ENVIRONMENTAL research, ENVIRONMENTAL protection research, AIR quality, AIR pollution, ORGANIC chemistry, ENVIRONMENTAL quality, AIR pollution monitoring, BIOORGANIC chemistry, and CARBON compounds

Three mathematical models of air quality (CMAQ, CMAQ- MADRID, and REMSAD) are applied to simulate the response of atmospheric fine particulate matter (PM2.5) concentrations to reductions in the emissions of gaseous precursors for a 10 day period of the July 1999 Southern Oxidants Study (SOS) in Nashville. The models are shown to predict similar directions of the changes in PM2.5 mass and component (sulfate, nitrate, ammonium, and organic compounds) concentrations in response to changes in emissions of sulfur dioxide (SO2), nitrogen oxides (NOx), and volatile organic compounds (VOC), except for the effect of SO2 reduction on nitrate and the effect of VOC reduction on PM2.5 mass. Furthermore, in many cases where the directional changes are consistent, the magnitude of the changes are significantly different among models. Examples are the effects of SO2 and NOx reductions on nitrate and PM2.5 mass and the effects of VOC reduction on organic compounds, sulfate and nitrate. The spatial resolution significantly influences the results in some cases. Operational model performance for a PM2.5 component appears to provide some useful indication on the reliability of the relative response factors (RRFs) for a change in emissions of a direct precursor, as well as for a change in emissions of a compound that affects this component in an indirect manner, such as via oxidant formation. However, these results need to be confirmed for other conditions and caution is still needed when applying air quality models for the design of emission control strategies. It is advisable to use more than one air quality model (or more than one configuration of a single air quality model) to span the full range of plausible scientific representations of atmospheric processes when investigating future air quality scenarios. [ABSTRACT FROM AUTHOR]

ORGANIC chemistry, WORK environment, STANDARD deviations, REGRESSION analysis, PHYSICAL & theoretical chemistry, VAPORIZATION in water purification, ORGANOMERCURY compounds, CONTAMINATION (Technology), and BIOORGANIC chemistry

We developed a personal exposure model using volatile organic compound data collected for teachers and office workers as part of the Boston Exposure Assessment in Microenvironments (BEAM) study. We included participant-specific time-activity and concentration measurements of residential outdoor, residential indoor, and workplace microenvironments, along with average concentrations in various dining, retail, and transportation microenvironments. We used a series of time-weighted personal exposure models to compare measured personal concentrations using median regression models, with bias estimates representing the difference between measured and modeled personal exposures. Incorporating only the outdoor microenvironment results in an unbiased estimate of personal exposure only for carbon tetrachloride. Adding the residential indoor microenvironment provides an unbiased estimate for trichloroethene as well. A model incorporating residential outdoor, indoor, and workplace microenvironments provides an unbiased estimate for the above compounds and chloroform, 1,4-dichlorobenzene, benzene, and α-pinene, and adding the transportation microenvironment adds ethylbenzene. A fully saturated model, including outdoor, indoor, workplace, transportation, and all other microenvironments, provides an unbiased estimate for the previously listed compounds along with tetrachloroethene and styrene. MTBE, toluene, o-xylene, d-limonene, formaldehyde, and acetaldehyde were not fully characterized even in the saturated model, emphasizing that additional time-activity and concentration information would more fully characterize personal exposure. [ABSTRACT FROM AUTHOR]

ORGANIC chemistry, CARBON nanotubes, NANOTUBES, PHYSICAL & theoretical chemistry, PROPERTIES of matter, SPECTRUM analysis, ENVIRONMENTAL engineering, PHYSICAL organic chemistry, and BIOORGANIC chemistry

