## Proposed Methodology for Specifying Atrazine Levels of Concern

for Protection of Plant Communities in Freshwater Ecosystems

Report To:

Environmental Fate and Effects Division

Office of Pesticide Programs

U.S. Environmental Protection Agency

Washington, DC

Final

September 15, 2010

Russell Erickson

Mid-Continent Ecology Division

National Health and Ecological Effects Laboratory

Office of Research and Development

U.S. Environmental Protection Agency

Duluth, MN23

1. INTRODUCTION

This document describes proposed methodology for setting levels of concern (LOCs) for atrazine in natural freshwater systems to prevent u nacceptably adverse effects on the aquatic plant communities in those systems. Effects on hum ans and possible effects on amphibian development are the subjects of separate efforts, a nd additional components of aquatic plant community atrazine risk assessment are described el sewhere. This first section defines the problem being addressed and describes a general str ategy for setting the LOC.

1.1 Requirements for the LOC Methodology

This methodology must be germane to the effects o f concern and the exposures expected within systems to which the LOC is applied. Regard ing effects, the concern is the productivity and composition of aquatic plant communities. Fort unately, atrazine has been the subject of many laboratory toxicity tests on individual aquati c plant species and of various studies on experimental aquatic ecosystems in which effects on plant communities have been documented. Certain shortcomings of such data for setting LOCs are discussed in Section 1.2 and a general strategy for using all the data to address these li mitations is presented in Section 1.3. More details on these data and how they can be applied t o LOCs are presented in Sections 2 and 3.

Regarding exposures, atrazine enters natural fres hwater systems primarily in rainfalldriven runoff, resulting in highly variable and epi sodic exposures that depend on rainfall distribution, atrazine application patterns, topography, and soil properties. Figure 1 provides three example time-series of atrazine exposures measured in streams in Indiana, Missouri, and Ohio from a set of exposure time-series provided by the Environmental Fate and Effects Division (EFED) of the U.S.EPA Office of Pesticide Programs (personal communication, Nelson Thurman). These highly variable exposures are in marked contrast to exposures typical of laboratory toxicity tests and experimental ecosystem studies, which have a defined duration and relatively constant or more smoothly declining concentrations. Thus, a primary issue for the LOC methodology is how to apply the experimental effects data in a manner relevant to such variable exposures.

Other than such exposure time-series, the LOC metho dology is not intended to address site-specific factors, such as physicochemical cond itions and the nature of the biological community. Addressing such conditions is not feasi ble from a standpoint of both effort/cost and knowledge of their influence on atrazine effects. Rather, this method will be generic in that any site with the same atrazine concentration time-seri es will be assessed as having the same risk. Figure 1. Examples of atrazine exposure time-serie s for natural freshwater systems.

Julian Dayμg/L Atrazine 0 50 100 150 200 250

0 50 100 150 200 250 300 350 0 10 20 30 40 50 0 50 100 150 200

MO01_05

Sandusky R. IN11_054

1.2 Shortcomings of Existing Assessment Approaches

For toxicity tests on individual plant species, p ast assessments of the risk of atrazine to aquatic plant communities (e.g., Solomon et al. 199 6; Giddings et al. 2000) have generally summarized the results of a toxicity test as a median effect concentration (EC 50 ), the concentration causing a 50% decrease in some measure of growth over the duration of the test. Average EC 50 s for each species are then used to describe a species sensitivity distribution (SSD), the cumulative percentage of species with EC 50 s less than a certain value (e.g., Figure 2). Risk can be characterized by how often exposures exceed certain percentiles in the SSD, and an LOC can be defined simply as the percentile providing some desired level of protection. For example, in Figure 2, an LOC of 10 μ g/L would be below the EC 50 s of 95% of the species and an LOC of 45 μ g/L would be below the EC 50 s of 80% of the species.

However, such LOCs have major shortcomings, espec ially for addressing the types of exposures in Figure 1:

(1) SSDs based just on EC 50 s provide limited information on the overall toxic impact to the assemblage of species used for the SSD. For exampl e, if an LOC is set to the 5 th percentile in this distribution, it only describes the concentrat ion at which the growth of a particular species is reduced by 50%. No information is provided on how much greater effects are for this species at higher concentrations, or how much lower effects ar e at lower concentrations. For other species, no information is given other than that their EC 50 s are less than or greater than the LOC. Much more information regarding effects is contained wit hin the data, but how should it be used?

(2) SSDs also do not address the issue of time. Ho w should effects be described for longer or shorter exposures and, especially, exposure concent rations that fluctuate? If the LOC is simply applied to the peak exposure, the exposure time-ser ies in the top panel in Figure 1 would be considered of most concern, but toxic impact would probably be higher for the middle timeseries and perhaps even for the lower time-series, because of the more prolonged and multiple exposure periods. Using average concentrations ove r a specified period also entails various problems and uncertainties. How should total impac t be assessed over an entire time-series?

(3) Although the EC 50 s in Figure 2 all describe plant growth in some fas hion, growth is measured in a variety of ways (final plant biomass, net chan ge in biomass, growth rate, oxygen evolution, carbon fixation, plant length, cell numbers, change s in chlorophyll) and over a wide range of exposure durations and conditions, such that these EC 50 s can have greatly different meaning regarding actual plant sensitivity. The spread of values in the SSD might therefore be due to Figure 2. Example of aquatic plant SSD based on dat a from Giddings et al. (2000).

EC 50 ( μ g/L Atrazine) 5 20 50 200 500 2000 10 100 1000Cumulative Percentage

0 20 40 60 80 1005

differences among test methods as well as differenc es among species. Such inconsistency in the meaning of EC 50 s will cause any LOC from the SSD to have uncertain meaning.

(4) Selecting the percentile in an SSD that constit utes an LOC is very problematic with regard to the level of protection afforded to a plant communi ty as a whole. Does the 5 th percentile in the SSD represent enough effect to be considered unacce ptable, especially for transient exposures such as in Figure 1, from which plant growth might quickly recover? Is the 20 th percentile too much effect? In most cases, this decision has been a judgment or policy call, justified in some cases and to some degree by information on effects in natural or experimental ecosystems.

