Market-based Pollutant Load Allocation Strategy for Integrat

arry X. Zhang¹ and Shaw L. Yu²
Department of Civil Engineering, University of Virginia, Charlottesville, VA 22903

Abstract An innovative approach for Total Maximum Daily Load (TMDL) allocation and implementation is the watershed-based pollutant trading. One of the most important technical challenges to establishing a pollutant trading program, i.e. setting of trading ratios, can be a very contentious issue and has been already listed as an obstacle by several pollutant trading programs. Given the inherent scientific uncertainty and lack of technical guidance in trading program, industries would generally like to see the ratios set as low as possible to lower the cost of complying with environmental regulation, while environmental groups would like to set the ratios as high as possible to ensure that trading programs will result in water quality improvement. However, most of the available studies did not provide an approach to explicitly addressing the issue of trading ratio determination. The objective of this paper is to introduce a practical methodology in estimating trading ratio from a TMDL allocation matrix of feasible scenarios.
By examining different “equivalent trading ratios (ETRs)”, which generally correlate non-linearly with selected pairs of allocation scenarios, the discussions focuses on the primary factors influencing trading ratio estimation and their potential impacts on TMDL implementation and watershed restoration. A case study is presented, which is based on the EPA-approved Nitrate TMDL Development for Muddy Creek, Virginia. The results show that with the increase in the relative percentage of nonpoint source load reduction, the ETR also increases. Determination of the ETR can provide a fast quantitative evaluation of the trade-offs among various combinations of point and nonpoint source control measures and their impacts on ambient water quality improvement. By further incorporating economic considerations (e.g. marginal treatment cost), this practical methodology will help enhance the scientific basis of trading ratio estimation and thus the public perception of TMDL-based pollutant trading program.

Key words: market-based, pollutant trading, trading ratio, margin of safety, TMDL, allocation, implementation, adaptive anagement, HSPF, BASINS

1. Introdcution

Total Maximum Daily Loads (TMDLs) are viewed as critical to attaining water quality goals and cleaning up impaired waters. EPA’s regulations define a TMDL as the sum of a point source load allocation (WLA), a nonpoint source allocation (LA) and a margin of safety (MOS), which is included to account for uncertainty. TMDL allocation is one of the required components for establishing TMDLs and its purpose is to create a technically feasible and reasonably fair division of the allowable load among various sources. Although there are many ways to express the distribution of the maximum allowable pollutant load, the concept of allocation is central to the TMDL process because it reinforces the importance of identifying which sources need to be addressed to eliminate the impairment (USEPA, 1999). On the other hand, allocation is first and foremost a policy decision on how to distribute costs among different stakeholders in order to achieve a water quality goal (NRC, 2001).

An innovative approach for TMDL allocation and implementation is watershed-based pollutant trading, which involves one source of a pollutant buying reductions in releases of that pollutant from another source elsewhere in the same watershed. Not only does trading offer a means of achieving water quality goals in a more cost-effective fashion, but it also can be used to encourage attainment of goals sooner than applicable deadlines or generating greater reduction than required by law. Therefore, EPA officially proposed water quality trading policy in May 2002 and released the final trading policy in January 2003. Water quality trading is a market-based approach to improving and preserving water quality. Trading can provide greater efficiency in achieving water quality goals in watersheds by allowing one source to meet its regulatory obligations by using pollutant reductions created by another source that has lower pollution control costs (USEPA, 2003). EPA policy recognizes that TMDL is the driver for most trading activity and nutrients (nitrogen and phosphorus in various forms) will be the pollutant most often traded (Wynn et. al., 2002).

One of the most important technical challenges in establishing a TMDL-driven trading program is how to determine the pollutant trading ratio. By definition, a trading ratio specifies how many units of pollutant reduction a source must purchase to receive credit for one unit of load reduction (USEPA, 1996). Setting of ratios can be a very contentious issue and has already been listed as an obstacle by several pollutant trading programs. Given the inherent scientific uncertainty and lack of technical guidance in trading program, industries would generally like to see the ratios set as low as possible to lower the cost of complying with environmental regulation, while environmental groups would like to set the ratios as high as possible to ensure that trading programs will result in improvements to water quality (USEPA, 1996; Caton, 2002). Numerous trading programs and projects have been ongoing or completed throughout the U.S. (e.g. Environomics, 1999; NWF, 1999; WERF, 2000a, 2000b, 2000c, 2000d, 2002). Table 1 lists the key information on EPA-funded pollutant trading demonstration project in the Lower Boise River watershed and five WERF-sponsored pollutant trading studies.

