The Need for Representative Ambient Air Carbon Monoxide Sampling

Recent investigations have indicated that ambient air CO measurements may not reflect population exposure to CO. The lack of correlation may be due to improper siting of CO instruments, improper interpretation of air quality data, or both. Studies of population carboxy-hemoglobin levels are evaluated and compared with ambient air data.,

No significant correlation was found between median population COHb levels and reductions in CO concentrations required to meet ambient air standards when calculations used to estimate reductions were based on the second highest 8 hour average. However, calculated reductions based on annual average concentrations and a trend analysis technique correlated significantly with COHb levels in five cities from which both CAMP and COHb data were available.

Studies to determine the nature of the relationship between ambient air CO concentrations and population COHb levels are needed. The differences between the Occupational Safety and Health Act Regulations and the National Ambient Air Standards for carbon monoxide should be scrutinized to determine if a redefinition of the standards or their applicability is warranted. A reevaluation of the controls necessary to make reductions in population COHb burden may be necessary.     

Characterization of Particulates from Power Plants

The emphasis on participate control from industrial processes has been shifted recently towards fine particulates, having diameters less than 3 microns. There exists an urgent need for more scientific information of fine particle characterization.^1,2 Coal and oil fired power plants are among the largest anthropogenic point sources of particulate matter.³ Limited knowledge is available on particle size distribution and trace metal composition in power plant emissions.^4-7 The morphological properties of particle emissions have been largely neglected. In this report we present some information on particle characteristics for an oil-fired and coal-fired power plant.     

An Overview of EPA’s Quality Assurance Program

Quality Assurance has been defined in many different ways to express concern for the “quality” of the output of some operation or function. For the purpose of this presentation, quality assurance is defined as the sum of those activities within an environmental monitoring or measurement program which are primarily for the purpose of documenting the precision, accuracy, representativeness, and completeness of the data that are produced. Further, we view quality assurance as a management tool which encompasses both administrative and technical functions which generally fall into three categories: (1) Prevention which includes those activities carried out prior to the proper placement and operation of the measurement system; (2) Evaluation which includes the continuous overview and checks on the performance of the systems and operators; and (3) Correction which includes changes made to improve or stabilize the capability and performance of the system.     

Heavy Duty Diesel Particulate Emission Factors

For the past several years, EPA has been measuring particulate emissions from a variety of heavy-duty diesel engines through contracts with Southwest Research Institute. Particulate emissions samples have been collected using an exhaust splitter to divert a fraction of the engine exhaust into a standard dilution tunnel. A small fraction of the diluted exhaust from the tunnel is pulled through a filter from which particulate mass and, in some cases, organic content of the particulate is determined. This paper discusses the sampling system and gives particulate emission factors that have been computed from truck and bus fuel consumption data as well as average truck and bus speed data from New York and Los Angeles (freeway and nonfreeway usage). Average particulate emission test results (steady state tests) for 2-stroke engines were 4.74 g/kg fuel and for 4-stroke engines were 2.64 g/kg fuel. Using average particulate emissions results, a particulate emission factor range of 0.8 to 1.3 g/km was computed. Nationwide diesel particulate emissions were calculated to be 88,000 metric tons per year.     

R&D Activities,Operating Experience Discussed at Fabric Alternatives Forum

ABSTRACT

Previously reported volatile organic compounds (VOC) radiocarbon (¹⁴C) measurements for 1992 summertime Atlanta, GA, have been compared with chromatographic data and emissions inventory predictions. The chromatographic approach that was used provided a more comprehensive VOC characterization than typically achieved, and the emissions inventory was research-grade level (date-, site-, and time-specific). The comparisons are in general agreement that biogenic emissions contribute only modestly (<10%) to the VOC content of the particular ambient samples that were collected and measured. The choices of sampling site (near city-center) and times (early morning and late evening) are major influences on the results, which consequently should not be regarded as representing the average VOC biogenic impact for the Atlanta area.     

Blast Furnace Casthouse Control Technology and Recent Emissions Test Data

By the fall of 1980, three new and seven retrofit casthouse emission control systems had been installed in North America. A number of prototype systems are being evaluated by steel companies and regulatory agencies. By 1981, the U.S. steel industry had made commitments to install controls on at least 58 additional casthouses in the U.S.

This paper describes the current status of casthouse control technology in the U.S. and Canada as of the spring of 1981, addresses future trends, and presents emissions test data collected in 1980 and 1981. Mass emissions and inhalable particulate test results from sampling at DOFASCO’s No. 3 blast furnace casthouse are presented. Roof monitor visible emissions data are also provided to describe performance of installed and demonstration systems including three J&L plants, Wheeling-Pittsburgh and Bethlehem Steel, and Inland’s new furnace.     

A Pilot Indoor-Outdoor Study of Organic Particulate Matter and Particulate Mutagenicity

Abstract

Many large metropolitan areas experience elevated concentrations of ground-level ozone pollution during the summertime “smog season”. Local environmental or health agencies often need to make daily air pollution forecasts for public advisories and for input into decisions regarding abatement measures and air quality management. Such forecasts are usually based on statistical relationships between weather conditions and ambient air pollution concentrations. Multivariate linear regression models have been widely used for this purpose, and well-specified regressions can provide reasonable results. However, pollution-weather relationships are typically complex and nonlinear—especially for ozone—properties that might be better captured by neural networks. This study investigates the potential for using neural networks to forecast ozone pollution, as compared to traditional regression models. Multiple regression models and neural networks are examined for a range of cities under different climate and ozone regimes, enabling a comparative study of the two approaches. Model comparison statistics indicate that neural network techniques are somewhat (but not dramatically) better than regression models for daily ozone prediction, and that all types of models are sensitive to different weather-ozone regimes and the role of persistence in aiding predictions.     

Rotary Kiln Incineration III. An Indepth Study—Kiln Exit/Afterburner/Stack Train and Kiln Exit Pattern Factor Measurements During Liquid CCl4 Processing

Temperature and stable species concentration data are presented from various locations within a full-scale rotary kiln incinerator firing natural gas/carbon tetrachloride/air. The data are being collected as part of a cooperative program involving university, industry and government participation. The overall goal of the program is to develop a more sophisticated understanding of and a predictive capability for rotary kiln and afterburner performance as influenced by basic design and operational parameters. Non-uniformities in stable species and temperature exist for this particular kiln, at the kiln exit, under certain operating conditions. Flow perturbations from within the kiln were found to persist into the afterburner, but not into the stack. High destruction and removal efficiencies (DRE’s) were achieved under the operating conditions of these tests through adequate secondary combustion processing.     

Rotary Kiln Incineration IV. An Indepth Study— Kiln Exit,Transition and Afterburner Sampling During Liquid CCl4 Processing

Detailed temperature and stable species concentration data are presented from the kiln exit, transition section and afterburner of a full-scale incinerator facility firing natural gas/carbon tetrachloride/air. The data are collected as part of a cooperative program involving university, industry and government participation. The overall goal of the program is to develop an understanding and predictive capability for rotary kiln and afterburner performance as influenced by basic design and operational parameters. The data demonstrate that nonuniformities in stable species and temperature exist, under certain operating conditions, at the kiln exit in the vertical direction only. Measurements from the transition section indicate that non-uniformities may exist within this region under certain operating conditions. Flow perturbations from within the kiln can persist into the afterburner, although the degree of nonuniformity is substantially reduced compared to either the kiln or transition sections. High destruction and removal efficiencies were achieved under all operating conditions of these tests through a combination of kiln and secondary combustion processing.     

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.