Total maximum daily load (TMDL) approach tosurface water quality management: concepts,issues, and applications

Amin Elshorbagy, Ramesh S.V. Teegavarapu, and Lindell Ormsbee

Abstract: The total maximum daily load (TMDL) approach is an emerging paradigm in surface water quality manage-ment and has been adopted and applied in many states in the US. The paper explains the conceptual development ofthe general TMDL process for surface water quality management of three water quality impairment constituents,namely, nutrients, pathogens, and acid load. The application of the TMDL approach to a stream impaired due to highacidic concentrations from mine drainage in southeastern Kentucky is explained and used to demonstrate the completedevelopment of the TMDL process. The paper highlights a number of issues, ranging from assessment of pollution-causing sources to development of practical methods for implementation of TMDLs.

Key words: total maximum daily loads (TMDLs), nutrients, pathogen and pH impairment, TMDL implementation.

Résumé : L’approche par la charge quotidienne maximale totale (TMDL) est un nouveau paradigme en gestion de laqualité des eaux de surface et elle a été adoptée et appliquée dans plusieurs états américains. Cet article explique le dé-veloppement conceptuel du processus TMDL général appliqué à la gestion de la qualité des eaux de surface pour troiscomposantes réduisant la qualité de l’eau, nommément les charges en nutriments, en agents pathogènes et en acides.L’application de l’approche TMDL à un cours d’eau affecté par de fortes concentrations d’acide provenant d’un drai-nage minier du sud-est du Kentucky est expliquée et utilisée pour illustrer le développement complet du processusTMDL. L’article souligne plusieurs difficultés, allant de l’évaluation des sources causant la pollution au développementde méthodes pratiques pour implanter les TMDL.

Mots clés : charges quotidiennes maximales totales (TMDLs), nutriments, réduction des agents pathogènes et du pH,implantation des TMDL.

[Traduit par la Rédaction] Elshorbagy et al. 448

Introduction

The natural ecology of the watershed is a dynamicallybalanced system that can be disturbed by any external force,such as human intervention. Any disturbance to such a sys-tem may significantly affect one or more of the watershedfunctions. If the watershed is dynamically stable, then re-moval of external forces that cause disturbance of equilib-rium will allow for the restoration of original conditions(Black 1996). The water quality parameters in a stream areexamples of indicators of such a stable equilibrium. Human

activities in a watershed may form point or nonpoint sourcesof pollution that degrade the health of the stream and renderit impaired for certain uses. On the other hand, controllingpollution loads and sources could restore, in most of thecases, the stream health. Streamflow (runoff) patterns, be-haviour, and quality are unique for each watershed. They are“fingerprints” of the contributing sources quantitatively andqualitatively. Nonpoint sources of pollution such as agricul-ture, mining, construction, salt-water intrusion, residualwaste, and land and subsurface waste disposal are to beproperly managed and controlled in the process of restora-tion of the hydrologic function of the watershed. This is animportant basis to ensure acceptable water quality throughbest management practices (BMPs) (ASCE 2001).

The objectives of watershed management are usuallythreefold: (i) rehabilitation of altered or abused watersheds(in some cases this is synonymous with restoration), (ii) pro-tection for sensitive watersheds from activities that mightlead to a need for rehabilitative measures, and (iii) enhance-ment of the water resource characteristics by manipulatingsome of the watershed features. BMPs are the practices thatentail any one or combination of more than one of the threeobjectives of watershed management. The total maximumdaily load (TMDL) program, although initiated in the 1972Clean Water Act in the United States, recently emerged asthe foundation to meet water quality standards in water bod-

Can. J. Civ. Eng. 32: 442–448 (2005) doi: 10.1139/L04-107 © 2005 NRC Canada

442

Received 8 December 2003. Revision accepted 28 October2004. Published on the NRC Research Press Web site athttp://cjce.nrc.ca on 6 May 2005.