Table 1 List of reference with available methodology for estimating the trading ratio No. Watershed Name State Pollutant of Concern Project Sponsor Trading Ratio 1 Long Island Sound Connecticut Nitrogen WERF and EPA “Attenuation ratio” determined by modeling 2 Cherry Creek Colorado Phosphorous WERF and EPA 1.4: 1 to 3:1 3 Fox Wolf Basin Wisconsin Phosphorous WERF and EPA 2:1 as reference value 4 Kalamazoo River Michigan Phosphorous WERF and EPA 2:1 (or 4:1, if no Agricultural BMPs implemented ) 5 Maryland (state-wide) Maryland Nitrogen WERF and EPA 2:1 proposed 6 Lower Boise River Idaho Phosphorous EPA Three kinds of ratios: delivery, location, and uncertainty ratios

Note: Chesapeake Bay study is not included, given its scale and different watershed characteristics.

One of the fundamental reasons that a trading ratio is often set higher (e.g. greater than 2) is to allow for uncertainty in the level of control needed to attain water quality standards, and to provide a buffer in case traded reductions are less effective than expected. For example, the effect of a reduction in discharge or the application of a best management practice cannot always be precisely predicted. Another reason is to account for differences in the impact of a pollutant in different locations. Because natural physical, chemical, and biological processes remove a pollutant as it is transported, the relative impact of one unit of pollutant discharged from different sources at different locations is not the same. Unfortunately, most of the available studies did not provide an approach to explicitly addressing the determination of the trading ratio. Instead, as shown in Table 1, a subjective selection method based on project needs and literature case studies is often employed in choosing the trading ratio (usually within a pre-specified range, e.g. between 1.5:1 and 4:1). The method for quantitatively determining the trading ratio in a TMDL allocation process is much needed. Therefore, the objective of this paper is to present a practical methodology in determining the trading ratio from a TMDL allocation matrix of feasible scenarios. Furthermore, primary factors influencing the trading ratio estimation and their potential impacts are discussed under the context of TMDL allocations and adaptive implementation plans.

2. Methodology

TMDL is usually expressed as a combination of point source load allocation (WLA) and nonpoint source allocation (LA). Theoretically, there may be many combinations of feasible management scenarios of WLA and LA that meet same ambient water quality target. Instead of direct comparing different ambient responses induced by unit weight of point source and NPS reduction (e.g. in lb), by first setting same water quality impact and subsequently comparing difference in load distribution between WLA and LA allows more convenient estimation of trading ratio. Thus, the basic concept for determining the trading ratio in this study is to calculate the gradient between differences of LAs from two allocation scenarios over their corresponding differences of WLAs, given same water quality improvement is achieved through various point source and NPS load reduction combinations.

A new term “equivalent trading ratio (ETR)” is defined to differentiate from the commonly used “trading ratio” term. Since a TMDL allocation matrix often includes a series of feasible management solutions, theoretically, ETR can therefore be calculated from any two allocation scenarios. ETRs are in general non-linearly correlated with selected pairs of allocation scenarios. The present paper discusses the primary factors affecting the trading ratio and their impacts on TMDL implementation and watershed restoration. Examples of these primary factors include uncertainty relating with long-term BMP performance, location of pollutant sources and their distance to the stream.

The case example presented herein was based a study entitled “Nitrate TMDL Development for Muddy Creek / Dry River, Virginia”, in which the state-of-the-art watershed model BASINS/HSPF was used as the modeling tool (Culver et al., 2000). Sections of Muddy Creek and the North River are designated for public drinking water use because they are less than 5 miles upstream of the intakes for the Bridgewater and Harrisonburg Water Treatment Plants (WTPs) on the North River. EPA Region 3 approved this first nutrient TMDL in Virginia in April 2000, which is subsequently used as case study during various research efforts.

3. Results and Discussion

3.1. Nutrient TMDL Development in Muddy Creek, Virginia

The Muddy Creek watershed in Virginia was selected as an example to demonstrate a practical method in estimating the trading ratio. This nitrate-TMDL, the first nutrient TMDL completed in Region 3, was approved by USEPA in 2000 (USEPA, 2000; Zhang et al., 2001; Culver et al., 2002a). The Muddy Creek watershed is located in Rockingham County, Virginia, approximately 15 miles to the west-northwest of Harrisonburg, Virginia. Sections of Muddy Creek and the North River are designated for public drinking water use because they are less than 5 miles upstream of the intakes for the Bridgewater and Harrisonburg Water Treatment Plants (WTPs) on the North River. Virginia’s water quality standard for nitrate in the reaches designated for drinking water is 10 mg/L nitrate as nitrogen (9 VAC 25-260-140). Historically, elevated nitrate concentrations have been recorded in Muddy Creek and occasionally for locations close to the Bridgewater Water Treatment Plant Intake. Nitrogen is attributed to both point and nonpoint sources in the watershed. The only active and significant permitted point source within the watershed is a poultry processing industry. In general, nonpoint source nitrogen originates from residential, agricultural, and natural sources. Specific nonpoint sources include land application of cattle manure and poultry litter, runoff from concentrated animal operations, grazing livestock, nitrogen-based fertilizer applications to agricultural and residential lands, septic tanks, atmospheric deposition, wildlife waste, and decaying organic matter.

