Articles | Volume 15, issue 20
https://doi.org/10.5194/amt-15-6051-2022
https://doi.org/10.5194/amt-15-6051-2022
Research article
 | 
21 Oct 2022
Research article |  | 21 Oct 2022

Source apportionment resolved by time of day for improved deconvolution of primary source contributions to air pollution

Sahil Bhandari, Zainab Arub, Gazala Habib, Joshua S. Apte, and Lea Hildebrandt Ruiz

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2022-76', Anonymous Referee #2, 19 Apr 2022
  • RC2: 'Comment on amt-2022-76', Anonymous Referee #3, 23 Jun 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Lea Hildebrandt Ruiz on behalf of the Authors (24 Aug 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (25 Aug 2022) by Mingjin Tang
RR by Anonymous Referee #2 (03 Sep 2022)
ED: Publish as is (03 Sep 2022) by Mingjin Tang
AR by Lea Hildebrandt Ruiz on behalf of the Authors (12 Sep 2022)  Author's response   Manuscript 
Short summary
We present a new method to conduct source apportionment resolved by time of day using the underlying approach of positive matrix factorization. We report results for four example time periods in two seasons (winter and monsoon 2017) in Delhi, India. Compared to the traditional approach, we extract a larger number of factors that represent the expected sources of primary organic aerosol. This method can capture diurnal time series patterns of sources at low computational cost.