For atrazine, the substantial number of experimen tal ecosystem studies provides an alternative to single-species toxicity tests that p artially addresses the above issues, especially issue 4. Although the nature of these systems and the effects measured in them can vary, the magnitude and acceptability of effects on an actual plant community are directly assessed. In fact, the 2003 Interim Re-registration Eligibility Decision for atrazine (U.S.EPA, 2003) noted concentrations of 10-20 μ g/L to be of concern based on experimental ecosyste m data. However, applying these data also entails difficulties. Tre atments are generally too few and measured endpoints too diverse to establish quantitative mea sures of effect that are consistent across studies; rather, results are typically classified a s large enough to be of concern or not (a “binary” yes/no assignment). Most importantly, the issue of time-variable exposures such as in Figure 1 can be addressed even less satisfactorily based on these experimental ecosystem studies than on single-species toxicity tests.

1.3 Overview of Proposed Strategy for Developing an LOC for Atrazine

To address the above issues, this document propos es a simple strategy for using data from both single-species toxicity tests and experim ental ecosystems. This strategy consists of two basic steps:

(1) More fully describe single-species plant toxici ty data using a quantitative measure of overall toxic effect that has clear and consistent meaning and applies to any exposure time-series. By describing the relative toxic severity of different exposures, such an index can help assess whether effects observed under one exposure regime should occur in another. Section 2 proposes such a measure, the “Plant Assemblage Toxi city Index” (PATI). This addresses issues 1-3 above, but still leaves unresolved the issue of what value of this measure is an LOC (issue 4).

(2) Use the experimental ecosystem data to assess h ow high PATI must be to expect unacceptable effects on the productivity and compos ition of aquatic plant communities. This combines the greater ecological relevance of the ex perimental ecosystems with an index that can relate the severity of toxicity for the exposure ti me-series in the experimental ecosystems to that for field exposures such as in Figure 1. Section 3 addresses how experimental ecosystem information can be used to establish an LOC for PAT I (=LOC PATI ).

Implementing this methodology is also conceptuall y simple. PATI is calculated for an exposure of interest in a natural system, and compa red to LOC PATI . Implementation examples and uncertainty issues are the subject of Section 4

.6

An important point to emphasize is that this LOC i s based on a measure of toxic effect (PATI) that is estimated from reported concentratio ns, in contrast to typical LOCs which are based on the concentrations themselves. For this d ocument, only this effects-based LOC PATI will be addressed. However, it is also possible to use the effects-based LOC PATI to develop an exposure concentration-based LOC that would be more convenient for assessing monitoring data or expected exposures. This is the subject of an o ngoing separate effort by EFED.7

2. PLANT ASSEMBLAGE TOXICITY INDEX

2.1 Definition of PATI

In order to quantify the overall effect of atrazi ne on an assemblage of plant species of interest, PATI simply expands the SSD concept by (a ) considering the entire growth inhibition vs. concentration curve (“toxicity relationship”) f or each plant species and (b) determining the average effect level across all species at each concentration. Figure 3 illustrates this with hypothetical relationships. The middle panel shows overlapping toxicity relationships for ten species constituting the assemblage of species. In the top panel, the EC 50 s for each species are used to create a traditional SSD, which just describes the cumulative percentage of the EC 50 s. For the bottom panel, the average magnitude of effect across all species at each concentration is used to create the PATI distribution. For example, at 50 μ g/L, the percent effects for species 1-10 are: 100, 82, 50, 18, 12, 9, 0, 0, 0, and 0, respectively. The average effect over all ten species is thus 27%, which is the PATI value at 50 μ g/L in the bottom panel (arrow).

Thus, rather than just providing the percentage of species that have an EC 50 below some concentration, PATI describes the percent reduction in plant production for the entire assemblage (weighting each species equally). This is more informative than the traditional SSD, but is still a simple concept and calculation.

However, the definition and calculation of PATI i llustrated in Figure 3 is not yet complete, because it does not address the issue of time. For a time-series of daily concentrations, there would need to be separate calculations for ea ch day to generate a time-series of daily PATI values. Because of the rapid recovery of growth ra tes after atrazine exposures (e.g. Abou-Waly et al. 1991, Desjardin et al. 2003), such daily PAT I values need not consider residual toxicity from exposures on previous days, but rather only th e toxicity for the current day’s exposure. These daily values would then be averaged over a pe riod consistent with the time scale of risk management goals, another simple calculation. For this report, this averaging will be designated with a subscript denoting the length of the averagi ng period (e.g., PATI 30d denotes a 30-d averaging period). Without a subscript, PATI will refer to daily or instantaneous values, or the general PATI concept. The selection of the averagi ng period is addressed in Section 4. Figure 3. Hypothetical comparison of toxicity relationships for several species (middle panel), t he SSD of EC 50 s for these species (top panel), and the plant assemblage toxicity index (bottom panel, PATI = the average of the curves in the middle panel).

Atrazine Concentration ( μ g/L) 20 50 200 500 10 100 1000Percent of Species with EC50BelowDesignated Atrazine Concentration

0 20 40 60 80 100

Atrazine Concentration ( μ g/L)

20 50 200 500 10 100 1000Percent Toxic Effectfor Individual Species

0 20 40 60 80 100

1 2 3 4 5 6 7 8 9 10

Atrazine Concentration ( μ g/L) 20 50 200 500 10 100 1000Plant Assemblage Toxicity Index(Average Effect Across All Species)

0 20 40 60 80 1008

An important implication of simply averaging daily PATI values over some averaging period is that a certain reduction in growth over 1 d is considered to be of equal importance to half that reduction persisting for 2 d, a quarter o f that reduction persisting for 4 d, etc. PATI assumes that such a simple summation/averaging of e ffects provides a useful index for the overall, relative effects of different time-series on plant communities, and deliberately does not address various cumulative effects or community pro cesses that might not react in such a linear fashion. However, community dynamics are driven on each day by the same growth reductions that are incorporated into PATI, so that PATI does embody the primary driving force for atrazine effects on plant communities. Even if community dy namics modify the relative severity of some time-series compared to that expected based just on PATI, such modifying processes would not be the primary driving force for effects and, in an y event, are not understood well enough to be satisfactorily addressed (e.g., Erickson, 2009).

2.2. Single-Species Plant Toxicity Test Data

Implementation of the PATI approach requires comp endia of the effects of atrazine on aquatic plants or statistical distributions describ ing these effects. Existing compendia of plant effects concentrations (ECs) (e.g., Giddings et al. 2000) have certain shortcomings regarding their applicability to risk assessment, which warra nted reanalysis of existing single-species toxicity tests. This section describes the shortco mings of concern, a new review and analysis of toxicity data, and a new compendium of plant toxici ty information more suitable for calculating PATI and for conducting atrazine risk assessments.

