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dc.contributor.authorGonzález-Figueredo, Carlos
dc.contributor.authorEgurrola-Hernández, E.A.
dc.contributor.authorDeAlba-Martínez, Hugo
dc.contributor.authorRamírez-Briseño, R.L.
dc.contributor.authorMagaña-Villegas, Elizabeth
dc.coverage.spatiales
dc.date.accessioned2019-08-28T18:31:02Z
dc.date.accessioned2023-03-16T14:28:29Z
dc.date.available2019-08-28T18:31:02Z
dc.date.available2023-03-16T14:28:29Z
dc.date.issued2018-03
dc.identifier.citationGonzález-Figueredo, C., Egurrola-Hernández, E. A., DeAlba-Martínez, H., Ramírez-Briseño, R. L., & Magaña-Villegas, E. (2018). Mobile App for Air Quality Guadalajara, Mexico. In Proceedings of Abstracts 11th International Conference on Air Quality Science and Application, University of Hertfordshire.es
dc.identifier.isbn978-1-5272-2150-5
dc.identifier.urihttps://hdl.handle.net/20.500.12032/72268
dc.descriptionAireAMG is a mobile app for air quality forecasting in the Guadalajara Metropolitan Area in Mexico. This app uses real-time air quality measurements of five criteria pollutants, along with meteorological data, to forecast air pollution concentration. A novel scheme of Artificial Neural Networks (ARN) coupled to Kalman filters is used to generate air quality predictions for up to 24 hours for each of the monitoring stations locations. We used these predictions to generate ozone and particulate matter dispersion maps, by interpolating with the Inverse Distance Weighted (IDW) method. AireAMG updates these forecasts and dispersion maps every hour, and is available for Android and iOS systems.es
dc.language.isoenges
dc.publisherInternational Conference on Air Quality Science and Application
dc.rights.urihttp://quijote.biblio.iteso.mx/licencias/CC-BY-NC-2.5-MX.pdfes
dc.subjectCalidad del Airees
dc.subjectApplicaciones Móvileses
dc.subjectRedes Neuronales Artificialeses
dc.titleMobile App for Air Quality Guadalajara, Mexico.es
dc.typeinfo:eu-repo/semantics/conferencePaperes


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