Ecological Risk Assessment - Part 5 pptx

29.2 SCREENING BENCHMARKS Screening benchmarks are concentrations of chemicals that are believed to constitute thresholdsfor potential toxic effects on some category of receptors exposed

Part V

Risk Characterization

Statements about single events can’t be decided by a calculator; they have to be hashed out byweighing the evidence, evaluating the persuasiveness of arguments, recasting the statements tomake them easier to evaluate, and all the other fallible processes by which mortal beings makeinductive guesses about an unknowable future

Pinker (1997)

Risk characterization is the phase of ecological risk assessment that integrates the exposureand the exposure–response profiles to evaluate the likelihood of adverse ecological effects anduses those results to synthesize a useful conclusion In other words, it is the process ofestimating and interpreting the risks and associated uncertainties There are two fundamen-tally different types of risk characterizations Screening assessments are intended to quicklyand easily divide risks into those that need more attention and those that can be ignored

providi ng risk estimat es for all asses sment end points (Ch apter 32)

Risk characterizations may be algorithmic in that they may use a standard procedure based on astandard set of input information using standard assumptions, scenarios, and models Algorith-mic approaches are used primarily in ecological risk assessments of pesticides and industrialchemicals (Luttik and van Raaij 2003; EPPO 2004) They are desirable in that context, becausethey are efficient and fair to all of the competitive products that come before a chemical regulator.They are popular with regulated parties, because the data requirements are clear, and the outcome

of a regulatory assessment can be predicted Algorithmic approaches are disadvantageous whenchemicals have properties that are not considered in the algorithm The obvious example isendocrine disruptors that are not addressed by standard test batteries or effects models

Alternatively, risk characterization may be performed ad hoc The advantage of ad hocapproaches is that they can be designed to provide the best estimate of risk and uncertaintygiven the types of information that are available and the particular circumstances of theassessment Ad hoc approaches have been used for contaminated sites, because the condi-tions and information sets are highly variable Ad hoc approaches are also employed whenassessments are highly contentious or when unusual issues such as developmental deformitiesare involved

Infer ence in risk charact erization takes diff erent form s de pending on the type of assessmentand the types of infor mation that are avail able They differ in how they use the avail able lines

of evidence to reach a con clusion In risk ch aracteriza tion, a line of evidence is an estimat e ofexpo sure an d a corres pondi ng exposure–r esponse relationshi p

Single line of eviden ce : The classic form of infer ence uses one line of ev idence, whi ch is eitherthe only availab le evidence or the best eviden ce For ch emicals, the most c ommon line ofevidence is an expo sure estimat e from a mathemati cal model and a num erical en dpoint from atoxic ity test

Weight of eviden ce : If mult iple lines of evidence are avail able, they may be joint ly sider ed The multiple lines may be from a single type of evidence (e.g., exp osure–r esponserelation ships from different tests) or from multiple types (e.g., chemical toxic ity tests, tests ofcon taminate d media , and biologi cal surveys )

con-Risk ch aracteriza tions may also be diff erentiated by the form of the infer ence

Rule -based inference : Risk asses sors may be provided with an inferen tial rule to de terminewhet her a risk is accepta ble The sim plest an d most common is: if the e xposure estimat eexceed s the benchmark effec ts level (i.e., HQ > 1; Secti on 31.1), the risk is unacce ptable

A more complex rule is: if the 90 th percent ile of the e xposure dist ribution exceeds the 10thpercen tile of the effec ts dist ribution, the risk is una cceptable (Section 30.5) Rule -basedinference is most common in algorithmic assessments of new chemicals However, an infer-ential rule may be developed for an individual assessment during the problem formulation(Ch apter 18) Rule-based inference may be applie d to screeni ng or defini tive asses sment s It isusuall y limit ed to a single line of evidence but , in its original form , the sedim ent qualit y triad

is a rule- based inferenti al method for three lines of evidence (Chap ter 32)

Ad hoc judgment: In many cases, risk characterizations include judgments concerningacceptability of a risk without a priori rules or guidance This approach provides the greatestflexibility and influence to the assessors, but lacks transparency and diminishes the role ofstakeholders and decision makers

Structured judgment: Many risk characterizations are too complex and the evidence tooambiguous to allow rule-based inference, but ad hoc judgment gives too much latitude toassessors In such cases, the assessor’s judgment can be guided by an inferential structureincluding organization of the input data by type of evidence, the use of standard consider-ations to evaluate the evidence, and scoring systems Examples of structures for judgment forcausal analys is and risk charact eriza tion are present ed in Chapt er 4 an d Chapter 32,respectively

Risk estimation: One may estimate risks and uncertainties and report them to a riskmanager who interprets the estimates and makes a decision Risk estimation is used indefinitive assessments and may be based on any number of lines of evidence Risk estimatesare essential if the results of risk characterization are to be used in an economic analysis,decision analysis, or other quantitative decision-support tool

Comparison of alternatives: Rather than characterizing risks from an agent or activity todetermine its acceptability, one may compare alternatives to determine which is preferable(Ch apter 33) Exa mples include alterna tive chemi cals with the same us e, alte rnative remedialactions for a contaminated or disturbed site, and alternative management plans for a forest.These approaches to inference are not mutually exclusive For example, it is often appro-priate to use structured judgment to determine whether significant effects are likely and then,

if the results are positive, use risk estimation to inform a decision

29 Criteria and Benchmarks

For various reasons , it is somet imes desir able to red uce the c omplex ities of expo sure–re sponserelationshi ps for v arious taxa, pro cesses, an d other eco logical pro perties to a single num berthat is presu med to be a suff iciently protect ive level Those that are us ed to separat eaccepta ble from unaccep table concen trations for regula tory purp oses are termed crite ria orstandar ds (hencef orth, sim ply crit eria) Thos e that are used for screenin g or priori tization aretermed screeni ng bench marks or screeni ng values

29.1 CRITERIA

Criter ia are con centrations of contam inants in water or other media that are intende d toconsti tute the bounds of regula tory accep tability given prescr ibed co nditions (Section 2.2).The only national ecological criteria in the United States are the acute and chronic NationalAmbient Water Quality Criteria (NAWQC) Criteria were proposed for sediments by theEnviron mental Pro tection Agency (EP A) but wer e conve rted to screeni ng gu idelines (Sect ion29.2.) The acu te NAW QC are calculated by the EPA as half the final acute value, whi ch is the5th percentile of the distribution of 48 to 96 h LC50values or equivalent median effectiveconcentration (EC50) values for each criterion chemical (Stephan et al 1985) The acuteNAWQC are intended to correspond to concentrations that would cause less than 50%mortality in 5% of exposed species in a relatively brief exposure Because the criterion isnot a no-effect level, the criterion is lowered if an important species is among the mostsensitive 5% (Figure 29.1) The chronic NAWQC are final acute values divided by the finalacute=chronic ratio, which is the geometric mean of quotients of at least three LC50=CV ratiosfrom tests of organisms belonging to different families of aquatic organisms (Stephan et al.1985) Chronic NAWQC are intended to prevent significant toxic effects in most chronicexposures Some, termed final residue values, are based on protection of humans or otherpiscivorous organisms rather than protection of aquatic organisms

Because criteria are applied to an entire state or nation, they should be derived in a way thataccounts for variance among sites and uncertainty Site-specific standards may incorporatesite properties to reduce either variance or uncertainty For example, the NAWQC for manymetals are functions of hardness, so that important sources of variance can be eliminated insite-specific applications (Spehar and Carlson 1984; Stephan et al 1985) Similarly, resultsfrom testing of local species may be used to modify national criteria in deriving site-specificstandards More broadly, standards may be derived for different classes of ecosystems (e.g.,freshwater and saltwater standards in the United States), different uses (e.g., agricultural,residential, commercial, and industrial land uses in Canadian soil guidelines), or differentlevels of protection (e.g., the designation of National Parks as Class I under the US CleanAir Act)

435

NAWQC are applicable regulatory criteria and are generally adequately protective, butthey are often not good risk estimators for particular sites If they are applied to a site,assessors should consider deriving site-specific criteria using the water effect ratio This is afactor for adjusting criteria to site water that may be derived using an EPA procedure (EPA1983; Office of Science and Technology 1994) It requires performing toxicity tests with thechemical in site waters, and, optionally, with site species (Figure 29.2) The time and expense

Ranked summary of cadmium GMAVs

Freshwater invertebrates Freshwater fish Freshwater amphibians (lowered to protect rainbow trout)

*

FIGURE 29.1 Acute and chronic ambient freshwater quality criteria for cadmium at 50 mg=L hardness

species and genera are geometrically averaged so the points are genus mean acute values (GMAVs).(From EPA (U.S Environmental Protection Agency), 2001 Update of Ambient Water Quality Criteriafor Cadmium, EPA-822-R-01-001, Office of Water, Washington, DC, 2006a With permission.)

Toxicity in site water = 0.4 mg/L

Toxicity in laboratory water

Water quality criterion = 0.06 mg/L

FIGURE 29.2 An illustration of the derivation and use of water effect ratios

requir ed to calculate site-spe cific criteri a co uld be worthw hile if the water ch emistry at a sitediffers significan tly from conven tional laborato ry test waters Othe rwise, the effor t is be tterexpend ed on tests of ambie nt waters (Sect ion 24.5).

Currently, in the United States, the methodology for deriving ambient water quality criteria

is being reexamined, and the risk assessment framework is being applied In particular,derivation of new criteria will begin with a problem formulation to determine the appropriateendpoints for the chemical, important exposure pathways, and the availability and utility ofunconventional effects data The more flexible approach is reflected in recent criteria andproposed criteria that use field data or novel modeling approaches (EPA 2000a, 2003a, 2004a,2006a) For suspended and bedded sediments, a framework for deriving regional or water-shed-specific values by multiple methods and weighing the results has been developed (EPA2006b)

Many nations have criteria for water and other media, and comments about the utility ofthe US criteria may not apply to them The utility of these criteria in risk assessments should

be considered where they are potentially applicable It is often appropriate to estimate the risk

of exceeding a criterion in addition to estimating risks to ecological endpoints

29.2 SCREENING BENCHMARKS

Screening benchmarks are concentrations of chemicals that are believed to constitute thresholdsfor potential toxic effects on some category of receptors exposed to the chemical in some medium.Since they are used for screening chemicals, they should be somewhat conservative so thatchemicals that do in fact cause effects at a particular site are not screened out of the assessment(Chap ter 31) It is mo re impor tant to ensure that hazardo us che micals are retained than to avoidretention of chemicals that are not hazardous However, excessive conservatism decreases thevalue of screening assessments, because effort is wasted on nonhazardous chemicals that mightbetter be expended on the truly hazardous ones Because of this deliberate conservatism, it isimportant to avoid adoption of screening benchmarks as remedial goals or other thresholds foraction without some additional assessment to determine that they are appropriate

There is little consensus about the best methods for deriving screening benchmarks Thefollowing alternatives are based on US practices Screening benchmarks used in Australia,Europe, and North America are reviewed by Barron and Wharton (2005)

29.2.1 CRITERIA ASSCREENINGBENCHMARKS

Criteria are commonly used as screening benchmarks because exceedence of one of these valuesconstitutes cause for concern The US NAWQC have been recommended for screening atcontaminated sites by the EPA (Office of Emergency and Remedial Response 1996) However,

it is not clear that they are sufficiently conservative, since they are assumed to be sufficientlyclose to the true threshold of effects to justify regulatory action and because of other concerns(Suter 1996c) These concerns are supported by the finding that nickel concentrations in awaste-contaminated stream on the Oak Ridge Reservation that were below chronic NAWQCwere nonetheless toxic to daphnids (Kszos et al 1992) When used for regulation of effluents—their intended purpose—these criteria achieve additional conservatism by being applied torelatively short exposure durations That conservatism does not apply to contaminated sites

29.2.2 TIERII VALUES

If NAWQC are not available for a chemical, the Tier II method described in the EPAProposed Water Quality Guidance for the Great Lakes System or a slight variation used at

OR NL may be applied (EPA 1993e; Suter and Ts ao 1996) Tier II va lues wer e de veloped sothat aquati c life crit eria could be conserva tively estimat ed with fewer da ta than are requir edfor the NAWQC Tier II values are conc entrations that woul d be expecte d to be higherthan NAW QC in no more than 20% of ca ses, if suff icient test data wer e obtaine d to calculateNAW QC For exampl e, if there is only one acu te value (LC50 or EC 50 ) for a ch emical, thatvalue is divided by 20.5 if it is a daphnid and 242 if it is not Equi valent factors are availablefor other numb ers of acu te values in Appen dix B of Suter and Tsao (1996) The sources ofdata for the Tier II values , and the pro cedure an d fact ors used to calculate the SAVs andSCVs, are presen ted by EPA (1993e ) and Suter a nd Tsao (1996).

