Appendix E
Comparing Visibility Control Strategies

Regional haze arises from the combined emissions of many sources. As discussed in Appendix D, control technologies are available to reduce the emissions from individual sources. It often is not clear which combination of controls can improve visibility most effectively.

The task of control strategy synthesis can be considered at two levels. First, the design effort is directed at defining the range of technically feasible solutions to the problem at hand. Second, the most attractive of the feasible solutions is adopted in the form of an actual control program.

A technically feasible solution consists of a combination of control equipment or regulations that would achieve visibility at least as good as that required by the goals set for the control program. Because full restoration of natural visibility might not be feasible, a realistic goal must be chosen initially if the following analysis procedures are to be of any use at all. Because there are thousands of contributing sources, there usually are many combinations of controls that could achieve the same visibility improvements and some that could not achieve the desired visibility improvements. The purpose of identifying solutions as feasible or infeasible is to clarify for decision-makers the group of solutions that could achieve desired air quality improvements. Source apportionment models, such as those discussed in Chapter 5, provide the technical basis for testing candidate control programs to determine whether they would solve the problem at hand.

From the group of feasible solutions, a solution must be chosen. The chosen solution usually is viewed as more attractive than other potential



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Protecting Visibility in National Parks and Wilderness Areas Appendix E Comparing Visibility Control Strategies Regional haze arises from the combined emissions of many sources. As discussed in Appendix D, control technologies are available to reduce the emissions from individual sources. It often is not clear which combination of controls can improve visibility most effectively. The task of control strategy synthesis can be considered at two levels. First, the design effort is directed at defining the range of technically feasible solutions to the problem at hand. Second, the most attractive of the feasible solutions is adopted in the form of an actual control program. A technically feasible solution consists of a combination of control equipment or regulations that would achieve visibility at least as good as that required by the goals set for the control program. Because full restoration of natural visibility might not be feasible, a realistic goal must be chosen initially if the following analysis procedures are to be of any use at all. Because there are thousands of contributing sources, there usually are many combinations of controls that could achieve the same visibility improvements and some that could not achieve the desired visibility improvements. The purpose of identifying solutions as feasible or infeasible is to clarify for decision-makers the group of solutions that could achieve desired air quality improvements. Source apportionment models, such as those discussed in Chapter 5, provide the technical basis for testing candidate control programs to determine whether they would solve the problem at hand. From the group of feasible solutions, a solution must be chosen. The chosen solution usually is viewed as more attractive than other potential

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Protecting Visibility in National Parks and Wilderness Areas solutions for some particular set of reasons: it is either the least expensive solution or the least burdensome solution from an administrative point of view. If the objective is to choose an economically attractive solution rather than an administratively convenient one, then the control-strategy design team has certain tools available to it that can be used to help identify cost-efficient control schemes. The tools are discussed below. The search for feasible solutions to a regional visibility control problem begins by constructing a mathematical model for the regional visibility problem. This model is on a larger conceptual scale than has been discussed earlier. At the core of the regional model is one of the emissions-to-air-quality models described in Chapter 5 for computing source contributions to ambient pollutant levels. Adjoined to this emissions-to-air-quality model is a model for translating air pollutant levels into effects on visibility. Then these two models are subjected to model verification tests over a base-case historical period when meteorological conditions, emissions, ambient pollutant concentrations, and atmospheric optical properties are known. The purpose is to demonstrate that the chosen models can produce accurate results in the presence of well-defined inputs and to demonstrate that cause and effect relationships in that particular airshed are understood. Following confirmation of the models' technical performance, the available emissions controls that could be applied to the problem are used to compute the emission rates from the sources that would prevail in the presence of each of the controls. Then, the effect of those controls on air quality is tested by application through the completed air quality and visibility models. Because the number of controls that must be tested for their effect on air quality and visibility is potentially quite large, care must be taken to structure an efficient search for feasible solutions. It usually is not practical to re-run an elaborate environmental model hundreds of times to learn about the properties of each control technique separately. Instead, for those models that are linear in emissions (e.g., most of the models with simplified chemistry), it is possible to perform the control evaluation without rerunning the models in their entirety. Transfer coefficients can be calculated for the linear models that state the pollutant concentration or light extinction increment at each receptor site per ton of emissions per day from each source. The transfer coefficients depend on meteorological conditions, the spatial location of the sources

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Protecting Visibility in National Parks and Wilderness Areas and receptors, and the age of the air parcels but they do not depend on emission rates. Therefore, they can be used outside the model to compute the future effect of each source in the presence of visibility controls. The transfer coefficients for each source, are multiplied by the new controlled emission rate from the source. Then, overall pollutant and light-extinction levels in the presence of a set of future controls can be synthesized quickly by adding the incremental effects of all controlled and uncontrolled sources on top of an estimate of background air quality and light extinction. The search for feasible solutions (e.g., sufficient combinations of controls) can be pursued quickly by exhaustive enumeration or with the assistance of linear programming techniques. For nonlinear environmental models (e.g., photochemical models for secondary particle formation), the search for feasible combinations of controls must be conducted by perturbing the model, either one source at a time, or by grouping controls with similar characteristics for evaluation (see Russell et al., 1988b). If the spatial distribution of the sources is thought to be less important than the overall level of emissions from the sources in a region, then the feasible control solutions can be approximated by progressively reducing all emissions of each chemical type by increments until a level of total emissions is found that is compatible with a solution to the visibility problem. (See Trijonis, 1972, and 1974 for examples of analysis with nonlinear models for ozone control). The least expensive solution to a regional air quality problem in many cases can be found by examination of the group of feasible solutions. In the case of problems that are described by linear environmental models, cost-effectiveness indexes can be constructed from available data that describe the incremental cost per unit of air quality improvement that is attributed to each available control technique. The indexes (in micrograms per cubic meter of pollutant concentration improvement per million dollars per year spent on a particular type of control, or extinction coefficient increment per unit cost) can be constructed from the output of a linear environmental model by multiplying the air quality transfer coefficient (as defined above, e.g., in micrograms per cubic meter per ton per day emitted) times the cost of control at that source (tons per day abated per dollar spent). These cost-effectiveness indexes often can be rank-ordered to indicate which controls provide the least expensive way

