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Environ Sci Technol. 2019 Aug 6;53(15):8925-8937. doi: 10.1021/acs.est.9b01897. Epub 2019 Jul 17.

Land-Use Regression Modeling of Source-Resolved Fine Particulate Matter Components from Mobile Sampling.

Author information

1
Department of Mechanical Engineering , Carnegie Mellon University , Pittsburgh , Pennsylvania 15213 , United States.
2
Center for Atmospheric Particle Studies , Carnegie Mellon University , Pittsburgh , Pennsylvania 15213 , United States.
3
Department of Environmental and Molecular Toxicology , Oregon State University , Corvallis , Oregon 97333 , United States.
4
Department of Civil, Architectural & Environmental Engineering , University of Texas at Austin , Austin , Texas 78705 , United States.

Abstract

This study presents land-use regression (LUR) models for submicron particulate matter (PM1) components from an urban area. Models are presented for mass concentrations of inorganic species (SO4, NO3, NH4), organic aerosol (OA) factors, and total PM1. OA is source-apportioned using positive matrix factorization (PMF) of data collected from aerosol mass spectrometry deployed on a mobile laboratory. PMF yielded a three-factor solution: cooking OA (COA), hydrocarbon-like OA (HOA), and less-oxidized oxygenated OA (LO-OOA). This study represents the first time that LUR has been applied to source-resolved OA factors. We sampled a roughly 20 km2 area of West Oakland, California, USA, over 1 month (mid-July to mid-August, 2017). The road network of the sampling domain was comprehensively sampled each day using a randomized driving route to minimize temporal and spatial bias. Mobile measurements were aggregated both spatially and temporally for use as discrete spatial observations for LUR model building. LUR model performance was highest for those species with more spatial variability (primary OA factors: COA R2 = 0.80, HOA R2 = 0.67) and lowest for secondary inorganic species (SO4 R2 = 0.47, NH4 R2 = 0.43) that were more spatially homogeneous. Notably, the stepwise selective LUR algorithm largely selected predictors for primary OA factors that correspond to the associated land-use categories (e.g., cooking land-use variables were selected in cooking-related PM models). This finding appears to be robust, as we demonstrate the predictive link between land-use variables and the corresponding source-resolved PM1 components through a subsampling analysis.

PMID:
31313910
DOI:
10.1021/acs.est.9b01897
[Indexed for MEDLINE]

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