Water Quality Trading with Equitable Total Maximum Daily Loads
Waste Load Allocation (WLA) strategies usually
intend to find economic policies for water resource management.
Water quality trading (WQT) is an approach that uses discharge
permit market to reduce total environmental protection costs. This
primarily requires assigning discharge limits known as total
maximum daily loads (TMDLs). These are determined by monitoring
organizations with respect to the receiving water quality and
remediation capabilities. The purpose of this study is to compare two
approaches of TMDL assignment for WQT policy in small catchment
area of Haraz River, in north of Iran. At first, TMDLs are assigned
uniformly for the whole point sources to keep the concentrations of
BOD and dissolved oxygen (DO) at the standard level at checkpoint
(terminus point). This was simply simulated and controlled by
Qual2kw software. In the second scenario, TMDLs are assigned
using multi objective particle swarm optimization (MOPSO) method
in which the environmental violation at river basin and total treatment
costs are minimized simultaneously. In both scenarios, the equity
index and the WLA based on trading discharge permits (TDP) are
calculated. The comparative results showed that using economically
optimized TMDLs (2nd scenario) has slightly more cost savings rather
than uniform TMDL approach (1st scenario). The former annually
costs about 1 M$ while the latter is 1.15 M$. WQT can decrease
these annual costs to 0.9 and 1.1 M$, respectively. In other word,
these approaches may save 35 and 45% economically in comparison
with command and control policy. It means that using multi objective
decision support systems (DSS) may find more economical WLA,
however its outcome is not necessarily significant in comparison with
uniform TMDLs. This may be due to the similar impact factors of
dischargers in small catchments. Conversely, using uniform TMDLs
for WQT brings more equity that makes stakeholders not feel that
much envious of difference between TMDL and WQT allocation. In
addition, for this case, determination of TMDLs uniformly would be
much easier for monitoring. Consequently, uniform TMDL for TDP
market is recommended as a sustainable approach. However,
economical TMDLs can be used for larger watersheds.
[1] USEPA “Water quality trading assessment handbook”, 2004, 1-120.
[2] S. Jamshidi, MH. Niksokhan, M. Ardestani “Surface Water Quality
Management Using Integrated Discharge Permit and Reclaimed Water
Market” Water Science and Technology, 70(5), 2014, 917-924.
[3] D. Collentine, “Including non-point sources in a water quality trading
permit program”, Water science and technology, 51(3-4), 2005, 47-53.
[4] R.A., Ranga Prabodanie, J.F. Raffensperger, M.W. Milke, “A pollution
offset system for trading non-point source water pollution permits”,
Environmental and Resource Economics, 45, 2010, 499-515.
[5] M.O. Ribaudo, J. Gottlieb, “Point-Nonpoint Trading – Can it Work?”,
Journal of the American Water Resources Association (JAWRA), 47(1),
2011, 5-14.
[6] B. Boyd, R. Greenwood, “Water quality trading: Assessment methods
and lessons”, Environmental Quality Management, 2005, 23-29.
[7] M.R., Nikoo, R., Kerachian, M.H. Niksokhan, “Equitable Waste Load
Allocation in Rivers Using Fuzzy Bi-matrix Games”, Water Resources
Management, 26(15), 2012, 4539-4552.
[8] M.H., Niksokhan, R. Kerachian, P. Amin, “A stochastic conflict
resolution model for trading pollutant discharge permits in river
systems”, Environmental Monitoring and assessment, 154, 2009, 219-
232.
[9] M. H., Niksokhan, R. Kerachian, M. Karamouz, “A game theoretic
approach for trading discharge permits in rivers”, Water Science and
Technology, 60(3), 2009, 793-804.
[10] N. P., Nguyen, J. S., Shortle, P. M. Reed, T. T. Nguyen, “Water quality
trading with asymmetric information, uncertainty and transaction costs:
a stochastic agent-based simulation”, Resource and Energy Economics,
35(1), 2013, 60-90.
[11] J. S. Kardos, C. C. Obropta, “Water quality model uncertainty analysis
of a point-point source phosphorus trading program”, Journal of the
American Water Resources Association (JAWRA), 47(6), 2011, 1317-
1337.
[12] G, Ghosh M, Ribaudo J, Shortle, “Baseline requirements can hinder
trades in water quality trading programs: Evidence from the Conestoga
watershed”. Journal of environmental management, 92, 2011, 2076-
2084.
[13] D. O’Grady, “Sociopolitical conditions for successful water quality
trading in the south nation river watershed, Ontario, Canada”, Journal of
the American Water Resources Association (JAWRA), 47(1), 2011, 39-
51.
