Integrated Environmental Assessment and Management — Volume 10, Number 4—pp. 595–601 © 2014 SETAC

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Learned Discourses: Timely Scientific Opinions Timely Scientific Opinions

Learned Discourses Editor Peter M. Chapman Golder Associates Ltd. 200-420 West Hastings Street Vancouver, BC V6B 1L1 [email protected]

SETAC’s Learned Discourses appearing in the first 7 volumes of the SETAC Globe Newsletter (1999–2005) are available to members online at http:\\communities.setac.net. Members can log in with last name and SETAC member number to access the Learned Discourse Archive.

A NOVEL APPROACH FOR GRAPHING CENSORED ENVIRONMENTAL DATA Timothy J Barrett,*y Rainie L Sharpe,z and M Ekram Azimy yGolder Associates Ltd., Calgary, Alberta, Canada zGolder Associates Ltd., Edmonton, Alberta, Canada  [email protected] DOI: 10.1002/ieam.1563

The effective presentation of censored environmental data is challenging, particularly when it comes to displaying values below analytical detection limits (DL) in an intuitive and transparent manner. There are multiple approaches for data analysis and presentation of censored environmental data. Substitution methods are the most common approach for incorporating censored data into statistical analyses, although more robust methods are available, such as maximum likelihood estimation or nonparametric methods (Helsel 2005). Graphical methods used to present univariate environ-

Data Analyses A novel approach for graphing censored environmental data, by Timothy J Barrett, Rainie L Sharpe, and M Ekram Azim Recommendations are provided for displaying values below analytical detection limits in an intuitive and transparent manner. Life Cycle Assessment Moving from the material footprint to a resource depletion footprint, by Kai Fang and Reinout Heijungs A resource depletion footprint aimed at addressing abiotic resource depletion is proposed rather than reliance on total mass of materials used for economic processes. Large Carnivore Ecology Factors influencing hunting success of carnivores, factors influencing vigilance of their prey: Are we just changing paper titles?, by Justice Muvengwi Future research into large carnivores and their prey should incorporate both vigilance and hunting success models for large carnivores. Flame Retardants Halogenated flame retardants in Canadian house dust, by Golnoush Abbasi, Amandeep Saini, Emma Goosey, and Miriam Diamond Halogenated flame retardants from consumer products are found in dust in indoor environments, resulting in exposures that could affect human health. Bioaccumulation Regulatory Approaches to the Bioaccumulation of Drugs: A Polar Bare Walks Into A Bar..., by Sigrun Kullik and A Graham M Rattray Environmental assessment of new active pharmaceutical ingredients should be precautionary and follow a weight-ofevidence approach which considers multiple lines of evidence. DOI: 10.1002/ieam.1578

mental data include boxplots (Phillips et al. 2012), bar graphs of mean concentrations with error bars (Kelly et al. 2010), and plots of confidence intervals for the mean (Hebert et al. 2013) with censored data represented in these plots using substitution methods. The method of substitution varies widely among studies and includes substitution of censored values with the DL (Kelly et al. 2010), one‐half the DL (Phillips et al. 2012), and zero (Hebert et al. 2013). The choice of the substitution value is subjective and influences the statistical results and graphical presentation of the censored data. The graphical methods described above are commonly used to present censored data in the field of environmental science but do not provide an accurate graphical representation of the censored data. More importantly the axis labels, figure captions, and footnotes of many plots of censored data do not provide sufficient information (e.g., the DL, sample size, and method of substitution for values below the DL) to correctly interpret the data set.

