SIBaR: a new method for background quantification and removal from mobile air pollution measurements
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Published:2021-08-26
Issue:8
Volume:14
Page:5809-5821
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ISSN:1867-8548
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Container-title:Atmospheric Measurement Techniques
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language:en
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Short-container-title:Atmos. Meas. Tech.
Author:
Actkinson BlakeORCID, Ensor Katherine, Griffin Robert J.
Abstract
Abstract. Mobile monitoring is becoming increasingly popular for
characterizing air pollution on fine spatial scales. In identifying local
source contributions to measured pollutant concentrations, the detection and
quantification of background are key steps in many mobile monitoring
studies, but the methodology to do so requires further development to
improve replicability. Here we discuss a new method for quantifying and
removing background in mobile monitoring studies, State-Informed Background Removal (SIBaR).
The method employs hidden Markov models (HMMs), a popular modeling
technique that detects regime changes in time series. We discuss the
development of SIBaR and assess its performance on an external dataset. We
find 83 % agreement between the predictions made by SIBaR and the
predetermined allocation of background and non-background data points. We
then assess its application to a dataset collected in Houston by mapping
the fraction of points designated as background and comparing source
contributions to those derived using other published background detection
and removal techniques. The presented results suggest that the SIBaR-modeled source contributions contain source influences left undetected by other techniques,
but that they are prone to unrealistic source contribution estimates when they
extrapolate. Results suggest that SIBaR could serve as a framework for
improved background quantification and removal in future mobile monitoring
studies while ensuring that cases of extrapolation are appropriately
addressed.
Funder
National Institute of Environmental Health Sciences
Publisher
Copernicus GmbH
Subject
Atmospheric Science
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