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

Area of Science:

• Environmental Science
• Atmospheric Chemistry
• Air Quality Modeling

Background:

• Understanding the spatial distribution of submicron particulate matter (PM1) components is crucial for assessing urban air quality and health impacts.
• Source apportionment of organic aerosol (OA) using positive matrix factorization (PMF) helps identify emission sources.
• Land-use regression (LUR) models are commonly used to predict air pollutant concentrations based on land-use characteristics.

Purpose of the Study:

• To develop and evaluate land-use regression (LUR) models for various PM1 components, including inorganic species and source-resolved organic aerosol (OA) factors.
• To investigate the application of LUR models to source-apportioned OA factors for the first time.
• To assess the relationship between specific land-use variables and PM1 components in an urban environment.

Main Methods:

• Mobile laboratory measurements of PM1 components using aerosol mass spectrometry in West Oakland, California.
• Source apportionment of OA using positive matrix factorization (PMF) into cooking OA (COA), hydrocarbon-like OA (HOA), and less-oxidized oxygenated OA (LO-OOA).
• Development of LUR models using aggregated mobile measurements and comprehensive road network sampling to minimize bias.

Main Results:

• LUR models showed higher performance for primary OA factors (COA R² = 0.80, HOA R² = 0.67) compared to secondary inorganic species (SO4 R² = 0.47, NH4 R² = 0.43).
• The LUR models successfully selected land-use predictors that corresponded to the identified OA sources (e.g., cooking land use for COA).
• A subsampling analysis confirmed a robust predictive link between land-use variables and source-resolved PM1 components.

Conclusions:

• LUR modeling is a viable approach for predicting the spatial distribution of source-resolved PM1 components, particularly primary OA.
• The study demonstrates the effectiveness of linking specific land-use categories to distinct PM1 sources in urban areas.
• These findings support the use of LUR models for targeted air pollution management strategies based on land-use planning.