Development of Phase and Seasonally Dependent Land-Use Regression Models to Predict Atmospheric PAH Levels

Area of Science:

• Environmental Science
• Atmospheric Chemistry
• Pollution Monitoring

Background:

• Polycyclic aromatic hydrocarbons (PAHs) are significant pollutants, particularly in China.
• Previous research predominantly focused on particle-associated PAHs, with limited investigation into gaseous PAHs.

Purpose of the Study:

• To establish distinct land use regression (LUR) models for predicting gaseous and particle-associated PAH concentrations in Taiyuan City.
• To identify key factors influencing PAH levels across different seasons (windy, non-heating, heating) and phases (gaseous, particle-bound).

Main Methods:

• Measurement of 15 representative PAHs in both gaseous and particle phases across 25 sampling sites.
• Development and validation of LUR models using leave-one-out cross-validation.
• Analysis of Acenaphthene (Ace), Fluorene (Flo), and benzo[g,h,i]perylene (BghiP) to understand influencing factors.

Main Results:

• LUR models demonstrated good performance for gaseous Ace (adj. R² = 0.14-0.82) and Flo (adj. R² = 0.21-0.85), while BghiP performed better in the particle phase (adj. R² = 0.20-0.42).
• Model accuracy was highest during the heating season (adj. R² = 0.68-0.83) compared to non-heating (adj. R² = 0.23-0.76) and windy seasons (adj. R² = 0.37-0.59).
• Gaseous PAHs were influenced by traffic emissions, elevation, and latitude, whereas BghiP was linked to point sources.

Conclusions:

• PAH concentrations exhibit significant seasonal and phase-dependent variations.
• Developing separate LUR models tailored to specific phases and seasons enhances the accuracy of PAH pollution prediction.
• Understanding these factors is crucial for targeted pollution control strategies in urban environments.