Evaluation of Regional-Scale Receptor Modeling

Area of Science:

• Atmospheric Chemistry and Physics
• Environmental Science
• Air Quality Modeling

Background:

• Accurate source apportionment of fine particulate matter (PM2.5) is crucial for effective air quality management.
• Receptor models are widely used to estimate source contributions, but their performance with complex datasets needs evaluation.
• Understanding regional contributions to PM2.5 is essential for addressing transboundary air pollution.

Purpose of the Study:

• To assess the ability of receptor models (Positive Matrix Factorization and Unmix) to estimate regional contributions to PM2.5.
• To evaluate the performance of the trajectory mass balance regression (TMBR) model in apportioning sulfate concentrations to source regions.
• To compare model-estimated source contributions with true contributions derived from synthetic data.

Main Methods:

• Generated synthetic PM2.5 chemical concentrations using the Community Multiscale Air Quality (CMAQ) model for two US sites.
• Employed Positive Matrix Factorization (PMF) and Unmix receptor models to analyze speciated PM2.5 data.
• Utilized the trajectory mass balance regression (TMBR) model with HYSPLIT trajectories and various meteorological inputs for sulfate apportionment.

Main Results:

• A seven-factor PMF solution explained 99% of data variability, capturing major sources at Brigantine (BRIG) but not distinguishing vehicle types.
• PMF factors at Great Smoky Mountains National Park (GRSM) did not align well with major PM2.5 sources or regional sulfate contributions.
• TMBR qualitatively agreed with true regional sulfate apportionments, identifying local regions as largest contributors, with results dependent on trajectory inputs.

Conclusions:

• Receptor model performance in source apportionment can vary significantly depending on the site and data complexity.
• TMBR shows potential for estimating regional sulfate contributions, but sensitivity to meteorological data and trajectory parameters requires further investigation.
• Accurate source identification and regional apportionment are critical for developing targeted air quality control strategies.