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ZENODO
Dataset . 2021
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2021
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2021
License: CC BY
Data sources: Datacite
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Dataset for prediction of Nitrogen Dioxide in Madrid city

Authors: Iskandaryan, Ditsuhi; Ramos, Francisco; Trilles, Sergio;

Dataset for prediction of Nitrogen Dioxide in Madrid city

Abstract

The dataset consists of nitrogen dioxide, meteorological data, and traffic data for the period of January-June 2019 and January-June 2020, which were generated taking into account the spatial distribution of the monitoring stations. Using the ArcGIS Pro software, a grid was created (Top -4,486,449.725263 m; Bottom - 4,466,449.725263 m; Left - 434,215.234430 m; Right - 451,215.234430 m) with a cell size having a width and height equal to 1000 m. There are 340 cells in total (20 by 17). Each cell value includes nitrogen dioxide, meteorological, and traffic attributes from assigned stations at a certain time. The cell value without stations was assigned to zero. The generated grid was exported as Comma Separated Values (CSV) files. Overall, 4344 and 4368 CSV files were generated every hour during the first six months of 2019 and 2020, respectively. Meteorological data include ultraviolet radiation, wind speed, wind direction, temperature, relative humidity, barometric pressure, solar irradiance, and precipitation, traffic data includes intensity, occupation, load, and average speed. The datasets have an hourly rate. The data were obtained from the Open Data portal of the Madrid City Council. There are 24 air quality monitoring stations, 26 meteorological monitoring stations, and more than 4,000 traffic measurement points (the location of the measurement points was provided on a monthly basis as these points changed monthly).

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Keywords

Air quality prediction, Machine learning, Spatiotemporal prediction

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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