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Protecting Visibility in National Parks and Wilderness Areas to improve air quality. Then, a number of those controls are selected to achieve a feasible solution. Alternatively, a linear programming cost-minimization calculation can be done to find the least expensive set of controls. The problems can be formulated in several ways; for example, Minimize C = cx, subject to Bx ≥ r, subject to Ax ≤ d, subject to Dx ≤ 1, O ≤ x ≤ 1, where C is the total cost of the control program; x is a vector of control method activity levels (if control measure i is adopted as part of the solution to the problem then xi = 1; if control measure i is not used than xi = 0); c is a vector whose elements state the annual cost of individual control measures if selected for application to the solution; r is a vector of pollutant concentration reduction requirements (or extinction coefficient reduction requirements) at each monitoring site; B is a matrix whose elements indicate the pollutant concentration reduction (or extinction coefficient reduction) at each monitoring site resulting from one unit of each control activity; A is a matrix of resource magnitude (e.g., natural gas) consumed when a control measure is selected; d is a vector of the limits on the physical resources available for the solution of the problem (e.g., limits, if any, on the total amount of natural gas that can be obtained in the region of interest); and D is a matrix of compatibility parameters that prevents the simultaneous application of two conflicting controls on the same source. Usually, costs are stated as equivalent annualized costs of operation in which the capital costs and salvage value (if any) of the control equipment are spread over the life of the equipment by using discounted cash-flow calculations that reflect the capital costs. The objective is to select those controls (x) that minimize the total cost of the overall regional control strategy subject to attaining the required environmental improvement while not consuming resources that are unavailable and without prescribing combinations of controls that work against each other. A large number of feasibility studies have been conducted to show that this method of economic analysis can be performed by using

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Protecting Visibility in National Parks and Wilderness Areas data from actual cases of air pollution control; those studies are summarized by Cass and McRae (1981). A more recent study by Harley et al. (1989) might be particularly relevant to visibility control because it illustrates that a single least-cost control-strategy study can combine the results of receptor-oriented CMB models for source apportionment of primary particles with the results of linear chemical models for secondary sulfate formation. Identification of the least expensive control strategy is more difficult in situations described by nonlinear environmental models than linear models. Nonlinear graphic solutions can be used to find the best strategy in some cases. (See Trijonis (1972, 1974) for a relevant example involving trade-offs between controls for hydrocarbons and for NOx in the case of ozone abatement.) Further research into development of methods for optimizing selection of control programs for nonlinear air quality problems is warranted. Certain economic incentive systems have been proposed as an alternative to the above engineering procedures for finding control programs that are economically attractive. Systems of transferrable licenses in which source owners trade rights among themselves to emit air pollutants can be used to limit total regional pollutant emissions. In one form of a transferrable licenses system, an absolute limit is set on total regional pollutant emissions. The right to emit those pollutants is broken into small increments, and those rights are auctioned to the highest bidder. Those source owners with low control costs will choose to install control equipment rather than pay for expensive emissions licenses. Those with high control costs will bid up the price of emissions licenses rather than install controls. Payments for the licenses can be used then to offset part of the installation costs of those owners that chose control programs over emissions licenses. After initial bidding, licenses to emit can be bought and sold in an open market to facilitate adjustments between source owners. The main advantage of such a system is that source owners can use their expertise in choosing control methods and minimizing total control costs. It is often argued that such market-based systems require that government regulators need less technical information about air quality problems. The notion is probably false. To determine the magnitude of the number of emissions licenses and the geographical extent of trading that

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Protecting Visibility in National Parks and Wilderness Areas will not hinder the pollution control program, the regulator needs to know the science of emissions and air quality. An example of source-apportionment modeling used to establish the proper number of transferrable permit units and to test for anomalous effects of geographic distribution is given by Cass et al. (1982). Although the procedures for selecting an optimal emission control strategy are widely available, it can be argued with some justification that they are seldom used effectively. There are a number of reasons for that. In many cases, legal mandates that carry short-time deadlines (a few months) for analysis of a complex problem simply preclude a careful technical analysis of the problem. Instead, regulatory boards are forced to guess about selecting a control program. In other cases, the human skills needed to analyze the problem are not available to the regulatory agencies. Given the large financial costs to society inherent in regional air pollution control, it is important to address the barriers to finding technically and economically sound solutions to regional visibility problems.

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PROTECTING VISIBILITY ~ NATIONAL PARKS I! not hinder the pollution control program, the regulator needs to ow the science of emissions and air quality. An example of source ~portionment modeling used to establish the proper number of transfer- ble permit units and to test for anomalous effects of geographic distri- ~tion is given by Cass et al. (1982~. Although the procedures for selecting an optimal emission control trategy are widely available, it can be argued with some justification hat they are seldom used effectively. There are a number of reasons for that. In many cases, legal mandates that carry short-time deadlines (a few months) for analysis of a complex problem simply preclucie a careful technical analysis of the problem. Instead, regulatory boards are forced to guess about selecting a control program. In other cases, the human skills needed to analyze the problem are not available to the regulatory agencies. Given the large financial costs to society inherent in regional air pollution control, it is important to address the barriers to finding technically and economically sound solutions to regional visibili- ty problems.