RESUMEN
An estimated 4.1 million barrels of oil and 2.1 million gallons of dispersants were released into the Gulf of Mexico during the Deepwater Horizon oil spill. There is a continued need for information about the impacts and long-term effects of the disaster on the Gulf of Mexico. The objectives of this study were to assess bioavailable polycyclic aromatic hydrocarbons (PAHs) in the coastal waters of four Gulf Coast states that were impacted by the spill. For over a year, beginning in May 2010, passive sampling devices were used to monitor the bioavailable concentration of PAHs. Prior to shoreline oiling, baseline data were obtained at all the study sites, allowing for direct before and after comparisons of PAH contamination. Significant increases in bioavailable PAHs were seen following the oil spill, however, preoiling levels were observed at all sites by March 2011. A return to elevated PAH concentrations, accompanied by a chemical fingerprint similar to that observed while the site was being impacted by the spill, was observed in Alabama in summer 2011. Chemical forensic modeling demonstrated that elevated PAH concentrations are associated with distinctive chemical profiles.
Asunto(s)
Contaminación por Petróleo , Hidrocarburos Policíclicos Aromáticos/análisis , Contaminantes Químicos del Agua/análisis , Monitoreo del Ambiente , Golfo de MéxicoRESUMEN
Classifications of geographic growing origin of three fresh fruits combining elemental profiles with various modeling approaches were determined. Elemental analysis (Ca, Cd, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, P, V, and Zn) of strawberry, blueberry, and pear samples was performed using inductively coupled plasma argon atomic emission spectrometer. Bulk stable carbon and nitrogen isotope analyses in pear were performed using mass spectrometry as an alternative fingerprinting technique. Each fruit, strawberry (Fragaria x ananassa), blueberry (Vaccinium caesariense/corymbosum), and pear (Pyrus communis), was analyzed from two growing regions: Oregon vs Mexico, Chile, and Argentina, respectively. Principal component analysis and canonical discriminant analysis were used for data visualization. The data were modeled using linear discriminant function, quadratic discriminant function, neural network, genetic neural network, and hierarchical tree models with successful classification ranging from 70 to 100% depending on commodity and model. Effects of Oregon subregional and variety classification were investigated with similar success rates.
Asunto(s)
Ambiente , Frutas/clasificación , Frutas/crecimiento & desarrollo , Isótopos/análisis , Oligoelementos/análisis , Argentina , Arándanos Azules (Planta)/química , Arándanos Azules (Planta)/crecimiento & desarrollo , Chile , Fragaria/química , Fragaria/crecimiento & desarrollo , Frutas/química , México , Oregon , Pyrus/química , Pyrus/crecimiento & desarrolloRESUMEN
The objective of this research was to demonstrate the feasibility of this method to differentiate the geographical growing regions of coffee beans. Elemental analysis (K, Mg, Ca, Na, Al, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Mo, S, Cd, Pb, and P) of coffee bean samples was performed using ICPAES. There were 160 coffee samples analyzed from the three major coffee-growing regions: Indonesia, East Africa, and Central/South America. A computational evaluation of the data sets was carried out using statistical pattern recognition methods including principal component analysis, discriminant function analysis, and neural network modeling. This paper reports the development of a method combining elemental analysis and classification techniques that may be widely applied to the determination of the geographical origin of foods.