Seasonal Variations in Aerosol Composition and Acidity at Shenandoah and Great Smoky Mountains National Parks

The concentration of elements Na through Pb, select ions, and organic carbon from fine (<2.5 µm) particles has been monitored at Shenandoah and Great Smoky Mountains National Parks from 1988 through 1995. The data obtained from 1988 through 1994 show that significant changes in the concentrations of many aerosol constituents occur on a seasonal basis. Particulate sulfate and organic carbon are shown to exhibit substantially higher concentrations during the summer, while sulfur dioxide and nitrate concentrations are highest during the winter.

A method for estimating the degree of neutralization of particulate sulfate is given. This method uses routinely measured aerosol elemental compositions because ammonium ion, the primary neutralizing species for sulfate, is not measured on a routine basis. Application of this method to the selected data set shows that sulfate aerosol is most acidic during summer with an average molar H_s (moles of hydrogen associated with sulfur) to S (moles of sulfur) ratio of approximately 4. This suggests the average sulfate particle during the summer has a molar coon slightly more acidic than ammonium bisulfate (NH₄HSO₄) which has a molar hydrogen to sulfur ratio of 5. Winter H_s to S ratios, however, are approximately 8, suggesting the aerosol is on average fully neutralized ammonium sulfate [(NH₄)₂SO₄].     

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.     

Daily Mortality in the Philadelphia Metropolitan Area and Size-Classified Particulate Matter

ABSTRACT

Time-series of daily mortality data from May 1992 to September 1995 for various portions of the seven-county Philadelphia, PA, metropolitan area were analyzed in relation to weather and a variety of ambient air quality parameters. The air quality data included measurements of size-classified PM, SO₄ ^2-, and H+ that had been collected by the Harvard School of Public Health, as well as routine air pollution monitoring data. Because the various pollutants of interest were measured at different locations within the metropolitan area, it was necessary to test for spatial sensitivity by comparing results for different combinations of locations. Estimates are presented for single pollutants and for multiple-pollutant models, including gaseous pollutants and mutually exclusive components of PM (PM_2.5 and coarse particles, SO₄ ^2- and non-SO₄ ^2- portions of total suspended particulate [TSP] and PM₁₀), measured on the day of death and the previous day.

We concluded that associations between air quality and mortality were not limited to data collected in the same part of the metropolitan area; that is, mortality for one part may be associated with air quality data from another, not necessarily neighboring, part. Significant associations were found for a wide variety of gaseous and particulate pollutants, especially for peak O₃. Using joint regressions on peak O₃ with various other pollutants, we found that the combined responses were insensitive to the specific other pollutant selected. We saw no systematic differences according to particle size or chemistry. In general, the associations between daily mortality and air pollution depended on the pollutant or the PM metric, the type of collection filter used, and the location of sampling. Although peak O₃ seemed to exhibit the most consistent mortality responses, this finding should be confirmed by analyzing separate seasons and other time periods.     

Characterization of Fine Particulate Matter Produced by Combustion of Residual Fuel Oil