Calibration of HSPF was a two-step process. The hydrology of the watershed was first calibrated before simulation of nitrogen could proceed. Model calibration is an iterative process where model parameters are varied within reasonable ranges until the model results adequately match the observed measurements. Detailed procedures for hydrological and water quality calibrations were included in several earlier publications (Culver et. al., 2000; Zhang et al., 2001 and Culver et al., 2002a).

3.2. Determination of the Equivalent Trading Ratio

Development of a TMDL allocation matrix considers different combinations of allocations that appear feasible and collectively can meet a TMDL’s desired load reductions, and a TMDL implementation plan carries out the best combination of control actions related to each allocation (USEPA, 2002).

The TMDL allocation matrix for Muddy Creek Nitrate TMDL includes a series of feasible management solutions combining both relatively constant point and storm-driven nonpoint source reductions (Table 2). Given the same water quality target is met (9.5 mg/L nitrate-nitrogen), a TMDL allocation matrix with five feasible scenarios, as shown in Table 2, is utilized for estimating the Equivalent Trading Ratio (ETR) by directly calculating from any two allocation scenarios following the methodology mentioned earlier in this paper.

Theoretically, there may be many combinations of load allocations between point source and nonpoint source pollution, given ambient same water quality target is met (Table 2). Instead of direct comparing different ambient responses induced by unit weight of point source and NPS reduction (e.g. in lb), comparing difference in load distribution between WLA and LA when achieving same water quality impact allows efficient calculation of trading ratio. The results from testing different pairs of allocation scenarios are given in Table 3.

Table 2 Allocation matrix of feasible scenarios for Muddy Creek nitrate TMDL Scenario Code Point

Source Crop Hay Pastures

2 and 3 Loafing Lots (LL) Peak

NO₃-N /mg/L Comments A 20 40 40 40 40 50 9.47 Most NPS Reduction from Sep.-Dec.,

LL (Jan.-Dec.) B 30 40 40 0 40 40 9.50 All NPS Reduction from Sep.-Dec. C 35 25 30 20 20 50 9.46 Most NPS Reduction from Sep.-Dec.,

LL (Jan.-Dec.) D 45 25 25 0 30 50 9.45 All NPS Reduction from Sep.-Dec. E 50 25 25 25 25 25 9.50 All NPS Reduction from Sep.-Dec.

Note: Numbers for each load are percent load reductions from current levels. Agricultural percent reductions are relative to the seasonal loading as indicated in comments.

Table 3Determination of Equivalent Trading Ratio from TMDL allocation scenarios (Zhang and Yu, 2003) Scenario Code Current Load /lb % of Point Source Load Reduction Point Source WLA /lb % of NPS Load Reduction Nonpoint Source LA /lb Equivalent Trading Ratio (ETR) C (Reference)

Point Source:
75 984

NPS Load:
3 799 336

Total: 3 875 320 35 49 390 7.0 3 529 454 N/A A 20 60 787 10.1 3 417 255 9.84 B 30 53 189 8.1 3 492 645 9.69 D 45 41 791 5.6 3 584 800 7.28 E 50 37 992 5.3 3 598 008 6.01

Note: Scenarios A-E all achieve the same ambient water quality standard 9.5 mg/L nitrate-nitrogen. Scenario C is used as reference because it is the finally selected allocation scenario in approved TMDL by EPA.

It can be seen from Table 3 that the effect of one unit of point source load reduction on in-stream water quality improvement is not necessarily equivalent to that of one unit of NPS load reduction. For example, Scenario A and C can achieve the same ambient water quality goal (9.5 mg/L NO₃-N). From Scenario A to C, there is a 15% increase of point source reduction while a 3.1% decrease of NPS reduction. This corresponds to 11,400 lbs of increased point source load reduction and 112,200 lbs of decreased NPS load reduction. In fact, Table 3 (scenario A and C) demonstrates the one unit weight of NPS load reduction is less effective than one unit of point source load reduction on improving ambient water quality (e.g. due to different fate and transport mechanisms by point and nonpoint source pollutants).

Furthermore, it is clearly shown that with the increase of relative percentage of nonpoint source load reduction in the total load reduction, the above-defined ETR also increases (see Figure 1). This could be qualitatively explained by the fact that the effect of one unit of NPS load reduction on ambient water quality improvement is not necessarily equivalent to (actually often less prominent than) that of one unit of point source load reduction. As pointed out by King and Kuch (2003), the difficulty of determining when and where a one-pound nutrient discharge reduction by a nonpoint source is equivalent to a one-pound increase in point source discharge somewhere else is a significant institutional obstacle to pollutant trading. This “scoring” problem is obviously more significant when attempting to regulate trades using standardized credits than when each trade can be evaluated on its individual merits.

