Comprehensive Profiling of Coal Tar and Crude Oil to Obtain Mass Spectra and Retention Indices for Alkylated PAH Shows Why Current Methods Err

• Published in: Environmental science & technology 2012-04, Vol.46 (7), p.3935-3942
• Main Authors: Zeigler, Christian D, Robbat, Albert
• Format: Article
• Language: English
• Subjects: Alkylation; Analytical chemistry; Applied sciences; Chemical compounds; Chemistry; Chromatographic methods and physical methods associated with chromatography; Chromatography; Chromatography, Gas; Coal Tar - chemistry; Crude oil; Electrons; Exact sciences and technology; Flame Ionization; Forensic Sciences; Gas chromatographic methods; Global environmental pollution; Mass spectrometry; Mass Spectrometry - methods; Petroleum - analysis; Pollutants; Pollution; Polycyclic aromatic hydrocarbons; Polycyclic Aromatic Hydrocarbons - chemistry; Retention; Sulfur; Toxicology
• ISSN: 0013-936X; 1520-5851
• DOI: 10.1021/es2030824

Investigators use C1 to C4 substituted polycyclic aromatic hydrocarbons (PAH) to assess ecological risk and to track fossil fuels and related pollutants in the environment. To quantify these compounds gas chromatography/mass spectrometry (GC/MS) is used. This work demonstrates single ion monitoring (SIM) or extraction (SIE) of full scan data produces inaccurate and imprecise concentration estimates due to incorrect homologue peak assignments. Profiling of coal tar and crude oil by automated sequential GC–GC/MS provided the retention windows and spectral patterns for each homologue to correctly quantify these compounds. Simultaneous pulsed flame photometric (sulfur-specific) detection differentiated PAH from polycyclic aromatic sulfur heterocycles and their alkylated homologues when they eluted within the same retention windows and had common ions. Differences between SIE and spectral deconvolution of GC/MS data based on multiple fragmentation patterns per homologue ranged from a few percent for C1 compounds to hundreds of percent for the higher alkylated homologues. Findings show current methods produce poor quality data adversely affecting forensic investigations, risk assessments, and weathering studies.