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

Area of Science:

• Environmental Science
• Atmospheric Chemistry
• Air Quality Modeling

Background:

• Sparsely monitored air pollutants pose challenges for accurate spatial prediction.
• Understanding the distribution of cooking organic aerosol (COA) and hydrocarbon-like organic aerosol (HOA) is crucial for air quality management.
• Existing land-use regression models often lack national scope and high spatial resolution.

Purpose of the Study:

• To assess the feasibility of developing national empirical models for predicting ambient concentrations of COA and HOA.
• To create high-resolution spatial models for these key air pollutants across the continental United States.
• To evaluate the transferability and robustness of these national empirical models.

Main Methods:

• Utilized aerosol mass spectrometry data for COA and HOA across the continental US.
• Selected monitoring sites to represent diverse land-use and source-activity variables.
• Developed land-use regression models and performed extensive cross-validation.
• Compared empirical model predictions with national chemical transport model (CTM) simulations.

Main Results:

• National empirical models explained approximately 60% of the spatial variability for both COA (R²=0.63) and HOA (R²=0.62).
• Models demonstrated robustness and reasonable transferability through cross-validation.
• Predicted concentrations revealed significant urban-rural and intra-urban variability, with hotspots in urban areas and along roadways.
• COA concentrations were predicted to be approximately two times higher than HOA concentrations nationally.
• Empirical predictions showed good spatial correlation with CTM simulations (R²=0.45 for COA, R²=0.4 for HOA).

Conclusions:

• National empirical models are feasible for predicting ambient COA and HOA concentrations at high spatial resolution.
• Commercial cooking emissions are a significant contributor to PM2.5 and warrant attention for air quality management.
• The developed models provide valuable tools for understanding and managing air quality, particularly concerning primary organic aerosols.