Determination of optimal air pollution control strategies

Seinfeld, John H.; Kyan, Chwan P.

doi:10.1016/0038-0121(71)90028-0

Cited by 32 publications

(12 citation statements)

References 33 publications

(35 reference statements)

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“…A related approach is based on the integration of the diffusion equation over an arbitrary fixed volume, as proposed initially by Seinfeld and Kyan (1971). The airshed is divided into an array of L cells, of equal or unequal volumes.…”

Section: (4)mentioning

confidence: 99%

Modeling Air Quality in Urban Areas: A Cell‐Based Statistical Approach

Guldmann¹,

Kim

2001

Geographical Analysis

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Statistical regression models are presented that explain the observed variations, across urban areas, in the concentrations of two major pollutants, ozone and carbon monoxide. Model specijkatwn and estimation are based on an explicit and new spatial framework derived from the theoretical concept of well-mixed cells, whereby the basic Fickian system of difusion equations is integrated over the regional space partitioned into a grid of large cells. The Concentration in each cell results from the balance of pollutant flows into and out of this cell and of pollutant emissions and removal within that cell, and i s expressed as the sum of two concentration contributions: (1) the local efect, dependent upon pollution-related factors around the measuring station, and (2) the regional g e d , dependent upon pollutant flows originating outside the local area. A large database is developed, making extensive use of CIS technology, to spatially relate such data as pollution measurements, meteorological factors, landuse characteristics, census socioeconomic data, and major highway network characteristics. The results con$rm the appropriateness of the well-mixed cell framework, are in line with general knowledge regarding the determinants of ozone and carbon monoxide concentrations, and clarify the role of transportation, residential fuel use, economic activities, natural environments, and meteorological factors such as temperature and solar radiation. About 50 percent of the variations in concentrations are explained by these models. Several areas of further research are outlined. a 31 percent decrease in aggregate pollution emissions, including decreases of 31 percent for carbon monoxide (CO), 37 percent for sulfur dioxide (SO,), 71 percent for particulate matter (PM,,), 98 percent Financial support from the Urban Affairs and Urban Assistance Program, Ohio Board of Regents, is gratefully acknowled ed. Many thanks are due to Phil Viton, Steve Gordon, and three anonymous referees of this journal for hekful comments and suggestions on earlier drafts. Jean-Michel Guldmann is a professor in the Jean-Michel Guldmann and Hag-Ye01 Kim / 157for lead (Pb), and 42 percent for volatile organic compounds (VOCs), and an increase of 17 percent for nitrogen oxides (NO,). More striking, these improvements have occurred while the population grew by 33 percent, the vehicle miles traveled (VMT) by 140 percent, and the gross domestic product (GDP) by 147 percent. However, this aggregate assessment masks the fact that, in 1999, 150 million tons of air pollution were released into the air, and approximately 62 million people lived in counties with monitored concentrations above the primary standards (those designed to protect public health) for one or more of the six principal pollutants, particularly ozone in the Northeast, California, some rural areas, and some national parks. Also, despite a 60-80 percent decline in per-car emissions since the 1960s, total emissions from mobile sources have not decreased proportionately, because more frequent an...

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Section: (4)mentioning

confidence: 99%

Modeling Air Quality in Urban Areas: A Cell‐Based Statistical Approach

Guldmann¹,

Kim

2001

Geographical Analysis

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“…The model we will employ is based on dividing the airshed into an array of well-mixed cells (Ulbrich, 1968;Reiquam, 1970;Seinfeld and Kyan, 1971;MacCracken et al, 1971 A dynamic material balance for species i in cell k is…”

Section: Dynamic Airshed Modelsmentioning

confidence: 99%

Real‐time control of air pollution

Kyan

Seinfeld

1973

AIChE Journal

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The problem of determining real-time (episode) 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 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. The theory is applied to a hypothetical study of the effect of implementation of the optimal control on September 29, 1969, in the Los Angeles basin. CHWAN SCOPEOne 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. The development of a theory for setting long-term controls is in progress Kyan, 1971, 1972;Trijonis, 1972). The objective of this paper is the presentation of a theory for the determination of short-term, or real-time, control for urban air pollution.The determination of real-time controls requires a number of elements: (1) a dynamic mathematical model of atmospheric pollutant concentrations, including meteorological variations and atmospheric chemistry, (2) a contaminant emissions inventory for the airshed, including the temporal and spatial variation of all primary pollutant emissions, (3) an enumeration of all feasible control strategies, and (4) some measure(s) of air quality. Given these elements, the problem is posed as choosing the set of control measures which just maintain air quality and do so at the minimum control level.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 (for example, natural gas) for high emission fuel (for example, 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. CONCLUSIONS AND SIGNIFICANCEThis paper consists of three parts: (1) the formulation of a general real-time air pollution control problem, (2) the development of a computational algorithm for solving the class of control problems which result, and (3) an application of the theory to a hypothetical study of the effect of implementation of the opimal control on September 29, 1969, in the Los Angeles basin. It is assumed that a mathematical model of pollutant b...

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“…If weather predictions indicate the possibility of forthcoming adverse meteorological situations, e.g., low inversion, light winds, heavy rain etc., then control measures can be taken by reducing the emission sources during the affected period from the viewpoint of air quality standard (Seinfeld et a/. 1 …”

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A distributed optimal control problem of gaseous pollutants by transient air pollution source

Dhar

Sinha

1992

International Journal of Environmental Studies

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Here we have investigated theoretically the distribution of horizontally homogeneous pollutant concentration, when the fixed emission sources are taken as controls with respect to time, such that the meansquare deviation of the pollutant concentration from the desired level (air quality standard) is minimised within the critical period over the entire boundary layer under impending adverse meteorological situation. KEY WORDS: Pollution, optimal control, air quality standard, point source. NOMENCLATURE X -horizontally integrated concentration distribution of the pollutant. C -desired level of the concentration (air quality standard) h -height of boundary layer / -height of the source u(t) -volume control (elevated source) co(t) -boundary control (area source) q { -horizontally integrated concentration of pollutant in the middle point of the interval s -length of interval into which the segment (0, h) is divided in n equal parts T -total time of the process t -time h -switching time Ti -switching time (dimensionless) = ktJS 2 -T_ __ -total time (dimensionless)_f= (kt/S 1 ) A 2r Ax -constraints of ¥I(T) where A t = Aj&lk (i = 1, 2)

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Determination of optimal air pollution control strategies

Cited by 32 publications

References 33 publications

Modeling Air Quality in Urban Areas: A Cell‐Based Statistical Approach

Modeling Air Quality in Urban Areas: A Cell‐Based Statistical Approach

Real‐time control of air pollution

A distributed optimal control problem of gaseous pollutants by transient air pollution source

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