In this specific nutrient TMDL case, comparison of simulated annual total nitrogen load from land uses and their contribution to stream is illustrated in Table 4. The nonpoint source load discharge from land surface is dominating with 96.5% of the total nitrogen load while point source discharge is only 3.5% of the total. However, when comparing the relative contribution between point and nonpoint source load in terms of their ambient water quality impact, the number is 34.7% for point source loads (the sum of NPDES discharger, cows in-stream and septic tanks) versus 65.3% for nonpoint source load. It is worth of noting that for modeling purpose, cows in-stream and septic tanks are considered direct deposition source to each stream segment.

One objective of Table 4 is to make a clear distinction between “NPS load deposited on land” and “NPS load contributed to stream”, since the ambient water quality impact between one unit weight of NPS load “on land” and “to stream” varies, given their different transport and transformation mechanisms.

Fig. 1. Equivalent Trading Ratio versus the percentage of WLA reduction (or LA reduction) over total load reductions (Zhang and Yu, 2003; Zhang and Yu, 2004)

Table 4 Comparison of simulated annual total nitrogen load from land uses and their contribution to Muddy Creek Land Use Total N from Land /lb % of Total N from Land Total N Contributed to Stream /lb % of Total N Contributed to Stream

Point Source Category Point Source 75 984 2.0% 75 984 19.7% Cows in Stream 47 718 1.2% 47 718 12.4% Septic Tanks 9 929 0.3% 9 929 2.6% Nonpoint Source Category Developed 212 019 5.5% 12 356 3.2% Farmstead 2 832 0.1% 5 070 1.3% Row Crop 1 039 824 26.8% 85 515 22.2% Pasture 3 577 756 14.9% 26 153 6.8% Pasture 1 789 775 20.4% 58 543 15.2% Pasture 2 117 286 3.0% 5 699 1.5% Loafing Lots 156 400 4.0% 30 111 7.8% Barren 32 0.0% 202 0.1% Forest 845 908 21.8% 28 142 7.3%

Note: Compiled and recalculated from Culver et al. (2000), USEPA (2000), Zhang (2000), and Zhang and Yu (2002b).

These results are consistent with the notion that greater portion of NPS load reduction in overall TMDL load reduction generally correlates with greater uncertainty, since long-term BMP performance at the watershed scale cannot always be precisely predicted (Zhang, 2000; Zhang and Yu, 2002a). As a result, it requires a large equivalent trading ratio to offset these uncertainty for achieving same water quality goal in the trading program.

3.3. Primary Factors Influencing Trading Ratio Estimation

(1) Equivalency and Uncertainty Factors

The trading ratios used in the above-referenced programs (Table 1) range from 1.4:1 to 4:1, which intend to address uncertainty and equivalence in nonpoint source loading and long-term reductions associated with site controls. The Lower Boise River trading program uses a combination of ratios that relate to relative locations and distances between sources, loading/reduction equivalence and a best management practice uncertainty factor (IDEQ, 2000). Trading ratios for Cherry Creek were set by looking at three primary factors, including uncertainty (institutional and scientific), net loading reduction (no net increase and environmental benefits of BMPs) and an additional margin of safety (WERF, 2000b). In certain cases, trading under the TMDL has not yet been fully defined, rather a framework for implementation is identified to best-utilize and adapt the trading tool for achieving reduction goals as point and non-point source implementation efforts are formalized (Kieser, 2002). The National Wildlife Federation (NWF, 1999) discusses the adequacy of trading ratios and concludes that a range of 2:1 to 4:1 is considered sufficient in most circumstances. Given the fact that a subjective selection is often employed in selecting trading ratio within a pre-specified range, one of the important technical areas that improvements could be made is to provide a clear explicit approach in estimating the trading ratio.

The “Equivalent Trading Ratios” listed in Table 3 range from 6.0 to 9.8, depending on the pair of scenarios chosen. It is worth of further emphasizing that the nonpoint source load numbers in the above calculation refers to those directly deposited on land surface, rather than “edge-of-farm” ones that discharge directly into the receiving waterbody after experiencing various transport and kinetic reaction processes. Therefore, the term “equivalent” is used to describe the trading ratio under this circumstance to differentiate from the commonly defined trading ratios in the literature.

Although the allocation process is primarily a policy decision, there is one important role that science can play in determining when actions can be considered “equivalent”. Water quality management actions are defined to be “equivalent” when their implementation achieves the designated use, taking uncertainty into consideration. There are two aspects of this definition of equivalency. First, equivalency is established with respect to ambient outcomes for the watershed and not in terms of pollutant loading comparisons, which is the way the allocations are described in the standard TMDL equation (e.g. Table 3 and Table 4). Second, the definition recognizes that equivalency must account for the relative uncertainty of different actions with respect to meeting the applicable water quality standard (NRC, 2001).