2.2.1. Issues in Interpreting and Applying Plant To xicity Test Results

ECs from plant toxicity tests can vary widely in both value and meaning depending on how tests are conducted and analyzed. For microalg ae, tests are usually conducted on cell suspensions under favorable (at least at test start ) conditions of temperature, light, and nutrients. These tests can involve various measurement endpoin ts, including (a) actual biomass; (b) surrogates for biomass such as cell counts, cell vo lume, optical density, or chlorophyll content; and (c) indicators of growth such as oxygen evoluti on or radioactive carbon fixation. The period over which measurements are made can vary from seve ral minutes to several weeks, and measurements might be reported at multiple times or only at the end of exposure. Biomass or biomass surrogates might be analyzed based on (a) b iomass values at various times during the exposure, (b) biomass increase (growth) at various times, (c) the area under the growth timeseries (AUC), and/or (d) specific growth rate (SGR) 1

. The meaning of an EC can be greatly affected by t est duration and by whether it is based on absolute biomass, growth, or SGR. To illustrate this, Figure 4 shows a hypothetical example involving exponential growth when the control SGR ( SGR C ) is 1/d and when a chemical exposure reduces the SGR to half of this value. Th e top panel shows the actual biomass vs. time

1 The specific growth rate (SGR) =dB(t)/dt/B(t), whe re B is biomass and t is time. SGR is thus the fra ctional rate of change of biomass with time and has units of invers e time. If SGR is constant, the growth rate is exp onential and B(t)=B(0)  e SGR
 t . Thus, if SGR is 1/d, this does not mean that the biomass will double in one day; rather the "compounding interest" of exponential growth will m ean that biomass actually increases to 2.7 times th e initial value – only over short periods will fraction growt h closely adhere to SGR (e.g., 1% growth over 0.01 d).9

in the control compared to the chemical exposure, w hile the bottom panel shows the percent reduction in the chemical exposure relative to cont rol for the SGR (constant at 50%), absolute biomass, and growth.

For growth, the treatment that is an EC 50 for SGR will be an EC 62 at 1 d, an EC 73 at 2 d, and an EC 88 at 4 d if the SGR C is 1/d. For absolute biomass, this concentration would be an EC 39 , EC 63 , EC 86 , respectively, at these times. For other values of SGR C , more widely ranging ECs can occur. Using absolute biomass can result in particularly misleading ECs when growth rates are modest. For example, when control growth is just a doubling of biomass over the duration of the test, an EC 50 for absolute biomass actually represents no growth.

Therefore, EC 50 s reported for absolute biomass, growth, and SGR will differ from each other, and these differences will vary with exposure duration and the SGR C . This is especially problematic when reports for toxicity tests just provide ECs, without sufficient information on absolute biomasses and/or SGRs as a function of time and concentration to calculate more consistent and meaningful measures of effect. Compendia that simply transcribe such reported EC 50 s can be describing a wide range of different effects, and assessments based on such compendia will be ill-defined.

Other factors make the meaning of reported plant ECs even less certain. As an algal suspension grows, the growth rate will decline beca use of nutrient depletion and self-shading. This departure from exponential growth will be most pronounced in the treatments with the highest growth rates (i.e., the control and low tox icant concentrations with little or no effect), so that the treatments with greater toxic effects migh t "catch up" as exposure duration increases, causing ECs to increase with time. In other words, the toxicity test actually can include stressors (nutrient/light limitations) in addition to the tox icant, and these other stressors can confound the effects of the toxicant. In fact, some standard pl ant test protocols were originally designed to assess nutrient limitations, and the durations were selected to result in nutrient depletion (e.g., Miller et al. 1978). When used for toxicants, this type of study design can result in complicated growth dynamics and relationships that are difficul t to interpret and apply. Tests can also have different photoperiods, which would also need to be considered in comparing ECs for growth (although ECs for SGR can be directly compared betw een different photoperiods). Figure 4. Variation of plant growth effects with ti me and measurement endpoint. Top panel shows exponential growth for the control SGR (solid line) and one-half of the control SGR (dashed line). Bottom panel converts this to percent effect on biomass (solid line), on increase in biomass (dashe d line), and on specific growth rate (dotted line).

Time (d) 0 1 2 3 4Plant Biomass (g)

0 5 10 15 20

Time (d) 0 1 2 3 4Percent Reduction Relative to Control

0 20 40 60 80 100 Initial Biomass

Specific Growth Rate Biomass Biomass Increase Biomass for 50% Reduction in Specific Growth Rate Control Biomass10

Schafer et al. (1994) provide a noteworthy exampl e of some of these problems. In a 10-d test in a flow-through system in which a constant c ontrol growth rate was maintained by replenishing the nutrient solution and periodically cropping biomass, they reported growth-based EC 50 s to drop from 50 μ g/L at 4 d to 20 μ g/L at 7 d to 10 μ g/L at 10 d. This is plausibly attributable to a constant relationship of SGR to c oncentration during these 10 d, so that a constant EC for growth rate translates into widely variant ECs for growth. These authors also reported an EC 50 of 350 μ g/L for a static, 3-d flask desk, indicating much l ess sensitivity compared both to the flow-through systems and to ph otosynthesis measurements made in the first day of these static tests. This apparent low er sensitivity is likely at least partly due to a h igh initial cell density (2

⋅ 10 5 cells/ml), which would have resulted at 3 d in a c ell density of 3

⋅ 10 8
cell/ml if a SGR C similar to that in the flow-through system had bee n maintained for the entire 3 d. Such a cell density would have resulted in both self-shading and nutrient depletion in the control, likely contributing to the apparent reduce d sensitivity. Increases with time for growthbased ECs are evident in other studies in the revie w presented later, although the opposite can also be true, indicating additional complexities.

Changes in cell condition other than light and nu trient limitations might also affect ECs and their dependence on test duration. For example , chlorophyll content per cell can increase with time to compensate for reduced photosynthesis. Mayer et al. (1998) reported the chlorophyll content of algal cells to increase by 1 0-fold in response to exposure to 200 μ g/L atrazine. Such changes in the chlorophyll content per cell make the use of chlorophyll as a surrogate for plant biomass inadvisable, likely mis representing toxic effects on biomass. For example, van der Heever and Grobbelaar (1996) repor ted effect concentrations in the same exposures to be about 2.5-fold higher when based on chlorophyll than when based on cell numbers or dry weight. Similarly, toxicants can al ter cell volume and mass (e.g., van der Heever and Grobbelaar 1996), creating differences among EC s based on cell count, cell volume, and cell weight, although these differences are much smaller than those due to the influence of chlorophyll, test duration, nutrient depletion, and light limitations.