29.2.3 B ENCHMARKS B ASED ON EXPOSURE –RESPONSE MODELS

Screen ing bench marks might be based on low percent iles of exposure–r espo nse relation ships

In particu lar, one can calcul ate an LC0 or EC 0 for chemi cals with apparent effects thresho lds.Alternat ivel y, the practice in human healt h risk asses sment of using the lower 95 % confide ncelimit on a be nchmark dos e (the EC10 ) can be applied to nonhum an organis ms (Linder et al.2004) Thi s value is consider ed by the US EPA to app roximatel y co rrespond to a no observedadverse effect level (NOAEL ) for human healt h effe cts, but is more consistent

29.2.4 T HRESHOLDS FOR S TATISTICAL SIGNIFICANCE

Test endpoints based on statistica l signi ficance are commonl y used as screening bench marks.The e ndpoint used varies among media and recept ors

Lowes t chroni c values : Chronic values (CVs ) are geomet ric means of no observed effectcon centrations (NO ECs) and lowest observed effe ct concentra tions (LOEC s) They are used

to calculate the chronic NAW QC , and may be present ed in place of ch ronic criteri a by theEPA when chro nic criteri a canno t be calcul ated (EPA 1985) CVs are not con servativeben chmark values

Wild life NO AELs : Screening ben chmarks for wildlife are conventi onally based onNOAE Ls from chronic or subch ronic toxicity tests with mamm als or birds The majorvaria bles in deriva tion of wildli fe benchmarks are the test en dpoints used an d whet herallom etric scali ng or safety fact ors are used Wildlif e bench marks use reprod uctive or othereffe cts as end points, allometr ic equ ations for inter species extrap olations, and factors to allowfor shortco mings in the test design (Sampl e et al 1996c; Office of Solid Waste and EmergencyRespons e 2005) The resul ting screeni ng dose, terme d the wi ldlife toxicity reference values(TRVs) must be co nverted to a concen tration in soil or other medium to screen those media(Efroy mson et a l 1997; Office of Solid Waste and Emergency Respons e 2005) That requ ires

an expo sure mod el (Chap ter 22)

29.2.5 T EST ENDPOINTS WITH S AFETY F ACTORS

Some states and EPA regions ba se screeni ng benchmarks on test e ndpoints divide d by safetyfact ors These fact ors do not have the scientif ic basis of the fact ors used to derive the Tier IIvalues (above) or the fact ors propo sed by Cal abrese and Bald win (Tab le 26.3) However, theuse of factors of 10, 100, or 1 000 have a long hist ory in the EPA (Dour son and Stara 1983;Nabho lz et al 1 997) (Table 26.1), an d such factors ca n be easily app lied to any test endp oint

29.2.6 DISTRIBUTIONS OFEFFECTSLEVELS

Sets of screening benchmarks for sediments and soils have been derived from distributions ofeffects or no-effects levels An estimate of the threshold effects concentration for a particular

chemi cal is derive d from a pe rcentile of the distribut ion of reported effects or no-effect sconcen trations Thes e concen trations vary due to varian ce in the phy sical and chemi calpropert ies of soil s or sedimen ts, varian ce among the measur ed responses , and varia nce inthe sensi tivities of the species or commun ities Therefor e, the benchmarks de rived in this waymay be tho ught to protect some propo rtion of combination s of specie s, responses , and media The foll owing are examples of this approach

Effect s range- low and e ffects range-med ian for sediment s: The Nation al Oceani c and mospheri c Admi nistratio n (NOAA ) uses three method s: (1) equ ilibrium partiti oning;(2) spiked sedim ent toxic ity test s; an d (3) field su rveys to develop exposure–r esponse rela-tions hips (Lon g et al 1995) Chem ical concentra tions obs erved or estimat ed to be associ atedwith biologi cal effe cts are ranked , and the low er 10th percent ile (effect s ran ge-low, ERL ) an dthe med ian (effect s range- media n, ER M) conce ntrations are identifi ed A variant of thisapproac h is Florida ’s Thresh old Effect s Lev els (MacD ona ld et al 1996)

At-Screenin g level concent rations : Thes e bench marks are derive d from synop tic data onsedim ent ch emical concen trations and benthic inverteb rate dist ributions They are estimat es

of the highest co ncentra tion that can be tolerated by a specified percent age of benthic species.Example s include the Ontario Minis try of the Environme nt Lowest and Severe Effect Levels(Pesaud et a l 1993)

Oak Ridge Nat ional Laborat ory benchm arks for soil : Bench marks for toxic ity to plants, soilinverteb rates, an d micr obial pro cesses have been developed from the 10th percen tile dist ri-butions of toxic ity test data (Efroy mson e t al 1997a, b)

29.2.7 EQUILIBRIUMPARTITIONINGBENCHMARKS

Equilibrium partitioning benchmarks are bulk sediment concentrations derived from aqueouscriteria or benchmark concentrations based on the tendency of nonionic organic chemicals topartition between the sediment pore water and sediment organic carbon and for metals to bebound to sulfides (Sect ion 22.3) The fundame ntal a ssumptions are that pore water is theprincipal exposure route for most benthic organisms and that the sensitivities of benthic species

is similar to that of the species tested to derive the aqueous benchmarks, predominantly thewater column species Examples include the US EPA’s equilibrium partitioning sedimentguidelines (EPA 2000b, 2002c–f) and consensus sediment guidelines for PAHs (Swartz 1999)

29.2.8 AVERAGEDVALUES AS BENCHMARKS

Sometimes the most sensitive response is thought to be too conservative, criteria for fying the best value are not apparent, and there is no agreement concerning how to extrapo-late to a safe level In such cases, benchmarks may be derived by simply averaging testendpoints that are deemed to be relevant and of sufficient quality This approach was used

identi-in the US EPA’s soil screenidenti-ing values for plants and soil identi-invertebrates (Office of Solid Wasteand Emergency Response 2005)

29.2.9 ECOEPIDEMIOLOGICALBENCHMARKS

When effects are observed in the field and the cause has been de termined (Chapter 4), theeffective exposure levels determined in those studies can be used as benchmarks at other sites.For example, tundra swans and other waterfowl were found dead or suffering toxicosis in theCoeur d’Alene Basin, Idaho, an area of lead mining Field and laboratory studies were used torelate sediment lead to dietary lead to lead body burdens and effects The result was anestimated toxic threshold of 530 mg lead per gram sediment dry weight and a lethal level of

1800 mg=g (Beyer et al 2000; Henny 2003)

29.2.10 SUMMARY OF SCREENING B ENCHMARKS

Curr ently the de velopm ent of screening benchmarks is inconsi stent across media The largeand relative ly consis tent body of data for aq uatic animals has led to the de velopm ent of morethan a doz en alternati ve types of ben chmarks Simi larly there are severa l alternati ve bench-marks for sedimen ts, but they have been developed for fewer chemic als Wildl ife benchmarksare nearly alw ays ba sed on NOEC values , so usually only one type of be nchmark is availa ble.How ever, there is consider able varian ce in what effects are included and in the exposuremodels used to extra polate back to soil concen trations Finally, bench marks for plan ts,invert ebrate s, an d micro bes in soil are inconsi stent and are ava ilable only for few chemi cals.Give n the lack of valida tion or even a common definiti on of validity , no singl e type ofben chmark can be demonst rated to be consis tently reliable When there are multiple bench-marks for a chemi cal an d none are clearly superi or, ‘‘cons ensus’ ’ ben chmark values may besim ply derive d by av eraging Swar tz (1999) derived a thres hold effe cts concentra tion for totalPAHs (0.3 mg =g OC) as the arithmet ic mean of five divers e bench marks He found that itwas a reasonab le thres hold value for PAH effec ts in inde pendent da ta sets from PAH-con taminate d sites Alternat ively, the unc ertainty co ncerning the most app ropriate bench-mark may be treat ed by cho osing the low est be nchmark for each chemi cal

Bec ause the degree of conserva tism of benchmarks is uncerta in, concerns that truly toxicchemi cals may be screened out may be relieved by using unc ertainty factors An exampl e ofthe use of unc ertainty fact ors for this purpo se is the eco logical risk assessment for the Rocky

Mo untain Arsenal, in which factors were a pplied to account for intrataxon variability,inter taxon variab ility, uncerta inty of critical effect, exp osure duratio n, en dpoint extrapo la-tion, and resid ual unc ertainty (Banton et al 1996) For each of these six issue s, a fact or of 1,

2, or 3 was applie d signi fying low , medium , or high uncerta inty, respect ivel y Clearl y, themagni tudes of these factors are not related to estimat es of actual varian ce or unc ertaintyassoci ated with each issue, and the multiplicat ion of fact ors bears no relat ionship to anyestimat e of the total uncerta inty in the ben chmarks Ho wever, uncerta inty factors pro vide anassura nce of conserva tism withou t appeari ng complet ely arbitrary An alternati ve is toderive unc ertainty factors based on estimat es of actual varian ce or uncerta inty An exampl e

is the pred iction inter vals on the inter taxon extrap olations and the unc ertainty factors on thepredict ion inter vals (PIs) for a given taxonom ic level present ed in Table 26.2 through

Table 2 6.5

30 Integrating Exposure and Exposure–Response

The primary task of risk characterization is to integrate the exposure estimates from theanalysis of exposure with the exposure–response relationships from the analysis of effects toestimate the nature and magnitude of risks In effect, response is estimated by solving theexposure–response function for the exposure estimate In most assessments, this task hasbeen performed by simple methods that require little thought However, as more attention

is paid to varia bility and uncerta inty (Chapter 5), probabil istic methods are be coming morecommon

30.1 QUOTIENT METHODS

If the analysis of exposure has generated a point estimate of exposure (e.g., the maximummeasured concentration) and the analysis of effects has reduced the exposure–responserelationship to a point (e.g., an LC50), integration of the two reduces to the quotient method.The hazard quotient (HQ) is the quotient of an exposure concentration (Ce) divided by atoxicological benchmark concentration (Cb):

Because this is a widely used assessment method, the terms have many representations InEurope, Ce is usually termed the predicted environmental concentration (PEC) and Cb istermed the predicted no effect concentration (PNEC) If exposure and effect are expressed asdoses, the HQ is equivalent [De=Db] The same simple model may be applied to a variety ofagents such as temperature, percent fines, and radiation Because of its simplicity, thequotient method is nearly always used in screening assessments, but it is also the mostcommon method of risk characterization in definitive assessments

Although some assessors have used Monte Carlo analysis (Chapter 5) to perform abilist ic analys es of HQs (as in the Hong Kon g exampl e, Sectio n 30.7.3 , an d Zolezz i et al.2005), they may be performed analytically (IAEA 1989; Hammonds et al 1994) The quotientmodel can be expressed as:

HQ will be approximately log-normal even if the distributions assigned to Ceand Cbare not(IAEA 1989; Hammonds et al 1994) Hence, the geometric mean of HQ is the antilog of thedifference of the means of the logs of Ceand Cb, and the geometric variance is the antilog ofthe sum of the variances of the logs of Ceand Cb

If the number of exposure values and effects values are finite, one may simply determine thedistribution of all possible values of HQ For example, in an assessment of risks to pondcommunities from pyrethroid pesticides, Maund et al (2001) determined the distribution ofquotients for 90th percentile concentrations in each of 72 pond categories with each acute andchronic toxicity datum (Figure 30.1).