One common scenario might be the need to establishing equivalency between nitrogen load reductions from a proposed agricultural BMP versus a proposed wastewater treatment plant improvement. To achieve equivalency, these load reductions must have the same effect on meeting the water quality standard, which would normally be determined by using a modeling approach. It is quite possible that the nitrogen load reductions at the sources (the agricultural BMP and the wastewater treatment plant) are different, but they are equivalent in that they are predicted to have an identical effect on the water quality standard. On the other hand, if the BMP and wastewater treatment improvement are both forecasted to have the same mean effect on the water quality standard, but the wastewater treatment improvement response has less uncertainty, then the actions are not equivalent.

Additional environmental benefit from NPS load reduction can be derived by promoting ultimate improvement goal to be established at higher and more optimal level of ecosystem function (e.g. habitat protection and restoration), whereas available trading studies typically focus on relatively small watersheds where individual pollutants are only targets (Kieser and Fang, 2002). Thus, certain type of NPS control measures and their associated ecological benefits should be given special consideration when weighing the equivalency and uncertainty factors between point source and NPS load allocation.

In developing a trading ratio, USEPA (2003) requires quantifying credits and addresses uncertainty. Specifically, where trading involves nonpoint sources, states and tribes should adopt methods that account for the greater uncertainty in estimates of nonpoint source loads and reductions. Greater uncertainty in nonpoint source estimates is due to several factors including but not limited to variability in precipitation, variable performance of land management practices, time lag between implementation of some practices and full performance, and the effect of soils, cover and slope on pollutant load delivery to receiving waters.

As noted by King and Kuch (2003), since the potential for a land management changes to reduce “edge-of-farm” nutrient emissions is far less certain than the “end-of-pipe” nutrient emissions from a point source, the expected (risk-adjusted) outcome of such trades, if they are allowed on a pound-for-pound basis, is an expected decline in water quality. In order to have trading systems that result in net reductions in expected nutrient discharges, and to take account of risks, most existing nutrient trading programs employ “trading ratios”. These ratios require the “uncertain value” of the nutrient discharge reductions from the nonpoint source to be greater than the “certain value” of the point source discharge they are intended to offset.

Therefore, where appropriate, states may elect to establish a reserve pool of credits that would be available to compensate for unanticipated shortfalls in the quantity of credits that are actually generated. These include monitoring to verify load reductions, the use of greater than 1:1 trading ratios between nonpoint and point sources, using demonstrated performance values or conservative assumptions in estimating the effectiveness of nonpoint source management practices, using site-specific or trade-specific discount factors, and retiring a percentage of nonpoint source reductions for each transaction or a predetermined number of credits (USEPA, 2003).

While uncertainty due to the variation in the expected performance for source control measures and management practices remains as the fundamental reason for higher trading ratio (e.g. 2:1 and 4:1), there are several other primary factors that can directly influence the estimation of a trading ratio.

(a) River Location Ratio: Pollutant sources in most watersheds are scattered along the river, and the entire pollutant load discharged by a source may not reach the mouth of the river. In the Lower Boise River study (IDEQ, 2000), these were developed using a mass balance model that accounts for inputs, withdrawals, and groundwater. For a given source, the location ratio is equal to the amount by which the phosphorus loading at a specific reference site would increase (or decrease) if one pound more (or less) was discharged at that location.

(b) Drainage Delivery Ratio: When a reduction is accomplished somewhere in a sub-watershed above the point of discharge to the impaired segment, drainage delivery ratios and site location factors will reduce the amount of marketable credits (IDEQ, 2000). This will be necessary because one unit pound reduction at a location up in a drain or tributary from the mouth of the river may not result in one unit pound reduction at the point of discharge to the stream due to the complex fate and transport mechanisms that affect phosphorus. Drainage delivery ratios account for transmission losses (e.g., uptake by vegetation, infiltration to groundwater, etc.) in a drain or tributary. Site location factors address the potential for diversion and reuse of water below the point of discharge to the drain or tributary.

In addition, in Chesapeake Bay Program (CBP, 2001), two other factors are proposed in assisting trading ratio estimation.

(c) Retirement ratio is applied to implement policy-driven or programmatic decisions to require that buyers or sellers donate part of all credit purchases or sales to the state or some other entity that will not apply the credits to offset loadings above its cap. For example, a 10% credit retirement requirement results in a 1.1 to 1 retirement ratio.

(d) Special need ratio would account for issues not addressed in other trading ratio (e.g. sensitive waters or areas needing additional protection).

(2) Economic Factor

Allocation becomes a difficult decision because the different combinations will have a different total cost and different levels of perceived fairness. Social and economic considerations complicate the allocation decision-making process and add new dimensions to the technical assistance tools that are needed to move from a purely scientific allocation scenario to control actions with a reasonable assurance of successful implementation (USEPA, 2002).

