NOx Removal with Combined Selective Catalytic Reduction and Selective Noncatalytic Reduction: Pilot-Scale Test Results

Pilot-scale tests were conducted to develop a combined nitrogen oxide (NO_x) reduction technology using both selective catalytic reduction (SCR) and selective noncatalytic reduction (SNCR). A commercially available vanadium- and titanium-based composite honeycomb catalyst and enhanced urea (NH₂CONH₂) were used with a natural-gas-fired furnace at a NO_x concentration of 110 ppm. Changes in SNCR chemical injection temperature and stoichiometry led to varying levels of post-furnace ammonia (NH₃), which acts as the reductant feed to the downstream SCR catalyst. The urea-based chemical could routinely achieve SNCR plus SCR total NO_x reductions of 85 percent with less than 3 ppm NH₃ slip at reductant/NO_x stoichiometries ranging from about 1.5 to 2.5 and SCR space velocities of 18,000 to 32,000 h^?1. This pilot-scale research has shown that SNCR and SCR can be integrated to achieve high NO_x removal. SNCR provides high temperature reduction of NO_x followed by further removal of NO_x and minimization of NH₃ slip by a significantly downsized (high-space velocity) SCR.     

Moving Bed Adsorption System for Control of VOCs from an Aircraft Painting Facility

An activated carbon moving bed system (10 to 100 acf m air flow) was tested for controlling VOC emissions from a commercial aircraft painting facility. The cross-flow moving adsorbent bed showed a VOC collection efficiency in the 77.1 to 99.6 percent range over a superficial gas velocity range of 27 to 185 ft/min (0.14-0.94 m/sec). The collection efficiencies were neither affected by a change in carbon flow rates from 5 to 8 Ib/hr (2.3 to 3.6 kg/hr) nor by a change in the gas superficial velocity from 27 to 185 ft/min. The VOC concentration in the emission stream from the painting hangar was found to vary by at least a factor of 20 (from 0.18 to 15 ppm) both over the five month period (during which the 15 system tests of about three hours each were conducted) and within a single eight hour work shift.     

Wind Tunnel Modelling of Roadways: Comparison with Mathematical Models

The assessment of air quality impacts from roadways is a major concern to urban planners. In order to assess future road and building configurations, a number of techniques have been developed, including mathematical models, which simulate traffic emissions and atmospheric dispersion through a series of mathematical relationships and physical models. The latter models simulate emissions and dispersion through scaling of these processes in a wind tunnel. Two roadway mathematical models, HIWAY-2 and CALINE-4, were applied to a proposed development in a large urban area. Physical modelling procedures developed by Rowan Williams Davies & Irwin Inc. (RWDI) in the form of line source simulators were also applied, and the resulting carbon monoxide concentrations were compared. The results indicated a factor of two agreement between the mathematical and physical models. The physical model, however, reacted to changes in building massing and configuration. The mathematical models did not, since no provision for such changes was included in the mathematical models. In general, the RWDI model resulted in higher concentrations than either HIWAY-2 or CALINE-4. Where there was underprediction, it was often due to shielding of the receptor by surrounding buildings. Comparison of these three models with the CALTRANS Tracer Dispersion Experiment showed good results although concentrations were consistently underpredicted.     

A Simple Neural Network for Estimating Emission Rates of Hydrogen Sulfide and Ammonia from Single Point Sources

Abstract

Neural networks have shown tremendous promise in modeling complex problems. This work describes the development and validation of a neural network for the purpose of estimating point source emission rates of hazardous gases. This neural network approach has been developed and tested using experimental data obtained for two specific air pollutants of concern in West Texas, hydrogen sulfide and ammonia. The prediction of the network is within 20% of the measured emission rates for these two gases at distances of less than 50 m. The emission rate estimations for ground level releases were derived as a function of seven variables: downwind distance, crosswind distance, wind speed, downwind concentration, atmospheric stability, ambient temperature, and relative humidity. A backpropagation algorithm was used to develop the neural network and is also discussed here. The experimental data were collected at the Wind Engineering Research Field Site located at Texas Tech University in Lubbock, Texas. Based on the results of this study, the use of neural networks provides an attractive and highly effective tool to model atmospheric dispersion, in which a large number of variables interact in a nonlinear manner.     

The Application of the Karl Fischer Oven for the Determination of Water in Consumer Products

Abstract

The direct Karl Fischer (KF) titration method has known interferences for measuring water content. In addition, in analyzing some paints, KF can fail to produce an accurate analysis. The California Air Resources Board (GARB) staff has developed a KF procedure that can be used to determine the water content of consumer products. The procedure uses an oven accessory to the titration system, and is based on a distillation method developed by the California Polytechnical University at San Luis Obispo (Cal Poly). Samples are diluted in l-methoxy-2-propanol (MPA), and an aliquot is injected into an enclosed oven system, where the MPA/water azeotrope is swept directly into the KF titration vessel. The technique is accurate and precise and, thus far, proves to be a fast and reliable method for analysis.     

