Revisiting hydrocarbons source appraisal in sediments exposed to multiple inputs

Area of Science:

• Environmental chemistry
• Geochemistry
• Sediment analysis

Background:

• Sediments in Guanabara Bay exhibit high degradation and widespread petrogenic pollution.
• Polycyclic Aromatic Hydrocarbon (PAH) ratios indicate pervasive combustion contamination but fail to differentiate sources.
• Traditional diagnostic ratios struggle with complex sediment inputs and degradation.

Purpose of the Study:

• To evaluate the efficacy of statistical methods versus traditional ratios for hydrocarbon source identification.
• To improve the discrimination of multiple hydrocarbon sources in contaminated sediments.
• To assess the combined influence of combustion, petrogenic inputs, and degradation on sediment composition.

Main Methods:

• Analysis of hydrocarbon and Polycyclic Aromatic Hydrocarbon (PAH) distributions in Guanabara Bay sediments.
• Application of traditional diagnostic ratios for source apportionment.
• Utilizing Principal Component Analysis (PCA) to discern superimposed contamination patterns.
• Employing multivariate linear regression (MLR) for quantitative source assessment.

Main Results:

• PCA successfully differentiated the petrogenic imprint from ubiquitous combustion contamination.
• Multivariate linear regression (MLR) quantified the predominance of combustion-derived contaminants.
• Statistical methods proved more effective than traditional ratios in complex sediment samples.
• Identified differential PAH degradation as a factor influencing source interpretation.

Conclusions:

• Principal Component Analysis (PCA) combined with multivariate linear regression (MLR) offers a robust approach for hydrocarbon source identification in complex environmental settings.
• Statistical methods overcome limitations of traditional ratios in distinguishing multiple, superimposed pollution sources.
• Understanding degradation processes is crucial for accurate source apportionment in contaminated sediments.