A. Elshorbagy.1 Centre for Advanced Numerical Simulation(CANSIM), Department of Civil and Geological Engineering,University of Saskatchewan, Saskatoon, SK S7N 5A9,Canada.R.S.V. Teegavarapu and L. Ormsbee. Department of CivilEngineering, University of Kentucky, Lexington, KY 40506-0281, USA.

Written discussion of this article is welcomed and will bereceived by the Editor until 31 August 2005.

1Corresponding author (e-mail: Elshorbagy@engr.usask.ca).

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ies. The TMDL process usually refers to the plan to developand implement the TMDL of a quantifiable pollutant thatachieves compliance with the standards (NRC 2001). Sec-tion 303(d) of the Clean Water Act and the US Environmen-tal Protection Agency (USEPA) Water Quality Planning andManagement Regulations (40 CFR Part 130) require statesto develop TMDLs for water bodies that are not meetingdesignated uses under technology-based controls for pollu-tion. Development of TMDLs of different pollutants at thewatershed level enables managers to enforce constraints onthe allowable level of activities, which makes the TMDL ap-proach come under the protection category of BMPs. If thelevel of activities or the water quality standards in waterbodies violate the recommended values from the TMDLstudy, a load reduction in the watershed could be easily sug-gested, which also makes the TMDL approach a candidatefor rehabilitation practices of BMPs.

This discussion paper is presented with the purpose ofbringing the TMDL approach to the fore and to the attentionof the watershed management community in Canada. Al-though the approach is not enacted in Canada, it can be asound foundation for research and implementation of waterquality management schemes. The next section explains theTMDL approach, and the following section outlines the pro-cess of developing a TMDL for different pollutants. Subse-quent sections present an application of the pH TMDL andimplementation guidelines. A section on challenges and re-search avenues of the TMDL issue is provided, and the pa-per closes with final remarks and conclusions.

Water quality management in the Canadiancontext

A number of water quality modeling and managementstudies have been undertaken in several provinces acrossCanada to assess the impairment of streams and water bod-ies. Concrete evidence of adoption and application of theTMDL approach in a form similar to that applied in the USis not available at this point in time, however. A literaturesearch of the application of TMDL or similar approaches inthe Canadian context has yielded a direction towards imple-mentation of such management strategies for improving thequality of impaired streams.

Total maximum daily load approach

Over the last few decades, water quality management hasbeen driven by the control of point sources of pollution andthe use of effluent-based water quality standards. Regula-tions that have required discharges to comply with certainstandards for “criteria pollutants” have been in place foryears. Today, pollutants such as nutrients, sediment, and de-terioration of pH level, which are often associated with non-point sources, are jeopardizing water quality. This hasshifted the focus of water quality management from effluent-based to ambient-based water quality standards (NRC 2001).This is what the TMDL is all about. A separate TMDLshould be developed for each pollutant under consideration(criteria pollutant) and for each impaired water body (e.g.,stream, lake). The TMDL program should include informa-

tion to identify point and nonpoint sources of pollution,along with their respective contributions to water quality im-pairment, and information needed to allocate load reductionamong sources.

Studies and research on different issues related to TMDLare ongoing. Chapra (2003) has outlined the modelingframework to help develop TMDLs for different water bod-ies. The Neuse River estuary in North Carolina has been astudy area for a few TMDL studies. Borsuk et al. (2003)proposed an integrated approach to TMDL development us-ing a Bayesian probability network model, and Bowen andHieronymus (2003) used the CE-QUAL-W2 model for thesame purpose and same study area. Systems analysis hasbeen also shown to be useful for waste-load allocation andTMDL development by considering a wide range of objec-tives and alternatives (Haith 2003).

