Abstract:
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Low-cost air pollution sensors, offering hyper-local characterization of pollutant concentrations, are becoming increasingly prevalent in environmental and public health research. However, low-cost data can be noisy and biased, and often need to be field-calibrated by co-locating low-cost sensors with reference-grade instruments. We show that the common procedure of regression-based calibration using co-located data underestimates high air-pollution concentrations, and fails to utilize the spatial correlation in concentrations. We propose a novel spatial filtering approach to co-location-based calibration of low-cost networks that mitigates the underestimation issue by using an inverse regression and incorporates spatial correlation by second-stage modeling of the true concentrations using a conditional Gaussian Process. Our approach works with one or more co-located sites in the network and is dynamic, leveraging spatial correlation with the latest available reference data. Through extensive simulations and an application on a low-cost PM2.5 network in Baltimore, we demonstrate how spatial filtering improves estimation of pollutant concentrations, especially peak concentrations.
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