A Multiple Pollutants Trading-Ratio System for River Water Quality Trading

Transcription

1 A Multiple Pollutants Trading-Ratio System for River Water Quality Trading P. H. B. Beiglou, R. Kerachian, M. R. Nikoo and S. T. O. Naeeni Abstract One of the effective approaches for water quality management in river system is trading of discharge permits. In addition to reducing total cost and maintaining water quality standards along the river, trading discharge permits can result in an equitable distribution of costs among dischargers and dischargers would have economic motivation to participate in process of trading discharge permits. In spite of extension of models for trading discharge permits, there are limited works focusing on trading discharge permits considering multiple pollutants simultaneously. In this paper, The Extended Trading-Ratio System (ETRS) proposed by Mesbah et al. (2009) is revised to consider multiple pollutants. The results of applying the proposed methodology in the Zarjub River illustrate its efficiency in river water quality management. Keywords Multiple-pollutants, The Zarjub River, Trading Discharge permits (TDP), Trading Ratio System I I. INTRODUCTION N recent decades, several models have been proposed for trading discharge permits in river systems. Eheart (1980) used effective cost of transferable discharge permit to control BODR5R and compared the obtained results with minimum uniform strategies. He introduced problem of developing a TDP system as a multi-variable optimization problem [1]. Ng and Eheart (2005) investigated effects of TDP on reliability of maintaining water quality. Hung and Shaw (2005) developed a new system named trading-ratio for trading discharge permits. They showed that the trading-ratio system (TRS) can take care of the location effect of a discharge and can achieve the predetermined standards of environmental quality at minimum aggregate abatement costs. [3]. Sarang et al. (2008) investigated TDP for multiple pollutants by presenting an analytic model based on weighting water quality variables [4]. Mesbah et al. (2009 and 2010) developed a model named ETRS by extending the TRS which considers DO as a water quality index [5]- [6]. They did not P. H. B. Beiglou is a M.Sc. Student at the School of Civil Engineering, University of Tehran, Tehran, Iran ( hatami.pouyan@ut.ac.ir). R. Kerachian is an Associate Professor at the School of Civil Engineering, University of Tehran, Tehran, Iran. He is also a member of center of Excellence for Engineering and Management of Civil Infrastructures, College of Engineering, University of Tehran (phone: ; fax: ; kerachian@ut.ac.ir). M. R. Nikoo is a Ph.D. Candidate at the School of Civil Engineering, University of Tehran, Tehran, Iran ( mohammadreza.nikoo@yahoo.com). S. T. O. Naeeni is an Assistant Professor at the School of Civil Engineering, University of Tehran, Tehran, Iran. He is also head of School of Civil Engineering, University of Tehran ( stnaeeni@ut.ac.ir). examine the simultaneous effects of multiple pollutants in their studies. Niksokhan et al. (2009 and 2010) developed TDP programs using cooperative game theory. Their model include two steps, allocating primary wastewater levels to discharger and reallocating costs equitably among dischargers and estimating final levels [7]- [8]. In this paper, a new model based on trading-ratio system is developed and examined for TDP with multiple pollutants. The model is developed considering three water quality variables, namely BODR5R, DO and NOR3R and some s for controlling pollution loads. The results of applying the methodology to the Zarjub River in northern part of Iran show its effectiveness and applicability for water quality trading in river systems. II. 1BMODEL FRAMEWORK Fig. 1 illustrates the framework of the proposed methodology for -based multiple-pollutant water quality trading. As shown in this figure, in this model, at first, some basic data such as main pollution sources, length of river zones, rive flow, number and distance of dischargers and their qualitative and quantitative properties of their loads, and costs of dischargers are gathered. Then, classification of dischargers is done using the obtained water quality data. In the next step, transfer coefficients must be calculated for three water quality variables by considering the interaction between multiple pollutants. The BODR5R and NOR3R coefficients are respectively denoted by tbij and t Nij. Also, the transfer coefficients based on the DO concentration are denoted by r Bij and r Nij, respectively. Then a number of s are considered for of wastewater of each discharger and the corresponding costs are calculated. The river assimilative capacity in each reach is estimated considering the standard levels for water quality variables BODR5R, DO and NOR3R. Based on the calculated capacities, the minimum level ( ) of each discharger is calculated for the first to the last discharger from the upstream to the downstream of the river. So the initial levels are allocated to dischargers without any trading. 387

2 Start pollutants into Anzali wetland. Fig. 2 illustrates the Zarjub River in the studied area. Gathering basic data from the study area Classifying dischargers and determination of discharge zone of each group of discharger Determining different s for pollution load reduction and calculating cost of each Calculating transfer coefficient and trading ratio coefficient of BOD 5, DO and NO 3 Calculating the assimilative capacity of each zone considering the standard levels of BOD 5, DO and NO 3 Allocating initial levels to each discharger considering the location of dischargers and the river assimilative capacity Calculating the levels ( s) of dischargers corresponding to the minimum total cost in initial level allocation Does the least cost differ from the initial total cost? YES NO Trading using modified ETRS Trade is not feasible and total cost cannot be reduced Fig. 2. The Zarjub River in the studied area [5] Determining total cost and for each discharger Fig. 1. A flowchart of the proposed methodology for multiple pollutants discharge permit trading in rivers In the trading process, an upstream discharger can cell discharge permit to a downstream discharger. An optimization model is developed and used to find the best trading strategy among dischargers which provide the least total cost. CASE STUDY The study area is a part of Zarjub River in north of Iran. This part of the river with a length of 24 kilometer stretches from the Rasht city in Northern Iran to the Caspian Sea. The Zarjub River is one the main rivers which discharges the most End The water quality condition of the Zarjub is very critical so that in some zones the water of this river is almost similar to municipal wastewater. Main source of pollutions is resulted from municipal and industrial activities near the Zarjub River. Results of measured water quality variables in the Zarjub River shows that in addition to BOD 5, DO and NO 3 variables, concentration of Coliform bacteria violates the river water quality standards. Considering that pathogens are fully removed through disinfection in wastewater plants, in this study, three water quality variables, namely, BOD 5, DO and NO 3 are assumed as water quality indicators. BOD 5 and NO 3 are also used to quantify pollution loads. A Qual2k-based water quality simulation model is developed and calibrated by considering the interaction of the mentioned water quality variables. Since constructing a plant for each individual waste load discharger is not acceptable considering the large construction cost of plants, 11 major municipal wastewater dischargers along the river are divided into four major groups. In this condition, it is assumed that the wastewater of each group of dischargers is transferred to its 388