Learned Discourse: Timely Scientific Opinions

Intent. The intent of Learned Discourses is to provide a forum for open discussion. These articles reflect the professional opinions of the authors regarding scientific issues. They do not represent SETAC positions or policies. And, although they are subject to editorial review for clarity, consistency, and brevity, these articles are not peer reviewed. The Learned Discourses date from 1996 in the North America SETAC News and, when that publication was replaced by the SETAC Globe, continued there through 2005. The continued success of Learned Discourses depends on our contributors. We encourage timely submissions that will inform and stimulate discussion. We expect that many of the articles will address controversial topics, and promise to give dissenting opinions a chance to be heard. Rules. All submissions must be succinct: no longer than 1000 words, no more than 6 references, and at most one table or figure. Reference format must follow the journal requirement found on the Internet at http:\\www. setacjournals.org. Topics must fall within IEAM’s sphere of interest. Submissions. All manuscripts should be sent via email as Word attachments to Peter M Chapman ([email protected]).

In a Nutshell…

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Integr Environ Assess Manag 10, 2014—PM Chapman, Editor

0.1

A

Nickel Concentration (mg/kg ww)

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C

B

0.08 0.07 0.06 0.05 0.04 0.03 0.02 0.01 0

2 Lake A 6

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Lake B 15

Lake C 20

Lake D 5

Lake A 6

Lake B 15

Lake n

Lake C 20

Lake D 5

Lake n

Lake A 6

Lake B 15

Lake C 20

Lake D 5

Lake n

Figure 1. Nickel concentrations measured in muscle tissue of Lake Chub collected from 4 lakes. (A) Modified censored boxplot with horizontal line at the DL ¼ 0.01 mg/kg ww. (B) Boxplot with values below the DL substituted as half the DL. (C) Bar graph of mean  SEM with values below the DL substituted as the DL.

The censored boxplot is an excellent graphical method proposed by Helsel (2005) that provides an intuitive and accurate graphical presentation of censored data. The method involves substituting the censored observations to a single value below the DL, creating a boxplot of the data, drawing a horizontal line at the DL (or maximum DL if there are multiple DLs for a data set), and censoring the boxplot below the DL (Helsel 2005). The boxplot is censored such that it only shows the distribution of the detected values above the horizontal line at the DL. The approximate percentage of the data set that is below the DL can be determined by the section of the boxplot that is censored (e.g., 25%–50% if the lower portion of the box is censored). We provide a modification of the censored boxplot that increases the amount of information provided in the plot and reduces the bias of the shape of the distribution when there are few values. Our method of graphing censored environmental data is to use censored boxplots when the sample size is 8 or more. When there are fewer than 8 values in a data set, the quartiles of a boxplot can be misleading, and the boxplot does not provide a good graphical representation, or understanding, of the data distribution. The boxplots are defined using the minimum value, the quartiles, and the maximum value. Values that are 1.5 times the interquartile range beyond the quartiles are considered outliers and are plotted as individual values. When outliers are present, the whiskers are truncated to the next value in the data set that is within 1.5 times the interquartile range beyond the quartiles. Values below the DL are represented as an open symbol at a proportion of the DL (e.g., half the DL), along with the number of observations below the DL reported beside the open symbol. Total sample sizes are reported on the boxplot below the x axis group labels and can be used to compare the proportion of values below the DL among groups. When there are fewer than 8 values in a data set, the individual values are plotted as solid symbols. Values below the DL are represented in the same manner as described above. An example of our graphing method is provided in Figure 1A, for a hypothetical data set showing Ni concentrations in muscle tissue of Lake Chub (Couesius plumbeus) collected from 4 lakes and reported with a DL of 0.1 milligrams per kilogram wet weight (mg/kg ww). Figures 1B and 1C present commonly used

graphing methods, which have been used in recent published studies (Kelly et al. 2010; Phillips et al. 2012). Figure 1B presents boxplots with values below the DL substituted as half the DL; Figure 1C presents the mean  1 SEM with values below the DL substituted as the DL. Misleading features of these plots include a misrepresentation of the variability in the data set (Figures 1B and C) and an overestimation of the true mean concentration (Figure 1C). Figure 1A shows a clear representation of the distribution of the data and the proportion of values below the DL in the data set. We suggest that our proposed modification of the censored boxplots described by Helsel (2005) provides the best graphical representation of censored environmental data.