While the HQ expresses how bad things are, a related concept, the margin of safety,expresses how good they are The relative margin of safety is simply the inverse of the HQ

A relative margin of safety of 100 suggests that the exposure concentration must be increased

by a factor of 100 to reach a toxic level The absolute margin of safety is the differencebetween a toxic level and the exposure level An absolute margin of safety of 100 mg=Lsuggests that the exposure concentration must be raised by that amount to reach a toxic level

An example of the use of margins of safety in ecological risk assessment is presented byNewsted et al (2002)

30.2 EXPOSURE IS DISTRIBUTED AND RESPONSE IS FIXED

Frequently, the exposure–response relationship is reduced to a point, such as a criterionvalue, but the exposure estimate is distributed The exposure distribution may come from thedistribution of measured concentrations in the environment, from Monte Carlo analysis of

a transport and fate model or from expert judgment In such cases, the probability ofexceeding the benchmark value (Cb) is the integral of the probability density function above

Cb(i.e., 1—the cumulative probability at Cb) An example of this approach is the analyses ofrisks to herons and egret s in Hong Kong with determ inate effects thresh olds (Sect ion 30.6)

FIGURE 30.1 Distributions of acute and chronic quotients for invertebrates from an assessment of risks

Maund, S.J., Travis, K.R., Hendley, P., Giddings, J.M., and Solomon, K.R., Environ Toxicol Chem.,

20, 687, 2001 With permission.)

30.3 BOTH EXPOSURE AND RESPONSE ARE DISTRIBUTED

Given distributions of exposure and response with respect to a common variable (e.g.,concentration), one may calculate risk as the probability that a random draw from theexposure distribution exceeds a random draw from the response distribution (Suter et al.1983) This concept of risk as the joint probability of exposure and effects distributions wasapplied to effects expressed as species sensitivity distributions (SSDs) by Van Straalen (1990)and Parkhurst et al (1996a,b) Risk is the integral of the product of the probability density ofthe exposure concentration Ceand the cumulative distribution of the benchmark concentra-tion Cb(Figure 30.2c) The derivation of this formula and alternatives, including a discreteapproximation, are clearly presented by Van Straalen (2002b) This is conceptually equivalent

to the prob abilist ic HQs (Sect ion 30.1) but is both clear er and more elegant

A variant of this approach is proposed by the ECOFRAM Aquatic Workgroup (1999) andapplied to pesticide risk assessments (Giddings et al 2005) as well as contaminated siteassessments (Moore et al 1999) From an exposure distribution (proportion of locations,times, or episodes, with respect to concentration) and an effects distribution (SSDs or otherexposure–response distributions) one can derive a plot of exposure proportions vs effectslevels that is called a risk curve (Figur e 30.3 an d Figure 30.4) Since both proportio ns ofexposures and responses are distributed with respect to concentrations, there are correspond-ing values of each The area under the curve is called the mean risk It is equivalent to riskestimated as a joint probability, discussed earlier

Probability density Probability density

Probability density Probability density

δ

δ

FIGURE 30.2 Graphical representations of the estimation of ecological risks (d) defined as the ability that exposure concentrations are greater than no-effect concentrations (NECs) The probabilitydensity of exposure concentrations is denoted as p(c), the distribution of NECs is denoted as n(c) P(C)and N(C ) are the corresponding cumulative distributions In a and c, both variables are distributed In band d, the exposure concentration is assumed to be constant (From Van Straalen, N.M., in

prob-L Posthuma, G.W Suter II, and T Traas, eds., Species Sensitivity Distributions in Ecotoxicology,Lewis Publishers, Boca Raton, FL, 2002 With permission.)

When exposure and effects distributions are used as part of a logical weighing of evidence(Ch apter 32), it may be appropri ate to logically inter pret them rather than calculati ng jointprobabilities For example, the following interpretation occurs in the risk assessment for fishcommunity of the Poplar Creek embayment of the Clinch River (Suter et al 1999).

Copper The distributions of ambient copper concentrations and aqueous test endpoints areshown in Figu re 30.5 The ambie nt concen trations were dissolved phase conc entrations in thesubreaches (3.04 and 4.01) with potentially hazardous levels of Cu The toxic concentrationswere those from tests performed in waters with hardness approximately equal to the site

Percent of species affected 0

0 20

water The ambient concentrations fall into two phases Concentrations below 0.01 mg=Ldisplay a fairly smooth increase suggestive of a log-normal distribution The upper end of thisphase of the distribution (above the 75th percentile of 4.01 and the 80th percentile of 3.04)exceed the lowest chronic value (CV) (a bluntnose minnow CV for reproductive effects).However, the distributions above the 90th percentile are not continuous with the other points.The break in the curve suggests that some episodic phenomenon causes exceptionally highconcentrations The two points in 4.01 and one in 3.04 that lie above this break exceedapproximately 90% of the CVs, approximately 30% of the acute values, and both the acuteand chronic National Ambient Water Quality Criteria (NAWQC) These results are suggest-ive of a small risk of chronic toxicity from routine exposures, but a high risk of short-termtoxic effects of Cu during episodic exposures in lower Poplar Creek embayment and theClinch River.

This sort of interpretation is a mixture of quantitative and qualitative analysis that can, as

in this case, provide more information than a purely quantitative analysis Had the AquaticRisk Assessment and Mitigation Dialog Group criterion been applied or a joint probabilitybeen calculated, the results would have been less ad hoc but would have provided less basisfor inference concerning the cause of observed effects and toxicity

30.4 INTEGRATED SIMULATION MODELS

When a mathematical simulation model, such as a chemical transport and fate model

function to the exposure model so that the model output is an effects level Similarly, when

a population or ecosystem model is used to estimate responses to exposures, an exposure level

A Probabilistic Risk Assessment, SETAC Press, Pensacola, FL, 2005 With permission.)

or an exposure model my be linked to produce an integrated model that estimates effects fromloading rates or a mbient level s (O’Nei ll et al 1982) Monte Carlo ana lysis (Chapter 5) is usedwith such models to estimate risks as probabilities of effects.

30.5 INTEGRATION OF SENSE AND NONSENSE

When integrating exposure with exposure–response relationships, it is essential to ensure thatthey can be combined in a way that makes sense, i.e., they must be concordant This requiresfirst that the common units be consistent This is not simply a matter of assuring that, forexample, the exposure concentration and the concentration in the exposure–response rela-tionship are both mg=L of copper If the response concentration is a 96 h LC50for dissolvedcopper, and the exposure concentration is an annual average of measured total copperconcentrations, they are not concordant Other measures of exposure or response must beused, or one of the measures must be adjusted to achieve concordance For example, a metalspeciation model may be used to estimate dissolved copper concentrations in the field and thepeak 96 h concentration might be estimated from the time series of measurements

Concordance becomes more complex when parameters are expressed as distributions andresults are expressed as probabilities For every distribution, it is essential to ask what isdistributed and with respect to what it is distributed Is a distribution of dose to mink(mg=kg=d) the distribution of the average dose across a mink population, the dose to the

10

FIGURE 30.5 Empirical distribution functions (species sensitivity distributions, SSDs) for acute toxicity

and for individual measurements of copper in surface water from two stream reaches Vertical lines areacute and chronic National Ambient Water Quality Criteria (NAWQC)

media n mink, or the dos e to an indivi dual mink occup ying a pa rticular locat ion? Is itdistribut ed with respect to space (e.g., from sampl ing points on a site), to time (e.g , fromyear- to-year variation in diet), to individua ls (e.g., from varian ce in size and dieta ry pr efer-ence), or to degree of belief (e.g., an expression of an asses sor’s unc ertainty concerning thedose esti mate)? If it was gen erated by Monte Car lo analysis of an exp osure model, the dosedistribut ion might be a hodgepo dge of variance of con sumption rates across individu als,varia nce in drinkin g wat er contam ination over time, varia nce in contam inant level sacross individ ual fish in a pond, variance in contam inant levels in mice across space, an dvaria nce in dieta ry composi tion across different studi es The best that could be said of such adose dist ribution is that the probabil ities express the asses sor’s uncerta inly con cerning dose asdegrees of be lief.

A response distribut ion (e.g , one derive d from a reproduct ive test) may also take differentform s In the sim plest case, a dose–res ponse distribut ion may be derive d for the propo rtionalreducti on in the num ber of live births per fema le That dist ribution might be us ed to estimat ethe average proporti onal reductio n at a given dos e, or the varia nce in the parame ters of thefitted mod el might be used to estimat e the dist ribution with respect to ind ividual fema les ofthe dose causing a given prop ortional redu ction (e.g., an ED10 ) If the test was performed withrats rather than mink, an unc ertainty fact or may be app lied resulting in a distribut ion of the

ED10 with respect to degree of belie f (Bo x 30.1)

Note that any pa ired exposure dist ribution and effe cts distribut ion from the previous tw oparagra phs superf icially app ear to be con cordant, becau se they are all proba bilities asfunctio ns of dose to mink How ever, the probabil ities ex press very different qualities The appro priate integ ratio n of ex posure an d response depend s on the asses sment endpo int,the prefer ences of the risk manage r, and the available infor mation In the sim plest case of themink exampl e, one might use an HQ The poin t esti mate of the an nual average daily dose forthe site might be divide d by the ED10 and the risk could be dec lared signi ficant if HQ > 1 Toinclude uncerta inty, one might sub jectivel y estimat e a low er confide nce bound of HQ= 100 foruse in screeni ng, or to be precau tionary One might estimat e the distribut ions of bothexposure and respon se dos es and esti mate the distribut ion analytical ly Howe ver, to a ctuallyestimat e the most likel y effe ct or the risks of prescribed levels of effect, one would need tosettle on a con cordant set of distribut ions of exp osure and response parame ters For exampl e,

to e stimate the probabil ity that a female mink on a con taminate d site experie nces a ductiv e de crement, one co uld use the dist ribution of the ED10 (i.e., the pr oportio n of femaleswith fecun dity at least 10% below control s) an d a distribut ion of dos e with respect toindivid ual female mink esti mated as describ ed in Secti on 22.11 Since both the exposu redose and the effective dose woul d be distribut ed with respect to indivi dual female mink, thejoint probab ility would be the prop ortion of fema les in the site popul ation estimat ed toexperi ence a 10% or great er redu ction in fecundi ty

repro-The problem of combini ng exposure an d response dist ributions may be simplified bydevisin g rules like the one provided by the Aquat ic Ris k Assessm ent and M itigation Dialo gueGroup (1994) (see also Solomon et al 1996) They reduced risk characterization for exposureand effects distributions to a dichotomous criterion; the risk is significant if the 90th percentile

of the distribution of aqueous concentrations exceeds the 10th percentile of the SSD though this method has been recommended by a distinguished group of scientists, thecriterion is not supported by legal or policy considerations In addition, it does not interpretthe distributions in terms of either variability or uncertainty It is simply an easy andconsistent rule, which may be adopted or adapted to an assessment if it makes sense.The possible combinations of distributions to characterize ecological risks are effectivelyinfinite, so this section can give only a sample of the possible problems in achieving concordance

Al-It is incumbent on those who perform probabilistic risk assessments to carefully consider what is

distributed, with respect to what it is distributed, and in what sense do probabilities from thedistribution constitute risks to the assessment endpoint If the nature of the probabilities isunclear in the assessor’s mind, it will not be clear to the users and reviewers of the assessment,and there is a good chance that it is wrong In such cases, assessors should consider seeking help orusing a less complex analysis.