Statistical Considerations in Determining the Health Significance of Constituents of Airborne Particulate Matter

ABSTRACT

Because of the U.S. Environmental Protection Agency’s (EPA) new ambient air quality standard for fine particles, the need is likely to continue for more detailed scientific investigation of various types of particles and their effects on human health. Epidemiology studies have become the method of choice for investigating health responses to such particles and to other air pollutants in community settings. Health effects have been associated with virtually all of the gaseous criteria pollutants and with the major constituents of airborne particulate matter (PM), including all size fractions less than about 20 gm, inorganic ions, carbonaceous particles, metals, crustal material, and biological aerosols. In many of the more recent studies, multiple pollutants or agents (including weather variables) have been significantly associated with health responses, and various methods have been used to suggest which ones might be the most important. In an ideal situation, classical least-squares regression methods are capable of performing this task. However, in the real world, where most of the pollutants are correlated with one another and have varying degrees of measurement precision and accuracy, such regression results can be misleading. This paper presents some guidelines for dealing with such collinearity and model comparison problems in both single- and multiple-pollutant regressions. These techniques rely on mean effect (attributable risk) rather than statistical significance per se as the preferred indicator of importance for the pollution variables.     

Estimating Maximum Concentrations for Open Path Monitoring Along a Fixed Beam Path

ABSTRACT

Researchers have applied open path optical sensing techniques to a variety of workplace and environmental monitoring problems. Usually these data are reported in terms of a path-average (or path-integrated) concentration. When assessing potential human exposures along a beam path, this path-average value is not always informative, since concentrations along the path can vary substantially from the beam average. The focus of this research is to arrive at a method for estimating the upper-bound in contaminant concentrations over a fixed open beam path. The approach taken here uses a statistical model to estimate an upper-bound concentration based on a combination of the path-average and a measure of the spatial variability computed from point samples along the beam path. Results of computer simulations and experimental testing in a controlled ventilation chamber indicate that the model produced conservative estimates for the maximum concentration along the beam path. This approach may have many applications for open path monitoring in workplaces or wherever maximum concentrations are a concern.     

Innovative Approach for Estimating Fugitive Gaseous Fluxes Using Computed Tomography and Remote Optical Sensing Techniques

ABSTRACT

This paper presents a new approach to quantify emissions from fugitive gaseous air pollution sources. The authors combine Computed Tomography (CT) with Path-Integrated Optical Remote Sensing (PI-ORS) concentration data in a new field beam geometry. Path-integrated concentrations are sampled in a vertical plane downwind from the source along several radial beam paths. An innovative CT technique, which applies the Smooth Basis Function Minimization method to the beam data in conjunction with measured wind data, is used to estimate the total flux from the fugitive source. The authors conducted a synthetic data study to evaluate the proposed methodology under different meteorological conditions, beam geometry configurations, and simulated measurement errors. The measurement errors were simulated based on data collected with an Open-Path Fourier Transform Infra-Red system. This approach was found to be robust for the simulated errors and for a wide range of fluctuating wind directions. In the very sparse beam geometry examined (eight beam paths), successful emission rates were retrieved over a 70° range of wind directions under extremely large measurement error conditions.     

A Comparative Analysis of Emissions Deterioration for In-Use Alternative Fuel Vehicles

Abstract

Although there have been several studies examining emissions from in–use alternative fuel vehicles (AFVs), little is known about the deterioration of these emissions over vehicle lifetimes and how this deterioration compares with deterioration from conventional vehicles (CVs). This paper analyzes emissions data from 70 AFVs and 70 CVs operating in the federal government fleet to determine whether AFV emissions deterioration differs significantly from CV emissions deterioration. An analysis is conducted on three alternative fuel types (natural gas, methanol, and ethanol) and on four pollutants (carbon monoxide, total hydrocarbons, non-methane hydrocarbons, and nitrogen oxides). The results indicate that for most cases studied, deterioration differences are not statistically significant; however, several exceptions (most notably with natural gas vehicles) suggest that air quality planners and regulators must further analyze AFV emissions deterioration to properly include these technologies in broader air quality management schemes.     

Estimation of Trends in Atmospheric Concentrations of Sulfate in the Northeastern United States

Abstract

Daily atmospheric concentrations of sulfate collected at six locations in the northeastern United States are regressed against meteorological factors, ozone, seasonal cycles, and time in order to determine if a significant trend in sulfate can be detected. The data used in this analysis were collected during the Sulfate Regional Experiment (SURE, 1977-1978) and the Eulerian Model Evaluation Field Study (EMEFS, 1988-1989). Ozone, specific humidity, and seasonal terms (reflecting the potential of the atmosphere for oxidation of sulfur dioxide) emerged as important explanatory variables. After accounting for the variability explained by environmental factors, the median estimated change in sulfate concentration from the six locations over the 11-year period is -22% (or -28% if ozone is not used as an explanatory variable). Although there are wide variations among locations, these changes are commensurate with an estimated 25% decline in sulfur emissions in the northeastern U.S. during the same period. These analyses provide insight into methods for detecting reductions in sulfate that may be expected to occur as a result of the Clean Air Act Amendments of 1990. Uncertainties in the estimates, with consideration of serial correlation in the data, imply a minimum detectable reduction of 10% using this modeling procedure with similar data availability.