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How To Deal with 100,000+ Substances, Sites, and Species: Overarching Principles in Environmental Risk Assessment A. Jan Hendriks* Department of Environmental Science, Institute for Wetland and Water Research, Faculty of Science, Radboud University, Nijmegen, The Netherlands and predicting for monitoring, modeling, and registration purposes. Yet, without evident links either to concentrations of chemicals or to response by populations and communities, relevance for management decisions is limited.
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OPPORTUNITIES FOR OVERARCHING PRINCIPLES AND SIMPLE MODELS Inherent data gaps can potentially be filled by modeling and regression analysis (Figure 1). Complex mathematical tools, such as ToxicoKinetic-ToxicoDynamic (TKTD) or food web models, have been built encompassing specific mechanisms at the molecular to community level. Unfortunately, such models have often been parameterized and validated for small sets of substances, sites, and species only. Application to new cases still requires substantial empirical research. Alternatively, data have been fit to statistical functions without underlying processes in mind, as in many Quantitative Structure Activity Relationships (QSARs) and Species Sensitivity Distributions (SSDs). While widely applicable in daily management, extrapolation beyond their domain, i.e. to other groups of substances or species, still requires rigorous testing. It there a way out? I believe there is if we are prepared to combine the best of both worlds. Rather than modeling detailed mechanisms, we might pay more attention to overarching principles to which most substances, soil−water types, and species obey (Figure 1). Vice versa, instead of going for statistical regressions with the highest explained variability, we might attach more value to meaningful equations of which the coefficients and exponents can be interpreted physically. A well-known success story is the relationship between the fate of organic chemicals and the octanol−water partition ratio Kow.1 Recently, it was demonstrated that this powerful descriptor also explains the distribution of (xeno-)biotic substances in organisms. Affinity to polar fat, neutral fat, lignin, and proteins, even including metabolic enzymes and membrane carriers, can be interpreted using, e.g. solvent theory.2 While statistical regressions for bioaccumulation are often used as such, intercepts and slopes are getting a physical−chemical meaning. Obviously, Kow only covers weak interactions between molecules, and the quest for universal descriptors of strong interactions, such as the covalent index for metals, continues. An often-overlooked descriptor explaining much, if not most, variability among water bodies and among species is size. The turnover of energy, water, carbon(dioxide), nutrients, and toxicants by objects as diverse as lakes, organisms, and engines scales to their mass in a remarkably similar, though sometimes slightly different, way.3,4 Time parameters, like residence time,
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ach second, one new chemical is added to the more than 65,000,000 already registered. In the EU, 100,000+ compounds are awaiting assessment, 1,500,000 contaminated sites potentially require cleanup, and unknown chemicals are responsible for up to 90% of the toxicity. Worldwide, 8,000,000+ species, of which 10,000+ are endangered, need protection. Studying 100,000+ substances at 100,000+ sites threatening 100,000+ species requires an intelligent approach. Traditionally, the strategy has been to determine chemical and toxicological properties of substances in laboratory experiments. Later (semi)field studies were included to cover multistress and multispecies settings. Yet, empirical research is severely limited by financial, practical, and ethical constraints. Simply said, we cannot cage otters to test the impact of 209 individual PCB congeners at the population level. When selecting chemicals and endpoints for our studies, we therefore have to ask ourselves what we really need to know and what we are prepared to leave undiscovered. For instance, decades after the detection of 2,3,7,8-TCDD, the deadliest synthetic chemical, its exact mode of action is still unknown. Unravelling the last 10% of its secrets may very well cost 90% of the total effort. Instead, we may shift our attention to less wellknown chemicals. Likewise, as chemicals are not the only threats, often not even the most important ones, empirical endpoints must be chosen in a way that allows comparison to effects of other environmental pressures. Gene expression and biomarker assays have been developed as additional tools to in vivo accumulation and toxicity tests. If explicitly framed in the cause−effect chains, in vitro tests play a vital role in interpreting © 2013 American Chemical Society
Published: March 26, 2013 3546
dx.doi.org/10.1021/es400849q | Environ. Sci. Technol. 2013, 47, 3546−3547
Environmental Science & Technology
Viewpoint
Figure 1. Similarities between scientific and artistic (Dutch painter styles) representations of systems. Data and realistic art aim at an exact observation of single cases. Optical artists and regressions capture information without theoretical underpinning in black boxes, allowing interpolation between measurements. Complex models and impressionism represent systems by a detailed notion of aspects (mechanisms) perceived to be important at first sight. Continued abstraction yields simple models and abstract paintings with essential elements (principles) allowing extrapolation to other cases. costs of energy conversion equipments. Environ. Sci. Technol. 2011, 45, 751−754. (4) Hendriks, A. J.; Schipper, A. M.; Caduff, M.; Huijbregts, M. A. J. Size relationships of water turnover in lakes: Empirical regressions suggest geometric scaling. J. Hydrol. 2012, 414−415, 482−490. (5) Stadnicka, J.; Schirmer, K.; Ashauer, R. Predicting concentrations of organic chemicals in fish by using toxicokinetic models. Environ. Sci. Technol. 2012, 46, 3273−3280.
age at death, and predator−prey oscillation periods scale inversely to size. River catchment area and animal home range are closely related to lake volume and organism mass, respectively. While environmental scientists have no problem with linking model parameters to chemical properties like Kow, most risk assessments still require many geo-hydrological and biological coefficients to be obtained empirically. Instead, we may take them from relationships to fundamental properties such as size and temperature. With this approach, simple onecompartment models become about equally accurate in predicting internal concentrations as multicompartment models requiring many data.5
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CONCLUSIONS Tackling 100,000+ chemicals, sites, and species requires integration of all data and models available (Figure 1). Financial, practical, and ethical constraints of empirical studies require careful selection of less well-known compounds and relevant endpoints. Overarching principles and simple, parameter-sparse models thereof cover more of those cases than detailed mechanisms and complex data-hungry modeling tools. Default parameter values can be obtained from relationships to chemical properties, geo-hydrological characteristics, and biological traits, like molecular, catchment, and organism size, to be overridden by empirical values if available.
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AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. Notes
The author declares no competing financial interest.
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REFERENCES
(1) Mackay, D. Finding fugacity feasible. Environ. Sci. Technol. 1979, 13, 1218−1223. (2) Hendriks, A. J.; Traas, T. P.; Huijbregts, M. A. J. Critical body residues linked to octanol−water partitioning, organism composition and LC50-QSARs: Meta-analysis and model. Environ. Sci. Technol. 2005, 39, 3226−3236. (3) Caduff, M.; Huijbregts, M. A. J.; Althaus, A. J.; Hendriks, A. J. Power-law relationships for estimating mass, fuel consumption and 3547
dx.doi.org/10.1021/es400849q | Environ. Sci. Technol. 2013, 47, 3546−3547
