Multivariate optimization of a green procedure for determination of emerging polycyclic aromatic nitrogen...

Emerging polycyclic aromatic nitrogen heterocycles (PANHs) contributes significantly to the health risk associated with inhaling polluted air. However, there is a lack of analytical methods with the needed performance to their determination. This study presents the optimization and validation for the first time of a green microscale extraction procedure for the determination of twenty-one PANHs, including carbazole, indole, and quinolone classes, in particulate matter (PM_2.5) samples by gas chromatography-mass spectrometry. A simplex-centroid mixture design and full factorial design (2³) were employed to optimize the following extraction parameters: type and volume of solvent, sample size, extraction time, and necessity of a cleanup step. Low limits of detection and quantification (LOD < 0.97 pg m^-3 and LOQ < 3.24 pg m^-3, respectively) were obtained in terms of matrix-matched calibration. The accuracy and precision of the method were adequate, with recoveries in three levels between 73 to 120% and intraday and interday relative standard deviations from 2.0 to 12.9% and 7.3 to 18.9%, respectively. The green character of the method was evaluated using the Analytical Greenness (AGREE) tool, where a score of 0.69 was obtained, indicating a great green procedure. The method was applied to PM_2.5 samples collected from sites with different characteristics; the concentrations ranged from 69.3 pg m^-3 (2-methylcarbazole) to 11,874 pg m^-3 (carbazole) for individual PANHs and from 2306 to 24,530 pg m^-3 for ∑21PANHs. Principal component analysis (PCA) and hierarchical clustering enabled discrimination of the sampling sites according to the PANHs concentrations. The score plots formed two distinct groups, one with samples containing higher concentrations of PANHs, corresponding to sites with a major influence from diesel emissions, and another group with minor PANH contents, corresponding to sites impacted by emissions from urban traffic and industrial activities.