Air Quality Trend of PM10. Statistical Models for Assessing the Air Quality Impact of Environmental Policies
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Otros documentos de la autoría: Vicente, Ana Belen; Juan, Pablo; Meseguer Costa, Sergio; Serra, Laura; Trilles, Sergio
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Air Quality Trend of PM10. Statistical Models for Assessing the Air Quality Impact of Environmental PoliciesFecha de publicación
2019Editor
MDPIISSN
2071-1050Cita bibliográfica
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. 5857Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://www.mdpi.com/2071-1050/11/20/5857Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
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 ... [+]
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. [-]
Publicado en
Sustainability, 2019, vol. 11, núm. 20, p. 5857Proyecto de investigación
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).Derechos de acceso
info:eu-repo/semantics/openAccess
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