VOC source apportionment: How monitoring characteristics influence positive matrix factorization (PMF) solutions

Area of Science:

• Environmental Science
• Atmospheric Chemistry
• Air Quality Management

Background:

• Positive Matrix Factorization (PMF) is crucial for identifying air pollutant sources to inform mitigation strategies.
• The accuracy of PMF source apportionment depends on the quality and characteristics of the monitoring data.
• Understanding data influences on PMF is key to refining Volatile Organic Compound (VOC) mitigation efforts.

Purpose of the Study:

• To investigate how monitoring data characteristics affect PMF solution accuracy for VOCs.
• To determine the optimal data requirements for effective PMF analysis in air quality management.
• To enhance the reliability of PMF in identifying specific VOC emission sources.

Main Methods:

• Applied PMF analysis to five VOC monitoring datasets from Colorado spanning four years.
• Evaluated the impact of temporal resolution, monitoring location, and species composition on PMF factor resolution.
• Assessed the model's ability to capture pollution spikes and specific source types.

Main Results:

• Generally consistent VOC source factors were identified, including oil extraction/processing, natural gas, vehicle exhaust, and gasoline.
• Comprehensive VOC species coverage was the primary determinant for resolving sources.
• PMF solutions did not effectively model pollution spikes or resolve hyperlocal/volatile chemical product sources.

Conclusions:

• High temporal resolution and a comprehensive suite of VOC species are essential for accurate PMF source identification.
• PMF analysis has limitations in modeling extreme pollution events and resolving certain localized sources.
• Informing VOC mitigation strategies requires optimized air monitoring data for PMF applications.