Although oxygen production and radiocarbon fixati on are arguably closely linked to biomass production, ECs based on these measures can also pose interpretation problems:

(a) They are often done over such short durations t hat apparent effects might be reduced because of the time it takes to fully induce the effects of a toxicant, unless there is sufficient pre-exposur e to the toxicant before the measurements are made. Fortunately, for atrazine, effects do appear to be induced quickly, such that EC 50 s based on oxygen measurements with just several mi nutes prior exposure have been reported to be similar to those based on biomass measurements (e.g., Turbak et al. 1986).

(b) Short-term radiocarbon fixation rates can conce ivably reflect gross or net photosynthesis (or a weighted combination of the two) depending on the disposition of the radioactive carbon in the organism. Williams et al. (1996) determined that r adiocarbon fixation over short periods approximates net photosynthesis for good growing co nditions (which would be expected in toxicity tests); therefore, radiocarbon fixation wi ll be assumed in this review to represent net photosynthesis.11

(c) Although oxygen production should parallel net photosynthesis, test methods using oxygen evolution measurements can involve extremes of oxyg en concentrations that might affect photosynthesis and/or respiration – either high, su persaturated levels as oxygen increases from initial levels, or low concentrations due to the me thodology involving an initial purging of oxygen. Studies with such extremes will not be use d in this review because of uncertainty about their impacts.

(d) Even when the test is such that oxygen producti on or radiocarbon fixation are arguably good surrogates for biomass production, the time-scale o f the measurements can affect their interpretation. Short-term values for oxygen produ ction or radiocarbon fixation for an approximately constant mass of algae are analogous to the SGR, whereas measurements long enough for substantial growth to occur would be ana logous to net cumulative growth, creating differences in the meaning of ECs similar to that f or growth vs. SGR. In one study (Larsen et al. 1986), the situation was especially complicated bec ause carbon-14 fixation was measured only during a short period at the end of a 24-h atrazine exposure, so that the measured fixation rate reflected both effects of the toxicant on the rate of carbon fixa tion per cell and the cumulative differences in cell density due to the preceding ex posure.
Macrophyte tests can be less susceptible to the i ssues of exponential growth and limiting conditions discussed above. Duckweed tests often s how exponential growth, but do not usually reach biomass levels sufficient to suppress growth rates (frond crowding or nutrient depletion). Other macrophytes have relatively modest and more l inear growth during the tests. However, the general issues raised above for microalgae shou ld still be considered in the interpretation of macrophyte tests and the definition of their ECs. For example, reduced photosynthesis can result in elongation of plant shoots with little or no bio mass increase, so that shoot length can be a poor surrogate for biomass growth (e.g., Fairchild et al . 1994, 1998). In addition, some macrophyte tests involve rhizomes, which contain resources to temporarily support growth that might obscure toxic effects, again making length a questi onable measure and even making weight problematic if only shoot biomass is measured. Fur thermore, if test protocols with cuttings result in slow growth (e.g., due to the absence of rooting ), variability can make it difficult to quantify toxic effects and/or make such toxic effects of unc ertain relevance to the field.

2.2.2. Review of Single-Species Plant Toxicity Test s
The inconsistency issues among single-species tox icity test ECs discussed above have not been adequately addressed in past reviews of atrazi ne toxicity (e.g., Solomon et al. 1996; Giddings et al. 2000) and might distort atrazine ri sk assessments. There was thus a need for better analyses of single-species plant toxicity te sts with atrazine to produce EC compendia which are more consistent, involving a “common curr ency” that can be more legitimately compared among tests and applied meaningfully to ri sk assessments. The SGR was selected as this “common currency” because it reduces the depen dence of ECs on test duration and is directly applicable to addressing effects of time v ariable exposure. In addition, rather than simply compiling information on EC 50 s, there was also a need for information on other e ffect levels, which is also inadequately addressed in pre vious compendia.

To this end, available single-species toxicity te sts with atrazine were reviewed for information regarding exposure conditions and effec ts by the Great Lakes Environmental Center12

(Traverse City, MI) under support from the Office o f Science and Technology of U.S.EPA’s Office of Water (EPA Contract 68-C-04-006, Work Ass ignment 4-34, Subtask 1-16). Journal articles and reports identified by this review as c ontaining potentially useful information were further analyzed by U.S.EPA to compile desired info rmation, which consisted of SGR-based EC 50 s, the steepness of the relationship of SGR vs. atr azine concentration, and the control SGR (SGR C ). The steepness is described here by the paramete r “Steep,” which is defined in Appendix A as part of the general mathematical form ulation for toxicity relationships. Appendix A also provides (a) guidelines and procedures used in EPA analyses and (b) a summary of each paper reviewed – describing how it was applied to t he compilation of SGR EC 50 , Steep, and SGR C . Table 1 provides the final compilation.

Although not included in the compilation because t hey were conducted in estuarine water near 10 ppt salinity, studies on Myriophyllum spicatum and Potamogeton perfoliatu s by Kemp et al. (1985) and Jones et al. (1986) are consistent w ith the vascular plant results in Table 1. For both these species, oxygen production-based reducti ons in photosynthesis (Kemp et al. 1985) indicated EC50s to be near or below 50 μ g/L in the first two weeks of exposure (although so me lessening of these effects was apparent in the ensu ing two weeks). For Potamogeton perfoliatus

, radiocarbon fixation-based reductions in photosynth esis (Jones et al. 1986) indicated the EC 50 to be between 50 μ g/L and 100 μ g/L.

2.3 Statistical Analysis of Toxicity Data

The SGR EC 50 data in Table 1 were log 10 transformed and subject to an analysis of variance using the general linear model (GLM) proce dure of Statistica (Version 8.0, StatSoft, Tulsa, OK, USA). A nested ANOVA showed no signific ant differences between genera within the larger taxonomic groups identified in Table 1, so the analysis was simplified to a one-way ANOVA on these taxonomic groups, with each test res ult being treated equally regardless of the number of tests within a genus. This analysis show ed significant differences among the taxonomicgroups, with the mean log 10 (EC 50 ) being 2.09 for green algae, 2.35 for diatoms/cryptomonads, 2.42 for blue-green algae, an d 1.93 for vascular plants (Table 2). These log values correspond, respectively, to median EC 50 s of 123, 224, 263, and 85 μ g/L. Standard errors on these mean log 10 (EC 50 )s varied from 0.07 to 0.12 (Table 2), depending on the number of observations on each group. It should be furthe r noted that the values for Chromalveolata and Cyanobacteria were based on only a few studies, und erscoring the uncertainty of these inter-taxa differences. The within-group variability did not differ significantly between taxonomic groups, with the within-group standard deviation ranging fr om 0.29 to 0.35 and the pooled value being 0.33 (Table 2). The overall, unweighted mean and s tandard deviation of all log 10 (EC 50 )s were 2.12 and 0.37, this higher standard deviation being due to including the intergroup variability. Basing the analysis on genus means rather than indi vidual observations produced similar values for the overall mean and standard deviation – 2.07 and 0.35.