REFERENCES Hebert CE, Campbell D, Kindopp R, MacMillan S, Martin P, Neugebauer E, Patterson L, Shatford J. 2013. Mercury trends in colonial waterbird eggs downstream of the oil sands region of Alberta, Canada. Environ Sci Technol 47:11785–11792. Helsel DR. 2005. Nondetects and data analysis: Statistics for censored environmental data. Hoboken (NJ): John Wiley and Sons. 250 p. Kelly EN, Schindler DW, Hodson PV, Short JW, Radmanovich R, Nielsen CC. 2010. Oil sands development contributes elements toxic at low concentrations to the Athabasca River and its tributaries. Proc Natl Acad Sci USA 107:16178–16183. Phillips PJ, Chalmers AT, Gray JL, Kolpin DW, Foreman WT, Wall GR. 2012. Combined sewer overflows: An environmental source of hormones and wastewater micropollutants. Environ Sci Technol 46:5336–5354.

MOVING FROM THE MATERIAL FOOTPRINT TO A RESOURCE DEPLETION FOOTPRINT Kai Fang*y and Reinout Heijungsyz yLeiden University, Leiden, the Netherlands zVU University Amsterdam, Amsterdam, the Netherlands *[email protected] DOI: 10.1002/ieam.1564

The concept of footprinting nowadays has been accepted by a large number of scholars as a proxy for anthropogenic pressure on the planet’s ecosystems. In view of the success of the ecological, water, and carbon footprints, it is not surprising that an expanding list of indicators with “footprint” in their names

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will be continuously introduced to the public. The recent appearance of the material footprint (MF) is an example (Schoer et al. 2012; Wiedmann et al. 2013). It is defined as the total mass of materials used for economic processes. By using this mass‐based MF indicator expressed in absolute terms, one can be clearly aware of the total resource needs of an economy. However, we argue that computing MF in this way is misleading from a life cycle perspective, because in the goal and scope definition of a life cycle assessment (LCA), material is treated as an upstream process before the manufacture of a product. This means that the MF is an analogous but different concept from product footprint—an ongoing European Commission policy initiative (EC 2013) assessing a broad set of impact categories to provide a comprehensive picture of the life cycle environmental performance of products, for the sake of product labeling. The MF, therefore, is expected to encompass a variety of environmental impacts associated with material extraction through the processing, distribution, storage, use, and disposal or recycling stages. Rather than furthering the discussion on approaches to a veritable MF that has not come up, we call for a shift in focus to scarcity—a critical issue which, in our view, the MF practitioners were intended to address. The failure to address scarcity is due to summing up the mass of raw materials with equal weights. To cite an example, we assume that Economy A and B both have a MF of 100 kg. This, however, does not mean anything except the total mass, because the truth might be that Economy A consumed 1 kg of Au and 99 kg of sands, and Economy B conversely consumed 99 kg of Au and 1 kg of sands! In that case, misleading decisions can be made as a consequence

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of neglecting the varying importance of different resources in terms of scarcity. There are several ways to quantify the scarcity of resources, such as exergy (available energy), surplus energy, and market price approaches. In life cycle impact assessment (LCIA), the impact of resource scarcity is evaluated by so‐called resource depletion potential (RDP), a form of characterization factor derived from characterization models reflecting the environmental mechanism of depletion in natural capital stocks (Hauschild et al. 2013). In theory, there are 2 branches of RDP, namely abiotic depletion potential (ADP) and biotic depletion potential (BDP) (Guinée and Heijungs 1995). However, the BDP is normally excluded from LCIA as most biotic resources can be reproduced by a production process. This is why deforestation, for example, would not be regarded as a depletion problem but a production process with its particular environmental impacts such as soil erosion, land degradation, and global warming. We herein propose a resource depletion footprint (RDF) aimed at addressing abiotic resource depletion. The rationale is that abiotic resources, such as minerals and fossil fuels, are a dominant contributor to the depletion of natural stocks. The RDF is calculated by multiplying the ADP by the extraction of resources, where ADP is specified as the ratio between 2 estimates, indicating how fast the remaining stocks of resources would be exhausted in comparison to a reference resource (such as Sb), both at the current rate of use. Figure 1 compares resource categories for which characterization factor ADPs are derived from Van Oers et al. (2002), which serves as an updated version of the baseline method proposed by Guinée and Heijungs (1995).

