Development And Evaluation Of Consensus-Based Sediment .
Gch. Envimn. Contam. Toxicol. 39, 20-31 (2000)DOI: 10.1007/sOM440010075!!,A R C H I V E S O FEnviranrnenta Contaminations n d Twxicw ogyDevelopment and Evaluation of Consensus-Based Sediment Quality Guidelinesfor Freshwater Ecosystems,D. D. a c o n a l dC., ' G. n e r s o lT.l , A. Berge?' MacDonald Environmental Sciences Ltd., 2376 Yellow Point Road. Nanaimo, British Columbia V9X 1W5, CanadaColumbia Environmental Resexch Center, U.S. Geological Surrey, 42W New Haven Road, Columbia, Missouri 65201, USA' 159.1410 Richmond Avenue, Houston, Texas 77006, USAReceived. 23 August 1999lAccepted: 13 January 2000!I!!!Abstract. Numerical sediment quality guidelines (SQGs) forfreshwater ecosystems have previously been developed using avariety of approaches. Each approach has ce tainadvantagesand limitations which influence their application in the sediment quality assessment process. In an effort to focus on theagreement among these various published SQGs, consensusbased SQGs were developed for 28 chemicals of concern infreshwater sediments (i.e., metals, polycyclic aromatic hydrocarbons, polychlorinated biphenyls* and pesticides). For eachcontaminant of concern, m o SQGs were developed from thepublished SQGs, including a threshold effect concentration(TEC) and a probable effect concentration (PEC). The resultantSQGs for each chemical were evaluated for reliability usingmatching sediment chemistry and toxicity data from field studies conducted throughout the United States. The results of thisevaluation indicated that most of the TECs (i.e., 21 of 28)provide an accurate basis for predicting the absence of sediment toxicity. Similarly, most of the PECs (i.e., 16 of 28)provide an accurate basis for predicting sediment toxicity.Mean PEC quotients were calculated to evaluate the combinedeffects of multiple contaminants in sediment. Results of theevaluation indicate that the incidence of toxicity is highlycorrelated to the mean PEC quotient (R2 0.98 for 347samples). It was concluded that the consensus-based SQGsprovide a reliable basis for assessing sediment quality conditions in freshwater ecosystems.Numerical sediment quality guidelines (SQGs; including sediment quality criteria, sediment quality objectives, and sediment quality standards) have been developed by various federal, state, and provincial agencies in North America for bothfreshwater and marine ecosystems. Such SQGs have been usedin numerous applications, including designing monitoring programs, interpreting historical data, evaluating the need fordetailed sediment quality assessments, assessing the quality ofConrspondence to:D. D. MacDonaldprospective dredged makrials, conducting remedial investigations and ecological risk assessments, and developing sedimentquality remediation objectives (Long and MacDonald 1998).Numerical SQGs have also been used by many scientists andmanagers to identify contaminants of concem in aquatic ecosystems and to rank areas of'concem on a regional or nationalbasis (e.g., US EPA 1 9 9 7 0 It is apparent, therefore, thatnumerical SQGs, when used in combination with other tools,such as sediment toxicity tests, represent a useful approach forassessing the quality of freshwater and marine sediments (MacDonald et al. 1992; US EPA 1992, 1996a. 1997a; Adams e! al.1992; Ingersoll er al. 1996, 1997).The SQGs that are currently being used in North America havebeen developed using a variety of approaches. The approachesthat have been selected by individual jurisdictions depend on thereceptors that are to be considered (e.g., sediment-dwelling organisms, wildlife, or humans), the degree of protection that is to beafforded, the geogaphic area to which the values are intended toapply (e.g., site-specific, regional, or national), and their intendeduses (e.g., screening tools, remediation objectives, identifyingtoxic and not-toxic samples, bioaccumulation assessment). Guidelines for assessing sediment quality relative to the potential foradverse effects on sediment-dwelling organisms in freshwatersystems have been derived using a combination of tlieoreticd andempirical approaches, primarily including the equilibrium partitioning approach (EqPA; Di Tom et al. 1991;NYSDEC 1994; USEPA 1997a), screening level concentration approach (SLCA; Persaud et al. 1993), effects range approach e m , Long andMorgan1991; Ingersoll etal. 1996), effects level approach @LA, Smith etal. 1996; Ingersoll et al. 1996), and apparent effects thresholdapproach (AETA; Cubbage et al. 1997). Application of thesemethods has resulted in the derivation of numerical SQGs formany chemicals of potential concem in freshwater sediments.Selection of the most appropriate SQGs for specific applications can be a daunting task for sediment assessors. This taskis particularly challenging because limited guidance is currently available on the recommended uses of the various SQGs.In addition, the numerical SQGs for any particular substancecan differ by several orders of magnitude, depending on thederivation procedure and intended use. The SQG selectionprocess is further complicated due to unceaaiuties regarding
