Receptor Model Applied to Patterns in Space (RMAPS)

Abstract

The physical and mathematical bases of a new receptor model based on spatially intensive data are presented. The model apportions the average concentration of a species as measured at many sites among several spatially distinct sources and can be applied to primary or secondary species. In the latter case, no assumptions concerning transformation or deposition rates are required. The methodology is a combination of the empirical orthogonal function approach that is well known in meteorology and the self-modeling multivariate modeling approach that has long been applied in chemometrics and multivariate receptor modeling of air quality data. A simple, geometrical example of the modeling approach is given.     

Henry's law constants of chlorinated solvents at elevated temperatures

Henry’s law constants for 12 chlorinated volatile organic compounds (CVOCs) were measured as a function of temperature ranging from 8 to 93 °C, using the modified equilibrium partitioning in closed system (EPICS) method. The chlorinated compounds include tetrachloroethylene, trichloroethylene, cis-1,2-dichloroethylene, vinyl chloride, 1,1,1-trichloroethane, 1,1-dichloroethane, 1,2-dichloroethane, chloroethane, carbon tetrachloride, chloroform, dichloromethane, and chloromethane. The variation in Henry’s constants for these compounds as a function of temperature ranged from around 3-fold (chloroethane) to 30-fold (1,2-dichloroethane). Aqueous solubilities of the pure compounds were measured over the temperature range of 8-75 °C. The temperature dependence of Henry’s constant was predicted using the ratio of pure vapor pressure to aqueous solubility, both of which are functions of temperature. The calculated Henry’s constants are in a reasonable agreement with the measured results. With the improved data on Henry’s law constants at high temperatures measured in this study, it will be possible to more accurately model subsurface remediation processes that operate near the boiling point of water.     

The Assessment of Emission Analysis Accuracy

An assessment of the accuracy of emission measurements is fundamental to an understanding of the ability of an organization to comply with emission regulations. Without a knowledge of the measurement accuracy, the significance of observed differences cannot be assessed. In addition, the average emission level that a group of engines must achieve for compliance is a function of the accuracy of the measurement of the exhaust constituents; i.e., the less accurate the measurement, the lower the average level must be to assure that essentially all engines pass the compliance tests. The accuracy assessment specifically included: 1) test instrument precision, 2) calibration gas accuracy and 3) sample error due to the distribution of data resulting from gas concentrations that are spatially non-uniform. In addition, the accuracy assessment required the consideration of the following general topics: the propagation of errors in a calibration hierarchy, the proper choice of error units, surveys of instrument precision, the evaluation of the significance of various instrument errors, the assessment of the significance of sampling error, and the consideration of methods for demonstrating representative samples and improving instrument precision. An accuracy assessment considering those factors resulted in the following conclusions: 1) Emission instrument precision is best expressed as percent of full scale. 2) The proper units for bias errors are percent of point. 3) Calibration-to-calibration repeatability may be utilized to estimate instrument precision. 4) Instrument precision shows wide variations from day to day. 5) Correcting past data by post-test calibration adjustments will not, in general, improve emission data altered by instrument drift. 6) Sampling error due to the distribution of emission concentrations in space is the single largest source of uncertainty in gas turbine emission measurements.     

Comparisons of EPA Rollback,Empirical/Kinetic,and Physicochemical Oxidant Prediction Relationships in the San Francisco Bay Area

This paper describes, compares and evaluates selected Oxidant Prediction Relationships {OPRs) in terms of projections of hydrocarbon emission reductions required for attainment of the former 0.08 ppm standard and the new 0.12 ppm standard in the San Francisco Bay Area in 1985. The OPRs analyzed are the LIRAQ physicochemical model, EPA’s Empirical Kinetic Modeling Approach (EKMA), linear and Appendix J rollback, and an empirical OPR based on local observations.

LIRAQ simulations indicated that to achieve the 0.12 ppm ozone standard, 1985 hydrocarbon emissions must be reduced by 27% from projected levels. The equivalent reductions derived from simple linear rollback, linear rollback with 0.04 ppm background, and the local empirical OPR were 32%, 45% and 37%, respectively. The LIRAQ simulations also showed that reduction of both hydrocarbon and NO_x emissions is less effective than reduction of hydrocarbons only. The attempt to apply EKMA failed because the Bay Area’s low hydrocarbon/NO_x ratios and observed ozone levels are not consistent with the standard EKMA isopleth curves.

For planning, proper OPR selection is important because the wide range in the projections of various OPRs translates into a correspondingly wide range in control costs. Physicochemical OPRs are preferred because they are verifiable; they account for complex topography, meteorology, and source distributions; and because they can treat a variety of control strategies. In the future, the uncertainties associated with the projections can be resolved by assessing trends in air quality on a regular basis and by upgrading and reapplying the prediction methodologies as new information becomes available.     

Tomato Root: Site of Initial Alteration in Minor Elements in Cadmium Treated Plants

Abstract

Societal and governmental pressures to reduce diesel exhaust emissions are reflected in the existing and projected future heavy-duty certification standards of these emissions. Various factors affect the amount of emissions produced by a heterogeneous charge diesel engine in any given situation, but these are poorly quantified in the existing literature. The parameters that most heavily affect the emissions from compression ignition engine-powered vehicles include vehicle class and weight, driving cycle, vehicle vocation, fuel type, engine exhaust aftertreatment, vehicle age, and the terrain traveled. In addition, engine control effects (such as injection timing strategies) on measured emissions can be significant. Knowing the effect of each aspect of engine and vehicle operation on the emissions from diesel engines is useful in determining methods for reducing these emissions and in assessing the need for improvement in inventory models. The effects of each of these aspects have been quantified in this paper to provide an estimate of the impact each one has on the emissions of diesel engines.     

