Source apportionment of particulate matter and selected volatile organic compounds with multiple time resolution data

Area of Science:

• Environmental Science
• Atmospheric Chemistry
• Public Health

Background:

• Fine particulate matter (PM2.5) and volatile organic compounds (VOCs) are key ambient air pollutants.
• Co-exposure to PM2.5 and VOCs is linked to significant human health risks.
• Accurate source apportionment is crucial for effective air quality management.

Purpose of the Study:

• To assess the feasibility of using a composite dataset of PM2.5 and VOCs for source apportionment.
• To improve the accuracy of source contribution estimates by integrating data with multiple time resolutions.
• To quantify the contributions of various sources to PM2.5 and VOC mixtures.

Main Methods:

• Combined hourly VOC speciation data with 12-h PM2.5 speciation data.
• Utilized an improved source apportionment model to analyze the composite dataset.
• Conducted sensitivity analyses by excluding PM2.5 data or reducing its temporal resolution.

Main Results:

• Successfully identified five distinct pollution source factors: vehicle 1, vehicle 2, industrial processing, transported regional, and secondary pollution.
• Vehicular emissions were the primary contributors to both VOCs (62%) and PM2.5 (35%).
• Transported regional (27%) and secondary pollution (25%) were significant contributors to PM2.5, but less so to VOCs (8% and 5%).

Conclusions:

• Integrating multi-resolution PM2.5 and VOC data enhances source apportionment model performance.
• Excluding PM2.5 data or using lower temporal resolution significantly reduces the number of identified sources and increases estimation errors.
• Composite datasets are vital for a comprehensive understanding of air pollution sources and their impacts.