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Physically constrained source apportionment (PCSA) for polycyclic aromatic hydrocarbon using the Multilinear Engine

Gui-Rong Liu¹, Guo-Liang Shi¹, Ying-Ze Tian¹

¹State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China.

The Science of the Total Environment

|September 21, 2014

Summary

This study introduces an improved physically constrained source apportionment (PCSA) method to identify sources of polycyclic aromatic hydrocarbons (PAHs) in Chengdu

Keywords:

Multilinear Engine 2 (ME2)-species ratios Polycyclic aromatic hydrocarbon (PAH)Positive matrix factorization (PMF)Source apportionment

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Area of Science:

Environmental Chemistry
Atmospheric Science
Chemical Engineering

Background:

Polycyclic Aromatic Hydrocarbons (PAHs) are significant air pollutants associated with various combustion sources.
Accurate source apportionment of PAHs is crucial for developing effective air quality management strategies.
Particulate matter (PM10 and PM2.5) plays a key role in transporting PAHs in the atmosphere.

Purpose of the Study:

To propose and apply an improved physically constrained source apportionment (PCSA) technology using the Multilinear Engine 2-species ratios (ME2-SR) method.
To quantify the sources of PM10- and PM2.5-associated polycyclic aromatic hydrocarbons (PAHs) in Chengdu during winter.
To compare the performance of the ME2-SR method with the positive matrix factorization (PMF) model for PAH source apportionment.

Main Methods:

Application of the Multilinear Engine 2-species ratios (ME2-SR) method for source apportionment.
Utilized positive matrix factorization (PMF) model as a comparative analysis tool.
Analyzed sixteen priority polycyclic aromatic hydrocarbon (PAH) compounds in PM10 and PM2.5 samples collected in Chengdu.

Main Results:

Mean ΣPAH concentrations in PM10 ranged from 70.65 to 209.58 ng/m(3), and in PM2.5 from 59.17 to 170.64 ng/m(3).
PMF model identified vehicular emission as the primary source (81.69% for PM10, 82.06% for PM2.5).
ME2-SR method indicated diesel (43.19% for PM10, 47.17% for PM2.5) and gasoline exhaust (34.94%, 32.44%) as major contributors, with PAH ratios closer to actual source profiles.

Conclusions:

The ME2-SR method provides physically constrained and satisfactory results for PAH source apportionment.
The study highlights the significant contribution of vehicular emissions to PAH pollution in Chengdu during winter.
The improved PCSA technology using ME2-SR offers a reliable approach for atmospheric pollutant source identification.