30.6 INTEGRATION IN SPACE

In regional assessments or other assessments at large spatial scales, the assessment mustintegrate risks over space The most common approach is to divide the area into reasonablyuniform units, estimate risks for each unit, and then generate a summary such as an area-weighted average effect or a distribution of effects across units to estimate risks on a site or in

a region The spatial units might be habitat types on a site, watersheds in a region, areas withdistinct types of disturbance or contamination, or other relevant divisions of the area being

BOX 30.1

Variability, Uncertainty, and Distributions of Effects

Conventionally, the relationship between an exposure variable (dose, concentration, duration,etc.) and a response variable (growth, death, etc.) is quantified using a distribution function

assess-ment, these functions are most commonly based on responses of exposed individual organisms(dose–response or concentration–response) or individual species (species sensitivity distributions,SSDs) These distribution functions are usually described as probability distributions withoutcareful thought as to what mechanism generated the distribution

Error: All individuals may be effectively the same or all species in a community may beeffectively the same and the distribution is due to random effects in the tests (these random effectsare termed error, but need not imply actual errors in conducting the tests) Hence, the output ofthe model is the probability of the prescribed response given the uncertainty due to experimentalerror (randomness)

Variability: There are actual differences among the individuals or species that are measuredwithout effective error Hence, the distribution describes that variability and the output of themodel is the deterministic proportion of individuals or species responding at a given exposurelevel This is the assumption underlying the estimation of risks to human populations, and themost common assumption in the case of species distributions, leading to the calculation of thepotentially affected fraction (PAF) of species

Identity: There are actual differences among the individuals or species that are measuredwithout effective error However, we are interested in the risk to an untested individual or species,rather than the proportion of individuals in a population or species in a community Thisinterpretation leads to an estimate of individual risks (probability of effects on an exposedorganism other than a member of the test population) or species risks (probability of effects on

an untested species) This is the assumption underlying the estimation of risks to human uals In the case of species distributions, this assumption implies that the endpoint species is arandom draw from the same population as the set of test species used to define the distribution.Extrapolation: The variability and uncertainty inherent in the test and the model fitted to thetest data are negligible relative to the uncertainty associated with extrapolation to the endpointspecies or community In such cases, we may ignore the variance among organisms or other test

deviation, range, or other distribution parameter to express extrapolation uncertainty

asses sed In most cases, the exp osure esti mates vary amon g units , but the expo sure–re sponserelationshi ps may vary as well due to diff erences in the biotic communi ties.

A sop histicate d elabora tion of this ap proach is found in the asses sment of aq uatic logic al risks from cotton pyrethr oids in Yazoo Count y, Mississipp i (Hendl ey e t al 2001;Travis and Hendley 2 001) The units wer e ponds and their associ ated watershe ds A geo-graphic infor matio n syst em (GIS) was used with trans port an d fate models to estimate90th percen tile concen trations in 597 ponds in the county an d compare them to SSDs foracute an d chron ic effects of the pesticides A furt her step woul d include spatial v ariance oreven spatial dynami cs of the en dpoint organ isms, popul ations, or co mmuni ties alon g with thespatial varia nce in expo sure

eco-Anothe r ap proach estimate s risks at points ; usuall y, points at whi ch soil or sedimen t hasbeen sampl ed an d a nalyzed Kriging, Thiesse n pol ygons, or some other geospat ial statistica lmethod is then used to de fine areas within whi ch risks fall in de fined ranges Thi s a pproach isapprop riate for organisms with littl e mobil ity such as plants and be nthic inverteb rates Iftoxic ity tests are perfor med on soil or sediment sampl es, and if the tests are measur es of effects

on an asses sment endpoint, this approach c an be applie d to tho se resul ts as well (Figur e 20.3).Figure 30.6 illustr ates a simple techn ique that is appropri ate for organisms with terr itories

or hom e ranges occurrin g on relative ly sim ple contam inate d sites A contam inant has be endumped or spilled at a point, and the average soil concen tration dro ps off app roximatel yexpon entially from that point as the area average d increa ses Equi valent curves could beplotted for other pa tterns of con taminatio n around a point Hori zontal dashed lines indica tesoil concentrations that are estimated to be thresholds for effects on small mammals andbirds The vertical dashed lines indicate the average home range or territory size for theendpoint species (shrew and woodcock in this case) If the average concentration falls belowthe effects concentrations before intersecting the home range size, not even one individual is

Area (acre) 0

Chronic lethal effect level for small mammals Reproductive effect level for song birds

Reproductive effect level for small mammals

Shrew Woodcock

FIGURE 30.6 Mean concentration of a contaminant as a function of averaging area centered on thepoint of maximum concentration Vertical lines indicate the home range area for a shrew and awoodcock Horizontal dashed lines indicate soil concentrations estimated to produce toxic dosesassociated with specific effects (Graphic redrawn from an unpublished graphic provided by C Menzie)

expecte d to be affected In Figu re 30.6, no effects on woodcock are exp ected, but a shrew isestimated to experience reproductive effects and has a marginal risk of death.

To estimate risks to multiple organisms with territories or home ranges on a contaminatedsite or other defined area, a GIS can be used to cover the area with polygons having the area(on the map scale) of the territory or home range Exposure levels in those areas can then beaveraged to estimate risks to those individuals or reproducing pairs

30.7 EXAMPLES

The following examples provide a taste of the range of approaches to integrating exposureand exposure–response information in ecological risk assessments

30.7.1 SHREWS ON AMERCURY-CONTAMINATED SITE

Talmage and Walton (1993) collected shrews on the mercury-contaminated floodplain of EastFork Poplar Creek, Tennessee They analyzed the mercury concentration in kidneys, thetarget organ, and compared them to the 20 mg=g threshold for mercury toxicity in rodents.They found that 75% of shrews exceeded that threshold

30.7.2 EGRETS ANDEAGLES INSOUTHFLORIDA

A large stormwater treatment pond in South Florida was found to have high methyl mercuryconcentrations To obtain a discharge permit, the State agreed to assess risks to great egretsand bald eagles foraging on the pond (Rumbold 2005) The exposure–response relationshipswere lowest observed adverse effect levels (LOAELs) divided by a factor of 3 Exposure wasestimated from concentrations in fish collected from the pond and from dietary uptakemodels for pre-nesting females and nestlings The nestling exposure model also includedmaternal mercury deposited in the eggs Monte Carlo simulation of the uptake models wasused to estimate the distributions of exposure, based on variance in the mercury concentra-tions in appropriate fish for each avian species The assessment found that risks of exceedingthe effects thresholds were low and similar to other areas in the region

30.7.3 EGRETS ANDHERONS INHONGKONG

Connell et al (2003) assessed risks from organochlorine compounds to the reproduction ofblack-crowned night herons and little egrets in the New Territories of Hong Kong Theyestimated exposure by analyzing contaminants in eggs and established that DDE posed thegreatest hazard They developed a concentration–response relationship using published stud-ies of the relationship between survival of young ardeids and DDE concentrations in eggs

survival By applying that value to the probability density functions for egg concentrations,they estimated that 12.4% of night herons and 40.9% of egrets were exposed at levelsexceeding the threshold Finally, they used Monte Carlo simulation to estimate the probabil-ity of exceeding the threshold given the uncertainty in the threshold However, they consid-ered only the possibility that the threshold was underestimated

In a companion study, Connell et al (2002) related the distributions of metal tions in heron and egret feathers to effects thresholds from the literature In this case, theMonte Carlo analysis used the observed distribution of concentrations in feathers and auniform distribution of the effects threshold between the highest level reported to have noeffects (3 mg=g mercury) and the lowest level reported to reduce reproductive success inardeids (5 mg=g)

concentra-30.7.4 BIOACCUMULATIVECONTAMINANTS IN ASTREAM

After completion of the remedial investigation for East Fork Poplar Creek in Oak Ridge,Tennessee, the US Department of Energy commissioned a new ecological risk assessment totest new probabilistic techniques (Moore et al 1999) Exposure of belted kingfishers and mink

to mercury and polychlorinated biphenyls (PCBs) was estimated using Monte Carlo tion of a multiroute model including inhalation, drinking, and feeding The exposure–response relationships were estimated using generalized linear modeling of published testdata A single best study was used for kingfishers, but for mink data from multiple tests werecombined to generate the dose–response functions The exposure and exposure–responsefunctio ns wer e co mbined to generat e risk curves (Figur e 30.8) Thes e an alyses showe dsignificant risks to mink and kingfishers from mercury and to mink from PCBs These resultsdiffered from the assessments for the remedial investigation, which found little risk topiscivorous wildlife However, that assessment did not use measured concentrations in fish,but rather modeled uptake based on contaminants in the sediment, which was the mediumthat would be remediated under Superfund (Burns et al 1997) The results by Moore et al.(1999) gave more similar results to a reservation-wide wildlife risk assessment that alsoused measured fish concentrations (Sample et al 1996b) The exception was PCBs inmink, and the difference was that Sample et al (1996b) used the LOAEL as a threshold.Consideration of the dose–response relationship showed that PCBs caused large reproductiveeffects at the LOAEL

simula-30.7.5 SECONDARYPOISONING INHAWAII

An ecological risk assessment of possible secondary poisoning in Hawaii provides an example

of Monte Carlo simulation of an integrated exposure and effects model (Johnston et al 2005).Broadcast baits containing rodenticides are consumed by invertebrates, which are in turn

0 20 40 60 80

100

Night herons Little egrets Findholt and Trost (1985) Henny et al (1984) Findholt (1984)

0 20 40 60 80 100

DDE Concentration in eggs (ng/g; log scale)

FIGURE 30.7 Cumulative distribution of concentrations of DDE in eggs for night herons and littleegrets and a model with confidence interval fitted to data on percent reduction in the survival of youngbirds as a function of DDE in eggs The vertical line is the estimated threshold effective concentration of

1000 ng=g (From Connell, D.W., Fung, C.N., Minh, T.B., Tanabe, S., Lam, P.K.S., Wong, B.S.F.,Lam, M.H.W., Wong, L.C., Wu, R.S.S., and Richardson, B.J., Water Res., 37, 459, 2003 Withpermission.)

con sumed by birds As illustr ated in Figure 30.9, the acute dose to Po’ouli (a honeycreeper )was esti mated from the distribut ion of co ncentra tions in snails and slugs from treat ed areas,and from estimat ed dist ributions of sn ail con sumption rates derived from caloric req uire-ments , fraction moll uscs in diets , and en ergy content of moll uscs The exp osure–r esponsemodel was de rived from distribut ions assign ed to the avian LD50 , the slope of the dose–respon se relationshi p, and the sensitiv ity of honeycreeper s relat ive to avian test sp ecies.

Mo dels were also develop ed for 5 and 14 d ex posures Median probab ilities of acute mort alitywer e 0.03% for adu lts and 0.57% for juveniles (Figur e 30.10)

30.7.6 ATRAZINE

The ecologi cal risk asses sment for atraz ine (Sect ion 32 4.4) illustr ates the use of risk curves

to integ rate exp osure and expo sure–r esponse distribut ions (Sect ion 30.3) The exposure–response distributions are SSDs The exposure distributions include both distributions ofmeasured concentrations and distributions of concentrations from Monte Carlo analyses ofmodels of pond scenarios

30.7.7 WARMINGSUBALPINEFORESTS

Bolliger et al (2000) estimated risks from climatic warming to the abundance and distribution

of tree species in the Alps The exposure–response relationships were logit regression models

of the presence or absence of each major tree species at locations as a function of fivebiophysical habitat properties This model was derived by fitting to forest inventory data,using a map of degree-days, radiation in July, summer frost frequency, July water budget, andslope Exposure was expressed as current conditions and three scenarios: warming of 100,

200, and 400 degree-days, converted into the biophysical variables The results showed littlechange in overall abundance, but distributions shifted, and species did not move together Inparticular, spruce and beech, which currently occur together became segregated, leaving thesubalpine belt dominated by spruce

30.8 SUMMARY

The critical step in the characterization of ecological risks is the integration of exposureestimates with exposure–response relationships to estimate effects or probabilities of effects

Diphacinone concentration

in snails (µg/g)

Weight of daily diet (g food/g bw)

Daily energy requirements (kJ/g bw)

Fraction of insects in diet

Fraction of snails in diet

Energy content of insects (kJ/g)

Energy content

of diet (kJ/g) Energy

content of

snails

(kJ/g)

Weight of snails (g/g bw)

diphacinone dose

Slope

LD

50

Interspecies extrapolation Probability of mortality

0 0.4 0.8 1.2

Log dose (mg/kg)

Weight of insects (g)

Dose

(mg/kg bw)

FIGURE 30.9 Diagram of a probabilistic ecological risk assessment for a single-day acute exposure of aHawaiian honeycreeper to diphacinone Dose is estimated from measured concentrations in snails andthe consumption of snails estimated from energy requirements, dietary habits, and energy contents of

values and a distribution of safety factors (1–25) for interspecies extrapolation (From Johnston, J.J.,Pitt, W.C., Sugihara, R.T., Eisemann, J.D., Primus, T.M., Holmes, M.J., Crocker, J., and Hart, A.,Environ Toxicol Chem., 24, 1557, 2005 With permission.)

