Contribution of point and small-scaled sources to the PM₁₀ emission using positive matrix factorization model

Area of Science:

• Environmental Science
• Atmospheric Chemistry
• Geochemistry

Background:

• Particulate matter (PM) poses significant environmental and health risks.
• Source apportionment of PM is crucial for effective air quality management.
• Positive Matrix Factorization (PMF) is a widely used receptor model for PM source identification.

Purpose of the Study:

• To evaluate the capability of the advanced Positive Matrix Factorization model version 5 (PMF5).
• To identify and quantify sources of small size particle contributions to ambient particulate matter in Zanjan, Iran.
• To assess the effectiveness of PMF5 in distinguishing specific pollution sources, such as mining tailing soils.

Main Methods:

• Ambient particulate matter samples were collected at two sites in Zanjan, Iran.
• 196 samples were analyzed for 15 chemical elements.
• The PMF5 model was applied to the chemical speciation data for source apportionment.

Main Results:

• Downtown: Soil particles (40.36%), fuel combustion/traffic (26.8%), tailing soils (Pb, Zn) (21.32%), and industrial emissions (Ni) (5.7%).
• Residential site: Industrial emissions (28.2%), tailing soils (Pb, Zn) (39.2%), soil (25.8%), and Cd/general pollutants (6.7%).
• PMF5 successfully identified tailing soils as a distinct source factor.

Conclusions:

• The PMF5 model demonstrated high capability in identifying and quantifying sources of ambient particulate matter.
• Tailing soils from mining activities were confirmed as a significant source of lead and zinc pollution.
• Other identified sources include traffic, fuel combustion, soil dust, and industrial emissions, highlighting the need for targeted control strategies.