ABSTRACT

Combustion experiments were carried out on four different residual fuel oils in a 732-kW boiler. PM emission samples were separated aerodynamically by a cyclone into fractions that were nominally less than and greater than 2.5 |j.m in diameter. However, examination of several of the samples by computer-controlled scanning electron microscopy (CCSEM) revealed that part of the PM_2.5 fraction consists of carbonaceous cenospheres and vesicular particles that range up to 10 |j.m in diameter. X-ray absorption fine structure (XAFS) spectroscopy data were obtained at the S, V, Ni, Fe, Cu, Zn, and As K-edges and at the Pb L-edge. Deconvolution of the X-ray absorption near edge structure (XANES) region of the S spectra established that the dominant molecular forms of S present were sulfate (26-84% of total S) and thiophene (13-39% of total S). Sulfate was greater in the PM_2.5 samples than in the PM₂₅₊ samples. Inorganic sulfides and elemental sulfur were present in lower percentages. The Ni XANES spectra from all of the samples agreed fairly well with that of NiSO₄, while most of the V spectra closely resembled that of vanadyl sulfate (VO?SO₄?xH₂O). The other metals investigated (i.e., Fe, Cu, Zn, and Pb) also were present predominantly as sulfates. Arsenic was present as an arsen-ate (As⁺⁵). X-ray diffraction patterns of the PM_2.5 fraction exhibit sharp lines due to sulfate compounds (Zn, V, Ni, Ca, etc.) superimposed on broad peaks due to amorphous carbons. All of the samples contain a significant organic component, with the loss on ignition (LOI) ranging from 64 to 87% for the PM_2.5 fraction and from 88 to 97% for the PM_2.5+ fraction. Based on ¹³C nuclear magnetic resonance (NMR) analysis, the carbon is predominantly condensed in graphitic structures. Aliphatic structure was detected in only one of seven samples examined.     

Pilot-Scale Demonstration of an Innovative Treatment for Vapor Emissions

ABSTRACT

Researchers from the National Renewable Energy Laboratory recently conducted a pilot-scale study at McClellan Air Force Base (AFB) in Sacramento, CA. The objective of the test was to determine the effectiveness of an ambient-temperature, solar-powered photocatalytic oxidation treatment unit for destroying emissions of chlorinated organic compounds from an air stripper. This paper reports test results and discusses applications and limitations of the technology.

A 10-standard-cubic-foot-per-minute (SCFM) (28.3 L/min) slip stream of air from an air stripper at Operative Unit 29-31 at McClellan AFB was passed through a reactor that contained a lightweight, perforated, inert support coated with photoactive titanium dioxide. The reactor faced south and was tilted at a 45° angle from vertical so that the light-activated catalyst received most of the available sunlight. An online portable gas chro-matograph with two identical columns simultaneously analyzed the volatile organic compounds contained in the reactor inlet and outlet air streams. Summa canister grab samples of the inlet and outlet were also collected and sent to a certified laboratory for U.S. Environmental

Protection Agency Method TO-14 analysis and verification of our field analyses. Three weeks of testing demonstrated that the treatment system's destruction and removal efficiencies (DREs) are greater than 95% at 10 SCFM with UV intensities at or greater than 1.5 milliwatts/square centimeter (mW/cm²). DREs greater than 95% at 20 SCFM were obtained under conditions where UV irradiation measured at or greater than 2 mW/cm². In Sacramento, this provided 6 hours of operation per clear or nearly clear day in April. A solar tracking system could extend operating time. The air stream also contained trace amounts of benzene. We observed no loss of system performance during testing.     

An Intelligent Emissions Controller for Fuel Lean Gas Reburn in Coal-Fired Power Plants

ABSTRACT

The application of artificial intelligence techniques for performance optimization of the fuel lean gas reburn (FLGR) system is investigated. A multilayer, feedforward artificial neural network is applied to model static nonlinear relationships between the distribution of injected natural gas into the upper region of the furnace of a coal-fired boiler and the corresponding oxides of nitrogen (NO_x) emissions exiting the furnace. Based on this model, optimal distributions of injected gas are determined such that the largest NO_x reduction is achieved for each value of total injected gas. This optimization is accomplished through the development of a new optimization method based on neural networks. This new optimal control algorithm, which can be used as an alternative generic tool for solving multidimensional nonlinear constrained optimization problems, is described and its results are successfully validated against an off-the-shelf tool for solving mathematical programming problems. Encouraging results obtained using plant data from one of Commonwealth Edison's coal-fired electric power plants demonstrate the feasibility of the overall approach.