The steepness parameter (Steep) data in Table 1 we re also log 10 transformed and subject to ANOVA. The ANOVAs showed no significance differ ences either between genera or the broader taxonomic groups. The within-group means r anged from -0.03 for the green algae and diatoms to -0.11 for the blue-green algae, with an overall mean of -0.05 (Table 2). The steepness distribution is therefore described here simply as this overall mean for log 10 (Steep)13

Table 1. Compiled data regarding atrazine toxicity to aquatic plants. All data pertain to the specif ic growth rate (SGR) of the plant. Compilation includes the EC 50 for the SGR, a steepness measure for a fitted logi stic relationship of SGR to atrazine concentration (Stee p=-d(SGR/SGR C )/d(log 10 (C ATZ )) at the EC 50 ), and the control SGR (SGR C ) under the test conditions. Italicized EC50s deno te values whose estimation required information on SGR C and/or steepness from other studies.

Genus SGR EC 50 ( μ g/L) Steep SGR C (d -1 ) Reference ARCHAEPLASTIDA (that includes tested green algae)

Ankistrodesmus 104 1.41 0.33 Burrell et al. 1985
119 Larsen et al. 1986

Chlamydomonas 378 0.65 Kallqvist and Romstad 1994
141 1.06 Schafer et al. 1993
67 Larsen et al. 1986 45 Hersh and Crumpton 1989

Chlorella 26 1.07 >1.4 Faust et al. 1993 37 Hersh and Crumpton 1989
91 0.47 0.26 Burrell et al. 1985
557 Larsen et al. 1986
480 Stratton 1984

Scenedesmus 87 Larsen et al. 1986
300 Stratton et al. 1984
39 0.73 Zagorc-Koncan 1996

Selenastrum 164 0.79 1.80 Mayer et al. 1998
1.93 Radetski et al. 1995
50 1.66 1.25 Caux et al. 1996 100 1.50 Versteeg 1990 131 0.62 1.75 Hoberg 1991A
70 Turbak et al. 1986
163 1.22 1.65 Roberts et al. 1990 125 1.07 1.01 Gala and Giesy 1990 110 0.90 Kallqvist and Romstad 1994 201 0.79 Kallqvist and Romstad 1994
236 1.01 van der Heever and Grobbelaar 1996
223 0.61 van der Heever and Grobbelaar 1997
101 1.61 0.97 Parrish 1978
78 Larsen et al. 1986 Stigeoclonium 317 Larsen et al. 1986
Ulothrix 159 Larsen et al. 1986 CHROMALVEOLATA (includes tested diatoms, cryptomona ds) Cryptomonas 494 1.15 Kallqvist and Romstad 1994

Cyclotella 462 1.22 Kallqvist and Romstad 1994
100 0.67 Millie and Hersh 1987
114 0.65 Millie and Hersh 1987
225 1.00 Millie and Hersh 1987
Navicula 217 1.08 1.03 Hughes et al. 1988 CYANOBACTERIA ( includes tested blue-green algae)

Anabaena 70 Stratton 1984
280 Stratton 1984
470 Stratton 1984 706 0.59 0.76 Hughes et al. 1988
286 Larsen et al. 1986

Microcystis 164 1.25 0.55 Parrish 1978 605 0.77 Kallqvist and Romstad 1994 Synechococcus 136 0.59 Kallqvist and Romstad 1994 ANGIOSPERMAE (includes tested vascular plants) Ceratophyllum 24 0.81 0.04 Fairchild et al. 1998

Elodea 65 0.38 0.07 Forney and Davis 1981
<38 0.02 Fairchild et al. 1998 204 0.52 0.09 Hoberg 2007 Hydrilla 118 0.99 Hinman 1989

Lemna 202 1.24 0.24 Hoberg 1991B 93 1.33 0.25 Hoberg 1993B 49 1.71 0.23 Hoberg 1993C
115 0.42 0.21 Fairchild et al. 1998 224 1.14 0.21 Hughes et al 1988 95 Kirby and Sheehan 1994 90 1.18 0.40 Desjardin 2003 Myriophyllum <150 0.02 Fairchild et al. 1998 Najas sp. 15 1.67 Fairchild et al. 1998 Potamogeton 63 0.69 Forney and Davis 1981
Vallisneria 141 0.40 Forney and Davis 198114

(corresponding to a median value for Steep of 0.89) and the overall observed standard deviation (0.18) (Table 2). Using genus means rather than in dividual observations resulted in a very similar log mean (-0.08) and standard deviation (0. 16). A correlation analysis also showed no significant correlation between log 10 (EC 50 ) and log 10 (Steep), so these parameters will be treated independently in any analyses. 2.4 Uncertainty of PATI Relationships

The toxicity data analyses here provide the basis for computing an overall measure of toxic impact on an assemblage of plant species as a function of concentration. However, this does involve some options regarding data selection and processing that will be relevant to uncertainty analyses presented in Section 4 of this document.

Figure 5 presents PATI computed directly from the individual tests in Table 1 (assuming the overall median steepness for any test without a measured steepness) vs. PATI based on the overall distributions of log 10 (EC 50 ) and log 10 (Steep) summarized in Table 2, which was estimated by generating 1000 randomly selected pairs of log 10 (EC 50 ) and log 10 (Steep) based on the means and standard deviations for these parameters in Table 2, and averaging effects over the 1000 concentration-effect relationships so defined. Negligible difference exists between these two PATI relationships, indicating that summary distributions are an adequate surrogate for direct use of measured relationships for individual species.

There also is uncertainty associated with PATI re lationships because of the finite amount of data. This uncertainty is reflected in the stan dard errors for the distribution means reported in Table 2, as well as uncertainty in the standard dev iations of the distributions. Figure 6 shows Figure 5. PATI relationships based on individual test data (dashed line) versus based on distributional summaries (solid line).