Freshwater Sediment Quality Guidelinesthe bioavailability of sediment-associated contaminants, theeffects of covarying chemicals and chemical mixmres, and theecological relevance of the guidelines (MacDonald et al. 2000). t is not surprising, therefore, that controversies have occurredover the proper use of these sediment quality assessment tools.This paper represents the thud in a series that is intended toaddress some of the difficulties associated with the assessment ofsediment quality conditions using various numerical SQGs. Theficst paper was focused on resolving the "mixture paradox" that isassociated with the application of empirically derived SQGs forindividual PAHs. In this case, the paradox was resolved by developing consensus SQGs for SPAHs (ie., total PAHs; Swaxtz1999). The second paper was Suected at the development andevaluation of consensus-based sediment effect concentrations fortotal PCBs, which provided a basis for resolving a similar mixmreparadox for that group of contaminants using empirically derivedSQGs (MacDonald et al. 2000). The results of these investigationsdemonstrated that consensus-based SQGs provide a unifying synthesis of the exis6ng guidelines, reflect causal rather than correlative effects, and account for the effects of contaminant mixturesin sediment (Swartz 1999).The purpose of this third paper is to further address uncertainties associated with the application of numerical SQGs byproviding a unifying synthesis of the published SQGs forfreshwater sediments. To this end, the published SQGS for 28chemical substances were assembled and classified into twocategories in accordance with their original narrative intent.These published SQGs were then used to develop two consensus-based SQGs for each contaminant, including a thresholdeffect concentration (TEC;below which adverse effects are notexpected to occur) and a probable effect concentration (PEC;abovewhich adverse effects are expected to occur more oftenthan not). An evaluation of resultant consensus-based SQGswas conducted to provide a basis for determining the ability ofthese tools to predict the presence, absence, and frequency ofsediment toxicity in field-collected sediments from variouslocations across the United States.21weight-normalized SQGs were utilized because the results of previousstudies have shown that they predicted sediment toxicity as well orhetter than organic carbon-normalized SQGs in field-collected sediments (Barnick et al. 1988; Long er al. 1995; Ingersoll el al. 1996; USEPA 1996a; MacDonald 1997).The effects-based SQGs that met the selection criteria were thengmuped to facilitate the derivation of consensus-based SQGs (Swanz1999). Specifically, the previouslypublished SQGs for the protectionof sediment-dwelling organisms in freshwater ecosystems weregrouved into two cateaories accordine to their orieinal narrative intent.incl;ding TECs and k s . The T@S were inteided to identify contaminant concentrations below which harmful effects on sedimentdwelling organisms were not expected. TECs include threshold effectlevels (TELs; Smith er al. 1996; US EPA 1996al. effect range lowvalues (ERLs; Long and Morgan 1991), lowest effect levels (LELs;Persaud et al. 1993), minimal effect thresholds (METs; EC and MENVIQ 1992). and sediment quality advisory levels (SQALs; US EPA1997a). The PBCs were intended to identify contaminant concentrations above which harmful effects on sediment-dwelling organismswere expected to occur frequently (MacDonald er al. 1996; Swaru1999). PECs include Drobable effect levels (PELS: Smith er al. 1996: .US EPA 1996a,, eifcct ran& medtan value; ( R i l sLone,and Mor6x1 19911: severe rilect leteli SELs: P e n a drr al. 1993). md toxlceffect thresholds (TETs: EC and MENVIO 1992:, Table 1)-,Following classification pf the published SQGs, consensus-basedTECs were calculated by determinine the eeometric mean of the SOGsthat were included in this category (Table 2). Likewise, consensusbased PECs were calculated by determining the geometric mean of thePBC-type values (Table 3). The geometric mean, rather than thearithmetic mean or median, was calculated because it provides anestimate of cenual tendency that is not unduly affected by extremevalues and because the distributions of the SQGs were not known(MacDouald er 01. 2000). Consensus-based TECs or PECs were calculated only if three of more published SQGs were available for achemical substance or group of substaaces. .