It may be deterministic or probabilistic based on variability or uncertainty in either ent The critical considerations are the relevance of any of the sources of variability anduncertainty to the decision and the concordance of the units of the two components and of thedistributions.

compon-Percent 0.000

and equivalent frequencies are presented as functions of percent occurrence (From Johnston, J.J., Pitt,W.C., Sugihara, R.T., Eisemann, J.D., Primus, T.M., Holmes, M.J., Crocker, J., and Hart, A., Environ.Toxicol Chem., 24, 1557, 2005 With permission.)

assess-. Indeter minate—ri sks that are not clear ly signi fican t or insi gnificant and must be resol ved

by furth er assessment or con siderati on of other issue s such as co sts, benefi ts, engineer ingfeasibil ity, or public concerns

. De manifes tis —ris ks that are clear ly signifi cant and need not be assessed further butshould be referred to the risk manager for remedi ation or control

These Latinisms are derived from legal terminology In particular, some risks or other legalissues are so small that they are considered trivial (de minimis) and therefore not worthy of thelaw’s attention (Travis et al 1987; Whipple 1987) Commonly, this three-part logic is reduced totwo parts by replacing de manifestis and indeterminate risks with a single category, nontrivialrisks In that case, the nontrivial risks are carried forward to subsequent assessments

Screening ecologi cal risk assessment s have a num ber of potenti al uses

To prompt acti on : In some cases, a screening assessment will reveal that risks are manif estlysignifi cant, and remedi al or pr eventative action sho uld be taken wi thout furt her data c ollec-tion or asses sment

To deter mine the need for furt her asses sment : A screeni ng assessment may reveal thatsignifi cant risks are highly unlikel y or that the risks are of low priority relative to otherrisks or relative to the likely costs of asses sment and managem ent

To d efine the scope of a definit ive asses smen t: A screenin g assessment may screen out certa incontam inants , recept ors, media , routes of exposure, or porti ons of a site by demo nstratingthat they are associated with negligible risks

To guide data collecti on : Data colle ction for subsequen t tiers of asses sment may be focused

on the hazards that have not been screened out as clearly insignificant or to focus testing ormodeling on routes of exposure or mechanisms of action that are likely to be associated with

an agent

In some assessment schemes, particularly rule-based assessments of industrial chemicals,there is no defini tive assessment in the sense that risks are neve r estimat ed (Figur e 3.8).Rather, data are generated and screening approaches are applied until the chemical falls intothe acceptable or unacceptable category

31.1 SCREENING CHEMICALS AND OTHER AGENTS

When characterizing risks in a screening assessment it is not necessary to estimate the nature,magnitude, or probability of effects, but it is necessary to assure that hazards fall into the

correct categor y It is especi ally important to avoid allowi ng a hazard to pass throug h thescreens sim ply becau se few or no data are avail able or be cause the haz ard is poorlycharact erize d.

31.1.1 QUOTIENTS

The standar d model for risk charact erization in screeni ng asses sment s is the ha zard quotient(HQ) (Section 30.1) The be nchmark and exposure levels may be de rived in a varie ty of ways.The bench mark concentra tion or dose may be a regula tory standar d or a value derive dspecif ically for screeni ng assessment s or a standar d test en dpoint (Chapter 29) The sim plestben chmark is the thres hold for toxico logical concern (T TC), ‘‘a level of exp osure to chemic alsbelow whi ch no significan t risk is expe cted to ex ist’’ (Kroes et al 2000) For exampl e, 0.01

m g=L has been proposed as a TTC for aquati c toxicity of organic chemi cals (De Wolf et al.2005) The exposure conce ntration for asses sment s of new pesticides or ind ustrial ch emicals isderive d by employ ing standar d use, relea se, or disposa l scenari os to standar d en vironmen talmodels such as in the Europ ean Union syst em for the evaluation of su bstance s (EUSE S) Forexisting contam ination, the de rivation of a n exposu re concentra tion is complex , and isdiscus sed with respect to contam inated sit e screeni ng (Sect ion 31.2)

Screen ing of mixtures of co ntaminan ts may emp loy tests of the mixt ure or models based onthe toxic ity of the con stituent s (C hapter 8) The only model that is rou tinely used forecologi cal screen ing asses sments of mixtures is the hazard index The assessor calculates :

where HI is the hazard index, Cei, the exposure concentration of chemical i, and Cbi, thecorresponding benchmark concentration If the sum is greater than 1, the mixture is potentiallyhazardous and must be retained for further assessment If the individual constituents must bescreened, one might retain those chemicals that contribute more than 10%, 1%, or some otherpercentile of the HI Alternatively, one may include chemicals with individual quotients greaterthan some value Parkhurst et al (1996a) recommend a minimum quotient of 0.3

If multiple ch emicals occur at potenti ally toxic co ncentra tions but the assum ption ofadd itive toxic ity is not reasonabl e, it is us eful to calcul ate the sum of toxic units ( S TU) as

an index of total toxicity (Sect ion 8.1.2) This pe rmits the asses sor an d reviewer s to comparethe relative contri butions of ch emicals to toxicity without necessa rily assum ing that thecon taminants are concen tration- additive TUs are quot ients of the conc entration of a che m-ical in a medium divide d by the standar d test endpoint concentra tion for that ch emical A TU

is sim ilar to an HQ and a STU is simila r to an HI except that, beca use TUs are used forcompa rative purposes rather than to dra w conclusi ons, a common test end point is use d ratherthan conserva tive be nchmarks or most relev ant test endpo ints The express ion of con centra-tion and the test endpoint vary among media ; for wat er they are typic ally the mean or upper95% con fidence lim it exposu re co ncentra tion an d the 48 h EC50 for Daphnia sp (the mostcommon aquati c test endp oint) The c hemicals that constitut e a potenti ally signi ficant com-ponent of toxicity (i.e., TU > 0.01) should be plotted for each reach or area for water,sedim ent, soil , a nd wildlife intake (e.g., Figure 20.2) The choice of a cutoff for inclusion isbased on the fact that acute values are used in calculating the TUs, and chronic effects canoccur at concentrations as much as two orders of magnitude below acute values Other valuesmay be used if specific circumstances warrant The height of the plot at each subreach is theSTU for that medium and subreach (Figure 20.2) This value can be conservatively inter-preted as the total toxicity-normalized concentration and therefore as a relative indication ofthe toxicity of the medium in that subreach

31.1.2 SCORING S YSTEMS

When data are unavailable or there are no simple models of the hazard being assessed, expertjudgment based on experience with the issues being assessed or similar issues may be used forscreening (UK Department of the Environment 2000) These may be qualitative (e.g., high,medium, low) or semiquantitative For example, for each hazard, scores (e.g., 1–5) may beapplied to the source, transport pathways, receptor exposure, and response, and then summed.Because risk assessments should be transparent and subject to replication, it is important toclearly characterize the bases for judgments This may be accomplished by developing a formalscoring system Scoring systems have been used for decades to rank the risks from chemicals orfrom more diverse sets of agents (Harwell et al 1992; Swanson and Socha 1997) However, toserve as screening tools, these systems should be calibrated to actual risks so that the total score

is at least roughly linearly related to risk and cutoff scores can be defined for the screeningcategories If scoring systems are subjective (i.e., not calibrated), it is important to avoid giving

an impression of scientific accuracy to the numeric results

31.1.3 SCREENING FOR P ROPERTIES

Chem icals and other agen ts may be sub ject to particular asses sment standar ds if they possesscerta in propert ies such as pe rsistence, or particular mod es of ac tion such as mutag enici ty orteratoge nicity For ex ample, the US Food Qual ity Pr otection Act requ ires screeni ng an dsubsequ ent testing of pesticides for endocrine- disrupting prop erties This screeni ng may beperformed using in vitro tests (ER-CALUX test for estrogenicity), simple rapid wholeorganism tests (e.g., use of Japanese medaka embryos to screen waters for teratogenicity),

or quantitative structure–activity relationships (QSARs) for particular modes of action

mode of action, it is screened out with respect to those standards

31.1.4 LOGICALCRITERIA

In some cases, particularly for agents other than chemicals, neither quantitative nor quantitative methods are feasible, but simple logical criteria may be applied to determinewhether a hazard exists For example, to screen nonnative plant species to determine whetherthey should be assessed, Morse et al (2004) asked: (1) Is the species established outsidecultivation in the region of interest? (2) Is the species established in conservation areas orother native species habitats in that region? If the answer to either question is no, the plantspecies is screened out from further assessment

semi-31.2 SCREENING SITES1

While the screening of chemicals, materials, and other agents is largely constrained to isons of exposure and effects metrics, assessments of contaminated sites are more complex.The primary purpose of screening is to narrow the scope of subsequent assessmentactivities by focusing on those aspects of the site that constitute credible potential risks.Screening is performed by a process of elimination Beginning with a site description and thefull list of chemicals that are suspected to constitute site contaminants, one can potentiallyeliminate:

compar-1

This section is based on Chapter 5 of Suter et al (2000).

Particular chemicals or classes of chemicals as chemicals of potential ecological concern. Particular media as sources of contaminant exposure

. Particular ecological receptors as credible assessment endpoints

. Ecological risks as a consideration in the remedial action

A secondary purpose of screening risk assessments is to identify situations that call foremergency responses A screening assessment may identify ongoing exposures that are causingsevere and clearly unacceptable ecological effects or potential sources of exposure that are likely

to cause severe and clearly unacceptable ecological effects in the immediate future In suchcases, the usual remedial schedule is bypassed to perform a removal action or other appropriateresponse No guidance is provided for such decisions because there are no generally applicablerules for defining an ecological emergency They must be identified ad hoc

Finally, screening assessments serve to identify data gaps Media or classes of chemicalsthat have not been analyzed, for which analyses are of unacceptably low quality or quantity

or for which the spatial or temporal distribution has been inadequately characterized, should

be identified during screening assessments This information serves as input to the assessmentplanning process for any subsequent assessments

Screening assessments of sites are performed at three stages:

1 When a site is initially investigated, existing information is collected, and a screeningasses sment is perfor med to guide the developm ent of an analys is plan (C hapter 18) Thescreeni ng assessment is used to help focus the analys is plan on those elemen ts of the sitethat require investigation and assessment

2 In a phased assessment process, a screening assessment is performed after the ary phase to guide the development of the subsequent phase by focusing investigations

prelimin-on remaining uncertainties cprelimin-oncerning credible potential risks

3 Finally, as a preliminary stage to the definitive assessment, a screening assessment isperformed to narrow the focus of the assessment on those contaminants, media, andreceptors that require detailed assessment

