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Fine particulate matter levels at four air sampling stations in the Houston, TX area are apportioned to quantify the impact of emissions from a local refinery during a reported emission event. Through quantification of lanthanum and lanthanides using a recently developed analytical technique, the impacts of emissions from fluidized-bed catalytic cracking (FCC) units are quantitatively tracked across the Houston region. The results show a significant (33-106-fold) increase in contributions of FCC emissions to PM2.5 compared with background levels associated with routine operation. This impact from industrial emissions to ambient air quality occurs simultaneously with a larger, regional haze episode that lead to elevated PM2.5 concentrations throughout the entire region. By focusing on detailed chemical analysis of unique maker metals (lanthanum and lanthanides), the impact of emissions from the FCC unit was tracked from the local refinery that reported the emission event to a site approximately 50 km downwind, illustrating the strength of the analytical method to isolate an important source during a regional haze episode not related to the emission event. While this source apportionment technique could separate contributions from FCC emissions, improved time-resolved sampling is proposed to more precisely quantify the impacts of transient emission events on ambient PM2.5.

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Fine particulate matter (PM) samples collected in a highway tunnel in Houston, TX, were analyzed to quantify the concentrations of 14 n-alkanes, 12 polycyclic aromatic hydrocarbons, and nine petroleum biomarkers, as well as 21 metals, with the ultimate aim of identifying appropriate tracers for diesel engines. First, an exploratory multivariate dimensionality reduction technique called principal component analysis (PCA) was employed to identify all potential candidates for tracers. Next, emission indices were calculated to interpret PCA results physically. Emission indices of n-heneicosane, n-docosane, n-tricosane, n-tetracosane, n-pentacosane, fluoranthene, and pyrene were correlated highly and increased strongly with percentage carbon present in the tunnel emanating from diesel vehicles. This suggests that these organic compounds are useful molecular markers to separate emissions from diesel and gasoline engines. Additionally, the results are the first quantification of the metal composition of PM with aerodynamic diameters smaller than 2.5 microm (PM2.5) emissions from mobile sources in Houston. PCA of trace metal concentrations followed by emission index calculations revealed that barium in fine airborne particles can be linked quantitatively to diesel engine emissions, demonstrating its role as an elemental tracer for heavy-duty trucks.

In the absence of a certified reference material, a robust microwave-assisted acid digestion procedure followed by inductively coupled plasma -mass spectrometry (ICP-MS) was developed to quantify rare earth elements (REEs) in fluidized-bed catalytic cracking (FCC) catalysts and atmospheric fine particulate matter (PM2.5). High temperature (200 °C), high pressure (200 psig), acid digestion (HNO3, HF, and H3BO3) with 20 minute dwell time effectively solubilized REEs from six fresh catalysts, a spent catalyst, and PM2.5. This method was also employed to measure 27 non-REEs including Na, Mg, Al, Si, K, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As, Se, Rb, Sr, Zr, Mo, Cd, Cs, Ba, Pb, and U. Complete extraction of several REEs (Y, La, Ce, Pr, Nd, Tb, Dy, and Er) required HF indicating that they were closely associated with the aluminosilicate structure of the zeolite FCC catalysts. Internal standardization using 115In quantitatively corrected non-spectral interferences in the catalyst digestate matrix. Inter-laboratory comparison using ICP-optical emission spectroscopy (ICP-OES) and instrumental neutron activation https://ntrs.nasa.gov/search.jsp? R=20060022633 2018-05-10T22:17:43+00:00Z analysis (INAA) demonstrated the applicability of the newly developed analytical method for accurate analysis of REEs in FCC catalysts. The method developed for FCC catalysts was also successfully implemented to measure trace to ultra-trace concentrations of La, Ce, Pr, Nd, Sm, Gd, Eu, and Dy in ambient PM2.5 in an industrial area of Houston, TX. Analytical novelty of the research. We have developed and quantitatively verified microwave digestion and inductively coupled plasma -mass spectrometry techniques to accurately and precisely measure all naturally occurring rare earth elements in the aluminosilicate matrices of fluidized-bed catalytic cracking catalysts and atmospheric fine particles. One important basis of our research is that to date, no certified reference material is available for these zeolite-based catalysts. Additionally, existing reference materials for atmospheric particles such as SRM 1648 from the National institute of Standards and technology only include four rare earths (La, Ce, Sm, and Eu) and that too only as uncertified elements. were detected in PM 2.5 samples from Houston's Ship Channel area. We demonstrate that the loss of FCC catalyst from the refinery was the primary source of REEs in ambient atmospheric fine particles and that increase in PM 2.5 mass was predominantly caused by the loss of FCC catalyst during the "upset" event. U N I V E R S I T Y of H O U S T O NI appreciate the opportunity to publish in Analytica Chimica Acta. As the corresponding author, please contact me at chellam@uh.edu if I can provide additional information.Sincerely, Shankar Chellam Associate Professor Department of Civil and Environmental Engineering Department of Chemical EngineeringMicrowave-assisted extraction of rare earth elements from petroleum refining catalysts and ambient fine aerosols prior to inductive...

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The use of nanomaterials in medicine depends largely on nanotoxicological evaluation in order to ensure safe application on living organisms. Artificial intelligence (AI) and machine learning (MI) can be used to analyze and interpret large amounts of data in the field of toxicology, such as data from toxicological databases and high-content image-based screening data. Physiologically based pharmacokinetic (PBPK) models and nano-quantitative structure–activity relationship (QSAR) models can be used to predict the behavior and toxic effects of nanomaterials, respectively. PBPK and Nano-QSAR are prominent ML tool for harmful event analysis that is used to understand the mechanisms by which chemical compounds can cause toxic effects, while toxicogenomics is the study of the genetic basis of toxic responses in living organisms. Despite the potential of these methods, there are still many challenges and uncertainties that need to be addressed in the field. In this review, we provide an overview of artificial intelligence (AI) and machine learning (ML) techniques in nanomedicine and nanotoxicology to better understand the potential toxic effects of these materials at the nanoscale.