Air Quality Monitoring Network Design Optimisation for Robust Land Use Regression Models
Ver/ Abrir
Impacto
Scholar |
Otros documentos de la autoría: Gupta, Shivam; Pebesma, Edzer; Mateu, Jorge; Degbelo, Auriol
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7037
comunitat-uji-handle3:10234/8635
comunitat-uji-handle4:
INVESTIGACIONMetadatos
Título
Air Quality Monitoring Network Design Optimisation for Robust Land Use Regression ModelsFecha de publicación
2018Editor
MDPIISSN
2071-1050Cita bibliográfica
GUPTA, Shivam, et al. Air Quality Monitoring Network Design Optimisation for Robust Land Use Regression Models. Sustainability (2071-1050), 2018, 10.5Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://www.mdpi.com/2071-1050/10/5/1442Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
A very common curb of epidemiological studies for understanding the impact of air
pollution on health is the quality of exposure data available. Many epidemiological studies rely on
empirical modelling techniques, ... [+]
A very common curb of epidemiological studies for understanding the impact of air
pollution on health is the quality of exposure data available. Many epidemiological studies rely on
empirical modelling techniques, such as land use regression (LUR), to evaluate ambient air exposure.
Previous studies have located monitoring stations in an ad hoc fashion, favouring their placement
in traffic “hot spots”, or in areas deemed subjectively to be of interest to land use and population.
However, ad-hoc placement of monitoring stations may lead to uninformed decisions for long-term
exposure analysis. This paper introduces a systematic approach for identifying the location of air
quality monitoring stations. It combines the flexibility of LUR with the ability to put weights on
priority areas such as highly-populated regions, to minimise the spatial mean predictor error. Testing
the approach over the study area has shown that it leads to a significant drop of the mean prediction
error (99.87% without spatial weights; 99.94% with spatial weights in the study area). The results of
this work can guide the selection of sites while expanding or creating air quality monitoring networks
for robust LUR estimations with minimal prediction errors. [-]
Publicado en
Sustainability, 2018, vol. 10, núm. 5Proyecto de investigación
The authors gratefully acknowledge funding from the European Commission through the GEO-C project (H2020-MSCA-ITN-2014, Grant Agreement Number 642332, http://www.geo-c.eu/)Derechos de acceso
info:eu-repo/semantics/openAccess
Aparece en las colecciones
- INIT_Articles [754]
- MAT_Articles [770]
El ítem tiene asociados los siguientes ficheros de licencia:
Excepto si se señala otra cosa, la licencia del ítem se describe como: © 2018 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/).