Air Quality Monitoring Network Design Optimisation for Robust Land Use Regression Models
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Other documents of the author: Gupta, Shivam; Pebesma, Edzer; Mateu, Jorge; Degbelo, Auriol
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comunitat-uji-handle2:10234/7037
comunitat-uji-handle3:10234/8635
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Title
Air Quality Monitoring Network Design Optimisation for Robust Land Use Regression ModelsDate
2018Publisher
MDPIISSN
2071-1050Bibliographic citation
GUPTA, Shivam, et al. Air Quality Monitoring Network Design Optimisation for Robust Land Use Regression Models. Sustainability (2071-1050), 2018, 10.5Type
info:eu-repo/semantics/articlePublisher version
https://www.mdpi.com/2071-1050/10/5/1442Version
info:eu-repo/semantics/publishedVersionSubject
Abstract
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. [-]
Is part of
Sustainability, 2018, vol. 10, núm. 5Investigation project
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/)Rights
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