Figure 1. Abiotic depletion potentials (ADPs) for characterizing abiotic resources against Sb, based on the estimation of planetary‐scale ultimate stocks and the extraction rate for the year 1999. Data derived from Van Oers et al. (2002).

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Integr Environ Assess Manag 10, 2014—PM Chapman, Editor Table 1. A proposal for RDF in comparison to the existing CF and an imitating WDF

Footprint

Environmental concern

Input/output flow

Impact characterization

Metric

CF

Climate change

Greenhouse emission

Global warming potential

kg CO2 equivalent

RDF

Resource scarcity

Abiotic extraction

Abiotic depletion potential

kg Sb equivalent

WDF

Water scarcity

Freshwater extraction

Water depletion potential

m3 H2O equivalent

CF ¼ carbon footprint; RDF ¼ resource depletion footprint; WDF ¼ water depletion footprint.

The RDF is distinguished from the MF as it uses a set of scientific‐based characterization factors as a substitute for arbitrarily equal weights, and the outcome is expressed in relative rather than absolute terms. As a result, it allows us to prioritize abiotic resources with respect to their relative scarcity and to translate the overall risk of abiotic resource depletion into a more understandable measure of kilograms. Moreover, the RDF implies a critical recognition, which has been neglected in many environmental footprints, that human demand for natural capital should be kept within the planetary boundaries of deaccumulation or regeneration, beyond which our planet will be no longer sustainable. Our proposal enables a harmonization of the RDF and C footprint (CF), in the sense that they both aggregate different components based on scientific characterization instead of subjective weighting. The CF has a broader base of acceptance than other existing footprints, because it is based on global warming potential (GWP)—the most complete and accurate characterization factor quantifying the contributions of an emission to climate change. To make transparent the role of characterization in footprinting, we provide a comparison among the CF, RDF, and an imitating water depletion footprint (WDF) (Table 1). The WDF has much in common with the RDF. Following this idea, one can easily formulate a suite of environmental footprints which characterize the extractions or emissions through their individual contributions to specific impact categories in a consistent manner. However, although the LCA community has taken an important step in characterizing multiple impact categories, the discrepancy between different characterization models for the same substance and impact category is still large. To fill in this gap, we argue for extensive interdisciplinary communication and collaboration between LCA and other footprint and nonfootprint developers.

REFERENCES [EC] European Commission. 2013. Annex II: Product environmental footprint (PEF) guide to the Commission recommendation (consolidated version). On the use of common methods to measure and communicate the life cycle environmental performance of products and organisations. [cited 2013 May 22]. Available from: http://www.lesenr.fr/les‐actualites/7_GUIDE_PEF.pdf Guinée JB, Heijungs R. 1995. A proposal for the definition of resource equivalency factors for use in product life‐cycle assessment. Environ Toxicol Chem 14: 917–925. Hauschild MZ, Goedkoop M, Guinée J, Heijungs R, Huijbregts M, Jolliet O, Margni M, De Schryver A, Humbert S, Laurent A, et al. 2013. Identifying best existing practice for characterization modeling in life cycle impact assessment. Int J Life Cycle Assess 18:683–697. Schoer K, Weinzettel J, Kovanda J, Giegrich J, Lauwigi C. 2012. Raw material consumption of the European Union—Concept, calculation method, and results. Environ Sci Technol 46:8903–8909.

