A modified receptor model for source apportionment of sediment polycyclic aromatic hydrocarbons

Area of Science:

• Environmental Chemistry
• Geochemistry
• Pollution Monitoring

Background:

• Polycyclic aromatic hydrocarbons (PAHs) pose significant risks to human health and ecosystems due to their persistence and toxicity.
• Lake sediments act as long-term archives for pollutants like PAHs, making sediment analysis crucial for pollution control.
• Existing receptor models for PAH source apportionment, such as APCA-MLR and PMF, have limitations including significant uncertainty in matrix rotation.

Purpose of the Study:

• To accurately analyze the sources of polycyclic aromatic hydrocarbons (PAHs) in Taihu Lake sediments.
• To improve upon existing receptor models for PAH source resolution.
• To assess the spatial distribution and contribution of different PAH sources in Taihu Lake.

Main Methods:

• Collection and analysis of PAH content in sediment samples from Taihu Lake.
• Development and application of an improved receptor model: Partition Computing (PMF-PC).
• Comparison of the PMF-PC model with traditional APCA-MLR and PMF models for accuracy and spatial consistency.

Main Results:

• High PAH concentrations were observed in Meiliang Bay, Zhushan Bay, Gonghu Bay, and nearshore areas.
• The improved PMF-PC model demonstrated superior numerical simulation accuracy and spatial distribution consistency compared to APCA-MLR and PMF.
• Biomass, coal combustion, and petroleum sources contributed 16.7%, 31.7%, and 51.6% to PAHs in Taihu Lake sediments, respectively, with fossil fuel sources concentrated nearshore.

Conclusions:

• The improved PMF-PC model is a more accurate and reliable tool for polycyclic aromatic hydrocarbon (PAH) source apportionment in lake sediments.
• Fossil fuel combustion is the dominant source of PAHs in Taihu Lake sediments.
• The PMF-PC model shows potential as a valuable tool for environmental pollution source analysis, despite requiring further algorithmic refinement.