Mostrar el registro sencillo del ítem
Air Quality Trend of PM10. Statistical Models for Assessing the Air Quality Impact of Environmental Policies
dc.contributor.author | Vicente, Ana Belen | |
dc.contributor.author | Juan, Pablo | |
dc.contributor.author | Meseguer Costa, Sergio | |
dc.contributor.author | Serra, Laura | |
dc.contributor.author | Trilles, Sergio | |
dc.date.accessioned | 2019-12-12T20:08:13Z | |
dc.date.available | 2019-12-12T20:08:13Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | VICENTE, Ana Belen, et al. Air Quality Trend of PM10. Statistical Models for Assessing the Air Quality Impact of Environmental Policies. Sustainability, 2019, vol. 11, núm. 20, p. 5857 | ca_CA |
dc.identifier.issn | 2071-1050 | |
dc.identifier.uri | http://hdl.handle.net/10234/185428 | |
dc.description.abstract | A statistical modelling of PM10 concentration (2006–2015) is applied to understand the behaviour, to know the influence of the variables to exposure risk, to treat the missing data to evaluate air quality, and to estimate data for those sites where they are not available. The study area, Castellón region (Spain), is a strategic area in the framework of EU pollution control. A decrease of PM10 is observed for industrial and urban stations. In the case of rural stations, the levels remain constant throughout the study period. The contribution of anthropogenic sources has been estimated through the PM10 background of the study area. The behaviour of PM10 annual trend is tri-modal for industrial and urban stations and bi-modal in the case of rural stations. The EU Normative suggests that 90% of the data per year are necessary to control air quality. Thus, interpolation statistical methods are presented to fill missing data: Linear Interpolation, Exponential Interpolation, and Kalman Smoothing. This study also focuses on testing the goodness of these methods in order to find the ones that better approach the gaps. After analyzing graphically and using the RMSE the last method is confirmed to be the best option. | ca_CA |
dc.format.extent | 18 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | MDPI | ca_CA |
dc.relation.isPartOf | Sustainability, 2019, vol. 11, núm. 20, p. 5857 | ca_CA |
dc.rights | © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | ca_CA |
dc.rights | Atribución 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-sa/4.0/ | * |
dc.subject | PM10 | ca_CA |
dc.subject | trend | ca_CA |
dc.subject | interpolation methods | ca_CA |
dc.subject | Kalman Smoothing | ca_CA |
dc.title | Air Quality Trend of PM10. Statistical Models for Assessing the Air Quality Impact of Environmental Policies | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | https://doi.org/10.3390/su11205857 | |
dc.relation.projectID | This work has been funded by the Generalitat Valenciana through the Subvenciones para la realización de proyectos de I+D+i desarrollados por grupos de investigación emergentes program (GV/2019/016). Sergio Trilles has been funded by the postdoctoral programme PINV2018–Universitat Jaume I (POSDOC-B/2018/12). | ca_CA |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.relation.publisherVersion | https://www.mdpi.com/2071-1050/11/20/5857 | ca_CA |
dc.type.version | info:eu-repo/semantics/publishedVersion | ca_CA |
Ficheros en el ítem
Este ítem aparece en la(s) siguiente(s) colección(ones)
-
INIT_Articles [748]
-
LSI_Articles [362]
Articles de publicacions periòdiques escrits per professors del Departament de Llenguatges i Sistemes Informàtics -
MAT_Articles [758]
Articles de publicacions periòdiques -
CAMN_Articles [566]
Excepto si se señala otra cosa, la licencia del ítem se describe como: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).