Air Quality Prediction Coverage based on date, heatmaps in the cities of Firenze, Pisa and Livorno on NOx.

The predictions are realised with a definition of 4x4 meters, at both 3 and 6 meters. The data are available in the Snap4City portal (https://servicemap.snap4city.org) and have been derived from the observations obtained through the sensor network and forecast models setup under the framework for the European TRAFAIR project.

Data e Risorse

Metadati del Dataset

Identificativo del dataset air_quality_prediction_coverage_date
Altro identificativo N/A
Temi del dataset Ambiente
Editore del Dataset N/A
Data di rilascio 09-06-2020
Data di modifica 09-06-2020
Copertura Geografica Area di competenza dell'unità organizzativa preposta
URI di GeoNames http://www.geonames.org/3165361
Lingue del dataset inglese, italiano
Estensione temporale N/A
Titolare Nome: Trafair
Codice IPA/IVA: trafair
Frequenza di aggiornamento continuo
Versione di N/A
Conforme a Standard: https://inspire.ec.europa.eu/documents/commission-regulation-eu-no-13122014-10-december-2014-amending-regulation-eu-no-10892010-0
Conforme a Standard: http://www.opengis.net/def/crs/EPSG/0/4326
Autore N/A

Informazioni supplementari

Nome campo Valore
Origine https://servicemap.snap4city.org/
Ultimo aggiornamento novembre 2, 2020, 17:58 (CET)
Contact email paolo.nesi@unifi.it
Contact name Paolo Nesi
End of temporal extent 2020-11-01T00:00:00.000Z
Start of temporal extent 2020-25-05T20:00:00.000Z
Theme ["http://publications.europa.eu/resource/authority/data-theme/ENVI"]
dcat_type http://inspire.ec.europa.eu/metadata-codelist/ResourceType/dataset
provenance The Predictions are visibile and available in the Snap4City platform (https://www.snap4city.org) and are based on the GRAL predictive Model.
sample_query https://wmsserver.snap4city.org/geoserver/Snap4City/wms?service=WMS&version=1.1.0&request=GetMap&layers=Snap4City:GRALheatmapPisa6m&bbox=10.3815320305478,43.6986204534205,10.4153738636442,43.7297698518152&width=768&height=542&srs=EPSG:4326&format=image/gif&time=2019-10-02T06:00:00.000Z
spatial POLYGON((10.160633321069001 43.88188014908394,11.473499532006501 43.88188014908394,11.473499532006501 43.448752618200714,10.160633321069001 43.448752618200714,10.160633321069001 43.88188014908394))
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