Van Oers L, De Koning A, Guinée JB, Huppes G. 2002. Abiotic resource depletion in LCA. Amsterdam, the Netherlands: Road and Hydraulic Engineering Institute, Ministry of Transport and Water. Wiedmann TO, Schandl H, Lenzen M, Moran D, Suh S, West J, Kanemoto K. 2013. The material footprint of nations. Proc Natl Acad Sci USA [cited December 2013]. Available from: DOI: 10.1073/pnas.1220362110

FACTORS INFLUENCING HUNTING SUCCESS OF CARNIVORES, FACTORS INFLUENCING VIGILANCE OF THEIR PREY: ARE WE JUST CHANGING PAPER TITLES? Justice Muvengwi*yz yUniversity of the Witwatersrand, Johannesburg, South Africa zBindura University of Science Education, Bindura, Zimbabwe *[email protected] DOI: 10.1002/ieam.1562

The large carnivore guild in the African savanna includes the cheetah (Acinonyx jubatus), leopard (Panthera pardus), lion (Panthera leo), hyena (Crocuta crocuta), and wild dog (Lycaon pictus). Hunting success is a key component of their survival, as these carnivores live on a tight energy budget (Gorman et al. 1998). Factors that influence hunting success of large carnivores can be environmental (e.g., time of day, brightness of moon, wind direction), directly related to the carnivores themselves (e.g., the age and sex of the hunters, the method of hunting used, number of participating members of the group), or other factors (e.g., prey size, prey abundance and competition, the antipredator behavior of prey animal species). Some of these factors equally affect both prey and predators (Van Orsdal 1984; Creel and Creel 1995). For example, the factors that influence prey species vigilance are the same as those that affect the hunting success of carnivores. Vigilance is generally defined as time spent with the head raised during periods of foraging, for species that rely on detecting predators by sight (Lima 1990; Lima and Dill 1990). A factor influencing the hunting success of a carnivore may also influence the catchability of a prey species leading to a “win win” factor (Figure 1). A “win win” factor herein is defined as a factor that results in both the prey and carnivore obtaining an advantage or disadvantage during foraging. Methods used to determine hunting success of carnivores and vigilance are usually the same—commonly direct observation of animals using binoculars and biotelemetry. However, it is unusual for a researcher to study, for example, both the vigilance behavior of impala (Aepyceros melampus) and the hunting success of a specific carnivore at the same study site. I suggest that future research into large carnivores and their prey incorporate both vigilance and hunting success models

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Figure 1. A conceptual model of likely factors influencing carnivore hunting success and vigilance of prey. Factors in bold influence vigilance; those in normal print influence hunting success. Visibility, vegetation structure, wind direction, and speed are the 4 factors that influence both the hunting success of carnivores and the vigilance behavior of prey directly. The broken 2‐way arrows indicate the interplay between environmental variables.

together. In this regard, I have 2 questions for scientists specializing in large carnivore ecology. First, based on the conceptual model in Figure 1, are we not just changing paper titles? Second, is there really a difference in the factors that influence hunting success of large carnivores and the vigilance behavior (catchability) of prey species?

REFERENCES Creel S, Creel NM. 1995. Communal hunting and pack size in African wild dogs, Lycaon pictus. Anim Behav 50:1325–1339. Gorman ML, Mills MGL, Raath J, Speakma JR. 1998. High hunting costs make African wild dogs vulnerable to kleptoparasitism by hyaenas. Nature 39: 479–481. Lima SL. 1990. Protective cover and the use of space: Different strategies in finches. Oikos 58:151–158. Lima SL, Dill LM. 1990. Behavioural decisions made under the risk of predation: A review and prospectus. Can J Zool 68:619–640. Van Orsdal KG. 1984. Foraging behaviour and hunting success of lions in Queen Elizabeth National Park, Uganda. Afr J Ecol 22:79–99.

