Excitation-Emission Matrix Spectroscopy for Analysis of Chemical Composition of Combustion Generated Particulate Matter.
Gaurav MahamuniJay RutherfordJustin DavisEric MolnarJonathan D PosnerEdmund SetoGregory V KorshinIgor V NovosselovPublished in: Environmental science & technology (2020)
Analysis of particulate matter (PM) is important for the assessment of human exposures to potentially harmful agents, notably combustion-generated PM. Specifically, polycyclic aromatic hydrocarbons (PAHs) found in ultrafine PM have been linked to cardiovascular diseases and carcinogenic and mutagenic effects. In this study, we quantify the presence and concentrations of PAHs with lower molecular weight (LMW, 126 < MW < 202) and higher molecular weight (HMW, 226 < MW < 302), i.e., smaller and larger than Pyrene, in combustion-generated PM using excitation-emission matrix (EEM) fluorescence spectroscopy. Laboratory combustion PM samples were generated in a laminar diffusion inverted gravity flame reactor (IGFR) operated on ethylene and ethane. Fuel dilution by Ar in 0% to 90% range controlled the flame temperature. The colder flames result in lower PM yields however, the PM PAH content increases significantly. Temperature thresholds for PM transition from low to high organic carbon content were characterized based on the maximum flame temperature (Tmax,c ∼ 1791 to 1857 K) and the highest soot luminosity region temperature (T*c ∼ 1600 to 1650K). Principal component regression (PCR) analysis of the EEM spectra of IGFR samples correlates to GCMS data with R2 = 0.988 for LMW and 0.998 for HMW PAHs. PCR-EEM analysis trained on the IGFR samples was applied to PM samples from woodsmoke and diesel exhaust, the model accurately predicts HMW PAH concentrations with R2 = 0.976 and overestimates LMW PAHs.
Keyphrases
- particulate matter
- polycyclic aromatic hydrocarbons
- air pollution
- heavy metals
- cardiovascular disease
- high resolution
- risk assessment
- metabolic syndrome
- wastewater treatment
- endothelial cells
- health risk assessment
- machine learning
- coronary artery disease
- energy transfer
- body composition
- artificial intelligence
- mass spectrometry
- solid state
- drinking water
- molecular dynamics
- simultaneous determination
- sewage sludge
- pluripotent stem cells