Minimizing the cost of air pollution control

Cass, Glen R.; McRae, Gregory J.

doi:10.1021/es00089a608

Cited by 20 publications

(9 citation statements)

References 16 publications

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“…Recent studies suggest that joint receptor-dispersion modeling efforts have had a 27:1 payoff in cost avoidance associa ted with misdirected control strategies. 2 Results from these studies indicate thait quantitative estimates of source contributions to TSP, fine and coarse mode mass can be identified with relative; uncertainties ranging from ±5 to 30%. 3 Receptor models are typically able to apportion between 80 and 90% of the measured background or urban aerosol mass to specific sources, chemical classes, or source groups (i.e., the crustal source group comprised of paved road dust, windblown soils, and other sources of similar composition).…”

Section: November 1982mentioning

confidence: 92%

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Particulate Dispersion Model Evaluation: A New Approach Using Receptor Models

Core¹,

Cooper²,

Hanrahan³

1982

Journal of the Air Pollution Control Association

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Section: November 1982mentioning

confidence: 92%

“…2 This paper focuses on the advantages and limitations of receptor and sourceoriented models. New approaches useful in evaluating the performance of particulate dispersion models are proposed and case studies discussing the joint application of these techniques are presented.…”

mentioning

confidence: 99%

Particulate Dispersion Model Evaluation: A New Approach Using Receptor Models

Core¹,

Cooper²,

Hanrahan³

1982

Journal of the Air Pollution Control Association

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“…1 Emission inventories are designed to systematically quantify the temporal and spatial distributions of the fluxes of primary pollutants and secondary pollutant precursors emitted by significant sources. This places emission inventories at the foundation of today's AQM strategies, and significant errors in inventories can, therefore, lead to the adoption of strategies that protect human health and the environment less effectively than possible.…”

Section: Introductionmentioning

confidence: 99%

Air Emission Inventories in North America: A Critical Assessment

Miller

Hidy²,

Hales³

et al. 2006

Journal of the Air & Waste Management Association

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Although emission inventories are the foundation of air quality management and have supported substantial improvements in North American air quality, they have a number of shortcomings that can potentially lead to ineffective air quality management strategies. Major reductions in the largest emissions sources have made accurate inventories of previously minor sources much more important to the understanding and improvement of local air quality. Changes in manufacturing processes, industry types, vehicle technologies, and metropolitan infrastructure are occurring at an increasingly rapid pace, emphasizing the importance of inventories that reflect current conditions. New technologies for measuring source emissions and ambient pollutant concentrations, both at the point of emissions and from remote platforms, are providing novel approaches to collecting data for inventory developers. Advances in information technologies are allowing data to be shared more quickly, more easily, and processed and compared in novel ways that can speed the development of emission inventories. Approaches to improving quantitative measures of inventory uncertainty allow air quality management decisions to take into account the uncertainties associated with emissions estimates, providing more accurate projections of how well alternative strategies may work. This paper discusses applications of these technologies and techniques to improve the accuracy, timeliness, and completeness of emission inventories across North America and outlines a series of eight recommendations aimed at inventory developers and air quality management decision-makers to improve emission inventories and enable them to support effective air quality management decisions for the foreseeable future. INTRODUCTIONAir quality management (AQM) in North America focuses on ensuring that concentrations of compounds in the ambient air are below the levels that are considered harmful to human health or the environment. Strategies developed to achieve these standards are based on reduction of emissions from specific source classes. The effectiveness of this approach depends on an accurate understanding of the relative contributions of the sources to ambient atmospheric pollution. An adequate knowledge of emissions sources and associated fluxes, both before and after emission controls are adopted, has long been recognized as a requirement for designing cost-effective air pollution control strategies. 1 Emission inventories are designed to systematically quantify the temporal and spatial distributions of the fluxes of primary pollutants and secondary pollutant precursors emitted by significant sources. This places emission inventories at the foundation of today's AQM strategies, and significant errors in inventories can, therefore, lead to the adoption of strategies that protect human health and the environment less effectively than possible. Emission inventory errors can be enormously expensive by requiring installation and operation of air pollution controls beyond the ...

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“…In the past few decades, researchers have reported various studies that describe methods for developing cost-effective control strategies for the emission least cost (ELC) and the ambient least cost (ALC) control problems. [1][2][3][4][5][6][7][8][9][10][11] These studies are based on a number of traditional optimization techniques, such as dynamic programming, integer programming, linear programming (LP), and mixed-integer programming (MIP). There is considerable difficulty, however, when these techniques are used for problems with pollutants that exhibit nonlinear atmospheric chemistry.…”

Section: Introductionmentioning

confidence: 99%

Conjunctive Use of Models to Design Cost-Effective Ozone Control Strategies

Brill

Ranjithan

2006

Journal of the Air & Waste Management Association

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The management of tropospheric ozone (O 3 ) is particularly difficult. The formulation of emission control strategies requires considerable information including: (1) emission inventories, (2) available control technologies, (3) meteorological data for critical design episodes, and (4) computer models that simulate atmospheric transport and chemistry. The simultaneous consideration of this information during control strategy design can be exceedingly difficult for a decision-maker. Traditional management approaches do not explicitly address cost minimization. This study presents a new approach for designing air quality management strategies; a simple air quality model is used conjunctively with a complex air quality model to obtain low-cost management strategies. A simple air quality model is used to identify potentially good solutions, and two heuristic methods are used to identify cost-effective control strategies using only a small number of simple air quality model simulations. Subsequently, the resulting strategies are verified and refined using a complex air quality model. The use of this approach may greatly reduce the number of complex air quality model runs that are required. An important component of this heuristic design framework is the use of the simple air quality model as a screening and exploratory tool. To achieve similar results with the simple and complex air quality models, it may be necessary to "tweak" or calibrate the simple model. A genetic algorithm-based optimization procedure is used to automate this tweaking process. These methods are demonstrated to be computationally practical using two realistic case studies, which are based on data from a metropolitan region in the United States.

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Minimizing the cost of air pollution control

Cited by 20 publications

References 16 publications

Particulate Dispersion Model Evaluation: A New Approach Using Receptor Models

Particulate Dispersion Model Evaluation: A New Approach Using Receptor Models

Air Emission Inventories in North America: A Critical Assessment

Conjunctive Use of Models to Design Cost-Effective Ozone Control Strategies

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