HALOGENATED FLAME RETARDANTS IN CANADIAN HOUSE DUST Golnoush Abbasi*y Amandeep Saini,y Emma Goosey,y and Miriam Diamondy yUniversity of Toronto, Toronto, Ontario, Canada *[email protected] DOI: 10.1002/ieam.1567

The presence of halogenated flame retardants (HFRs) in indoor dust has provided strong evidence of their release from consumer products and household materials into indoor environments. We examined the relationship between HFRs in dust and the surface of hard polymer casings of electronic products suspected of containing HFRs. This research was motivated by the need to minimize human exposure from indoor sources and ecosystem exposure from emissions also originating from indoor sources. Concentrations of 13 polybrominated diphenyl ethers (PBDEs) and 10 halogenated replacements were analyzed in

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dust samples collected from 35 homes and 10 offices in Toronto (ON, Canada). At each location, Br content, an indicator of presence of brominated flame retardants, was measured using a portable X‐ray fluorescence (XRF) analyzer at the surface of hard polymer casings of products. For products with Br greater than 0.1%, a surface area of 5  5 cm2 was sampled with medical wipes (n ¼ 60) for subsequent analysis by gas chromatography–mass spectrometry (GC‐MS). Measured concentrations of “novel” flame retardants (NFRs) in dust were similar to those of PBDEs. Tris(1,3‐ dichloropropyl)phosphate (TDCPP), 2‐ethylhexyl‐2,3,4,5‐ tetrabromobenzoate (TBB), and bis(2‐ethylhexyl)‐tetrabromophthalate (TBPH) dominated NFRs in dust with average concentrations of approximately 3500, 1000, and 650 ng/g, respectively. In 60% of dust samples, TDCPP concentrations exceeded those of PBDEs. PBDE concentrations were comprised of mostly BDE‐47, BDE‐99, and BDE‐209 with average concentrations of approximately 400, 850 and 800 ng/g of dust, respectively. Product wipes taken from casings of flat screen TVs had the highest average concentration of decabromodiphenyl ethane (DBDPE) (600 ng/wipe), whereas average concentrations of BDE‐209, BDE‐99, and BDE‐47 were approximately 2.5, 1.5, and 1 ng/wipe, respectively. Cathode ray tube or CRT‐TVs had the highest average concentration of BDE‐209 of approximately 23 000 ng/wipe. Octabromotrimethyl‐phenyllindane (OBIND) and TBB were also found in CRT‐TVs at average concentrations of approximately 20 ng/wipe for each compound. In audio‐video devices such as DVD and cassette players, BDE‐209 dominated PBDEs with an average concentration of 500 ng/wipe, whereas DBDPE and TBB dominated the NFRs with an average concentration of approximately 100 ng/wipe of each compound. Computer casings had elevated concentrations of BDE‐99, BDE‐47, and BDE‐209 with average concentrations of approximately 90, 40, and 20 ng/wipe, respectively, whereas TBB and TBPH were measured at average concentrations of approximately 200 and 100 ng/wipe, respectively. Generally, higher HFR concentrations in product surface wipes were correlated with higher HFR concentrations in dust. Congeners associated with octa‐BDE and deca‐BDE and DPDPE were less abundant in dust relative to manufactured products, which is consistent with their very low vapor pressure. Conversely, penta‐BDE congeners (BDE‐47, BDE‐ 99), TDCPP, TBB, and TBPH, which have high vapor pressures, were more abundant in dust than manufactured products. The sources of BDE‐47 and BDE‐99, TBB, and TBPH, could have been polyurethane foam products in furniture, but an unexpected finding was these compounds in some hard polymer casings of electronic devices that also could have been the source for dust. TDCPP was not measured in manufactured products that were sampled; the likely source was polyurethane foam products. Our results provide evidence of the migration of additive HFRs from consumer products to indoor environments. Although the heavier compounds with lower vapor pressure are assumed to be less volatile than lighter ones, the high concentration of these compounds in manufactured products has led to their elevated concentrations in indoor environments, subsequently raising concerns about human exposure to these compounds due to their toxicity and bioaccumulative potential.

