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Air Quality Monitoring Network Design Optimisation for Robust Land Use Regression Models

Shivam Gupta (), Edzer Pebesma (), Jorge Mateu () and Auriol Degbelo ()
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Shivam Gupta: Institute For Geoinformatics, Westfälische Wilhelms-Universität, 48149 Münster, Germany
Edzer Pebesma: Institute For Geoinformatics, Westfälische Wilhelms-Universität, 48149 Münster, Germany
Jorge Mateu: Department of Mathematics, Universitat Jaume I, 12071 Castelló de la Plana, Spain
Auriol Degbelo: Institute For Geoinformatics, Westfälische Wilhelms-Universität, 48149 Münster, Germany

Sustainability, 2018, vol. 10, issue 5, 1-27

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, 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.

Keywords: air quality monitoring; land use regression; monitoring location optimisation; simulated annealing; spatial mean prediction error (search for similar items in EconPapers)
JEL-codes: Q Q0 Q2 Q3 Q5 Q56 O13 (search for similar items in EconPapers)
Date: 2018
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