Epidemiological analyses of lung cancer incidence and PM2.5 levels in England, using the UKBB data set.</li> </ul> <p>mouse_RNA_seq</p> <ul> <li>Analysis of RNA-seq data derived from lung tumour tissue of pollution-exposed mice.</li> </ul> <p>mouse_WGS</p> <ul> <li>Analysis of WGS data derived from lung tumour tissue of pollution-exposed mice.</li> </ul> <p>normal_lung</p> <ul> <li>Analysis of ddPCR for EGFRm from normal lung tissue from the TRACERx and PEACE cohorts.</li> <li>Analysis of Duplex-seq data from normal lung tissue from the PEACE and BDRE cohorts.</li> </ul>
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>
A data-driven supervised machine learning approach to estimating global ambient air pollution concentrations with associated prediction intervals
Open the record for dataset details and reuse information.
Data for: Urban form and its impacts on air pollution and access to green space: A global analysis of 462 cities
Open the record for dataset details and reuse information.
Characteristic and spatiotemporal variation of air pollution in Northern China based on correlation analysis and clustering analysis of five air pollutants
<p>Data for "Characteristic and spatiotemporal variation of air pollution in Northern China based on correlation analysis and clustering analysis of five air pollutants"</p>
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Allen Brain Atlas
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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.