One suggestion might be to choose the combination of actions that minimizes the total cost. However, this may result in a cost distribution that places most of the burden on the customers of the treatment plant. An alternative may be to reduce loads from the plants and from runoff by the same proportion. However, this leaves unanswered whether any cost responsibility should fall on the irrigators. Other combinations of actions would have other cost distribution effects.

Overall, TMDL implementation and watershed restoration involves actions taken to reduce all the stressors responsible for the impairment. The allocation of financial and legal responsibility for taking those actions will fall on stakeholders in the watershed, who may not receive public subsidies for taking such actions. Because of these cost consequences, stakeholders want to be sure that water quality standards are appropriate and that total load limits and the limits proposed on other stressors are necessary to secure the designated use.

Although economic consideration is not the main focus of this paper, it is one integral component of the pollutant trading program (e.g. marginal treatment cost). For example, trading ratio of 3:1 or 4:1, in effect, increase the cost of purchasing credits to offset a unit of point source discharge by 300% or 400% over the cost of achieving an offset on a one-for-one basis. Higher trading ratios reduce the economic value of a credit (the treatment costs it displaces) and, all other things equal, make it more cost-effective for point sources to treat waste on-site rather than purchasing credits (King and Kuch, 2003).

By further incorporating economic consideration, it will enhance the scientific basis and thus public perception with the determination of trading ratio for more informed decision in overall TMDL-based pollutant trading program. For example, Faeth (2000) estimated the cost of removing one unit weight (e.g. in lb) of phosphorus under different management scenarios in three agricultural watersheds in Midwest (Minnesota River; Saginaw Bay, MI; and Rock River, WI). The cost of phosphorus control for point source performance requirement (without trading) is two to six times equivalent to the cost of point source control scenario with trading. Or in other words, using trading between point and non-point sources, there could be a reduction of over 40% and up to 80% for total treatment costs.

(3) Trading Ratio and NPDES Permit

For National Permit Discharge Elimination System (NPDES) dischargers, all existing regulatory and enforcement provisions continue to apply and a discharger is always responsible for meeting its permit requirements. USEPA (2003) emphasizes that standardized protocols are necessary to quantify pollutant loads, load reductions, and credits. States and tribes should develop procedures to account for the generation and use of credits in NPDES permits and discharge monitoring reports in order to track the generation and use of credits between sources and assess compliance.

Existing methods for translating point source wasteload allocations into NPDES permits could be improved. In terms of the permit mechanism, larger scale trading programs may want to consider the use of general or watershed permits to facilitate the exchange of pollution reduction credits and reduce paperwork while retaining federally enforceable permits for all NPDES participants (Wynn et al., 2002).

In Truckee River pollutant trading project, a range of trade ratios was developed depending on the type of source reduction and project. Based on the ratio and the pounds reduced, the discharge credit was calculated and incorporated into the NPDES permit prior to credits being available for permit compliance (Bhimani et al., 2002). Furthermore, in the Lower Boise River study, the State keeps a good record of trading document and corresponding permit, which will be reviewed every five years. Adjustments will be made, if necessary, in coordination with the five-year NPDES permit cycle.

3.4. Pollutant Trading in the Context of Adaptive Management

In general, EPA supports implementation of water quality trading by states, interstate agencies and tribes where trading reduces the cost of implementing TMDLs through greater efficiency and flexible approaches or compliance with water quality-based requirements. The objectives of pollutant trading are to establish economic incentives for voluntary pollutant reductions from point and nonpoint sources within a watershed and offset new or increased discharges resulting from growth in order to maintain levels of water quality that support all designated uses (USEPA, 2003). In addition, EPA supports cross-pollutant trading for oxygen-related pollutants where adequate information exists to allow the assessment of impacts on water quality. Reducing upstream nutrient levels to offset a downstream biochemical oxygen demand or to improve a depressed in-stream dissolved oxygen level are examples of cross-pollutant trading.

Adaptive implementation is the application of the scientific method to decision-making. It is a process of taking actions of limited scope commensurate with available data and information to continuously improve our understanding of a problem and its solutions, while at the same time making progress toward attaining a water quality standard (NRC, 2001).

Plans for future regulatory rules and public spending should be tentative commitments subject to revision as we learn how the system responds to actions taken early on. Regardless of what immediate actions are taken, there may not be an immediate ambient response in waterbody or biological condition. For example, there may be significant time lags between when actions are taken to reduce nutrient loads and resulting changes in nutrient concentrations. This is especially likely if nutrients from past activities are tightly bound to sediments or if nutrient-contaminated groundwater has a long residence time before its release to surface water. Technically, explicit quantification of trading ratio remains a challenge in pollutant trading program, given equivalency and uncertainty inherent in TMDL allocation and implementation process.