Atrazine Concentration ( μ g/L) 20 50 200 500 10 100 1000Plant Assemblage Toxicity Index

0 20 40 60 80 100 Table 2. Summary statistics for SGR-based toxicity data from Table 1.

Taxonomic Group log(EC 50 ) log(Steep)

Mean Std. Dev. Std. Err. of Mean Mean Std. Dev. Std. Err. of Mean
Green Algae 2.09 0.33 0.07 -0.03 0.17 0.04

Diatoms/Cryptomonads 2.35 0.29 0.12 -0.03 0.12 0.05

Blue-green Algae 2.42 0.35 0.12 -0.12 0.15 0.07

Vascular Plants 1.93 0.34 0.09 -0.07 0.23 0.06

Overall 2.12 0.37 0.06 -0.05 0.18 0.0315

how PATI based on the overall distribution in Table 2 would vary by changing the mean of the log 10 (EC50)s and the standard deviation of the log 10 (EC50)s to their lower and upper 95% confidence limits. As would be expected, there are marked shifts in the PATI relationships, and the effects of this on assessment uncertainty will be examined in Section 4.

The PATI relationships in Figures 5 and 6 represe nt an assemblage of plant species and tests defined by the available data, but different assemblages are also possible by preferentially selecting or weighting particular taxa. Figure 7 c ontrasts PATI relationships based (a) on the overall distributions of log 10 (EC 50 ) and log 10 (Steep) in Table 2, (b) the separate distributions in Table 2 for the four major taxa, and (c) a composit e distribution based on equal representation of the four major taxa. Again as expected, the select ed assemblage also has a marked effect on the PATI relationship. The effects of various options for defining the plant assemblage on assessment uncertainty also will be examined in Sec tion 4.

Figure 7. Comparison of PATI for the overall species composition (solid line) to green algae(short dash), diatoms(long dash), blue-green algae(dash-dotted), vascular plants(dash-double dotted), and a composite of the four taxa (dotted).

Atrazine Concentration ( μ g/L) 20 50 200 500 10 100 1000Plant Assemblage Toxicity Index

0 20 40 60 80 100 Figure 6. Comparison of best estimate of overall PATI relationship (solid line), to 95% confidence limits for the mean of the EC 50 s (dashed-dotted lines) and to 95% confidence limits for the standard deviation of the EC 50 s (dashed lines).

Atrazine Concentration ( μ g/L) 20 50 200 500 10 100 1000Plant Assemblage Toxicity Index

0 20 40 60 80 1001617

3. USING EXPERIMENTAL ECOSYSTEM DATA TO SPECIFY THE LOC

3.1 Experimental Ecosystem Data Set

For atrazine, an extensive set of experimental ec osystem tests have documented effects of atrazine on plant community structure and function. These tests cannot be used directly to set an LOC because they can’t be directly applied to expos ures such as those in Figure 1. However, they can be used to decide what level of PATI shoul d be considered an LOC, thus using PATI to extrapolate the experimental ecosystem information to the field exposures. Briefly, PATI values are computed for each experimental ecosystem exposu re and the relationship of these PATI values to observed effects in these ecosystems is u sed to specify LOC PATI . Then, PATI values are calculated for the field exposures and compared to this LOC PATI . One consequence of this is that LOC PATI

reflects the types and levels of effects considere d unacceptable in these experimental systems, rather than simply making a j udgement of what magnitude of singlespecies toxicity is acceptable. Thus, PATI is not used directly to define the effects of concern, but rather provides a relative scale against which the experimental ecosystem data can be assessed, and thereby extrapolated to field exposur es.
. A fundamental assumption in using these experimen tal ecosystem data is that they collectively describe a relationship of effects to exposure that is relevant to the probability of effects (risk) occurring in natural freshwater syst ems. In other words, it is assumed that natural aquatic plant communities will generally react adve rsely if subjected to the same exposures that elicited adverse effects in the experimental ecosys tem studies. This assumption is inherent in any assessment that extrapolates toxicity experimen ts to the field, and the use of experimental ecosystems arguably provides a better basis than do single-species toxicity tests.

Analyzing the available experimental ecosystem da ta was the subject of a separate effort by the EFED, who provided a data compilation for th e LOC methodology development described here (personal communication, Brian Ander son, 2010). Appendix B provides the portions of this data compilation relevant to the a nalysis here, including an exposure profile, an effects determination, and a literature citation fo r each experimental ecosystem atrazine treatment. These data are summarized in Figure 8, where each experimental ecosystem treatment is characterized by the duration over whi ch effects were assessed, the average atrazine concentration over this duration, and whether there were unacceptably adverse effects on the system. This figure is only intended to provide a general illustration of the nature of the experimental ecosystem data that will be used in de veloping a PATI-based LOC. It does not address the fact that the exposure concentrations d ecline over the test duration to various degrees among these different data; these declines are docu mented in Appendix B. This decline further complicates application of such data to the types o f field exposures in Figure 1 and is a major reason for needing a toxicity measure such as PATI, which combines the effects of duration and time-variable concentrations into a single measure upon which the LOC can be based.

Figure 8 illustrates two important issues that nee d to be addressed in deriving a PATIbased LOC. First, diversity among the experimental approaches and the typically small number of treatments precluded characterizing each experim ental ecosystem treatment with a comparable, quantitative measure of effect. Theref ore, LOC specification must rely on a binary (acceptable vs. unacceptable) characterization of e ffect. Second, although the exposures that18

resulted in adverse effects are somewhat separated from those that did not cause adverse effects, substantial overlap exists between these two groups , especially in the 10-20 μ g/L range. This is presumably due to the combined effect of (a) differ ent sensitivities of the experimental systems to atrazine, (b) random variability in system respo nse to atrazine, and (c) differences among test methodologies affecting how well atrazine effects c an be detected. The next section will consider how both the binary nature of the experime ntal ecosystem data and this variability/overlap issue should be addressed in sp ecifying a PATI-based LOC.