Understanding adsorptive interactions between organic contaminants and carbon nanotubes is critical to both the environmental application of carbon nanotubes as special adsorbents and the assessment of the potential impact of carbon nanotubes on the fate and transport of organic contaminants in the environment. The adsorption of organic compounds with varied physical-chemical properties (hydrophobicity, polarity, electron polarizability, and size) to one single-walled carbon nanotube (SWNT) and two multiwalled carbon nanotubes (MWNTs) was evaluated. For a given carbon nanotube, the adsorption affinity correlated poorly with hydrophobicity but increased in the order of nonpolar aliphatic < nonpolar aromatics < nitroaromatics, and within the group of nitroaromatics, the adsorption affinity increased with the number of nitro-functional groups. We propose that the strong adsorptive interaction between carbon nanotubes and nitroaromatics was due to the π-π electron-donor-acceptor (EDA) interaction between nitroaromatic molecules (electron acceptors) and the highly polarizable graphene sheets (electron donors) of carbon nanotubes. Additionally, we attribute the stronger adsorption of nonpolar aromatics compared to that of nonpolar aliphatics to the π-electron coupling between the flat surfaces of both aromatic molecules and carbon nanotubes. For tetrachlorobenzene, the bulkiest adsorbate, adsorption affinity (on a unit surface area basis) to the SWNT was much stronger than to the two MWNTs, indicating a probable molecular sieving effect. [ABSTRACT FROM AUTHOR]

VOLATILE organic compounds, BIOREACTORS, AIR pollution, BIOTRANSFORMATION (Metabolism), ORGANIC chemistry, BIOORGANIC chemistry, CARBON compounds, BIOCHEMICAL engineering equipment, and CHEMICAL reactors

A predicting model is proposed to evaluate metabolic byproducts accumulation and process performance in suspended growth reactors treating air emissions contaminated with volatile organic compounds (VOCs). The model presented integrates a multistep kinetic model and a general mechanistic model describing bioreactor operation. This integrated model is based on general equations modeling, both mass transport and the mechanisms underlying pollutant biotransformation and byproducts accumulation, and can be applied to a wide range of operating conditions (VOC substrate, O2, and nutrients limitation) The model was tested for predicting benzyl alcohol (BA) accumulation in a chemostat reactor treating toluene. BA accumulates in Pseudomonas putida Fl cultures degrading toluene as a result of methyl monooxygenation reaction parallel to the main TOO degradation pathway. The operational conditions leading to BA accumulation are evaluated through simulations assays. Simulation results indicate that BA accumulation occurs when other substrates rather than toluene are limiting. Therefore, operation under toluene limitation is highly recommended to ensure not only the detoxification goals but also to avoid potential mutagenic effects of BA over the microbial culture. [ABSTRACT FROM AUTHOR]

MATRICES, FACTOR analysis, ORGANIC chemistry, CARBON compounds, BIOORGANIC chemistry, PHYSICAL organic chemistry, and ORGANIC compounds

One hundred and twenty five particulate matter samples that were collected over a 2 year period at the St. Louis Midwest Supersite were analyzed for 24 hour average organic carbon (OC), elemental carbon (EC), and particle-phase organic compound (molecular markers) concentrations. Over 100 organic compounds along with measurements of silicon and aluminum were analyzed using a factor analysis based source apportionment model, positive matrix factorization (PMF), which has been widely used in the past with elemental data but not organic molecular markers. Four different solutions (7, 8, 9, and 10 factor solutions) to the PMF model were explored to consider the stability of the source apportionment results, which were found to be reasonably stable. The eight-factor solution was further explored and compared to a parallel chemical mass balance (CMB) source apportionment modeling result that used a subset of the PMF data. A base case eight-factor PMF solution resolved two point source factors, two winter combustion factors, a biomass-burning factor, a mobile source factor, a secondary organic aerosol factor, and a resuspended soil factor. An optimized eight- factor case was also examined, which was formulated by removing three extreme point source impacts observed in the base case, to better understand the nonpoint sources. In the optimized case, the daily OC explained by the biomass burning shows good agreement with the corresponding CMB source, with a slope of 0.93 ± 0.03. Likewise, the average OC explained by the optimized PMF resuspended soil factor showed good correlation with the CMB road dust apportionment, but there was a significant bias between the two results. The optimized PMF OC from one of the winter combustion factors showed good correlation with the CMB natural gas combustion apportionment but also has a significant bias. In both cases, PMF analysis factored one mobile source controlled by hopanes and streranes, which did not correlate well with any of the three CMB mobile sources. Although the most of the molecular markers were clustered with the PMF model in a manner consistent with prior knowledge of these organic compounds, one significant deviation was observed. Cholesterol, used in the past as a tracer for meat smoke, was found to largely associate with road dust, which raises questions on the suitability of cholesterol as a tracer for meat smoke in the midwestern U.S. [ABSTRACT FROM AUTHOR]