東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Stream water quality management: A ...

Ali, Md. Kamar.

Linked to FindBook

Google Book

Amazon

博客來

Stream water quality management: A stochastic mixed-integer programming model.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Stream water quality management: A stochastic mixed-integer programming model./
Author:	Ali, Md. Kamar.
Description:	108 p.
Notes:	Chairs: Tim T. Phipps; Jerald J. Fletcher.
Contained By:	Dissertation Abstracts International63-06A.
Subject:	Economics, Agricultural. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3055903
ISBN:	0493710469

Stream water quality management: A stochastic mixed-integer programming model.
Ali, Md. Kamar.

Stream water quality management: A stochastic mixed-integer programming model. - 108 p.

Chairs: Tim T. Phipps; Jerald J. Fletcher.

Thesis (Ph.D.)--West Virginia University, 2002.

The MIP model solves for the location and maximum capacity of treatment plants to be built throughout the watershed which will provide the optimal level of treatment throughout the year.

ISBN: 0493710469Subjects--Topical Terms:

626648
Economics, Agricultural.

Stream water quality management: A stochastic mixed-integer programming model.
LDR:03464nam 2200337 a 45 001 936487
005 20110510
008 110510s2002 eng d
020 $a 0493710469
035 $a (UnM)AAI3055903
035 $a AAI3055903
040 $a UnM $c UnM
100 1 $a Ali, Md. Kamar. $3 1260177
245 1 0 $a Stream water quality management: A stochastic mixed-integer programming model.
300 $a 108 p.
500 $a Chairs: Tim T. Phipps; Jerald J. Fletcher.
500 $a Source: Dissertation Abstracts International, Volume: 63-06, Section: A, page: 2318.
502 $a Thesis (Ph.D.)--West Virginia University, 2002.
520 $a The MIP model solves for the location and maximum capacity of treatment plants to be built throughout the watershed which will provide the optimal level of treatment throughout the year.
520 $a Water quality management under the US Environmental Protection Agency's watershed approach requires that water quality standards be maintained throughout the year. The main purpose of this research was to develop a methodology that incorporates inter-temporal variations in stream conditions through statistical distributions of pollution loading variables. This was demonstrated through a general stochastic cost minimization mixed-integer linear programming (MIP) model. Traditional approaches for addressing variability in stream conditions are unlikely to satisfy the assumptions on which these methodologies are founded or are inadequate in addressing the problem correctly when distributions are not normal.
520 $a The proposed methodology involves estimating the parameters of the distribution of pollution loading variables from simulated data and using those parameters to regenerate a suitable number of random observations in the optimization process such that the new data preserve the same distribution parameters. All stream segments in the watershed are assigned the same randomly drawn value in a particular draw to reflect the high spatial correlation in loadings between segments. The methodology was tested with water quality data for the Paint Creek watershed in West Virginia. The objective of the empirical model was to minimize costs for implementing pH TMDLs for the watershed by determining the level of treatment required to attain water quality standards under stochastic stream conditions. The output of the model provided total minimum costs for treatment and selection of the spatial pattern of the least-cost technologies for treatment. To minimize costs, the model utilized a spatial network of streams in the watershed, which provides opportunities for reducing costs by trading pollution control among different sources. The results were used to estimate the costs attributable to intertemporal variations and the costs of different settings for the ‘margin of safety’.
520 $a The application of the methodology, however, is not limited to the estimation of TMDL implementation costs. For example, it could be utilized to estimate costs of antidegradation policies for water quality management and other watershed management issues.
590 $a School code: 0256.
650 4 $a Economics, Agricultural. $3 626648
650 4 $a Engineering, Environmental. $3 783782
650 4 $a Hydrology. $3 545716
690 $a 0388
690 $a 0503
690 $a 0775
710 2 0 $a West Virginia University. $3 1017532
773 0 $t Dissertation Abstracts International $g 63-06A.
790 $a 0256
790 1 0 $a Fletcher, Jerald J., $e advisor
790 1 0 $a Phipps, Tim T., $e advisor
791 $a Ph.D.
792 $a 2002
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3055903