3 corresponding municipal wastewater plant. The transfer coefficient and trading ratio matrix is calculated based of results of the calibrated water quality simulation model (Tables I to IV). General properties of each group of dischargers are given in Table V. TABLE I TRANSFER COEFFICIENT FOR BOD 5 ( Bij ) (100 kg BOD 5 )/(mg/l DO) TABLE II TRANSFER COEFFICIENT FOR NO 3 ( r Nij ) (100 kg NO 3 ) /(mg/l DO ) TABLE III TRADING RATIO FOR BOD 5 ( t Bij ) r secondary and tertiary levels of a typical municipal wastewater. TABLE VI TREATMENT SCENARIOS FOR GROUP 1 OF DISCHARGERS Scenario Discharged Discharged NO 3 BOD 5 (mg/l) (mg/l) Cost TABLE VII TREATMENT SCENARIOS FOR GROUP 2 OF DISCHARGERS Scenario Discharged Discharged NO 3 BOD 5 (mg/l) (mg/l) Cost TABLE IV TRADING RATIO FOR NO3 ( t Nij ) Zone number TABLE V PROPERTIES OF EACH GROUP OF POLLUTERS Flow 3 ( m / s ) Concentration of entry BOD 5 to plant Concentration of entry NO 3 to plant Corresponding population TABLE VIII TREATMENT SCENARIOS FOR GROUP 3 OF DISCHARGERS Scenario Discharged Discharged NO 3 number BOD 5 (mg/l) (mg/l) Cost ( U.S. $) Initial values of discharge permits for BOD 5 and NO 3 loads by considering the initial assimilative capacity based on DO are obtained through TRS and ETRS methods (Tables X, XI and XII). In order to find the optimum strategies and TDP for each group of waste load dischargers, 12 s is determined and considered for each group of dischargers. These s are combinations of primary, 389

4 TABLE IX TREATMENT SCENARIOS FOR GROUP 3 OF DISCHARGERS Scenario Discharged Discharged NO 3 number BOD 5 (mg/l) (mg/l) Cost TABLE X INITIAL BOD 5 DISCHARGE PERMITS (KG / DAY) T Bi TABLE XI INITIAL NO 3 DISCHARGE PERMITS (KG / DAY) T Ni ) TABLE XII INITIAL DO RECEPTION CAPACITY (KG / DAY) T DOi As it can be seen in Tables X and XI, zones 3 and 4 have reached to a critical condition. To solve this problem, the amounts of upstream discharge permits must be corrected. Considering obtained values, we calculate dominant pollutant reception capacity for each zone which is equal to the smallest value calculated for each zone. These values are initial discharge permits in each zone which the dischargers can trade it and make profit from selling their permits. III. RESULTS AND DISCUSSION As mentioned earlier, using existing data, initial pollutant discharge permits are determined so that the water quality indicators meet the standards. The levels (s) of dischargers are determined for two states of before and after trading (Tables XIII and XIV). TABLE XIII INITIAL TREATMENT SCENARIO Discharger Initial TABLE XIV INITIAL TREATMENT SCENARIO COSTS AND TOTAL COST Total Discharger cost Initial cost Now, using the developed optimization model, the minimum cost of system is determined for each state of trade between any groups of dischargers. In order to compare results of the trading model with minimum cost condition (without trade), a least cost model is developed. The final s and total costs in this condition are presented in Table XV. TABLE XV FINAL TREATMENT SCENARIO COSTS Discharger Total cost (10 3 U.S. $) Final cost (U. S. $) As shown in Table XV, it is feasible to reduce the total cost by trading discharge permits among dischargers. In this way, pollution load dischargers not only meeting river quality standards but also reduce their costs. Therefore, the trading strategies are determined by developing and utilizing an optimization model. The results show that only one trade between dischargers 1 and 4 is possible (Table XVI) Discharger TABLE XVI RESULTS OF PRE TRADING AND AFTER TRADING I Initial Treatment after trading Cost of based on initial (U. S. $) Cost of after trading Cost reduction Sum Feasibility of trade means that concentration of water quality variables in system must not deviate from standards. As seen in Table XVI, discharger 1 by changing its from 11 to 12 provides discharger 4 with the possibility of changing his level from 12 to 11. In this way, system cost decreases from to US Dollars. The gain resulted from system cost reduction in the trade state, must been divided equitably between dischargers 1 and 4 in a way that motivates two dischargers to cooperate. 390

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