Articles | Volume 14, issue 8
Atmos. Meas. Tech., 14, 5809–5821, 2021
https://doi.org/10.5194/amt-14-5809-2021
Atmos. Meas. Tech., 14, 5809–5821, 2021
https://doi.org/10.5194/amt-14-5809-2021

Research article 26 Aug 2021

Research article | 26 Aug 2021

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

Blake Actkinson et al.

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Cited articles

Actkinson, B., Ensor, K., and Griffin, R. J.: Time Series Comparisons, Model Code, and a Demo Dataset for SIBaR: A New Method for Background Quantification and Removal from Mobile Air Pollution Measurements, Zenodo [data set], https://doi.org/10.5281/zenodo.5022590, 2021. 
Apte, J. S., Messier, K. P., Gani, S., Brauer, M., Kirchstetter, T. W., Lunden, M. M., Marshall, J. D., Portier, C. J., Vermeulen, R. C. H., and Hamburg, S. P.: High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data, Environ. Sci. Technol., 51, 6999–7008, https://doi.org/10.1021/acs.est.7b00891, 2017. 
Baldwin, N., Gilani, O., Raja, S., Batterman, S., Ganguly, R., Hopke, P., Berrocal, V., Robins, T., and Hoogterp, S.: Factors affecting pollutant concentrations in the near-road environment, Atmos. Environ., 115, 223–235, https://doi.org/10.1016/j.atmosenv.2015.05.024, 2015. 
Brantley, H. L., Hagler, G. S. W., Kimbrough, E. S., Williams, R. W., Mukerjee, S., and Neas, L. M.: Mobile air monitoring data-processing strategies and effects on spatial air pollution trends, Atmos. Meas. Tech., 7, 2169–2183, https://doi.org/10.5194/amt-7-2169-2014, 2014. 
Brantley, H. L., Hagler, G. S. W., Herndon, S. C., Massoli, P., Bergin, M. H., and Russell, A. G.: Characterization of Spatial Air Pollution Patterns Near a Large Railyard Area in Atlanta, Georgia, Int. J. Environ. Res. Public. Health, 16, 535, https://doi.org/10.3390/ijerph16040535, 2019. 
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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, applied to an extensive mobile dataset, and compared with other previously published techniques used to estimate background. The results suggest that the method is a promising framework for background estimation.