3.2 Quantification of PATI-Based LOC from Binary Da ta

To introduce the analysis of the binary experiment al ecosystem effects data as a function of PATI, a more familiar analysis will be discussed first. Mortality in a toxicity test also involves a binary response – an individual organism either dies or not. Mortality data is often plotted as the fraction of a group of organisms tha t died (at an observation time) vs. the concentration to which the group was exposed, shown in the left panel of Figure 9. However, such data can also be plotted based on the response of each individual organism (0 if alive, 1 if dead), shown on the right panel of Figure 9, in whi ch offsets are used to show points that actually have the same concentrations. Probit analysis is a common method applied to such data to generate a sigmoidal relationship for the probabili ty of mortality at each concentration, this relationship being the same in the left and right p anels because both panels represent the same information and analysis. Because probit analysis uses the binary response o f the individual organism as the basic observation, it is actually more directly related t o the right panel of Figure 9 than to the left. Furthermore, if individual organisms all have diffe rent exposures, the presentation format of the left panel cannot be used (i.e., no groups of repli cate organisms upon which to compute fraction survival), but a plot such as in the right panel ca n still be done and probit analysis is still appropriate. For example, if the offsets for the p oints in the right panel of Figure 9 actually Figure 8. Effects of atrazine on experimental ecos ystems as a function of exposure duration and avera ge concentration. Closed triangles denote adverse eff ects, open triangles no effects.

Test Duration (days) 5 20 50 200

10 100Average Concentration (μg/L Atrazine)

1 10 100 1000 1000019

represented different concentrations, probit analys is could still be applied even without replicate points at the same concentration. The experimental ecosystem data provide an analys is situation analogous to the survival data in the right panel of Figure 9. Figure 10 rep lots the experimental ecosystem data from Figure 8 as binary effects (1 if there is an effect , 0 if there isn’t) vs. a PATI 90d value. For the purpose of this example: (a) The overall distribution of toxicity values in Table 2 was used as the basis for PATI. (b) A 90-d average is used because it is longer tha n all of the experimental ecosystem treatments without effect and all but a few of the treatments with effects (Figure 8). The actual selection of toxicity distributions and the averaging period is addressed in Section 4. Figure 9. Probit analysis as an example of binary d ata analysis. For a hypothetical toxicity test, le ft panel shows fraction (of 10 organisms) which died at each expos ure concentration while right panel plots individua l organism response as 0 (if survived) and 1 (if dead). Lines denote probit relationship for probability of deat h.

Concentration 0.2 0.5 2 5 0.1 1 10Probability of Mortality

0.0 0.2 0.4 0.6 0.8 1.0

Concentration 0.2 0.5 2 5 0.1 1 10 0.0 0.2 0.4 0.6 0.8 1.0

Figure 10. Experimental ecosystem data plotted as e ffect/no effect versus PATI 90d , fitted to a logistic relationship for the probability of an effect versus PATI, this probability being 50% when PATI 90d equals ≈ 2.

Plant Assemblage Toxicity Index 0.2 0.5 2 5 20 50 0.1 1 10 100Probability of Adverse Effect

0.0 0.2 0.4 0.6 0.8 1.0

LOC PATI =220

The example values here are only intended to demons trate how the experimental ecosystem data can be used to derive an LOC PATI
. Although there is a clear increase in the probabi lity of effects as PATI 90d increases in Figure 10, there also is considerable overlap betwe en effects and no effects with respect to PATI 90d , especially in the 1 to 5 range for PATI 90d . This variability/overlap issue was already noted regarding Figure 8, and should be viewed here in terms of any particular PATI 90d value having a probability of eliciting an effect across the variety of experimental ecosystem studies used here. That there is a probability, rather tha n a certainty, of having an adverse effect at any PATI 90d value is again indicative of sensitivity differenc es among the systems and/or various experimental uncertainties. Across all PATI 90d values, there would be an underlying relationship for this probability, illustrated by the curve on F igure 10.

This probability relationship can be quantified us ing probit or similar analyses. Field et al. (1999, 2002) applied binary logistic regression analysis to sediment toxicity assessments of a similar nature (i.e., relating binary effect data t o an exposure concentration). Rather than the Gaussian distribution-based relationship of probit analysis, Field et al. (1999, 2002) applied a very similar, but simpler, probability relationship based on the logistic equation. For describing the probability of effects in the experimental ecos ystem set as a function of PATI 90d , this logistic probability expression can be formulated as:

(Equation 1)

where P is the probability (percent scale) of an ad verse effect at a PATI 90d value, PATI 50% is the PATI 90d value at which P=0.5 (50% chance of an effect over the range of experimental ecosystems), and S is a steepness parameter (>0) fo r the relationship.

Although P is the underlying probability of an act ual adverse effect, this equation is not appropriate for analyzing the data in Figure 10 bec ause it does not reflect certain errors in the statistical analysis regarding whether an experimen tal ecosystem treatment is concluded to have an adverse effect. Most importantly, Type I error (the probability of concluding a treatment has an effect when it actually does not) is typically s et at 0.05. This means that, although the actual probability of an adverse effect approaches zero as PATI approaches zero per Equation 1, the probability of stating that there is an effect does not approach zero, but rather approaches 0.05. Type II error (the probability of concluding a trea tment does not have an effect when it actually does) will also affect the curve, but it is not pos sible to adjust for this without detailed information on the statistical power of the various tests. However, because Type II error will go to zero as concentration increases, it will not aff ect the upper asymptote of the curve like Type I error affects the lower asymptote, and thus will no t affect the basic sigmoidal shape of the curve being fitted. The binary regression used in the LO C methodology will therefore use a logistic model with a lower asymptote of 0.05, modifying Equ ation 1 as follows:

(Equation 2)



# ( ) ( ) 10 50% 10 90 50%
90 log log 1 1

1 1 10 d
d S S PATI PATI PATI PATI P

− = = + +



## ( ) ( )

50%

50% 1 0.05

1 S

PATI PATI
data S

PATI PATI P + ⋅

= +21

where P data refers to the probability of a data point being st ated to have an effect, in contrast to P being the actual probability of having an effect.

Using Equation 2, a maximum likelihood analysis was conducted on the data in Figure 10 to generate estimates for the equation parameters, PATI 50% and S. Using these parameter estimates, the curve in Figure 10 was calculated, u sing Equation 1 so the curve shows the actual estimated P, not P data . Once estimated, this curve provides a basis for making risk management decisions regarding what PATI value is considered a n LOC. For example, in the example of Figure 10, a risk management decision to use P=0.5 would result in an LOC PATI of about 2.

It should be noted here, that although LOC PATI =2 might seem to be a low value (2% reduction in average growth rate across the plant a ssemblage), this is an average value across all species for 90 d. For the days with more intense e xposure during this period, the average effects on the assemblage would be much greater than 2%, an d even greater for the sensitive species within the assemblage. Over 4 d, growth could be r educed by nearly 50% for the entire assemblage and completely suppressed for many speci es, while still satisfying this average. For the shortest exposure in Figure 1, there could be a lmost complete cessation of growth while still satisfying PATI 90d =2. Furthermore, just a 2% reduction in growth eve ry day over several weeks can “compound” to a substantial change in assemblag e biomass and composition.