Integr Environ Assess Manag 10, 2014—PM Chapman, Editor

REGULATORY APPROACHES TO THE BIOACCUMULATION OF DRUGS: A POLAR BARE WALKS INTO A BAR… Sigrun A Kulliky and A Graham M Rattray*y yHealth Canada, Ottawa, Ontario, Canada *[email protected] DOI: 10.1002/ieam.1566

Regulatory frameworks for the environmental risk assessment of new active pharmaceutical ingredients (APIs) generally incorporate assessment of persistence, bioaccumulation, and inherent toxicity (PBT). Assessing APIs for bioaccumulation in nontarget organisms presents unique challenges for regulators, due to their diverse chemistry, potency at low doses, and often very specific mode and/or mechanism of action (Daughton and Brooks 2011). Bioaccumulation, the sequestration of a substance in an organism at higher concentrations than in the surrounding environment, is a process that encompasses the balance between absorption through all routes of exposure and loss through biotransformation and elimination (Gobas et al. 2009). Although the selection of specific evaluation criteria for bioaccumulation varies in different jurisdictions, the criteria are generally based on octanol‐water partition coefficient (KOW) cutoff values and/or costly in vivo tests that consume large numbers of experimental animals (Gobas et al. 2009). Current Canadian regulatory criteria for the identification of substances that have the potential to bioaccumulate include a bioaccumulation factor (BAF) 5000, a bioconcentration factor (BCF) 5000, or a log KOW 5 (Gobas et al. 2009). These criteria, like regulations in many other jurisdictions, are based on the “lipid‐water partitioning” approach developed in the 1970s and 1980s for legacy persistent organic pollutants (POPs) that partition to lipids, have a narcotic mode of action, and whose uptake occurs through passive diffusion with no metabolism (Gobas et al. 2009). The majority of APIs are ionizable compounds whose uptake across biological membranes varies with environmental pH and may have active, in addition to passive, mechanisms of uptake (Daughton and Brooks 2011). Many have a nonnarcotic mode of action and may affect the physiology and behavior of nontarget organisms in an ecologically relevant manner at levels that are well below the screening criteria used during PBT assessments.

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Exposure scenarios for APIs also differ considerably from those for legacy POPs. Generally, bioaccumulation assessments begin with a determination of the persistence of a compound and are concerned with far‐field effects (Gobas et al. 2009). APIs by contrast usually have relatively short half‐lives in the environment. This is counterbalanced by pseudopersistence resulting from their continuous release in the near‐field, particularly near wastewater treatment and aquaculture facilities, and in agricultural fields to which manure or biosolids have been applied (Daughton and Brooks 2010). Assessing exposure scenarios for new APIs prior to marketing is therefore fundamentally different from evaluating substances targeted by traditional PBT profiling and is, by necessity, based on predicted environmental concentrations (PECs) rather than actual environmental monitoring data (Ankley et al. 2007). KOW, the most common metric for assessing bioaccumulation potential, does not require animal testing but is problematic for polar APIs, as dissociation at environmentally relevant pH may alter a substance’s partitioning behavior (Daughton and Brooks 2011). Methods that consider pH in relation to KOW provide an alternative for assessing partitioning behavior of ionic APIs, but additional empirical data are required for the integration of these metrics into mechanistic models of bioconcentration (Daughton and Brooks 2011). Quantitative‐structure‐activity relationships (QSARs) and mass‐balance models, which often contain embedded QSARs, are frequently used during regulatory assessment. However, uptake and tissue concentrations of polar APIs may not occur exclusively through passive diffusion or be related to hydrophobicity, as assumed by most current models, and accounting for metabolism is problematic (Nichols et al. 2009). Although models that explicitly incorporate a mechanistic approach to bioconcentration of ionogenic substances are being developed (Armitage et al. 2013), a lack of high quality empirical data for uptake and bioconcentration of APIs in biota, especially under field conditions, has hindered their validation (Daughton and Brooks 2011). Current standard in vivo test protocols are also not ideal for assessing polar APIs for bioaccumulation, because the results are not easily linked to biological effects and do not give any information on possibly relevant metabolites. Metabolism and the lack of measures of internal exposure, which may be much higher relative to external concentrations, are particularly significant sources of uncertainty in BCF/BAF testing (Daughton and Brooks 2011). Uptake for ionizable APIs is highly variable depending on the environmental pH of the test system and may cause adverse effects at nonsteady state tissue concentrations, as many APIs induce toxicity through chemical‐target receptor interactions and not due to internal narcotic thresholds (Daughton and Brooks 2011). Because the intrinsic properties, potential environmental effects, and exposure scenarios of polar APIs

