SIBaR: a new method for background quantification and removal from mobile air pollution measurements
<p>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 th...
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Copernicus Publications
2021-08-01
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doaj-6ae96d41f9694757ba9cb893e1a1b7a82021-08-26T07:32:29ZengCopernicus PublicationsAtmospheric Measurement Techniques1867-13811867-85482021-08-01145809582110.5194/amt-14-5809-2021SIBaR: a new method for background quantification and removal from mobile air pollution measurementsB. Actkinson0K. Ensor1R. J. Griffin2R. J. Griffin3Department of Civil and Environmental Engineering, Rice University, Houston, TX 77005, USADepartment of Statistics, Rice University, Houston, TX 77005, USADepartment of Civil and Environmental Engineering, Rice University, Houston, TX 77005, USADepartment of Chemical and Biomolecular Engineering, Rice University, Houston, TX 77005, USA<p>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.</p>https://amt.copernicus.org/articles/14/5809/2021/amt-14-5809-2021.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
B. Actkinson K. Ensor R. J. Griffin R. J. Griffin |
spellingShingle |
B. Actkinson K. Ensor R. J. Griffin R. J. Griffin SIBaR: a new method for background quantification and removal from mobile air pollution measurements Atmospheric Measurement Techniques |
author_facet |
B. Actkinson K. Ensor R. J. Griffin R. J. Griffin |
author_sort |
B. Actkinson |
title |
SIBaR: a new method for background quantification and removal from mobile air pollution measurements |
title_short |
SIBaR: a new method for background quantification and removal from mobile air pollution measurements |
title_full |
SIBaR: a new method for background quantification and removal from mobile air pollution measurements |
title_fullStr |
SIBaR: a new method for background quantification and removal from mobile air pollution measurements |
title_full_unstemmed |
SIBaR: a new method for background quantification and removal from mobile air pollution measurements |
title_sort |
sibar: a new method for background quantification and removal from mobile air pollution measurements |
publisher |
Copernicus Publications |
series |
Atmospheric Measurement Techniques |
issn |
1867-1381 1867-8548 |
publishDate |
2021-08-01 |
description |
<p>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.</p> |
url |
https://amt.copernicus.org/articles/14/5809/2021/amt-14-5809-2021.pdf |
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