TMDL development

In this paper, three brief examples of pH, nutrient, andpathogen TMDLs are provided with the purpose of exempli-fying the approach using different water quality parameters.The pH example, which is provided in detail, helps highlightan important issue of quantifying a pollutant load of a pa-rameter that is usually measured as a nonquantitative indexvalue. Detrimental reduction in the pH level in a water body(e.g., stream) can be the result of acid mine drainage (AMD)due to mining activities. The application in this paper isbased on data collected from a watershed in the state ofKentucky. Nutrients (e.g., total phosphorous) are a classicalexample of a quantifiable pollutant load that may result fromagricultural activities and effluent from wastewater treatmentplants. In southeastern Kentucky, there is a severe problemof pathogens in the streams because of the existence ofstraight pipes (discharging raw sewage from homes directlyto the streams), failing septic tanks, and the remains of live-stock in the region. A brief explanation and referral to theliterature on both nutrient and pathogen TMDLs are pro-vided in this paper.

pH TMDL

In the process of coal mining, iron sulphide (FeS2) is un-covered and exposed to the oxidizing action of oxygen in theair (O2), water, and sulphur-oxidizing bacteria. The endproducts are ferrous iron (FeSO4) and acid solution (H2SO4).A slow subsequent oxidation to ferric iron (Fe2(SO4)3) oc-curs. The ferric acid solution is further diluted and neutral-ized in a receiving stream, and the pH rises. The ferric iron(Fe3+ or Fe2(SO4)3) hydrolyses and brownish yellow ferrichydroxide (Fe(OH)3) precipitates and may remain suspendedin the stream, indicating that there has been production ofsulphuric acid that causes the low pH. According toOrmsbee et al. (2004), the overall relationship shown in thefollowing equation indicates that a net of 4 mol of H+ areliberated for each mole of pyrite (FeS2) oxidized, causing itto be an extremely acidic weathering reaction:

[1] 4FeS 15O 14H O 8H SO 4Fe(OH)2 2 2 2 4 3+ + ↔ +

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The TMDL describes the maximum amount of pollutant astream can assimilate without violating water quality stan-dards. The units of the load measurement are mass per unittime (e.g., mg/h). In the case of pH, it is measured in Stan-dard units, with no associated mass unit. In this paper, theapproach proposed by Ormsbee et al. (2004) and approvedby the USEPA is adopted. That is, the total load is expressedin terms of an equivalent hydrogen ion load. Since the activ-ity of the hydrogen ion load in a water column can be deter-mined by measuring the pH, the relationship betweenhydrogen load and pH can be expressed as follows:

[2] { }H pH+ −= 10

where pH is the negative logarithm of the H+ ion activity inmoles per litre. The actual molar concentration [H+] is re-lated to the measured activity {H+} as follows:

[3] [ ] { }/H H+ += γ

where γ is an activity coefficient that is dependent on theionic strength µ of the source water (Fig. 1) (Snoeyink andJenkins 1980). The ionic strength of a given source watercan be approximated using the total dissolved solids (TDS)in milligrams per litre (Snoeyink and Jenkins 1980) as fol-lows:

[4] µ = × ×−( . )25 10 5 TDS

The atomic weight of hydrogen is 1 g/mol, so the concen-tration of hydrogen ions in moles per litre and grams perlitre is the same. For a given day, the multiplication of aver-age flow rate by the concentration of ions results in the ionload for that day in grams per litre. Therefore, based on aminimum pH value of 6.0 and the flow rate, the TMDL ofhydrogen ions can be calculated. An envelope of the TMDLcan be obtained as a function of the flow rate, taking intoconsideration the activity coefficient γ.

Margin of safetyTMDLs are comprised of the sum of individual waste-

load allocations (WLA) for point sources, load allocations(LA) for nonpoint sources and natural background levels,and margin of safety (MOS), which accounts for uncertaintyin the relation between pollutant loads and the quality of thereceiving water body (Ormsbee et al. 2004). The TMDL canbe generically described by the following equation:

[5] TMDL sum(WLA) sum(LA) MOS= + +

TMDL approach for addressing nutrientimpairment of streams

Nutrients such as phosphorus, nitrogen, and carbon areknown to be vital to sustaining aquatic ecosystems. Anabundance of these nutrients will accelerate the naturaleutrophication process of a water body, however, and is alsoconsidered to be an interference with desirable water uses(Thomann and Mueller 1987). The eutrophication processsuggests an increase in nutrients that leads to nuisance algaeblooms, or more commonly periphyton (rooted algae) inswift-moving fresh waters. These algae blooms pose manyproblems for a body of water. The physical congestion pro-hibits recreational boating and swimming and degrades the

visual aesthetics of the water body. The most detrimentaleffect is the oxygen demand created by the algae bloomsduring respiration, which chokes off the less resilient andstatic zooplankton (small aquatic animal life) that cannot es-cape areas of low dissolved oxygen. Human inclusions inthe ecosystem by means of wastewater treatment effluentsand (or) agricultural–fertilization practices are typical causesfor the imbalance of nutrients in the ecosystem. Nutrient im-pairment of streams is a common problem plaguing manystreams and water bodies.

A number of models and modeling environments areavailable today to develop TMDLs for water bodies andstreams impaired due to nutrients when the point and non-point sources are considered. PLOAD in the BASINS mod-eling environment (USEPA 2002) is generally used toestimate seasonal or annual loadings to feed simple eutro-phication models or to test the effectiveness of the imple-mented TMDL (post-implementation monitoring). SWAT inBASINS (USEPA 2002) can model pesticide and nutrientcycling, bacteria transport, erosion, and sediment transport.SWAT is most suitable for TMDL development for water-sheds dominated by agricultural lands. Hydrological simula-tion program Fortran (HSPF) in BASINS (USEPA 2002) canbe used to simulate nutrient loadings over rural and urbanwatersheds. The protocol developed by the USEPA for nutri-ent TMDLs (USEPA 1999) reports a number of modelingenvironments suitable for developing TMDLs and two exam-ple applications of TMDLs in two separate watersheds in theUS.

Simple fate and transport models are also considered valu-able and applicable in many situations where the processesgoverning the pollutant loads can be modeled easily by con-sidering only the dominant processes, which can beexpressed as simplified first-order decay equations for pol-lutants. An example of such a simple model is the one devel-oped by Ormsbee and Blandford (2000). In their work theyaddress the issue of TMDL development for a nutrient(phosphorus) impaired stream due to point and nonpointsources. Fate and transport of phosphorus through a streamsystem are modeled using a spatially distributed kinematicwave model. A distributed model based on kinematic theoryis used for flow routing, since it determines flows in bothtime and space and is also useful in modeling streams wherethe lateral flows may constitute a majority of the total flow.By combining pollutant fate and transport equations with a

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Fig. 1. Activity coefficients of H+ as a function of ionic strength(Snoeyink and Jenkins 1980).

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kinematic wave model, both flow and pollutant concentra-tions are simultaneously determined at various locationsalong the stream. Two physical transport processes thatdominate the movement of the pollutant, namely advectionand dispersion, are simulated using simplified fate and trans-port equations seamlessly integrated with the kinematicwave model.

TMDL approach for pathogen impairment

An excessive amount of bacteria would indicate an in-creased risk of pathogen-induced illnesses to human beings.Fecal coliform is generally used as an indicator bacteria toassess the degree of impairment related to water qualitystandards–criteria for bacteria for designated water uses(USEPA 1986). The designated uses are recreation (primaryand secondary contact), public water supplies, and protec-tion of aquatic life.

The main sources of pathogen contamination of waters aredue to point and nonpoint sources. Point sources are mainlythe wastewater treatment plants and combined sewer out-flows (CSOs) (Novotny et al. 1989). Continuous simulationof indicator bacteria loadings can be undertaken using HSPFavailable under the BASINS (USEPA 2002) modeling envi-ronment. In many studies, pathogen decay is modeled as afirst-order decay process, and in a few cases build-up andwash-off processes are also considered. In large rural and ur-ban watersheds, HSPF is applicable for simulation of dailypathogen loadings and therefore is useful for developingTMDLs. Muddy Creek (MCTEW 1999) is an example ofsuccessful pathogen TMDL studies that have been reportedin the past using the BASINS modeling environment. TheUSEPA provides a detailed protocol (USEPA 2001) for de-veloping pathogen TMDLs and with modeling approachesthat range from screening-level models to comprehensivemodeling procedures.

