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

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 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.

SIBaR: a new method for background quantification and removal from mobile air pollution measurements

“…Both the code and data are available on request. Additionally, time series comparisons for all 312 time series taken in the campaign, as well as a demo of the SIBaR partitioning step, are available at https://doi.org/10.5281/zenodo.5022590 (Actkinson et al, 2021). Data are also free to download from Ope-nAQ (https://openaq.org/#/project/28974, Environmental Defense Fund, 2021).…”