- !,,.Evaluation of the SQGsIThe consensus-based SQGs were critically evaluated to determine ifthe? would urnvide effective tools for assessine sediment aualitv,cond tionsin ireshw terc:osystema Spc: lic llg,the rcllsbilit) si theMaterials and Methods ndiv dualor combtncd coniensur-blsed TECs and PECc ior assessumscdimcnt quality con&ttons was evalustcd by detcrmlnlng their predi:tive ah htyIn this smdy, predlcuve hbility IS defined as the abllityDerivation of the Consensus-Based SQGsof the vanous SQGs to cone:rly clissriy field-collected sediments astoxlc or not lour, bnrcd on ihc melsured co lcenrr uonsoi chemlualA stepwise approach was used to develop the consensus-based SQGsconuminnnls. Thc predicuve ability of the SQGs was rvaluated usme.for common contaminants of concern in freshwater sediments. As aa three-step process.first step, the published SQGs that have been derived by variousIn the first step of the SQG evaluation process, matching sedimentinvestigators for assessing the quality of freshwater sediments werechemistry and biological effects data were comp ledfor various freshcollated. Next, the SQGs obtained from all sources were evaluated towater locations in the United States. Because the data sets weredetermine theirapplicibility to this study. To facilitate this evaluation,generated for a wide variety of purposes, each study was evaluated tothe supporring documentation for each of the SQGs was reviewed. Theassure the aualiw of the data used for evaluatine the oredictive abilitvcollated SQGs were further considered for use in this smdy iE (1) theof the S Q G (Ling et al. 1998; Ingersoll and Mac dnald1999). Asmethods that were used to derive the SQGs were readily apparent; (2)result of this evaluation, data from the following freshwater locationsthe SQGs were based on emvirical data that related contaminantwere identified for use in this paper: Grand calumet i v e andr Indianacourcntrattons to M leffecis on sediment-jwelling orgmlsms orHarbor Canal, IN (Hoke er al. 1993; Giesy et al. 1993; Burton 1994:were intended to be prcdictrve of etfcc son sediment-dwelhngDorkin 1994): Indiana Harbor. IN (US EPA 1993a 1996a 1996b):.organ.isms (i.e., not simply an indicator of background contamination); andBuff*" v eNY; . RIS EPA 199%; 1996a): aglna;, River, hll(3) the SQGs had been derived on a de novo basis (ie., not simply . E P 1993b. 1996a): Clark Fork &vet, .\IT (USFWS 1993,. .\1111to!vnadopted from another jurisdiction or source). It was not the intent ofReservoir, MT (L'SFWS 1993):L w e r Columbia River. WA (Johnsonthis paper to collate bioaccumulation-based SQGs.and h'orton 1988): I w c Foxr River and Green B3y. M l (Call er olThe SQGs that were expressed on an organic carbon-normalized1991): Polomac River. DC (Schlrkn er 21. 1994: M'adc el a ; . 1994:basis were convened to dry weight-normalized values at 1% organice r , (Dlcksun era1 1389. CS EP.4\'ehsl;y era1 1994,. n r u k t \ , TXcarbon (MacDonald er al. 1994, 1996; US EPA 1997 ).The dry1996a). L'pper hlisslss ppiRiver, htU lo h10 \US EP.I 1996a. 1997h,,-.a.CS
21D. D. MacDonald er 01.Table 1. Descriptions of the published freshwater SQGs that have been developed using vnrious approachesType of SQGAcronymThreshold effect concentration SQGsLowest effect levelApproachLELSLCAThreshold effect levelTELWEAEffect r a n g e l o wERLWEATEL-HA28WEAMimmai effect thresholdMETSLCAChronic equilibrium partitioningthresholdSQALEqPASELSLCAProbable effect levelPELWE9Effect range-medianERMThreshold effect level for Hyalellaazreca in 28-day testsProbable effect concentration SQGsSevere effect levelIProbable effect level for Hyalellnazteca in 28-day tests'PEL-HA28,WEAWEADescriptionReferenceSediments are considered to be clean tomarginally polluted. No effects on themajority of sediment-dwellingorganisms are expected below thisconcentration.Represents the concentration below whichadverse effects are expected to occuronly rarely.Represents the chemical concentrationbeiow which adverse effects would berarely observed.Represents the concentration below whichadverse effects on survival or growth ofthe amphipod Hyalella azfeca areexpected to occur only rarely (in 28day tern).Sediments are considered to be clean tomarginally polluted. No effects on themajority of sediment-dwellingorganisms are expected beiow thisconcentration.Represents the concentration in sedimentsthat is eredicted to be associated withconcentrations in the interstitial waterbelow a chronic water quality criterion.Adverse effects on sediment-dwellingorganisms are predicted to occur onlyrareiy below this concentration.Persaud et al.