Application of pH TMDL to Cane Branchwatershed

The 1998 303(d) list of waters for Kentucky (Kentucky Di-vision of Water 1998) indicates 2.0 mi. (1 mi. = 1.609 km) ofCane Branch, from the headwaters, do not meet the desig-nated use for both contact recreation (swimming) andaquatic life. The Cane Branch is entirely contained withinMcCreary County in southern Kentucky (Fig. 2) and pro-vides an example of impairment caused by AMD. Bitumi-nous coal mine drainage, such as that found in the CaneBranch watershed, contains very concentrated sulphuric acidand high concentrations of metals, especially iron, manga-nese, and aluminium.

Mining activities in the Cane Branch watershed occurredprior to 1977. Its waters were monitored as early as 1978 bythe Kentucky Division of Water (DOW). The DOW reportedthat Cane Branch could not support aquatic life and swim-ming use supports based on observed pH readings below5.0. The observed cause of pH impairment was surface min-ing activities and resource extraction.

In developing TMDLs for pH impairment when the viola-tions are caused by nonpoint sources on small intermittentstreams, such as Cane Branch, frequently used flow criteria

like 7Q10 flow are not practical, simply because flow mayactually be zero. In recognition of the inherent difficultiesassociated with imposition of a “no-exceedance” pH crite-rion on potentially intermittent streams, the Kentucky DOWhas decided to use the lowest 1 year average discharge of themost recent 10 year flow record as the flow basis for settingthe appropriate TMDL. The lowest 10 year mean annual dis-charge for Cane Branch is estimated to be 0.025 m3/s(0.9 cfs). Use of this discharge yields a TMDL of 2.27 g/day(0.005 lb/day) of hydrogen ions.

In developing a pH TMDL for Cane Branch, a conserva-tive activity coefficient of 1.0 is assumed, thus providing foran implicit margin of safety in determining the associatedTMDL. Once the TMDL for the watershed has been deter-mined, the associated load must be allocated between bothpoint loads (waste-load allocations) and nonpoint sourceloads (load allocations). Since there are no known permittedpoint sources in this watershed, the waste-load allocationsare assumed to be zero. Thus, the remaining load allocationsare directly equal to the associated TMDL.

Predicted load assessmentTo provide a more recent characterization of the pH levels

in the watershed, the University of Kentucky and MurrayState University collected data from the watershed at the siteindicated in Fig. 3 (A1). A summary of the results obtainedfrom the site is shown in Table 1. Based on a physical in-spection of the watershed, it is hypothesized that the degra-dation of the pH in the stream is directly related to oxidationof sulphur that occurs as runoff flows over the spoil areas as-sociated with previous mining activities in the basin. Usingthe most recent monitoring data, an inductive model was de-veloped for the monitoring site that relates total hydrogenion loading to streamflow. The model developed for sub-basin 1 is shown in Fig. 4, which shows that the total loadincreases as a function of flow, illustrating the significant re-lationship between the pH degradation and nonpoint sources.The developed relationship may be used to predict total ionloading to a stream on the basis of streamflow. The predictedhydrogen ion load for the site may be obtained using thecritical discharge (0.9 cfs) along with the associated load re-lationship (Fig. 4). Application of this approach yields thepredicted load for the site of 26.31 g/day (0.058 lb/day).

Load reduction allocationOnce a TMDL is developed for a watershed, the required

load reductions can be found by determining the amount ofload that must be reduced to lower the in-stream load to alevel at or below the TMDL. Separate TMDLs (and associ-ated load reductions) can be developed for individualsubbasins within the watershed. In the current study, theTMDLs and associated load reduction are developed for thesubbasin identified in Fig. 3. Attainment of the individualload reductions should then meet the TMDL requirement forthe complete watershed. This could be accomplished by sub-tracting the incremental TMDL from the incremental pre-dicted load for the subbasin. Based on a critical flow of0.025 m3/s (0.9 cfs) and using Fig. 4, the predicted load is26.31 g/day (0.058 lb/day). This approach allocates the totalload reduction for Cane Branch (site A1). The TMDL at apH level of 6.0 is 2.27 g/day (0.005 lb/day), as mentioned

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earlier. Therefore, the required load reduction in the water-shed is equal to 24.04 g/day (0.053 lb/day), which is the pre-dicted load minus the TMDL.

