Source contribution analysis of PM_2.5 using Response Surface Model and Particulate Source Apportionment Technology...

Area of Science:

• Environmental Science
• Atmospheric Chemistry
• Air Pollution Modeling

Background:

• Particulate matter (PM2.5) pollution necessitates accurate source identification for effective policy.
• Existing models like PSAT have limitations in quantifying nonlinear secondary PM2.5 formation.
• The Pearl River Delta (PRD) faces significant PM2.5 challenges requiring advanced source apportionment.

Purpose of the Study:

• To comparatively analyze PM2.5 source contributions in the PRD using RSM and PSAT.
• To evaluate the performance of RSM and PSAT in modeling primary and secondary PM2.5 formation.
• To identify key emission sources contributing to ambient PM2.5 in PRD cities.

Main Methods:

• Comparative analysis of two advanced source contribution modeling techniques: Response Surface Model (RSM) and Particulate Source Apportionment Technology (PSAT).
• Application of models to PM2.5 data from the Pearl River Delta (PRD) region.
• Assessment of model capabilities in handling linear (primary) and nonlinear (secondary) PM2.5 formation processes.

Main Results:

• Both RSM and PSAT reasonably predict primary PM2.5 source contributions.
• RSM demonstrates superior ability in quantifying nonlinear secondary PM2.5 formation compared to PSAT.
• Regional sources are the largest contributors (42-66%) to PRD urban PM2.5, followed by dust (27-34%) and mobile sources (16-30%).

Conclusions:

• RSM is better suited for modeling complex, nonlinear PM2.5 precursor interactions.
• Dust and mobile sources are significant anthropogenic contributors to PRD PM2.5.
• Coordinated city-scale emission reductions and enhanced control of dust and mobile sources are recommended for PRD PM2.5 mitigation.