Site screening assessments are final assessments only when they indicate that no potentialhazards to ecological receptors exist Otherwise, they should prompt the parties to theremedial process to consider the need for additional data Whether or not additional dataare collected, a screening assessment that indicates that a site is potentially hazardous must befollowed by a more definitive baseline ecological risk assessment that provides estimates ofthe risks and suggests whether remedial actions are needed

31.2.1 SCREENINGCHEMICALS ATSITES

At many sites, concentrations in environmental media will be reported for more than 100chemicals, most of which are reported as undetected at some defined limit of detection Theassessor must decide which of these constitute chemicals of potential ecological concern(COPECs): which detected chemicals constitute a potential ecological hazard and which

of the undetected chemicals may pose a hazard at concentrations below the reporteddetection limits The concern about undetected chemicals results from the possibility thatthe detection limit may be higher than concentrations that cause toxic effects This screening

is done for each medium by applying one or more of the following criteria:

1 If the chemical is not detected and the analytical method is acceptable, the chemical may

be excluded

2 If the wastes deposited at the site are well specified, chemicals that are not constituents

of the waste may be excluded

3 If the concentration of a chemical in a medium is not greater than background trations, the chemical may be excluded

concen-4 If the application of physicochemical principles indicates that a chemical cannot bepresent in a medium in significant concentrations, the chemical may be excluded

5 If the chemical concentration is below levels that constitute a potential toxicologicalhazard, the chemical may be excluded

In the United States the list of chemicals to be screened is assumed to include the EPA TargetCompound and Target Analyte Lists Chemicals known to be associated with the sitecontaminants but not on those lists, particularly radionuclides, should be included as well.Specific methods for applying these criteria are presented in the following subsections Theorder of presentation is logically arbitrary, i.e., the screening methods can be applied in anyorder, and the order used in any particular assessment can be based on convenience Inaddition, it is not necessary to use all of the five screening criteria in a screening assessment.Some criteria may be inappropriate to a particular medium or unit, and others may not beapplicable because of lack of information

Other criteria may be used, particularly if they are mandated by regulators or other riskmanagers For example, the California EPA specifies that chemicals should be retained if theyhave regulatory standards, if they are difficult to treat, or if they are highly toxic or highlybioaccumulative, even if they occur at very low concentrations (Polisini et al 1998) In theRisk Assessment Guidance for Superfund, the EPA has specified that chemicals found in lessthan 5% of samples may be excluded These criteria are not recommended here, because theyare not risk-based

31.2.1.1 Screening Against Background

Waste sites should not be remediated to achieve concentrations below background; therefore,baseline risk assessments should not normally estimate risks from chemicals that occur atbackground concentrations Chemicals that occur at background concentrations may benaturally occurring, may be the result of regional contamination (e.g., atmospheric deposition

of cesium-137 from nuclear weapons testing or mercury from incinerators and coal tion), or may be local contaminants that have been added in such small amounts that theirconcentrations in a medium have not been raised above the range of background concentra-tions Screening against background requires that two issues be addressed First, whatlocations constitute background for a particular site? Second, given a set of measurements

combus-of chemical concentrations at background locations, what parameter combus-of that distributionconstitutes the upper limit of background concentrations?

It must be noted that it has been the policy of the US EPA not to screen againstbackground (Office of Solid Waste and Emergency Response 2002) While the Agency doesnot promote cleaning a site to below background concentrations, it states that consideration

of background in screening assessments is likely to yield misleading results Misleadingscreens against background are certainly possible, but a well-conducted screen can be animportant and useful component of the assessment process (LaGoy and Schulz 1993; Smith

et al 1996)

31.2.1.1.1 Selection of Background Data

Background sites should be devoid of contamination from wastes or any other local source.For example, water from a location upstream of a unit cannot be considered background if

there are outfalls or waste sites upstream of that location To ensure that there is no localcontamination, a careful survey of watersheds for potential background water or sedimentsites should be performed, and for terrestrial sites, the history of land use must be determined.For example, although Norris Reservoir, upstream of Oak Ridge, Tennessee, is quiteclean relative to Watts Bar Reservoir, the reservoir receiving contaminants from OakRidge, the occurrence of a chloralkali plant in the Norris Reservoir watershed has eliminated

it as a background site for mercury In theory, if a local source releases a small and characterized set of contaminants, a location that is contaminated by it could be used as abackground site for other chemicals However, wastes and effluents are seldom sufficientlywell defined Background can be defined at multiple scales: regional, local, and unit-specific.Each scale has its advantages and disadvantages

well-National or regional background concentrations may be available from existing sources such

as the US Geological Survey or state publications (Shacklette and Boerngen 1984; ‘Slayton andMontgomery 1991; Toxics Cleanup Program 1994) National or regional background concen-trations are advantageous in that they provide a broad perspective It is not sensible to remove

or treat soils at a site for a metal concentration that is higher than local background but wellwithin the range of concentrations of that metal at uncontaminated sites across the region(LaGoy and Schulz 1993) However, one must be careful when using national or regionalbackground values to ensure that the concentrations were measured in a manner that iscomparable to the measurements at the waste site For example, concentrations of metalsfrom aqueous soil extractions should not be compared to total soil concentrations Becausethe use of national or regional background concentrations is often less conservative than the use

of local or unit-specific concentrations, the latter is often favored by regulators

Local background measurements are generally the most useful Local backgrounds areconcentrations of chemicals in environmental samples collected to represent an entire site or ageologically homogeneous portion thereof Examples include the background soil data for theOak Ridge Reservation (Watkins et al 1993) That study systematically collected soils fromall geological units on the site It provided high-quality data that was agreed by all parties torepresent local background and its variance In most cases, background measurements areless systematic Examples include water collected upstream of a site or soil collected beyondthe perimeter of a site Local background measurements present some major disadvantages.Because samples are typically collected in the vicinity of the unit, there is some danger ofundetected contamination In addition, because local background measurements are oftenpoorly replicated in space or time, their variance is often poorly specified However, becausethe natural variance in background concentrations is lower in the vicinity of an individual sitethan across a nation or region, use of unit-specific background values is more likely (com-pared to background estimates on a larger scale) to suggest that concentrations on the site areabove background

Local background should be used when possible, with care taken to ensure that ground variance is specified and that samples from background and contaminated locationsare comparable For example, because aqueous concentrations of naturally occurring chem-icals are sensitive to hydrologic conditions, background samples should be taken at the sametime as contaminated samples Regional and national background values can be used tocheck the reasonableness of local and unit-specific background values

back-31.2.1.1.2 Quantitative Methods for Comparison to Background

Various methods may be used for comparison of site concentrations to background trations Because concentrations of chemicals at uncontaminated reference sites are variable,

concen-a definition of the upper limit of bconcen-ackground must be specified Some regulconcen-ators use simplerules such as specifying that chemicals should be retained unless the maximum concentration

of the chemical in a medium on the unit is less than twice the mean background tion Other possible limits include the maximum observed value at reference sites, a percentile

concentra-of the distribution concentra-of reference concentrations, or tolerance limits on a percentile If there aremultiple reference sites, one might use the site with the highest concentrations as bestrepresenting the upper limits of background, or statistically combine the sites to generate adistribution of concentrations across uncontaminated sites

31.2.1.1.3 Treatment of Background in Multimedia Exposures

Wildlife species are exposed to contaminants in food, water, and soil If concentrations of achemical in all media are at background levels, the chemical can be screened out However, ifconcentrations in one or more of the media are above background, the chemical cannot beeliminated from consideration in any of the media with respect to that wildlife endpoint,because all sources of the chemical contribute to the total exposure

31.2.1.1.4 When Is a Concentration Not Comparable to Background?

If there is reason to believe that a chemical occurs in a form that is more toxic or morebioavailable than at background sites, it may be a chemical of concern even at concentrationsthat are within the range of background values An example from Oak Ridge is the acidic andmetal-laden leachate from the S-3 waste ponds that entered Bear Creek Because metals aremore bioavailable in acidic than in neutral waters, metal concentrations in a stream down-gradient of the ponds that were within the range of background waters were not screened out.Considerations should include the major physical–chemical properties of the waste, such as

pH, hardness, and concentrations of chelating agents relative to properties of the ambientmedia, and the species of the chemical in the waste relative to the common ambient species

31.2.1.1.5 Screening Biota Contamination Using Background Concentrations

for Abiotic Media

It is possible to use background values for abiotic media to screen biota as sources ofexposure to herbivores and predators For example, if all metals in soil are at backgroundconcentrations, it can be assumed that plant and earthworm metal concentrations are also atbackground Similarly, if concentrations in both water and sediment are at background levels,concentrations in aquatic biota can be assumed to be at background

31.2.1.1.6 Screening Future Exposure Concentrations Against Background

If exposure concentrations may increase in the future, current concentrations should not beused to exclude chemicals from the baseline assessment, because future exposure scenariosmust also be addressed If the increased future exposures might result from movement of acontaminated ambient medium such as soil or groundwater, concentrations measured inthose media should be screened against background For example, if a plume of contaminatedgroundwater might intersect the surface in the future, concentrations in the plume should bescreened against background If the increased future exposures might result from changes in asource such as the failure of a tank, the contaminant concentrations predicted to occur inambient media might often not be screened against background concentrations That isbecause regulators often argue that the modeled future concentrations are, by definition,additions to background Therefore, even if the predicted concentrations are within the range

of local concentrations, they are not ‘‘really background.’’

31.2.1.2 Screening Against Detection Limits

Chemicals that are not detected in any sample of a medium may be screened out if thedetection limits are acceptable to the risk manager For example, EPA Region IV indicated

that the Contract Laboratory Program’s Practical Quantification Limits could be used forthis purpose (Akin 1991) It should be noted that this screening criterion is not risk-based It isentirely possible that undetected chemicals pose a significant risk to some receptors and mayaccount for effects seen in media toxicity tests or biological surveys The use of this criterion isbased entirely on a policy that responsible parties should not be required to achieve lowerdetection limits than are provided by standard US EPA methods An alternative is to use thelimit of detection as the exposure concentration in the screening assessment.

Care should be taken when eliminating undetected chemicals that are known to mulate to concentrations in biota that are higher than in inorganic media In particular,organic mercury and persistent lipophilic organic compounds such as polychlorinated bi-phenyls (PCBs) and chlordane may occur in significant amounts in aquatic biota when theycannot be detected in water or even sediment If there are known sources of these chemicals,they should not be screened out until biota have been analyzed

bioaccu-31.2.1.3 Screening Against Waste Constituents

If the waste constituents are well specified either because they were well documented at thetime of disposal or because they are still accessible to sampling and analysis (e.g., leakingtanks), chemicals that are not waste constituents should be screened out However, this is notpossible at many sites because of imperfect record keeping, loss of records, poor control ofwaste disposal, disposal of ill-defined wastes, or occurrence of wastes in forms that do notpermit definitive sampling and analysis

31.2.1.4 Screening Against Physical–Chemical Properties

Chemicals can be screened out if their presence in significant amounts in a medium can beexcluded by physicochemical principles For example, volatile organic compounds (VOCs) wereexcluded from the risk assessment for Lower Watts Bar Reservoir, Tennessee, because anyVOCs in Oak Ridge emissions would be dissipated by the time the contaminated waters reachedthe mouth of the Clinch River Similarly, atmospheric routes of exposure have been eliminatedfrom ecological risk assessments at most units, because significant atmospheric concentrationsare implausible given the nature and concentrations of the soil and water contaminants

31.2.1.5 Screening Against Ecotoxicological Benchmarks

Chemicals that occur at concentrations that are safe for ecological receptors can be excluded

as COP ECs Thi s screen is pe rformed us ing HQs (Sect ion 31.1) If the bench mark tration or dose for a chemical exceeds its conservatively defined exposure concentration ordose, the chemical may be screened out Although this is the typical approach to screening fortoxic hazards, some risk managers require that the benchmark exceed the exposure level bysome factor (e.g., 2 or 10) These safety factors are used because the benchmark or exposurelevels are not conservative or not sufficiently conservative The screening method developed

concen-by Parkhurst et al (1996a) uses a factor of 3 A much more elaborate set of factors can befound in Kester et al (1998)

