Preprints
https://doi.org/10.5194/acp-2020-1303
https://doi.org/10.5194/acp-2020-1303

  27 Jan 2021

27 Jan 2021

Review status: a revised version of this preprint was accepted for the journal ACP and is expected to appear here in due course.

The MAPM (Mapping Air Pollution eMissions) method for inferring particulate matter emissions maps at city-scale from in situ concentration measurements: description and demonstration of capability

Brian Nathan1,2, Stefanie Kremser2, Sara Mikaloff-Fletcher1, Greg Bodeker2, Leroy Bird2, Ethan Dale2, Dongqi Lin3, Gustavo Olivares4, and Elizabeth Somervell4 Brian Nathan et al.
  • 1NIWA, Wellington, New Zealand
  • 2Bodeker Scientific, Alexandra, New Zealand
  • 3University of Canterbury, Christchurch, New Zealand
  • 4NIWA, Auckland, New Zealand

Abstract. MAPM (Mapping Air Pollution eMissions) is a two-year project whose goal is to develop a method to infer particulate matter (PM) emissions maps from in situ PM concentration measurements. Central to the functionality of MAPM is an inverse model. The input of the inverse model includes a spatially-distributed prior emissions estimate and PM measurement time series from instruments distributed across the desired domain. Here we describe the construction of this inverse model, the mathematics underlying the retrieval of the resultant posterior PM emissions maps, the way in which uncertainties are traced through the MAPM processing chain, and plans for future development of the processing chain. To demonstrate the capability of the inverse model developed for MAPM, we use the PM2.5 measurements obtained during a dedicated winter field campaign in Christchurch, New Zealand, in 2019 to infer PM2.5 emissions maps on city scale. The results indicate a systematic overestimation in the prior emissions for Christchurch of at least 40–60 %, which is consistent with some of the underlying assumptions used in the composition of the bottom-up emissions map used as the prior, highlighting the uncertainties in bottom-up approaches for estimating PM2.5 emissions maps.

The paper also presents the results of two sets of observing system simulation experiments (OSSEs) that explore how measurement uncertainties affect the computation of the derived emissions maps, and the extent to which using emissions maps from one day as the prior for the next day improves the ability of the inversion system to characterize the emissions sources. We find in the first case that a smaller number of high-accuracy instruments performs significantly better than a higher number of low-accuracy instruments. In the second case, the results are ultimately inconclusive, showing the need for further investigations that are beyond the scope of this study.

Brian Nathan et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2020-1303', Anonymous Referee #1, 12 Mar 2021
  • RC2: 'Comment on acp-2020-1303', Peter Rayner, 09 Apr 2021
  • AC1: 'Comment on acp-2020-1303', Brian Nathan, 21 May 2021

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2020-1303', Anonymous Referee #1, 12 Mar 2021
  • RC2: 'Comment on acp-2020-1303', Peter Rayner, 09 Apr 2021
  • AC1: 'Comment on acp-2020-1303', Brian Nathan, 21 May 2021

Brian Nathan et al.

Data sets

MAPM Campaign PM Data Ethan Dale, Stefanie Kremser, Jordis Tradowsky, Greg Bodeker, Leroy Bird, Gustavo Olivares, Guy Coulson, Elizabeth Somervell, Woodrow Pattinson, Jonathan Barte, and Jan-Niklas Schmidt https://doi.org/10.5281/zenodo.4023402

Brian Nathan et al.

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Latest update: 21 Sep 2021
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Short summary
The MAPM project showcases a method to improve estimates of PM2.5 emissions through an advanced statistical technique that is still new to the aerosol community. Using Christchurch, NZ as a testbed, the robustness and limitations of this approach are first demonstrated with pseudo-data experiments. Then, measurements from a field campaign in winter 2019 are incorporated. An overestimation from local inventory estimates is identified. This technique may be exported to other urban areas in need.
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