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Determination of Optimal Air Pollution Control Strategies

Citation

Kyan, Chwan Pein (1973) Determination of Optimal Air Pollution Control Strategies. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/H6WX-2303. https://resolver.caltech.edu/CaltechTHESIS:11122019-165102707

Abstract

One of the important environmental problems facing urban officials today is the selection and enforcement of air pollutant emission control measures. These measures take two forms: long-term controls (multi-year legislation, such as the Federal new car emission standards through 1976) and short-term controls (action taken over a period of hours to days to avoid an air pollution episode). What is required for each form of control is a methodology for the systematic determination of the "best" strategy from among all those possible. In this thesis, a general theoretical framework for the determination of optimal air pollution control strategies is presented for both long-term and real-time controls.

For the long-term control problem, it is assumed that emission control procedures are changed on a year-to-year basis. The problem considered is to determine the set of control measures that minimizes the total cost of control while maintaining specified levels of air quality each year. It is assumed that an airshed model exists which is capable of predicting pollutant concentrations as a function of source emissions in the airshed. Both single-year and multi-year problems are treated. Computational methods are developed based on mathematical programming techniques. The theory and computational methods developed are applied to the evaluation of long-term air pollution control strategies for the Los Angeles basin. Optimal strategies for the control of carbon monoxide, nitrogen dioxide and ozone for 1973 to 1975 in the Los Angeles basin have been obtained.

The problem of determining real-time (short-term) air pollution control strategies for an urban airshed is posed as selecting those control measures from among all possible such that air quality is maintained at a certain level over a given time period and the total control imposed is a minimum. The real-time control is based on meteorological predictions made over a several hour to several day period. A computational algorithm is developed for solving the class of control problems that result.

Typical control measures include restrictions on the number of motor vehicles allowed on a freeway, reduced operation of power plants, and substitution of low emission fuel (e.g. natural gas) for high emission fuel (e.g. coal) in power plants. The control strategy is assumed to be enforced over a certain period, say, one hour, based on meteorological predictions made at the beginning of the period. The strategy for each time period could be determined by an air pollution control agency by means of a computer implementing the algorithm presented. The theory is applied to a hypothetical study of implementation of the optimal control on September 29, 1969 in the Los Angeles basin.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:Chemical Engineering
Degree Grantor:California Institute of Technology
Division:Chemistry and Chemical Engineering
Major Option:Chemical Engineering
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Seinfeld, John H.
Thesis Committee:
  • Seinfeld, John H.
Defense Date:14 May 1973
Funders:
Funding AgencyGrant Number
CaltechUNSPECIFIED
NSFUNSPECIFIED
John A. McCarthy FoundationUNSPECIFIED
Earle C. Anthony FoundationUNSPECIFIED
Li Ming Endowment FundUNSPECIFIED
Record Number:CaltechTHESIS:11122019-165102707
Persistent URL:https://resolver.caltech.edu/CaltechTHESIS:11122019-165102707
DOI:10.7907/H6WX-2303
Related URLs:
URLURL TypeDescription
https://doi.org/10.1021/i160036a012DOIArticle adapted for Proposition III.
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:13572
Collection:CaltechTHESIS
Deposited By: Melissa Ray
Deposited On:14 Nov 2019 01:33
Last Modified:21 Dec 2019 01:25

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