Chemicals at contaminated sites typically occur as mixtures One common approach to thisproblem is to screen the individual chemicals and assume that this issue is covered by theconservatism of the screening process: any chemical that significantly contributes to toxicity on

a site will be retained by the conservative screening process An alternative is to explicitly modelthe combined toxicity, which is typically done using an HI based on additive toxicity (Section31.1) That is, if the sum of the HQs for the chemicals in the mixture, based on normalizedtoxicity benchmarks, is greater than 1, the mixture is retained for further assessment

Petrol eum and other complex an d poor ly specified mate rials present a particular prob lem.Curren tly the most common app roach for sit es con taminate d with petroleu m an d its products

is to screen total petrol eum hydroca rbon (TP H) con centrations agains t TPH benchmarks How ever, a repres entat ive chemi cal approach co uld be used to screen agains t ben chmarkconcen trations For exampl e, when screeni ng a mixtu re of polycycl ic aro matic hydroc arbons(PAH s) in soil, asses sors can not obtain be nchmarks or good toxic ity da ta from which toderive ben chmarks for man y con stituent s In that case, it is app ropriate for the sake ofscreeni ng to use a PAH for whi ch a benchmark is available an d whi ch the asses sor isconfide nt is more toxic than average to repres ent all constituen ts of the mixtu re

The calcula tion of exposure con centra tions to be compared to the benchmarks depends onthe charact eristics of the recept or In general , a co ncentra tion sho uld be used that repres ents areasonabl e maxi mum exposure given the ch aracteris tics of the medium and recepto r Thefundame ntal dist inction that mu st be made is betw een recept ors that average their exp osureover space or tim e an d those that have essential ly con stant exposure

. Terrestrial wildlife, like humans, move across a site potentially consuming soil, vegetation, oranimal foods from locations that vary in their degree of contamination Therefore, meanconcentrations over space provide reasonable estimates of average exposure levels For theconservative estimate to be used in the screening assessment, the 95% upper confidence limit(UCL) on the mean is as appropriate as in human health assessments (Office of Emergencyand Remedial Response 1991, Office of Solid Waste and Emergency Response 2003).. Fish an d oth er aqua tic organisms in flowing waters a verage their expo sures over time.Therefor e, the 95% UC L on the tempor al mean is a reasonabl y conserva tive estimat e ofchronic aqu eous exp osure con centrations If aqueo us con centrations are known to behighly varia ble in time and if periods of high co ncentra tion that persi st for extend edduratio ns can be identi fied, the ave raging period should corres pond to those periods . Wildlif e that feed on aquati c biota average their dietary exp osure across their preyorganis ms Therefor e, they average their expo sure ov er space and tim e (i.e., ov er theirfeedin g range and ov er time as their prey respon d to va riance in water quality) The 95%UCL on that mean is a reasonabl y co nservative estimat e of expo sure con centra tions.. Soil and sedim ent co ncentra tions are typic ally relative ly constant over time, an d plan ts,invertebrat es, an d micr obes are imm obile or effe ctively imm obile Ther efore, there iseffecti vely no averagi ng of concentra tions ov er space or time The reasona ble maxi mumexposure for those media an d recepto rs is the maxi mum observed co ncentra tion, if areasonabl e number of sampl es ha ve been analyze d Some organ isms occup y that max-imally contam inated soil or sedim ent or would oc cupy it if it were not toxic Therefor e,exceedence of ec otoxico logical benchmarks at any locat ion impl ies a potential risk tosome recept ors Alternat ively, an uppe r percent ile of the dist ribution of concen trations(e.g., 90th percentile) could be used Such percentiles would be more consistent thanmaxima because they are less dependent on sample size The US EPA recommends use ofthe 95% UCLs on mean soil concentrations for soil invertebrates and plants if many dataare available (Office of Solid Waste and Emergency Response 2003), but that implies thatthese organisms are averaging across the site

Screening against wildlife benchmarks requires specification of individual wildlife species, sothat the concentration in the contaminated medium corresponding to the screening bench-mark dose can be estimat ed using an approp riate expo sure model (Sect ion 22.8) Even ifendpoint species have not yet been selected for the site through the assessment planningprocess, species should be selected for screening The chosen species should include poten-tially sensitive representatives of trophic groups and vertebrate classes that are potentially

exposed to contaminants on the site The US EPA’s surrogate species for deriving soilscreening levels are: meadow vole (mammalian herbivore), short-tailed shrew (mammalianground insectivore), long-tailed weasel (mammalian carnivore), mourning dove (avian grai-nivore), American woodcock (avian ground insectivore), and red-tailed hawk (avian carni-vore) (Office of Solid Waste and Emergency Response 2003) For screening assessments, thesespecies are assumed to be monophagous For example, the short-tailed shrew is assumed toeat only earthworms.

If no appropriate benchmark exists for a chemical that cannot be screened out byother criteria, an effort should be made to find or develop a benchmark for that chemical.However, in some cases, there are no appropriate toxicity data available for a chemical–receptor combination In such cases, the chemical cannot be eliminated by toxicity-basedscreening The media in which such chemicals occur should not be eliminated from furtherassessment

31.2.1.6 Screening Species Against Area

Wildlife endpoint species that have large home ranges relative to areas contaminated are oftenretained by screening assessments that assume that they spend 100% of their time in thecontaminated area They are then found to have very low risks in definitive assessments thatconsider spatial distributions of exposure This wasted effort can be avoided by identifying anallowable contaminated area for screening purposes One approach uses a percentage (e.g.,2%) of an animal’s home range (Tannenbaum 2005b) Hence, since the smallest home rangefor a red fox is 123.5 acres, if the contaminated area were less than 2.5 acres, that specieswould be screened out An alternative approach is based on density An allowable contam-inated area might be the area expected to contain a certain number of organisms of anendpoint species (e.g., 4) (Tannenbaum 2005a)

These approaches, like other screening criteria, must be applied with care If a ated area is particularly attractive, because it provides water, mineral nutrients, or otherfeatures that do not occur elsewhere, screening based on area should be avoided or at leastapplied carefully Also, the acceptability of area screening criteria should be determined byconsulting the decision maker in advance

contamin-31.2.2 EXPOSURECONCENTRATIONS FORSITES

The following issues must also be considered when deriving exposure concentrations forscreening assessments of contaminated sites

. The screening methods described here presume that measured chemical concentrationsare available to define exposure Use of measured concentrations implies that concentra-tions are unlikely to increase in the future Where concentrations may increase in thefuture due to movement of a contaminated groundwater plume, failure of waste contain-ment, or other processes, future concentrations must be estimated and used in place ofmeasured concentrations for the future scenarios For screening assessments, simplemodels and assumptions such as exposure of aquatic biota to undiluted groundwaterare appropriate

. For large sites, it is appropriate to screen contaminants within subunits such as streamreaches rather than in the entire site to avoid diluting out a significant contaminantexposure The division of a site into areas or reaches should be done during the devel-opment of the analysis plan and should take into consideration differences in contamin-ant sources and differences in habitat

Some benchmarks are de fined in term s of specific forms or species of ch emicals Wh enforms are not specified in the data availa ble for the screenin g asses sment, the most toxicform shou ld be assum ed unless there are co mpelling reasons to be lieve that oth er form spredomi nate For example, it has been ge nerally reco gnized and confir med in studi es atOak Ridge that the hexaval ent form of ch romium is convert ed to trivalent chromi um inhumid soils, sedimen ts, and wat ers; therefo re, EPA Region IV recomm ended agains tassum ing that hexaval ent chro mium is present in signi ficant amounts on that site.. Measurem ents of chemi cals in ambie nt media often include a mixture of detect ed con-centra tions an d nonde tects with associ ated detect ion limit s For screeni ng of soil an dsedimen t, the maxi mum value is sti ll availa ble in su ch cases How ever, 95% UCLs on themean concentration cannot be derived directly If time and resources permit, these valuesshould be estimated using a maximum likelihood estimator or a product limit estimator(Box 20.1) Othe rwise, the 95% UC L ca n be calcul ated using the detect ion limits as if theywere observed values If the chemical was not detected in any sample and the analyticaltechniques did not achieve detection limits that were agreed to be adequate by the riskmanager, the reported limit of detection should be screened in place of the maximum or95% UCL value.

31.2.3 SCREENINGMEDIA

If the screening of chemicals does not reveal any COPECs in a particular medium, and ifthe data set is considered adequate, that medium may be eliminated from further consider-ation in the risk assessment However, if toxicity has been found in appropriate tests of themedium or if biological surveys suggest that the biotic community inhabiting or usingthat medium appears to be altered, the assessor and risk manager must consider whatinadequacies in the existing data are likely to account for the discrepancy between the lines

of evidence, and must perform appropriate investigations or reanalysis of the data to resolvethe discrepancy

31.2.4 SCREENINGRECEPTORS

If all media to which an endpoint receptor is exposed are eliminated from consideration, thatreceptor is eliminated as well For wildlife species that are exposed to contaminants in water,food, and soil, this means that all three media must be eliminated Aquatic biota can beeliminated if both water and sediment have been eliminated Plants and soil heterotrophs can

be eliminated if soil has been eliminated Any evidence of significant exposure to ants or injury of the receptor would prevent its elimination from the assessment

contamin-31.2.5 SCREENINGSITES

A site can be eliminated from a risk assessment if all endpoint receptors for that type of unithave been eliminated However, it must be noted that even when there are no significant risksdue to contaminant exposures on the site, the risk assessment must address fluxes of contam-inants that may cause ecological risks off site or incidental use of the site by wildlife, whichmay cause risks to wide-ranging wildlife populations

31.2.6 DATAADEQUACY ANDUNCERTAINTIES

Screening ecological risk assessments performed at the intermediate stages of a phasedassessment process or as the initial step in the definitive assessment should have adequatedata quality and quantity because the data set used should be the result of a proper

assessment plan However, the initial screening assessment is likely to have few data for somemedia, and the data quality may be questionable because the data are not adequately quality-assured or adequately documented to perform a full data evaluation Sets of encountered datashould at least be evaluated as far as possible to eliminate multiple reports of the samemeasurement, units conversion errors, and other manifest flaws Further, because it isimportant to avoid screening out any potentially hazardous chemicals, the available datashould be evaluated to eliminate data with an anticonservative bias Once data evaluation hasbeen carried out as far as possible, the proper response to remaining questions of dataadequacy is to perform the screening assessment with the available data and describe theinadequacies of the data and the resulting uncertainties concerning the results Highlyuncertain screening results for a medium should then constitute an argument for making abroad chemical analysis part of the plan for the next phase of the assessment.