Implementation guidelines

Limestone (CaCO3) can be used to neutralize acidic wa-ters for streams with pH < 6.0. This has been demonstratedand confirmed by recent studies (Clayton et al. 1998):

[6] CaCO 2H H CO Ca3 2 32+ → ++ +

Therefore, the total mass of CaCO3 required to neutralize1 g of hydrogen ions (H+) can be obtained by dividing themolecular weight of CaCO3 (100) by the molecular weightof two hydrogen atoms (2):

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Date Flow (m3/s)a pH

1 March 0.007 (0.23) 5.26 March 0.010 (0.34) 5.2

13 March 0.014 (0.48) 4.721 March 0.072 (2.53) 4.925 March 0.042 (1.48) 5.029 March 0.078 (2.76) 4.9

2 April 0.070 (2.48) 5.118 April 0.041 (1.46) 4.730 April 0.011 (0.39) 4.5

aValues in parentheses are in cfs.

Table 1. Details of flow and pH sam-pling results at site A1 in 2002.

Fig. 2. Location of the Cane Branch watershed in southeastern Kentucky, US. HUC, hydrologic unit code. Approximate scale is1:2 000 000.

Fig. 3. Cane Branch watershed sampling site A1. Fig. 4. Discharge versus H+ ion loading for pH for site A1.

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[7] mass of limestone

= ×50 mass of hydrogen ions g/day)(

The estimated annual load of limestone can be estimated asfollows:

[8] annual mass of limestone

= × ×365 50 mass of hydrogen ions g/day)(

It is worth mentioning that some of the limestone added tothe stream may not be readily available as soluble CaCO3,and the existence of other metal ions, such as Fe, Al, andMn, needs an additional amount of limestone for their neu-tralization. Streams with pH < 4.5 usually have some ofthese metal ions present in the acid mine drainage (Snoeyinkand Jenkins 1980). Although this problem has been simplytackled by applying rates of two to four times the calculatedamounts of limestone in Kentucky and West Virginia, an ef-fective way to deal with it is by determining the amount oflimestone required through calculating the total acidity inthe water column (Ormsbee et al. 2004).

The total acidity, which is normally defined as a measureof the concentration of acids, can be determined by titratinga water sample with a standard solution of a strong base(NaOH) to an observed end-point pH of 8.3. The mass ofNaOH required to raise the sample pH to 8.3 is (Ormsbee etal. 2004)

[9] acidity (as mg CaCO ) [50 000 (mL of NaOH)3 =× (normality of NaOH)] [(mass of sample used (mg)]/

A relationship between pH and total acidity can be devel-oped for a given stream using measured values of pH andacidity. When such measurements of total acidity are absent,as in the case of Cane Branch, a regional curve for the rela-tionship between required hydrogen ion reduction (to raisethe pH up to 8.3) and the corresponding load of CaCO3 canbe developed from data available in surrounding watersheds.Figure 5 is an example of such a regional curve for the re-gion surrounding the Cane Branch watershed. Loading val-ues produced using Fig. 5 increase the pH to 8.3 (based onthe definition of total acidity). Thus the amount of CaCO3estimated using this approach will be conservative. UsingFig. 5 and the required load reduction, the amount of CaCO3loading can be estimated as follows:

[10] CaCO (g/day) 301.75 24.113 = ×= =7274 g/day 2.66 t /year

Challenges and future research directions

The TMDL approach to water quality management comeswith a variety of challenges and research avenues that hy-drologists and water quality engineers need to explore. Theneed for watershed models that facilitate the decision-making process and have the ability to describe the transi-tion from first-order streams to higher order streams andrivers is one of these challenges (Thomann 1998). Anotherchallenge, which seemingly contradicts the first, is the rec-ommendation made by the US National Research Council(NRC 2001) that in many situations an exceedingly simplemodel is all that is needed for TMDL development. Detailed

mechanistic models for TMDL development in data-poorsituations should not be advocated. Basically, the issue ofsimplicity versus complexity in water quality modelingneeds to be addressed. Stow et al. (2003) have concludedthat the predictive accuracy of water quality parameters wasno better in the more process oriented spatially detailedmodels than in the aggregate probabilistic model.

Public participation and accountability have becomeincreasingly important in water quality related issues. Stake-holders should be involved early in TMDL and model devel-opment to demystify the modeling process and enhanceconfidence in the model (Maguire 2003).

Developing the TMDL for a water body (stream) relies onidentifying a critical flow. Apparently in many cases, such asin the case of intermittent streams, critical flow is deter-mined in a probabilistic sense, and therefore modeling forthe purpose of developing the TMDL should take into ac-count statistical and probabilistic approaches in defining im-pairment and critical flows and developing the TMDL. Stowet al. (2003) and Shabman and Smith (2003) have addressedthe issue of the statistical approach, including using aBayesian network, in developing TMDLs. The door is wideopen for more research in this direction. Developing dy-namic (time varying) or seasonal TMDLs may be perceivedas a simplified version of the probabilistic approach. At leastdynamic or seasonal TMDLs can help address the issue ofdifferent loading and different critical flow and thus assimi-lating capacity during different times of the year.

In this paper, a margin of safety (MOS) has been consid-ered in accounting for uncertainty in developing the TMDL.This is one way of handling the uncertainty in many aspectsof the process. Because of the natural variability in waterquality parameters and the limits of predictability, however,a small MOS may result in non-attainment of the water qual-ity goal, and a large MOS may be costly and inefficient.Since parameters involved in TMDL determination areprobabilistic and the MOS is a measure of uncertainty, theMOS should be addressed through a formal uncertainty anderror-propagation analysis (NRC 2001). The protocols devel-oped by the USEPA (1999, 2001) for nutrients and patho-gens are continuously being revised and updated based onthe experiences and lessons learned from the appliedTMDLs in the US.

Last but not least, the current methodology for determin-ing the TMDL is designed for each pollutant individually.

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Fig. 5. CaCO3 loading versus required hydrogen ion reduction.

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For example, a pH value between 6.0 and 9.0 could be ac-ceptable for a warm water aquatic habitat, but the questionis, in the case of the existence of other stressors (pollutants),will a pH value of 6.0, for example, be tolerable. The issueof developing the TMDL for multiple stressors is anotherwide venue and challenge for researchers.

Conclusions

The total maximum daily load (TMDL) approach is beingincreasingly adopted in the US to counter the further impair-ment of already impaired rivers and surface-water bodies.This paper outlines the general process for the developmentof TMDL approaches for management of surface water qual-ity impaired by increased concentrations of three major wa-ter quality constituents. The paper also illustrates thedevelopment of the TMDL approach for a pH-impairedstream in Kentucky, US, and highlights the technical andpractical aspects of implementation. Although the TMDLapproach is conceptually simple, the development of practi-cal management strategies and the implementation of recom-mended load reductions are specific to a stream or watershedunder consideration. The paper reports several issues thatrange from the preliminary TMDL development process tothe final implementation phase. The TMDL approach iswidely accepted in the US as a conceptually valid mecha-nism for addressing stream and watershed impairment is-sues, and through this paper the authors aim to provide ageneral overview of lessons learned from the US experience.It should be noted, however, that the approach is still underfurther investigation and research to refine issues at both thetechnical and implementation levels.

Acknowledgements

The first author would like to acknowledge the financialsupport provided to him through the Natural Sciences andEngineering Research Council of Canada (NSERC) Discov-ery Grant Program.

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