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differ substantially from those of legacy POPs, traditional measures of bioaccumulation potential are not likely to reliably identify bioaccumulative APIs and metabolites that may pose an environmental risk. This could result in false positives that lead to costly, unnecessary testing, or false negatives that may have unanticipated environmental consequences. APIs are typically manufactured or imported in much lower volumes than industrial substances, and PECs usually do not reach the action limits at which submission of any ecotoxicity testing data is required in many jurisdictions. Assessments of polar APIs for bioaccumulation may need to occur at lower action limits and include a targeted testing strategy that minimizes costs and the number of animals used in testing. Current Canadian regulations for the notification of new substances do not include a requirement for in vitro bioaccumulation data (Weisbrod et al. 2009). APIs are data rich substances with extensive pharmacokinetic documentation detailing their absorption, distribution, metabolism, and elimination (ADME) processes that could be leveraged by regulators in determining their bioaccumulation potential (Ankley et al. 2007). Any new regulatory framework assessing APIs should have action limits that are appropriate for the high potency of APIs at low concentrations, give environmental assessors access to ADME data packages, and include dynamic referencing so that they are easily adaptable as new techniques in toxicology and environmental chemistry become available for bioaccumulation assessments of ionic APIs. Environmental assessment of APIs should be precautionary and follow a weight‐of‐ evidence (WOE) approach that considers multiple lines of evidence. Future research efforts need to be focused on validating new approaches to testing and developing standard testing protocols and appropriate guidance for their use in a regulatory context.

REFERENCES Ankley GT, Brooks BW, Huggett DB, Sumpter JP. 2007. Repeating history: Pharmaceuticals in the environment. Environ Sci Technol 41:8211–8217. Armitage JM, Arnot JA, Wania F, Mackay D. 2013. Development and evaluation of a mechanistic bioconcentration model for ionogenic organic chemicals in fish. Environ Toxicol Chem 32:115–128. Daughton CG, Brooks BW. 2011. Active pharmaceutical ingredients and aquatic organisms. In: Beyer WN, Meador JP, editors. Environmental contaminants in biota: Interpreting tissue concentrations, 2nd ed. Boca Raton (FL): CRC. p 286–347. Gobas FAPC, de Wolf W, Burkhard LP, Verbruggen E, Plotzke K. 2009. Revisiting bioaccumulation criteria for POPs and PBT assessments. Integr Environ Assess Manag 5:624–637. Nichols JW, Bonnell M, Dimitrov SD, Escher BI, Han X, Kramer NI. 2009. Bioaccumulation assessment using predictive approaches. Integr Environ Assess Manag 5:577–597. Weisbrod AV, Sahi J, Segner H, James MO, Nichols J, Schultz I, Erhardt S, Cowan‐ Ellsberry C, Bonnell M, Hoeger B. 2009. The state of in vitro science for use in bioaccumulation assessments for fish. Environ Toxicol Chem 28:86–96.