Therefore, longer-term actions should be formulated in recognition of emerging and innovative strategies for watershed restoration. The performance after adopting the TMDL implementation plan can be further evaluated and verified based on the collection of additional monitoring data and data analysis. For example, selection of different trading ratio could result in different ambient water quality improvement over longer period of time. An adaptive implementation plan would specify analyses of specific long-term alternatives, a schedule for such analyses to be conducted, and a mechanism for supporting such analyses. However, if there is significant uncertainty about the effect of certain control actions, those who bear the costs may resist taking such actions without further evidence of their worth. Adaptive implementation would support a cautious approach of taking low-cost actions with a high degree of certainty about the outcome, while taking parallel longer-term actions to improve forecast capabilities and revise control strategies during the TMDL implementation and watershed restoration phase.

4. Conclusions

This paper presents a practical methodology in explicitly estimating pollutant trading ratio. By examining different “equivalent trading ratios (ETRs)”, which generally correlate non-linearly with selected pairs of allocation scenarios, it provide a fast quantitative evaluation of the trade-offs among various combinations of point and nonpoint source control measures and their impacts on ambient water quality improvement. By further incorporating economic consideration (e.g. marginal treatment cost), this practical methodology will help enhance scientific basis of trading ratio estimation and thus public perception in TMDL-based pollutant trading program.

Acknowledgements

Credits. This work was in part derived from original research conducted at University of Virginia. The manuscript was revised from Zhang and Yu (2003).

Authors. Dr. Harry X. Zhang is a Senior Engineer and Project Manager with Parsons Corporation in Fairfax, Virginia. Dr. Shaw L. Yu is a Professor in the Department of Civil Engineering at University of Virginia in Charlottesville, Virginia. Correspondence should be addressed to Harry X. Zhang, Parsons Corporation, 10521 Rosehaven Street, Fairfax, Virginia 20190.

References

Bhimani, S., McDonald, S., Dennis, G., and Gray, R. 2002. Evaluating Water Quality Impacts of Watershed Alternatives using an Integrated Modeling Approach on the Truckee River, WEFTEC’02, September 28 – October 2, Chicago, IL.

Bicknell, B. R., Imhoff, J.C., Kittle, J. L, Jr., and Donigian, A. S., Jr. 1996. Hydrological Simulation Program – FORTRAN, User’s Manual for Release 11, Environmental Research Laboratory, USEPA, Athens, GA.

Caton, P. 2002. Developing an Effluent Trading Program to Address Nutrient Pollution in the Providence and Seekonk Rivers, M. Sc. thesis - May 2002, Center for Environmental Studies, Brown University,

Chesapeake Bay Program (CBP). 2001. Chesapeake Bay Program Fundamental Principle and Guidelines, EPA 903-B-01 CBP/TRS 254/01,

Culver, T. B., Naperala, T. R., Potts, A., Zhang, X., Neeley, K., and Yu, S. L. 2002a. Case Study of Impact of Total Maximum Daily Load Allocations on Nitrate Leaching. ASCE Journal of Water Resources Planning and Management, 128(4): 262-270.

Culver, T. B., Neeley, K., Yu, S. L., Zhang, X., Potts, A., and Naperala, T. R. 2002b. The Muddy Creek / Dry River Nitrate Total Maximum Daily Load: Leading by Example. Water Resources Update (March 2002), 5-13.

Chow, V. T., Maidment, D. R. and Mays, L. W. 1988. Applied Hydrology, McGraw-Hill Inc.

Environmental Trading Network (ETN). 2002. Great Lakes Trading Network – Program and Project Summaries,

Environomics, Inc. 1999. A Summary of U.S. Effluent Trading and Offset Projects. Report prepared for USEPA Office of Water.

Faeth, P. 2000. Fertile Ground: Nutrient Trading’s Potential to Cost-Effectively Improve Water Quality, World Resources Institute, Washington, DC,

Fontaine, T. A. and Jacomino, V. M. F. 1997. Sensitivity Analysis of Simulated Contaminated Sediment Transport. Journal of the American Water Resources Association, 32 (2): 313-326.

Idaho Department of Environmental Quality (IDEQ). 2000. Lower Boise River Effluent Trading Demonstration Project,

Kieser, M. S. 2002. Moving Beyond the Bells and Whistles: Implementation Plan Formulation for a Kalamazoo River/Lake Allegan Phosphorus TMDL. WEF 2002 National TMDL Science and Policy Conference, November 13 – 16, Phoenix, AZ.

Kieser, M. S., and Fang, F. 2002. Water Quality Trading: Another Tool for the Watershed Tool Box. 2002 Illinois Environmental Conference, August 15-16, Chicago, IL.

King, D. M., and Kuch, P. J. 2003. Will Nutrient Credit Trading Ever Work? An Assessment of Supply and Demand Problems and Institutional Obstacles, Environmental Law Institute News & Analysis, 33 ELR 10352.

