2024-03-29T13:24:52Zhttps://repositori.uji.es/oai/requestoai:repositori.uji.es:10234/1771632023-03-09T11:27:44Zcom_10234_43662com_10234_9com_10234_7037col_10234_43643col_10234_8635
Repositori UJI
author
Gupta, Shivam
author
Pebesma, Edzer
author
Mateu, Jorge
author
Degbelo, Auriol
2018-10-31T18:59:44Z
2018-10-31T18:59:44Z
2018
GUPTA, Shivam, et al. Air Quality Monitoring Network Design Optimisation for Robust Land Use Regression Models. Sustainability (2071-1050), 2018, 10.5
2071-1050
http://hdl.handle.net/10234/177163
https://doi.org/10.3390/su10051442
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.
eng
© 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/). Atribución 4.0 Internacional
air quality monitoring
land use regression
monitoring location optimisation
simulated annealing
spatial mean prediction error
Air Quality Monitoring Network Design Optimisation for Robust Land Use Regression Models
info:eu-repo/semantics/article
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