Preliminary results show that the use of this intelligent controller will also enable the determination of the most cost-effective operating conditions of the FLGR system by considering, along with the optimal distribution of the injected gas, the cost differential between natural gas and coal and the open-market price of NO_x emission credits. Further study, however, is necessary, including the construction of a more comprehensive database, needed to develop high-fidelity process models and to add carbon monoxide (CO) emissions to the model of the gas reburn system.     

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.     

Estimates of Particle Hygroscopicity during the Southeastern Aerosol and Visibility Study

ABSTRACT

Aerosol water content was determined from relative humidity controlled optical particle counter (ASASP-X) size distribution measurements made during the Southeastern Aerosol and Visibility Study (SEAVS) in the Great Smoky Mountains National Park during summer 1995. Since the scattering response function of the ASASP-X is sensitive to particle refractive index, a technique for calibrating the ASASP-X for any real refractive index was developed. A new iterative process was employed to calculate water mass concentration and wet refractive index as functions of relative humidity. Experimental water mass concentrations were compared to theoretically predicted values assuming only ammonium sulfate compounds were hygroscopic. These comparisons agreed within experimental uncertainty. Estimates of particle hygroscopicity using a rural aerosol model of refractive index as a function of relative humidity demonstrated no significant differences from those made with daily varying refractive index estimates. Although aerosol size parameters were affected by the assumed chemical composition, forming ratios of these parameters nearly canceled these effects.     

Methodology for Siting Ambient Air Monitors at the Neighborhood Scale

Abstract

In siting a monitor to measure compliance with U.S. National Ambient Air Quality Standards (NAAQS) for par-ticulate matter (PM), there is a need to characterize variations in PM concentration within a neighborhood-scale region to achieve monitor siting objectives. A simple methodology is provided here for the selection of a neighborhood-scale site for meeting either of the two objectives identified for PM monitoring. This methodology is based on analyzing middle-scale (from 100 to 500 m) data from within the area of interest. The required data can be obtained from widely available dispersion models and emissions databases.

The performance of the siting methodology was evaluated in a neighborhood-scale field study conducted in Hudson County, NJ, to characterize the area’s inhalable particulate (PM₁₀) concentrations. Air monitors were located within a 2- by 2-km area in the vicinity of the Lincoln Tunnel entrance in Hudson County. Results indicate the siting methodology performed well, providing a positive relationship between the predicted concentration rank at each site and the actual rank experienced during the field study. Also discussed are factors that adversely affected the predictive capabilities of the model.     

Assessing Sorbents for Mercury Control in Coal-Combustion Flue Gas

Abstract

Sorbent injection for Hg control is one of the most promising technologies for reducing Hg emissions from power-generation facilities, particularly units that do not require wet scrubbers for SO₂ control. Since 1992, EPRI has been assessing the performance of Hg sorbents in pilot-scale systems installed at full-scale facilities. The initial tests were conducted on a 5000-acfm (142-m³/min) pilot baghouse. Screening potential sorbents at this scale required substantial resources for installation and operation and did not provide an opportunity to characterize sor-bents over a wide temperature range.

Data collected in the laboratory and in field tests indicate that sorbents are affected by flue gas composition and temperature. Tests carried out in actual flue gas at a number of power plants also have shown that sorbent performance can be site-specific. In addition, data collected at a field site often are different from data collected in the laboratory, with simulated flue gas mixed to match the major components in the site’s gas. To effectively estimate the costs of Hg sorbent systems at different plants, a measure of sorbent performance in the respective flue gases must be obtained. However, injection testing at multiple facilities with large pilot systems is not practical.

Over the past five years, fixed-bed characterization testing, modeling studies, and bench-scale injection testing have been undertaken to develop a low-cost technique to characterize sorbent performance in actual flue gas and subsequently to project normalized costs for Hg removal prior to full-scale demonstration. This article describes the techniques used and summarizes field-testing results from two plants burning Powder River Basin (PRB) coal for commercial activated carbon and several other sorbent types. Full-scale projections based on the results and data collected on larger-scale systems also are included.