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

Gupta Shivam, Pebesma Edzer, Mateu Jorge, Degbelo Auriol

Abstract

A very common curb of epidemiological studies for understanding the impact of air pollution on health. Many epidemiological studies rely on empirical modeling techniques, such as land use regression (LUR), to evaluate ambient air exposure. Previous studies have located monitoring stations in an ad hoc fashion, favoring 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 to identifying the location of air quality monitoring stations. It combines the flexibility of LUR with the ability to put weight on priority areas such as highly-populated regions, to minimize the spatial mean predictor error. 99.87% without spatial weights (99.87% without spatial weights in the study area). LUR estimations with minimal prediction errors.

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