Chapter 28
Triazine Herbicides: Ecological Risk Assessment
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in Surface Waters
Keith R. Solomon and Mark J. Chappel Centre for Toxicology, University of Guelph, Guelph, Ontario N1G 2W1, Canada Triazine herbicides are widely used pesticides in North America. Residues of these substances are found in many surface waters and ecological effects in these ecosystems are a possible concern. A probabilistic risk assessment technique was used to assess therisksassociated with these substances in surface waters. The exposure characterization concentrated on monitoring datafromUS and Canadian watersheds with a focus on high-use areas. The effects characterization showed that phytoplankton and aquatic plants were the most sensitive organisms followed by, in decreasing order of sensitivity, arthropods and vertebrates such as fish. Based on an integrative risk assessment using laboratory bioassay data and environmental monitoring datafromwatersheds in high-use areas, it was concluded that, in general, the triazines do not pose a significant risk to the aquatic environment. Although some inhibitory effects on algae, phytoplankton or macrophyte production may occur in small streams vulnerable to agricultural runoff, these effects are likely to be transient and quick recovery of the ecosystem is expected. A subset of surface waters, principally small streams in areas with intensive use of triazines, may be at greater risk. In these cases, site-specific risk assessments should be conducted to assess possible ecological effects in the context of the uses to which these ecosystems are put and the effectiveness and cost-benefits of anyriskmitigation measures that may be applied. Trace amounts of pesticides have been found in a number of aquatic systems in North America. These pesticide residues are primarily those of compounds that have the propensity for movement and some persistence in the environment. Residues are more often detected in aquatic ecosystems that are located close to areas of high pesticide use and where environmental factors, such as intensity of precipitation, increase the likelihood for runoff into aquatic ecosystems. Therisksassociated with the use of atrazine in North American surface waters have been assessed using probabilistic techniques (1) and this approach has been applied for other substances as well (2). This paper is based on procedures recommended by the US EPA (3) and focuses on the use of probabilistic ©1998 American Chemical Society In Triazine Herbicides: Risk Assessment; Ballantine, L., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 1998.
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358 procedures for assessing risks from the herbicides; atrazine [6-chloro-N-ethyl-N'-(lmethylethyl-l,3,5-triazine-2,4Kiiamine], simazine [6-chloro-N,N'-diethyl-1,3,5-triazine-2,4diamine] and cyanazine [2-[[4-chloro-6-(ethylamino)-l ,3,5-triazin-2-yl]amino]-2methylpropionitrile] in aquatic ecosystems in the US and Canada.
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Assessing Risks Most risk assessments are carried out using a tiered approach. The use of tiered approaches has several advantages. The initial use of conservative criteria allows substances that truly do not present a risk to be eliminated from the risk assessment process, thus allowing the focus of expertise to be shifted to more problematic substances. These tiers begin with a simple "worst-case" estimation of environmental concentration which is compared with the effect level for the most sensitive organism (the hazard quotient approach). If the result of this comparison suggests no risk, no regulatory action would be necessary. If the result suggests a potential risk, further tiers of risk assessment with more realistic and more complete exposure and effects data can be applied to the problem. Emulating (4) we have used the following tiers in the assessment: 1) hazard quotient, 2) probabilistic risk characterization and, 3) characterization of ecological relevance. The Quotient Approach. Traditionally, characterizing hazards at the level of the organism has been conducted by comparison of the concentration of the stressor/s found in the environment to the responses reported for the stressor/s in laboratory tests. The simplest approach to this is the use of hazard quotients. Hazard quotients are simple ratios of exposure and effects. For example: x
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Hazard ~ ^ P concentration Effect concentration The application of hazard quotients to ecotoxicological risk assessment has been accomplished by comparing the effect concentration of the most sensitive organism or group of organisms to the highest exposure concentration (a worst-case hazard scenario). Depending on the response used to characterize the effect (No Observed Effect Concentration, LC50, etc.), this hazard quotient may be made more conservative by the use of a safety or uncertainty factor such as, for example, division of the effect concentration by 20 (5). This is done to allow for unqualified uncertainty in the estimations or measurements of effect and exposure. The hazard quotient approach is based on similar procedures used in human health risk assessment and therefore fails to acknowledge the very significant differences between human health and ecosystem risk assessment (5). In contrast to human health protection, individual organisms in the ecosystem are regarded as transitory and, because they are usually part of a food chain, are, in an ecological sense, expendable (7). In addition, ecosystem functions are usually highly conserved. The absence of one or more species may have no effect on ecosystem function and ecosystems are, in general, less sensitive than their most sensitive component (7).
In Triazine Herbicides: Risk Assessment; Ballantine, L., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 1998.
Downloaded by NORTH CAROLINA STATE UNIV on September 30, 2012 | http://pubs.acs.org Publication Date: May 14, 1998 | doi: 10.1021/bk-1998-0683.ch028
359 The hazard quotient approach with uncertainty factors is conservative and is useful where little data on effect or exposure concentrations are available (4). However, where more data are available and the varia tion in the response of organisms in the environment is better defined, the use of large uncertainty factors may be unnecessar ily overprotective. The hazard quotient approach also fails to consider the range of variation that may exist in terms of realworld exposures to the substance in ques tion.
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Undetectable/ j The Probabilistic Approach. Expressing residues k ι the results of a refined risk characterization ^ ^ ! > Increasing concentration, log s c a / e - ^ analysis as a distribution of toxicity values rather than a single point estimate is an C approach presently being used in higher tiers is ° Ο « of risk assessment by several regulatory ^ I that , agencies (8) and others (1, 2, 4). A major *1 Probability concentration y, will take on a SDetectable advantage of this approach is that it uses all residues value of / J relevant single species toxicity data and, * / I / 1 when combined with exposure distributions, 1 / , allows quantitative estimations ofrisksto Increasing concentration, log scale-^ ecosystems. The principle of the probabilistic ap < IS •s s proach is illustrated in Figure 1. It is well / I* known that many parameters and measures I! a? 90 « < are distributed in a consistent manner (9) and that, from these distributions, it is possible to Cnténohôf If (10th / assessment centile) estimate the likelihood that any particular 100 1000 10 measure will be observed in subsequent sampling of the same population (for example, height of individual humans, Figure 1. Illustration of the use of the Figure 1-A). This also applies to concentra probabilistic approach. In D, horizontal arrows tions of substances in the environment. show the intercept of the 10 centile of the However, in this case, data are often cen toxicity distribution with the environmental sored by the limits of analytical detection (Figure 1-B) and they arefrequentlylog- concentration distribution and indicates the probability that this concentration will be normally distributed. When plotted as a exceeded. cumulative frequency distribution using a probability scale on the Y axis (Figure 1-C), these distributions approximate a straight line which can be used to estimate the likelihood that a particular concentration of the substance
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