Estimating mean molecular weight, carbon number, and OM∕OC with mid-infrared spectroscopy in organic particulate matter samples from a monitoring network

Yazdani, Amir; Dillner, Ann M.; Takahama, Satoshi

doi:https://doi.org/10.5194/amt-14-4805-2021

Articles | Volume 14, issue 7

https://doi.org/10.5194/amt-14-4805-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-14-4805-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 14, issue 7

Research article

|

08 Jul 2021

Research article |

| 08 Jul 2021

Estimating mean molecular weight, carbon number, and OM∕OC with mid-infrared spectroscopy in organic particulate matter samples from a monitoring network

Amir Yazdani, Ann M. Dillner, and Satoshi Takahama

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Final revised paper (published on 08 Jul 2021)
Supplement to the final revised paper
Preprint (discussion started on 23 Mar 2020)
Supplement to the preprint

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Review', Anonymous Referee #1, 08 Jun 2020
- AC1: 'Response to Referee', Satoshi Takahama, 18 Jul 2020
RC2: 'Review for “Estimating mean molecular weight, carbon number, and OM/OC with mid-infrared spectroscopy in organic particulate matter samples from a monitoring network” by Yazdani et al.', Anonymous Referee #2, 19 Jun 2020
- AC2: 'Response to Referee', Satoshi Takahama, 18 Jul 2020

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Satoshi Takahama on behalf of the Authors (18 Jul 2020) Author's response Manuscript

ED: Publish as is (11 Aug 2020) by Paolo Laj

AR by Satoshi Takahama on behalf of the Authors (27 Aug 2020)

Short summary

We propose a spectroscopic method for estimating several mixture-averaged molecular properties (carbon number and molecular weight) in particulate matter relevant for understanding its chemical origins. This estimation is enabled by calibration models built and tested using laboratory standards containing molecules with known structure, and can be applied to filter samples of PM_2.5 currently collected in existing air pollution monitoring networks and field campaigns.