Preprints
https://doi.org/10.5194/amt-2021-5
https://doi.org/10.5194/amt-2021-5

  28 Jan 2021

28 Jan 2021

Review status: this preprint is currently under review for the journal AMT.

SIBaR: A New Method for Background Quantification and Removal from Mobile Air Pollution Measurements

Blake Actkinson1, Katherine Ensor2, and Robert J. Griffin1,3 Blake Actkinson et al.
  • 1Department of Civil and Environmental Engineering, Rice University, Houston, TX 77005, USA
  • 2Department of Statistics, Rice University, Houston, TX 77005, USA
  • 3Department of Chemical and Biomolecular Engineering, Rice University, Houston, TX 77005, USA

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 modelling technique that detects regime changes in time series. We discuss the development of SIBaR and assess its performance on an external dataset. We find 86 % agreement between the predictions made by SIBaR and the predetermined allocation of background and non-background data points. We compare five-minute averages of SIBaR-derived background NOx measurements to five-minute averages of NOx measurements taken by a stationary monitor sitting 70 m above ground level near downtown Houston, finding greater disagreement between SIBaR and the stationary monitor than the disagreement between other background detection techniques and the same stationary monitor. We then assess its application to a data set collected in Houston, TX, by mapping the fraction of points designated as background and comparing source contributions to those derived using other published background detection and removal techniques. Results suggest that SIBaR could serve as a framework for improved background quantification and removal in future mobile monitoring studies.

Blake Actkinson et al.

Status: open (until 14 Apr 2021)

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Blake Actkinson et al.

Blake Actkinson et al.

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Short summary
This paper describes the development of a new method used to estimate background from mobile monitoring time series. The method is tested on a previously published dataset, compared to measurements from a stationary monitor used to represent background in previously published work, and is applied for the first time on a Houston mobile monitoring dataset. Results suggest the method is a promising framework for background estimation.