[14] JW, Eheart, T, Ling Ng, “Role of effluent permit trading in total
maximum daily load programs: Overview and uncertainty and reliability
implications”. Journal of environmental engineering ASCE, 130(6),
2004, 615-621
[15] A., Pejman, G., Nabi Bidhendi, A., Karbassi, N. Mehrdadi, M. Esmaeili
Bidhendi, “Evaluation of spatial and seasonal variations in surface water
quality using multivariate statistical techniques”, International Journal
of Environmental Science and Technology, 6(3), 2009, 467-476.
[16] E., Feizi Ashtiani, M. H. Niksokhan, M. Ardestani, “Multi-objective
Waste Load Allocation in River System by MOPSO Algorithm”,
International Journal of Environmental Research, in press.
[17] P. R., Kannel, S., Lee, Y.S., Lee, S. R. Kanel, G. J. Pelletier,
“Application of automated Qualt2kw for water quality modelling and
management in the Bagmati River, Nepal”, Ecological Modelling, 202,
2007, 503-517.
[18] A. M. Baltar, D. G. Fontane, “Use of multi-objective particle swarm
optimization in water resource management”, Journal of water resource
planning and management, 134(3), 2008, 257-265.
[19] A. Azadnia, B. Zahraie, “Optimization of nonlinear Muskingum method
with variable parameters using multi-objective particle swarm
optimization”, World environmental and Water Resources Congress,
ASCE, 2010, 2278-2284
[20] Ashtiani, E. F., Niksokhan, M. H., & Jamshidi, S. (2015), “Equitable
fund allocation, an economical approach for sustainable waste load
allocation” Environmental monitoring and assessment, 187(8), 1-11.
[21] I., Rahimi, K. Qaderi, A.M. Abasiyan, “Optimal Reservoir Operation
Using MOPSO with Time Variant Inertia and Acceleration
Coefficients”, Universal Journal of Agricultural Research, 1(3), 2013,
74-80.
[22] D. H. Burn, J.S. Yulianti “Waste-load allocation using genetic
algorithms”, Journal of Water Resource Planning and Management,
127(2), 2001, 121-129.
[1] USEPA “Water quality trading assessment handbook”, 2004, 1-120.
[2] S. Jamshidi, MH. Niksokhan, M. Ardestani “Surface Water Quality
Management Using Integrated Discharge Permit and Reclaimed Water
Market” Water Science and Technology, 70(5), 2014, 917-924.
[3] D. Collentine, “Including non-point sources in a water quality trading
permit program”, Water science and technology, 51(3-4), 2005, 47-53.
[4] R.A., Ranga Prabodanie, J.F. Raffensperger, M.W. Milke, “A pollution
offset system for trading non-point source water pollution permits”,
Environmental and Resource Economics, 45, 2010, 499-515.
[5] M.O. Ribaudo, J. Gottlieb, “Point-Nonpoint Trading – Can it Work?”,
Journal of the American Water Resources Association (JAWRA), 47(1),
2011, 5-14.
[6] B. Boyd, R. Greenwood, “Water quality trading: Assessment methods
and lessons”, Environmental Quality Management, 2005, 23-29.
[7] M.R., Nikoo, R., Kerachian, M.H. Niksokhan, “Equitable Waste Load
Allocation in Rivers Using Fuzzy Bi-matrix Games”, Water Resources
Management, 26(15), 2012, 4539-4552.
[8] M.H., Niksokhan, R. Kerachian, P. Amin, “A stochastic conflict
resolution model for trading pollutant discharge permits in river
systems”, Environmental Monitoring and assessment, 154, 2009, 219-
232.
[9] M. H., Niksokhan, R. Kerachian, M. Karamouz, “A game theoretic
approach for trading discharge permits in rivers”, Water Science and
Technology, 60(3), 2009, 793-804.
[10] N. P., Nguyen, J. S., Shortle, P. M. Reed, T. T. Nguyen, “Water quality
trading with asymmetric information, uncertainty and transaction costs:
a stochastic agent-based simulation”, Resource and Energy Economics,
35(1), 2013, 60-90.
[11] J. S. Kardos, C. C. Obropta, “Water quality model uncertainty analysis
of a point-point source phosphorus trading program”, Journal of the
American Water Resources Association (JAWRA), 47(6), 2011, 1317-
1337.
[12] G, Ghosh M, Ribaudo J, Shortle, “Baseline requirements can hinder
trades in water quality trading programs: Evidence from the Conestoga
watershed”. Journal of environmental management, 92, 2011, 2076-
2084.