Screening assessments also must consider the relevance of historical data to currentconditions Issues to consider in deciding whether data are too old to be useful include thefollowing:

. If contamination is due to a persistent and reasonably stable source that has beenoperating since before the date of the historic data, the data are likely to be relevant.. If the source is not persistent and stable and the chemical is not persistent (e.g., itdegrades or volatilizes), the data are unlikely to be relevant

. If the ambient medium is unstable or highly variable, historic data are less likely to berelevant Examples include highly variable aqueous dilution volumes and scouring ofsediments

. Human actions, particularly those taken to stabilize the wastes or partially remediate thesite, may make historic data irrelevant

31.2.7 PRESENTATION OF ASITESCREENINGASSESSMENT

Because the screening assessment is not a decision document, the documentation of ascreening assessment should be brief and need not provide the level of documentation orexplanat ion that is required of a definiti ve asses sment (C hapter 35) The resul ts can bepresented in two tables The first table lists the chemicals that were retained with reasonsfor retaining any chemicals despite their passage through the screening benchmarks andbackground The second table lists those chemicals that were rejected with the reason forrejection of each These results may be presented for each medium, for each spatial unit within

a site, and for each endpoint entity exposed to a medium The assessment that generated theseresults should be presented in the format of the framework for ecological risk assessment

important to do the screening assessment correctly, it is also important to ensure that theproduction of a screening assessment does not become an impediment to completion of adefinitive assessment and remediation

The following information is important to support the screening results:

. Rationale for the list of chemicals that was screened

. Sources of the site, reference, and background contaminant concentrations

. Justification of the use of any preexisting concentration data

. Methods used to derive any site-specific background concentrations

. Criteria used to determine the adequacy of the concentration data set

. Sources of existing screening benchmark values

. Methods used to derive any new screening benchmark values

31.3 EXAMPLES

Because they are a preliminary procedure, screening ecological risk assessments are rarelypublished in the open literature However, Region V (2005a,b) has published screeningassessments that represent the state-of-practice as part of its guidance for ecological riskassessment of contaminated sites These assessments use simple conservative assumptionssuch as 100% area use factor and 100% bioavailability and risk characterizations based ondeterministic HQs The conclusions represent the roles that screening assessments can play.Camp Perry was found to have low ecological risks given current conditions, so it wasrecommended that efforts be focused on isolating the source, so that conditions will notworsen Ecological risks were potentially significant at the Elliot Ditch=Wea Creek site, so theassessors recommended additional studies to reduce uncertainties The examples includedevaluation of the presence of endangered species, site-specific toxicity testing, bioaccumula-tion studies, and residue analyses of ecological receptors

32 Definitive Risk Characterization by

Weighing the Evidence

When you follow two separate chains of thought, Watson, you will find some point of intersectionwhich should approximate the truth

Sherlock Holmes, in The Disappearance of Lady Frances Carfax

Risk characterization for definitive risk assessments consists of integrating the availableinformation about exposure and effects, analyzing uncertainty, weighing the evidence, andpresenting the conclusions in a form that is appropriate to the risk manager and stakeholders.The integration of exposure and effects information should be carried out for each line ofevidence independently so that the implications of each are explicitly presented This makesthe logic of the assessment clear and allows independent weighing of the evidence For eachline of evidence, it is necessary to evaluate the relationship of the measures of effect to theassessment endpoint, the quality of the data, and the relationship of the exposure metrics inthe exposure–response data to the exposure metrics for the site The actual characterizationfor ecological risk assessment is then performed by weight of evidence (Suter 1993a; EPA1998a) Rather than simply running a risk model, ecological risk assessors should examine allavailable data from chemical analyses, toxicity tests, biological surveys, and biomarkers, andapply appropriate models to each to estimate the likelihood that significant effects areoccurring or will occur and to describe the nature, magnitude, and extent of effects on thedesignated assessment endpoints ‘‘Because so many judgments must be based on limitedinformation, it is critical that all reliable information be considered’’ (The Presidential=Congressional Commission on Risk Assessment and Risk Management 1997)

32.1 WEIGHING EVIDENCE

All types of evidence have strengths and weaknesses as bases for inference By comparingmultiple lines of evidence with independent strengths and weaknesses it is possible to identifyinferences that are strongly supported and avoid those that have little support In addition,consideration of all available and relevant evidence provides assurance to stakeholders thatevidence is not being ignored or covered up (National Research Council 1994) Finally, theuse of multiple lines of evidence provides a sort of replication in situations, such as contam-inated or disturbed ecosystems, that are not replicated (Cavalli-Sforza 2000) If we apply threeindependent techniques to an assessment and get the same answer, it is analogous to applyingthe same technique to three different ecosystems and getting the same answer Ecological riskcharacterization by weight of evidence is widely practiced and numerous methods have been

devise d (Ch apman et al 2002) Analyzing the weigh t of evidence is also the be st generaltechni que for determini ng causat ion in ecologi cal e pidemiology (Ch apter 4).

Thr ee term s a re critical to this discus sion

Typ e of eviden ce : A categor y of evidence used to charact erize risk Eac h type of eviden ce isqua litatively diffe rent from others used in the risk charact eriza tion Type s of evidence aretypic ally charact erized by the source of the exp osure–r esponse relationshi p The most com-monly used types of e vidence in ecologi cal risk asses sments of co ntaminan ts are (1) biologicalsurveys , (2) toxic ity tests of con taminate d media , and (3) toxic ity test s of indivi dual chemi cals.Line of eviden ce : An exposure–r espon se relationshi p and a co rrespondi ng esti mate ofexpo sure A line of evidence (e.g , a fathe ad minnow LC50 and a 24 h maximum conc entrationestimat ed using EXAM S) is an inst ance of a type of e vidence (e.g., laborat ory test endpo intsand modeled exposure level s) There may be mult iple lines of eviden ce for a type of evidence

in a risk charact erization

Weight of eviden ce : A pro cess of identi fying the best-supp orted risk cha racterization giventhe existence of multiple lines of eviden ce Eviden ce to be weighed may include multiple lines

of ev idence of a single type or multiple types of evidence, each represe nted by one or morelines of evidence

Ther e are at least four app roaches to weighing evidence in ecologi cal risk charact eriza tion.Best line of eviden ce : The sim plest ap proach is to assemb le all avail able lines of evidence,evaluat e their stre ngths and weakne sses, identify the strong est, and use it to cha racterize therisks This approach is most ap plicable when one line of evidence is much strong er than theothers because of its qua lity or its relev ance to the case In suc h cases, one would not wish todilut e or cast doubt upon a high-qua lity line of evidence by combini ng it with lines of evidencethat are likel y to be mis leading This approach also has an advantag e in that the methods andresul ts are relative ly clear an d simp le to present The selec tion of the best line of evidenceshou ld be based on attribut es like those developed for the M assachus etts weigh ting andscori ng system (Table 32.1)

Tiered asses sment : In this app roach, lines of evidence (data and associ ated models) aredevelop ed sequenti ally, beginni ng wi th the simplest a nd cheap est More comp lex an d expen-sive lines of evidence a re added until one gives a suff iciently clear resul t Thi s is a varia nt onthe best line of evidence approach in that only one line of evidence is used in each tier and therisk c haracteriza tion is based on the results of the highest impl ement ed tier This app roach isillustr ated by pe sticide ecologica l risk asses sment s (Sect ion 32.4.4)

Num erical weighting and scoring : Numer ical wei ghts can be applie d to results for multiplelines of evidence to generat e scores indicating the degree of support A system of this sort wasgenerat ed for asses sing ecologi cal risks at contam inate d sites in Massac husett s (Massa chusettsWeight -of-E vidence Work Group 1995; Menzie et al 1996) Each line of evidence is scored forthe indicated response (pres ence or ab sence of ha rm and high or low response) , and then given

a 1 to 5 score for eac h of 10 attribut es and finally each attribut e is weighted 0 to 1 for itsrelative importance The 10 attributes have to do with the strength of association of theevidence with the site, the quality of the evidence, and the study design and execution (Table32.1) Finally, the concurrence among the lines of evidence is scored This system, inevitablywith some modification, has been successfully applied to real sites (Johnston et al 2002) Likeother rule-based methods of risk characterization, numerical weighting and scoring is efficientand consistent but may not be optimal for an individual case

Best explanation: Abductive inference, as opposed to deductive and inductive inference,reasons from a collection of evidence to the best explanation (Josephson and Josephson 1996)(Sect ion 4.3) The be st exp lanation is one that accounts for the ap parent discrep ancies amonglines of evidence as well as the concurrences For example, differences in bioavailability oftenexplain differences in apparent toxicity between laboratory toxicity tests and ambient water

toxicity tests The best developed example of abductive inference in ecological risk terization is the sediment quality triad, discussed below.

charac-This chapter is devoted to weighing evidence to determine the best characterization of anecological risk Risk characterizations based on a best line of evidence or numerical weightingand scoring systems are program-specific and largely self-explanatory In addition, they impli-citly assume that each line of evidence is independent Reasoning to the best explanation allowsassessors to use knowledge of the relationships among lines of evidence to characterize the risks

32.2 SEDIMENT QUALITY TRIAD: A SIMPLE AND

CLEAR INFERENCE METHOD

Inference to the best explanation as an approach to weighing multiple lines of evidence is bestillustrated by the sediment quality triad (Long and Chapman 1985; Chapman 1990) The

TABLE 32.1

Attributes for Scoring Lines of Evidence

Relationship of the line of evidence and the assessment endpoint

Degree of association The extent to which the response in the exposure–response relationship is

representative of, or correlated with, the assessment endpoint Exposure–response The extent to which the exposure–response relationship is quantified and the

strength of the relationship Utility The certainty, scientific basis, and sensitivity of the methods used to generate the

exposure estimate and the exposure–response relationship Data quality

Quality of data The quality of the methods used to generate the exposure estimate and the

exposure–response relationship, including measurements, tests, and models Study design

Site specificity Representativeness of the media, species, environmental conditions, and habitat

type relative to those at the site; for non-site-specific assessments, the scenario

is considered instead Sensitivity The ability of the exposure–response relationship to define endpoint effects of

concern and to discriminate them from background variance or confounding causes

Spatial representativeness Spatial overlap or proximity of the area sampled to generate the exposure estimate

or the exposure–response relationship and the area being assessed Temporal representativeness Temporal overlap of the time when samples or measurements were taken to

generate the exposure estimate or the exposure–response relationship and the time when effects were being induced or the frequency or duration of sampling

or measurement relative to the temporal pattern of exposure and response Quantitative measure The degree to which the magnitude of response can be quantified by the line

of evidence Standard method The degree to which exposure estimates or exposure–response relationships were

generated using data produced by relevant standard methods and standard models Source: Adapted from Menzie, C., Henning, M.H., Cura, J., Finkelstein, K., Gentile, J., Maughan, J., Mitchell, D.,

et al., Hum Ecol Risk Assess., 2, 277–304, 1996; Massachusetts Weight-of-Evidence Work Group, Draft Report:

A Weight-of-Evidence Approach for Evaluating Ecological Risks, 1995 (the Attributes are Somewhat Reworded, and the Explanations are Entirely Different).

three types of evidence from a site that form the triad are sediment chemistry, sedimenttoxicity, and sediment invertebrate community structure Measures used for each line ofevidence must be chosen and evaluated to be appropriately sensitive, so as not to causefrequent false negative or false positive results For example, analyses of sediment chemicalsmust be sufficiently sensitive to detect potentially toxic concentrations, but they should also

be compared to effects benchmarks or background concentrations to assure that trivial levelsare not counted as positive results

Assuming that all three components can be assigned a dichotomous score (þ=) and aredetermined with appropriate sensitivity and data quality, the rules in Table 32.2 can be used

to reach a conclusion concerning the induction of effects by contaminants The assumptionsare critical For example, in situation 5 the conclusion that the community alteration is notdue to toxic chemicals depends on the assumptions that the chemical analyses include allpotentially significant toxicants and are sufficiently sensitive, and that the sediment toxicitytest was well conducted and was representative of the field exposure conditions and of thesensitivity of the more sensitive members of the community If any of the data are notsufficiently sensitive or of high quality, one must weigh the evidence taking those issuesinto consideration The sediment quality triad was developed for estuarine sediments andhas been widely applied in those systems (Chapman et al 1997) It has been adapted to thesoft sediments of streams, rivers, and reservoirs (Canfield et al 1994) and is applicable inprinciple to contaminated soils

An alternative inferential triad, the exposure–dose–response triad, has been proposed forassessment of risks from contaminated water or sediments by Salazar and Salazar (1998).Exposure is estimated by analysis of the ambient medium, dose by analysis of tissue chem-istry, and response by surveys of community properties or toxicity tests of the contaminated

TABLE 32.2

Inference Based on the Sediment Quality Triad

degradation

pollution-induced degradation

present at nontoxic levels

with the potential to cause degradation

are not sufficient to significantly modify the community

degradation

is not due to toxic chemicals Source: Chapman, P.M., Sci Total Environ., 97=98, 815–825, 1990 With permission.

Responses are shown as either positive (þ) or negative (), indicating whether or not measurable and potentially significant differences from control=reference conditions are determined.