The Onset of Electrical Breakdown in Dust Layers: I,Microsparking Described by Paschen’s Law

The onset of electrical breakdown in dust layers has been studied for hand-deposited dust layers in a parallel plate geometry. It was found that the breakdown was an ordinary electron avalanche process originating in voids within the dust layer and obeying Paschen’s Law. The size of voids where breakdown occurs was in the range of 10 to 20 μm for the layers used. The distribution of particle sizes in a sample Influences its breakdown through changes in the average void dimension where breakdown takes place. Water vapor in the test environment, which affects the electrical conduction mechanism prior to breakdown, lowered the average electric field required to initiate breakdown. Moderate compaction of the sample had little or no effect on its breakdown behavior.     

Design and Performance Characterization Strategy Using Modeling for Biofiltration Control of Odorous Hydrogen Sulfide

Abstract

Biofilter, dynamic modeling software characterizing contaminant removal via biofiltration, was used in the preliminary design of a biofilter to treat odorous hydrogen sulfide (H₂S). Steady-state model simulations were run to generate performance plots for various influent concentrations, loadings, residence times, media sizes, and temperatures. Although elimination capacity and removal efficiency frequently are used to characterize biofilter performance, effluent concentration can be used to characterize performance when treating to a target effluent concentration. Model simulations illustrate that, at a given temperature, a biofilter cannot reduce H₂S emissions below a minimum value, no matter how large the biofilter or how long the residence time. However, a higher biofilter temperature results in lower effluent H₂S concentrations. Because dynamic model simulations show that shock loading can significantly increase the effluent concentration above values predicted by the steady-state model simulations, it is recommended that, to consistently meet treatment objectives, dynamic feed conditions should be considered. This study illustrates that modeling can serve as a valuable tool in the design and performance optimization of biofilters.     

Lubrication Oil Reservoir Mist Elimination

Visible emissions from lubrication oil reservoir vents on stationary internal combustion engines, compressors and turbines can be virtually eliminated through the use of properly engineered fiber beds. The fiber bed is more successful than other approaches at eliminating visible emissions because of the inherent low pressure drop, minimal or non-existent maintenance requirements, and proven collection efficiency. In fact, with fiber bed technology, visible emissions can be reduced to virtually zero percent opacity. This paper reviews the applicable emission standards, explores the nature of the lubrication oil vent (LOV) oil mist, describes some of the equipment that has previously been used to control LOV emissions, and details the application of fiber beds for this purpose.     

A comprehensive investigation and assessment of mercury in intertidal sediment in continental coast of Shanghai

The aims of this paper were to survey the total Hg levels and distribution character in intertidal sediment in continental coast of Shanghai, and identify the environment factors that might influence the sediment Hg concentrations, and to assess the pollution degree and potential ecological risk of Hg in sediment. Eighty-eight surface sediment samples and 18 sediment cores were collected for Hg contamination analysis. Physicochemical properties including Eh, particle size, content of total organic carbon (TOC), and acid volatile sulfide (AVS) were also measured. Index of geo-accumulation (I _geo) and potential ecological risk index were used respectively to assess the pollution levels and the ecological risk of sediment Hg. The average of total Hg concentrations in surface sediments was 107.4?±?90.9 ng/g with the range from 0 to 465.9 ng/g. Higher Hg concentrations were generally found in surface sediments near sewage outfalls and the mouth of rivers. Total Hg concentrations were significantly correlated with TOC (p?<?0.05) both in surface (r?=?0.24) and core (r?=?0.29) sediments, but not with the other environment factors (Eh, AVS, and particle size). Geo-accumulation index indicated that Hg contamination in intertidal sediments was generally at none to moderate degree, while potential ecological risk index demonstrated that the risk caused by Hg were at moderate to considerable level. Intertidal sediment in continental coast of Shanghai has generally been contaminated by Hg, and it might pose moderate to considerable risk to the local ecosystem. The Hg contamination is related more to the coastal pollution sources and complicated hydrodynamic and sedimentary conditions than the other environment factors studied.     

Further Validation of Artificial Neural Network-Based Emissions Simulation Models for Conventional and Hybrid Electric Vehicles

Abstract

With the advent of hybrid electric vehicles, computer-based vehicle simulation becomes more useful to the engineer and designer trying to optimize the complex combination of control strategy, power plant, drive train, vehicle, and driving conditions. With the desire to incorporate emissions as a design criterion, researchers at West Virginia University have developed artificial neural network (ANN) models for predicting emissions from heavy-duty vehicles. The ANN models were trained on engine and exhaust emissions data collected from transient dynamometer tests of heavy-duty diesel engines then used to predict emissions based on engine speed and torque data from simulated operation of a tractor truck and hybrid electric bus. Simulated vehicle operation was performed with the ADVISOR software package. Predicted emissions (carbon dioxide [CO₂] and oxides of nitrogen [NO_x]) were then compared with actual emissions data collected from chassis dynamometer tests of similar vehicles. This paper expands on previous research to include different driving cycles for the hybrid electric bus and varying weights of the conventional truck. Results showed that different hybrid control strategies had a significant effect on engine behavior (and, thus, emissions) and may affect emissions during different driving cycles. The ANN models underpredicted emissions of CO₂ and NO_x in the case of a class-8 truck but were more accurate as the truck weight increased.