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.
Highlights
Understanding air pollution exposure is important, as it has been linked to various adverse health conditions (Caplin et al, 2019; Zhang et al, 2018)
In running State-Informed Background Removal (SIBaR) on the campaign nitrogen oxides (NOx) measurements, we note that the empirical classifier designates 96 % of the original time series to be correctly classified for a 50 % threshold
We illustrate that SIBaR provides a defensible mechanism to quantify and remove background from air pollution monitoring data time series
Summary
Understanding air pollution exposure is important, as it has been linked to various adverse health conditions (Caplin et al, 2019; Zhang et al, 2018). A key component of mobile monitoring analysis is identifying ambient background levels, defined here as measured air pollution concentrations independent of local source influences (Brantley et al, 2014). Background quantification is vital from both policy and exposure perspectives, as it is important to assess the contribution of local sources to pollution concentrations accurately. The wide variance in the approaches used is problematic, as estimates of source contributions to measurements have been shown to be sensitive to the technique used (Brantley et al, 2014). To improve the replicability and power of mobile monitoring studies, a more consistent technique for background estimation is needed. Actkinson et al.: SIBaR: a new method for background quantification and removal
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