Laroche, A., Gallichand, J., Lagace, R., and Pesant, A. 1996. Simulating Atrazine Transport with HSPF in an Agricultural Watershed. Journal of Environmental Engineering, 122 (7): 622-630.

Moore, L. W., Mathewy, H., Tyree, T., Sabatini, D., and Klaine, S. J. 1988. Agricultural Runoff Modeling in a Small West Tennessee Watershed. ASCE Journal of Water Pollution Control Federation, 60 (2): 242-249.

National Research Council (NRC) (2001). Assessing the TMDL Approach to Water Quality Management, Washington DC.

National Wildlife Federation (NWF).1999. A New Tool for Water Quality: Making Watershed-based Trading Work for You. Northeast Natural Resource Center, Montpelier, VT.

Schneider, J. 2001. Commonwealth of Virginia Muddy Creek Nitrate TMDL.WEF 2001 National TMDL Science Issues Conference, March 4-7, St. Louis, MO.

USEPA. 1991. Guidance for Water Quality-Based Decisions: The TMDL Process, EPA 440/4-91-001, Washington DC.

USEPA. 1996. 1996 Comments on Watershed-Based Trading Proposed Framework (Draft),

USEPA. (1999a). Draft Guidance for Water Quality-based Decisions: The TMDL Process (Second Edition), EPA Report 841-D-99-001

USEPA. 1999b. Protocol for Developing Nutrient TMDLs (First Edition). Report EPA 841-B-99-007, November, 1999, Washington DC.

USEPA. 2000. Joseph Piotrowski, Acting Director, Water Protection Division, USEPA, Letter of Approval for the Muddy Creek, Rockingham County TMDL for Nitrate to Mr. Larry Lawson, VADEQ, April 27, 2000

USEPA. 2002. The Twenty Needs Report: How Research Can Improve the TMDL Program, EPA Report 841-B-02-002.

USEPA.2003. EPA 2003 Water Quality Trading Policy.

Water Environment Research Foundation (WERF). 2000a. Nitrogen Credit Trading in the Long Island Sound Watershed, Report D00300.

Water Environment Research Foundation (WERF). 2000b. Phosphorus Credit Trading in the Cherry Creek Basin: An Innovative Approach, Report D00310.

Water Environment Research Foundation (WERF). 2000c. Phosphorus Credit Trading in the Kalamazoo River Basin: Forging Nontraditional Partnerships, Report D00311.

Water Environment Research Foundation (WERF). 2000d. Phosphorus Credit Trading in the Fox-Wolf Basin: Exploring Legal, Economic, and Technical Issues, Report D00312.

Water Environment Research Foundation (WERF). 2002. Nitrogen Credit Trading in Maryland: A Market Analysis for Establishing a Statewide Framework, Report 97IRM5E.

Wynn, L. H., Batchelor, D., Podar, M. 2002. Reinvigorating EPA’s Policy and Support for Water Quality Trading, WEF 2002 National TMDL Science and Policy Conference, November 13 – 16, Phoenix, AZ.

Zhang, H.X. 2000. The Critical Flow-Storm Approach and Uncertainty Analysis for the TMDL Development Process, Ph.D. Dissertation, Department of Civil Engineering, University of Virginia, Charlottesville, Virginia.

Zhang, H.X., Yu, S. L., and Culver, T.B. 2001. The Critical Flow-Storm Approach for Nitrate TMDL Development in the Muddy Creek Watershed, Virginia. WEFTEC’01, October 13-17, Atlanta, Georgia.

Zhang, H. X., and Yu, S. L. 2002a. Uncertainty Analysis of Margin of Safety in a Nutrient TMDL Modeling and Allocation. Water Environment Federation – Watershed’02, February 23 – 27, Fort Lauderdale, Florida.

Zhang, H. X., and Yu, S. L. 2002b. A Conceptual Model of the Critical Flow-Storm Approach for TMDL Development. ASCE 2002 Water Resources Planning and Management Annual Conference, May 19-22, Roanoke, Virginia.

Zhang, H. X. and Fillmore, L. A. 2003. Is the TMDL on Target? Rigorous uncertainty analysis can help ensure that a TMDL includes a reasonable margin of safety. Water Environment and Technology, 15(6): 27-31.

Zhang, H. X., and Yu, S. L. 2003. Estimating the Pollutant Trading Ratio from the TMDL Allocation Matrix, WEF 2003 National TMDL Science and Policy Conference, November 16-19, Chicago, IL

Zhang, H. X. and Yu, S. L. 2004. Applying the First-Order Error Analysis in Determining the Margin of Safety (MOS) Term for TMDL Computations. ASCE Journal of Environmental Engineering, 130(6): 664-673.

1. Senior Engineer and Project Manager, Parsons Corporation, 10521 Rosehaven St., Fairfax, VA 22030, Email: [email protected]

2. Professor, University of Virginia, Department of Civil Engineering, Charlottesville, VA 22903, Email: [email protected]