Determination of polycyclic aromatic hydrocarbons from ambient air particulate matter using a cold fiber solid phase microextraction gas chromatography-mass spectrometry method

Polycyclic aromatic hydrocarbons (PAH) from ambient air particulate matter (PM) were analyzed by a new method that utilized direct immersion (DI) and cold fiber (CF) SPME-GC/MS. Experimental design was used to optimize the conditions of extraction by DI-CF-SPME with a 100μm polydimethylsiloxane (PDMS) fiber. The optimal conditions included a 5min equilibration at 70°C time in an ultrasonic bath with an extraction time of 60min. The optimized method was validated by the analysis of a NIST standard reference material (SRM), 1649b urban dust. The results obtained were in good agreement with certified values. PAH recoveries for reference materials were between 88 and 98%, with a relative standard deviation ranging from 5 to 17%. Detection limits (LOD) varied from 0.02 to 1.16ng and the quantification limits (LOQ) varied from 0.05 to 3.86ng. The optimized and validated method was applied to the determination of PAH from real particulate matter (PM10) and total suspended particulate (TPS) samples collected on quartz fiber filters with high volume samplers.     

Mid‐ and Near‐Infrared Spectroscopic Determination of Carbon in a Diverse Set of Soils from the Brazilian National Soil Collection

Abstract

Calibrations for soil carbon content measured by combustion (total carbon, TC) and chromate oxidation by a modified Walkley‐Black method (Walkley‐Black carbon, WBC) from the Brazilian National Soil Collection were made using Fourier‐transform near (1100 to 2500 nm; NIRS) and mid‐infrared diffuse reflectance (2,500 to 25,000 nm; DRIFTS) spectroscopy combined with partial least squares (PLS). Calibration sets of sample populations of different carbon ranges, soil taxonomic classes, and soil textural groups were established. These are for TC ranges between 0.4 to 555.0, 0.4 to 99.1, and 0.4 to 39.9 g kg^?1: for WBC 0.2 to 401.0, 0.2 to 66.0, and 0.2 to 66.0, and 0.2 to 30.0 g kg^?1: for soil taxonomic classes Ferralsols and Acrisols; and for soil textural groups very clayey, clayey, and medium textures were examined. Calibrations obtained for the largest TC and WBC ranges were better compared to the lower ones, but lower root mean squared deviation (RMSD) and relative difference (RD=RMSD/mean value) were found for the lower carbon ranges. Taxonomic soil class was not an adequate criterium for calibration set formation. Soil texture had effect on calibrations, especially using NIR, because of the particle size effect to which NIR was more sensitive than mid‐IR. In general, DRIFTS showed better performance than NIRS. NIRS only outperformed DRIFTS when used with calibration set fairly homogeneous in its particle size distribution. Results demonstrated that while calibrations can be developed using either DRIFTS or NIRS for even a very diverse set of soil samples, which will determine C over a wide range of concentrations inherent in such a diverse set, it is desirable to seperate sample populations by soil textural properties and choose the adequate spectral range (NIR or mid‐IR) based on the textural group, for calibration development to achieve more accurate results.     

Sampling of benzene in environmental and exhaled air by solid-phase microextraction and analysis by gas chromatography–mass spectrometry

Benzene is classified as a Group I carcinogen by the International Agency for Research on Cancer (IARC). The risk assessment for benzene can be performed by monitoring environmental and occupational air, as well as biological monitoring through biomarkers. The present work developed and validated methods for benzene analysis by GC/MS using SPME as the sampling technique for ambient air and breath. The results of the analysis of air in parks and avenues demonstrated a significant difference, with average values of 4.05 and 18.26 μg m⁻³, respectively, for benzene. Sampling of air in the occupational environment furnished an average of 3.41 and 39.81 μg m⁻³. Moreover, the correlations between ambient air and expired air showed a significant tendency to linearity (R ² = 0.850 and R ² = 0.879). The results obtained for two groups of employees (31.91 and 72.62 μg m⁻³) presented the same trend as that from the analysis of environmental air.     

A deep learning-based simulator for comprehensive two-dimensional GC applications

Among the main approaches for predicting the spatial positions of eluates in comprehensive two-dimensional gas chromatography, the still under-explored computational models based on deep learning algorithms emerge as robust and reliable options due to their high adaptability to the structure and complexity of the data. In this work, an open-source program based on deep neural networks was developed to optimize chromatographic methods and simulate operating conditions outside the laboratory. The deep neural networks models were fit to convenient experimental predictors, resulting in scaled losses (mean squared error) equivalent to 0.006 (relative average deviation = 8.56%, R² = 0.9202) and 0.014 (relative average deviation = 1.67%, R² = 0.8009) in the prediction of the first- and second-dimension retention times, respectively. Good compliance was observed for the main chemical classes, such as environmental contaminants: volatile, semivolatile organic compounds, and pesticides; biochemistry molecules: amino acids and lipids; pharmaceutical industry and personal care products and residues: drugs and metabolites; among others. On the other hand, there is a need for continuous database updates to predict retention times of less common compounds accurately. Thus, forming a collaborative database is proposed, gathering voluntary findings from other users.