(1993)Sediments are considered to be heavilypolluted. Adverse effects on themajority of sediment-dwellingorganisms are expected when thisconcentration is exceeded.Represents the concentration above whichadverse effects are expected to occurfrequently.Represents the chemical eoncentrauonabove which adverse effects wouldfrequently occur.Represents the concentration above whichadverse effects on survival or arowth ofthe amphipod Hyalella azteco &eexpected to occur frequently (in 28-daytests).Sediments are considered to be heavilypolluted. Adverse effects on sedimentdwelling organisms are expected whenthis concentration is exceeded.Persaud et al,(1993) Toxic effect thresholdTETSLCAand Waukegan Harbor, IL (US EPA 1996a; Kemble el al. 1999).These studies provided 17 data sets (347 sediment samples) withwhich to evaluate the predictive ability of theSQGs. These s M i e s alsorepresented a broad range in both sediment toxiclty and contamination;roughly 50% of these samples were found to be toxic based on theresults of the various toxlcity tests (the raw data from these studies aresummarized in IngersoU and MacDonald 1999).In the second step of the evaluauon, the measured concentration ofeach substance in each sediment sample was compared to the corresponding SQG for that substance. Sediment samples were predicted toSmith et al. (1996)Long and Morgan(1991)US BPA (1996a);Ingersoll et al.(1996)EC and MENVIQ(1992)Bolton et 01. (1985):Zaha (1992); USEPA (1997a)Smith et al. (1996)Long and Morgan(1991)US EPA (19968);Ingersoil er al.(1996) EC and MENVIQ(1992)be not toxic if the measured concenrrations of a chemical substnncewere lower than the corresponding TEC. Similarly, samples wenpredicted to be toxic if the corresponding PECs were exceeded infield-collected sediments. Samples with contaminant concentrationsbetween the TEC and PEC were neither predicted to be toxic nornontoxic (ie., the individual SQGs are not intended to provide guidance w i h n this range of concentrations). The compuisons of measured concentrations to the SQGs were conducted for each of the 28chemicals of concern for which SQGs were developed.In the third step of the evaluation, the accuracy of each predicuon
Freshwater Sediment Quality Guidelines23Table 2. Sediment quality guidelines for metals in freshwater ecosystems that reflect TECs (r.e., below which harmful effects are unlkely tobe observed)Threshold Effect sBased TECSQALMetals (in mgikg ncPolycyclic aromatic hydrocarbons (in g k aceneFluoranthenePyreneTotal PAHsPalychlorinated biphenyls (in &@kg DW)Toal PCBsOrganochlorine pesticides (in @kg DW)ChlordaneDieldrinSum DDDSum DDESum DDTTotal DDTsEndrinHeptachlor epoxideLindane (gamma-BHC)TEL Threshold effect level; dry weight (Smlth et al. 1996)LEL Lowest effect level, dry weight (Persaud er al. 1993)MET Minimal effect threshold; drj weight (EC and MENVIQ 1992)ERL Effect range low: dry weight (Long and Morgan 1991)E L H A 2 8 Threshold effect level for Hyalella azteca; 28 day test; dry weight (US EPA 1996a)SQAL Sediment quality advisory levels; dry weight at 1% OC (US EPA 1997a)NG No guidelinewas evaluated by determining if the sediment sample acNally wastoxic to one or more aauatic o eanisms.as indicated bv the results of"v3nOus sediment toxrcity rests (Ingersoll and MacDonald 1999,. Thefollow ngresponses of aquattc orcanlsms to contaminant challcnzes(ie. toxicity test endpoinfs) were ;sed as indicators of toxicity in thisassessment (i.e., sediment samples were designated as toxic if one ormore of the following endpoints were significantly different from theresponses observed in reference or control sediments), including amphipod (Hyalella azteca) survival, growth, or reproduction; mayfly(flexagmia limbata) survival or growth; midge (Chironomus tentans0' Chironomus ripariu) survival or growth: midge deformities; oligochaete (Lumbriculw variegarus) survival: daphnid (Ceriodnphniadubia) survival; and bacterial (Photobacterium phosphoreum) lumi- 'nescence (i.e., Microtox). In contrast, sediment samples were designated as nontoxic if they did not cause a significant response in at leastOne of these test endpoints. In this sNdy, predictive ability wasCalculatedas the ratio of the number of samples that were correctly classified as toxic or nontoxic to the total number of samples that werepredicted to be toxic or nontoxic using the various SQGs (predictiveability was expressed as a percentage).The criteria for evaluating the reliability of the consensus-basedPBCs were adapted from Long er al. (1998). These criteria are intended to reflect the narrative intent of each tvoe. of SOG- (i.6.srdiment toxic tyshould be obselv:d on.y rarely bciou the TEC and l o ubel d frequenrly obsencd above thc PEC,. SpecificaUy, chc individttal TECs were considwed to rovidea reliable basla for 3ssessinethe quality of freshwater sediments if more than 75% of %esedimentsamples were correctly redictedto be not toxic. Similarlv, the indi,,dual PEC for tach s"brv;mce was consldcred robe reliabie iigrcxcrthan 75% of lhc sediment ,ampler were cumectly prcilcted to rox uuring the PEC Therefore. the mgcr levels of both fdsc posiuvcs ( r e . .ramplcs incorre.-rl) classified as toxic) and false nepsrivcr ( d e , s m plcs incorrecrly clas iiedas nor toxic: w3s 25"o urine the TEC andPEC. To assure that the results of the predictive ability evaluation wereI.I1I!!I I'//,I
Table 3. Sediment quality guidelines for metals in freshwater ecosystems that reflect PECs (i.e., above which 1obse
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