Preprints
https://doi.org/10.5194/amt-2022-76
https://doi.org/10.5194/amt-2022-76
 
30 Mar 2022
30 Mar 2022
Status: this preprint is currently under review for the journal AMT.

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

Sahil Bhandari1, Zainab Arub2, Gazala Habib2, Joshua S. Apte3,4, and Lea Hildebrandt Ruiz5 Sahil Bhandari et al.
  • 1Department of Mechanical Engineering, University of British Columbia, Vancouver, Canada
  • 2Department of Civil Engineering, Indian Institute of Technology Delhi, New Delhi, India
  • 3Department of Civil and Environmental Engineering, UC Berkeley, California, USA
  • 4School of Public Health, UC Berkeley, California, USA
  • 5McKetta Department of Chemical Engineering, The University of Texas at Austin, Texas, USA

Abstract. Present methodologies for source apportionment assume fixed source profiles. Since meteorology and human activity patterns change seasonally and diurnally, application of source apportionment techniques to shorter rather than longer time periods generates more representative mass spectra . Here, we present a new method to conduct source apportionment resolved by time of day using the underlying approach of positive matrix factorization (PMF). We call this approach “time-of-day PMF” and statistically demonstrate the improvements in this approach over traditional PMF. We report on source apportionment conducted on four example time periods in two seasons (winter and monsoon 2017), using organic aerosol measurements from an Aerosol Chemical Speciation Monitor (ACSM). We deploy the EPA PMF tool with the underlying Multilinear Engine (ME-2) as the PMF solver. Compared to the traditional seasonal PMF approach, we extract a larger number of factors as well as PMF factors that represent the expected sources of primary organic aerosol using time-of-day PMF. By capturing diurnal time series patterns of sources at a low computational cost, time-of-day PMF can utilize large datasets collected using long-term monitoring and improve the characterization of sources of organic aerosol compared to traditional PMF approaches that do not resolve by time of day.

Sahil Bhandari et al.

Status: final response (author comments only)

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

Sahil Bhandari et al.

Sahil Bhandari et al.

Viewed

Total article views: 462 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
288 165 9 462 24 6 5
  • HTML: 288
  • PDF: 165
  • XML: 9
  • Total: 462
  • Supplement: 24
  • BibTeX: 6
  • EndNote: 5
Views and downloads (calculated since 30 Mar 2022)
Cumulative views and downloads (calculated since 30 Mar 2022)

Viewed (geographical distribution)

Total article views: 448 (including HTML, PDF, and XML) Thereof 448 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 01 Jul 2022
Download
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.