National Exposure Models for Source-Specific Primary Particulate Matter Concentrations Using Aerosol Mass Spectrometry Data

Provat K. Saha, Albert A. Presto, Steve Hankey, Benjamin N. Murphy, Chris Allen, Wenwen Zhang, Julian D. Marshall, Allen L. Robinson

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

This paper investigates the feasibility of developing national empirical models to predict ambient concentrations of sparsely monitored air pollutants at high spatial resolution. We used a data set of cooking organic aerosol (COA) and hydrocarbon-like organic aerosol (HOA; traffic primary organic PM) measured using aerosol mass spectrometry across the continental United States. The monitoring locations were selected to span the national distribution of land-use and source-activity variables commonly used for land-use regression modeling (e.g., road length, restaurant count, etc.). The models explain about 60% of the spatial variability of the measured data (R2 0.63 for the COA model and 0.62 for the HOA model). Extensive cross-validation suggests that the models are robust with reasonable transferability. The models predict large urban-rural and intra-urban variability with hotspots in urban areas and along the road corridors. The predicted national concentration surfaces show reasonable spatial correlation with source-specific national chemical transport model (CTM) simulations (R2: 0.45 for COA, 0.4 for HOA). Our measured data, empirical models, and CTM predictions all show that COA concentrations are about two times higher than HOA. Since COA and HOA are important contributors to the intra-urban spatial variability of the total PM2.5, our results highlight the potential importance of controlling commercial cooking emissions for air quality management in the United States.

Original languageEnglish (US)
Pages (from-to)14284-14295
Number of pages12
JournalEnvironmental Science and Technology
Volume56
Issue number20
DOIs
StatePublished - Oct 18 2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Chemistry
  • Environmental Chemistry

Keywords

  • aerosol mass spectrometry
  • fine particulate matter
  • spatial modeling

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