[13] D. O’Grady, “Sociopolitical conditions for successful water quality
trading in the south nation river watershed, Ontario, Canada”, Journal of
the American Water Resources Association (JAWRA), 47(1), 2011, 39-
51.
[14] JW, Eheart, T, Ling Ng, “Role of effluent permit trading in total
maximum daily load programs: Overview and uncertainty and reliability
implications”. Journal of environmental engineering ASCE, 130(6),
2004, 615-621
[15] A., Pejman, G., Nabi Bidhendi, A., Karbassi, N. Mehrdadi, M. Esmaeili
Bidhendi, “Evaluation of spatial and seasonal variations in surface water
quality using multivariate statistical techniques”, International Journal
of Environmental Science and Technology, 6(3), 2009, 467-476.
[16] E., Feizi Ashtiani, M. H. Niksokhan, M. Ardestani, “Multi-objective
Waste Load Allocation in River System by MOPSO Algorithm”,
International Journal of Environmental Research, in press.
[17] P. R., Kannel, S., Lee, Y.S., Lee, S. R. Kanel, G. J. Pelletier,
“Application of automated Qualt2kw for water quality modelling and
management in the Bagmati River, Nepal”, Ecological Modelling, 202,
2007, 503-517.
[18] A. M. Baltar, D. G. Fontane, “Use of multi-objective particle swarm
optimization in water resource management”, Journal of water resource
planning and management, 134(3), 2008, 257-265.
[19] A. Azadnia, B. Zahraie, “Optimization of nonlinear Muskingum method
with variable parameters using multi-objective particle swarm
optimization”, World environmental and Water Resources Congress,
ASCE, 2010, 2278-2284
[20] Ashtiani, E. F., Niksokhan, M. H., & Jamshidi, S. (2015), “Equitable
fund allocation, an economical approach for sustainable waste load
allocation” Environmental monitoring and assessment, 187(8), 1-11.
[21] I., Rahimi, K. Qaderi, A.M. Abasiyan, “Optimal Reservoir Operation
Using MOPSO with Time Variant Inertia and Acceleration
Coefficients”, Universal Journal of Agricultural Research, 1(3), 2013,
74-80.
[22] D. H. Burn, J.S. Yulianti “Waste-load allocation using genetic
algorithms”, Journal of Water Resource Planning and Management,
127(2), 2001, 121-129.
@article{"International Journal of Earth, Energy and Environmental Sciences:70445", author = "S. Jamshidi and E. Feizi Ashtiani and M. Ardestani", title = "Water Quality Trading with Equitable Total Maximum Daily Loads", abstract = "Waste Load Allocation (WLA) strategies usually
intend to find economic policies for water resource management.
Water quality trading (WQT) is an approach that uses discharge
permit market to reduce total environmental protection costs. This
primarily requires assigning discharge limits known as total
maximum daily loads (TMDLs). These are determined by monitoring
organizations with respect to the receiving water quality and
remediation capabilities. The purpose of this study is to compare two
approaches of TMDL assignment for WQT policy in small catchment
area of Haraz River, in north of Iran. At first, TMDLs are assigned
uniformly for the whole point sources to keep the concentrations of
BOD and dissolved oxygen (DO) at the standard level at checkpoint
(terminus point). This was simply simulated and controlled by
Qual2kw software. In the second scenario, TMDLs are assigned
using multi objective particle swarm optimization (MOPSO) method
in which the environmental violation at river basin and total treatment
costs are minimized simultaneously. In both scenarios, the equity
index and the WLA based on trading discharge permits (TDP) are
calculated. The comparative results showed that using economically
optimized TMDLs (2nd scenario) has slightly more cost savings rather
than uniform TMDL approach (1st scenario). The former annually
costs about 1 M$ while the latter is 1.15 M$. WQT can decrease
these annual costs to 0.9 and 1.1 M$, respectively. In other word,
these approaches may save 35 and 45% economically in comparison
with command and control policy. It means that using multi objective
decision support systems (DSS) may find more economical WLA,
however its outcome is not necessarily significant in comparison with
uniform TMDLs. This may be due to the similar impact factors of
dischargers in small catchments. Conversely, using uniform TMDLs
for WQT brings more equity that makes stakeholders not feel that
much envious of difference between TMDL and WQT allocation. In
addition, for this case, determination of TMDLs uniformly would be
much easier for monitoring. Consequently, uniform TMDL for TDP
market is recommended as a sustainable approach. However,
economical TMDLs can be used for larger watersheds.", keywords = "Waste load allocation (WLA), Water quality trading
(WQT), Total maximum daily loads (TMDLs), Haraz River, Multi
objective particle swarm optimization (MOPSO), Equity.", volume = "9", number = "6", pages = "737-5", }