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Measuring Sub-23 Nanometer Real Driving Particle Number Emissions Using the Portable DownToTen Sampling System

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PM_1.0 marker-based diagnostic ratios and PMF-based source apportionment for quantifying oxidative potential in a

Chaehyeong Park¹, Seoyeong Choe¹, Myoungki Song¹

¹Particle Pollution Research and Management Center, Department of Environmental Engineering, Mokpo National University, Muan, 58554, Republic of Korea.

Scientific Reports

|June 20, 2026

Summary

This study identifies polycyclic aromatic hydrocarbons (PAHs) from PET and wood waste combustion as key contributors to PM1.0 pollution in Korea. New diagnostic ratios help distinguish sources like coal, PET, and wood waste combustion.

Keywords:

Oxidative potential PM1.0 PMF Picene Polycyclic aromatic hydrocarbons Positive matrix factorization

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Area of Science:

Environmental Chemistry
Atmospheric Science
Air Pollution Analysis

Background:

Combustion sources emit hazardous polycyclic aromatic hydrocarbons (PAHs), complicating the isolation of specific contributions to ambient PM1.0.
Accurate source apportionment of PM1.0 is crucial for understanding and mitigating air pollution impacts.

Purpose of the Study:

To develop and validate a diagnostic framework for identifying and quantifying combustion sources of PM1.0 using molecular markers and positive matrix factorization (PMF).
To differentiate contributions from PET waste combustion, wood waste combustion, and coal combustion.

Main Methods:

Collected PM1.0 samples at 6-hour resolution in western Korea, analyzing chemical composition, PAHs (including picene), molecular markers (levoglucosan, mannosan, terephthalic acid, etc.), and oxidative potential (QDTT-OP).
Applied positive matrix factorization (PMF) to resolve source contributions.
Developed a PMF-constrained diagnostic framework using picene-normalized ratios and a composite marker (LMT*) to categorize PAHs (L-PAHs and H-PAHs).

Main Results:

PMF identified PET waste combustion (28.8%) and wood waste combustion (18.1%) as dominant sources, with coal combustion contributing 4.1% to PM1.0 mass.
Elevated picene levels and higher QDTT-OP correlated with combustion-dominated air masses.
Proposed screening thresholds successfully differentiated between coal, PET, and wood waste combustion based on marker ratios.

Conclusions:

PET and wood waste combustion are significant sources of PM1.0 and associated PAHs in the study area.
The developed diagnostic framework provides effective tools for source apportionment of combustion-related air pollution.
Understanding these sources is vital for targeted air quality management strategies.