However, it also important to remember that LOC PATI is a relative index that is used here to compare experimental ecosystem results and to ap ply them to other exposure time-series. It is the experimental ecosystem results that define the magnitude of effects that are of concern, not LOC PATI . Unless LOC PATI is so extreme as to invalidate any utility in comp aring different exposure time-series or to raise questions about th e validity of the experimental ecosystem data, its value is not an appropriate basis for addressin g the level of protection being provided.2223

4. IMPLEMENTATION OF PATI-BASED RISK ASSESSMENT MET HODOLOGY

4.1 Example Field Exposure Time-Series

Figure 1 provided three example field exposure time -series (chemographs) for use in the problem definition. In this section, method parame terization and performance evaluations will involve a larger subset (Figure 11) of the chemogra phs supplied to this effort by EFED. Of these chemographs, numbers 1, 3, 5, 7, 9, and 12 are as m easured in various monitoring efforts from which EFED compiled examples. The remaining chemog raphs are modifications of the measured ones to examine the effect of altering the magnitude/duration/timing of exposure events and to provide a greater diversity of exposu res for evaluating the methodology.

Figure 11. Example atrazine chemographs used in met hodology evaluations.

0 50 100 150 200 250 300 350 0 50 100 150 200 250
IN11_05_1Peak

0 50 100 150 200 250 300 350 0 10 20 30 40 50 0 50 100 150 200 250 300 350 0 2 4 6 8 10 0 50 100 150 200 250 300 350 0 10 20 30 40 50 0 50 100 150 200 250 300 350 0 10 20 30 40 50

0 50 100 150 200 250 300 350 0 10 20 30 40

Julian Day 0 50 100 150 200 250 300 350 0 10 20 30 40 50 0 50 100 150 200 250 300 350 0 10 20 30 40 0 50 100 150 200 250 300 350 0 5 10 15 20 25 0 50 100 150 200 250 300 350Concentration (μg/L Atrazine)

0 10 20 30 40 50 0 50 100 150 200 250 300 350 0 10 20 30 40

0 50 100 150 200 250 300 350 0 50 100 150 200 RegA_Res_1Peak

IL08_06_HiLo IL08_06_HiHi

Sandusky_HiLo Sandusky_HiMed

IN03_05_Orig_HiMedLo IN03_05_HiMedLo

Maumee_MedHiLo Sandusky_MedHiLo

RegG_Res_longdur MO01_05_MO01 1

3

5

7

9 10 8 6 4 2

12 1124

4.2 Parameterization Issues for PATI-based LOCs

Implementing a PATI-based methodology requires spec ifying (a) the toxicity distributions (EC 50 and steepness) to use in PATI calculations and (b) the period over which to average daily PATI values.

4.2.1 Averaging Period – Issues and Options

Because exposure outside the averaging period is c onsidered inconsequential by PATI, this period needs to be long enough to encompass (a ) exposures of significance to establishing LOC PATI from the experimental ecosystems (Figure 8) and (b ) effects expected from seasonal field exposures (Figure 11). However, it should no t be any longer than necessary, in order to avoid uncertain inferences regarding (a) cumulative effects of low concentrations and (b) widely separated exposures that are independent regarding ecological effects. A 90-d averaging period would include all or almost all periods of effect i n the example chemographs of Figure 11. It also encompasses the duration of all but a few of t he experimental ecosystems in Figure 8; most importantly, it includes all the no-effect treatmen ts – not doing so would underestimate how much exposure can occur without causing effects. T he few experimental ecosystem treatments with durations longer than this are of no consequen ce to setting the LOC PATI , because they simply verify significant effects for high PATI val ues. From another perspective, the 90-d averaging period includes the entire PATI range of overlap between effects and no effects.

To evaluate the suitability of 90 d as the averagi ng period and the consequences of other choices, sensitivity analyses below will address ho w risk determinations would differ for a longer (120-d) and a shorter (60-d) averaging perio d, this shorter period still encompassing almost all the experimental ecosystem overlap regio n and almost all significant exposures in the example chemographs. A 30-d averaging period is al so included in this sensitivity analysis to document the impact of a period that is arguably to o short, in that it is less than the duration of a substantial percentage of the experimental ecosyste ms treatments without effects and poorly covers periods of substantial exposure in the examp le chemographs.

4.2.2 Toxicity Parameter Distributions – Issues and Options

The review and analysis of single-species toxicity test data in Sections 2.2 and 2.3 provide the basis for specifying toxicity parameter distributions for PATI calculations, but there are options and uncertainties in applying this info rmation, which were already discussed to some degree in Section 2.4:

(a) Should PATI calculations be directly based on t he discrete data points in Table 1, or should the methodology follow the typical assessment pract ice of using the data to estimate sensitivity distributions (Table 2), and basing calculations on such distributions?

(b) Should the methodology be weighted in some mann er for taxonomic groups, or follow standard practice (e.g., typical SSDs) of not adjus ting for the relative representation of different taxa in the available data?25

(c) Should calculations be based on average results for each species or genus, or on individual tests?

The strategy here was to use a default starting po int of the distributions based on all the available, individual toxicity observations (the “o verall” distributions of log 10 (EC 50 ) and log 10 (Steep) summarized in Table 2). Sensitivity analys es were conducted to determine how much effects determinations varied for alternatives from this default, including (a) the use of discrete data points in Table 1 vs. these distribut ions, (b) various weightings of the major taxonomic groups, and (c) the use of genus means vs . individual observations. Based on this sensitivity analysis, decisions can be made regardi ng how these issues should be addressed in the final methodology.

4.3 Sensitivity Analyses for PATI-Based LOCs

4.3.1 Sensitivity Analysis for Averaging Period

Using the overall (default) toxicity parameter dis tributions specified in Table 2, effects assessments were made for each of the example chemo graphs in Figure 11, using averaging periods of 30, 60, 90, and 120 d. These assessment s proceeded as follows:

(a) The daily PATI values for each experimental eco system exposure were calculated. As illustrated in Figure 3, this involves computing, f or each daily exposure concentration C, an average effect across a set of species whose toxici ty relationships are summarized by the distributions of log 10 (EC 50 ) and log 10 (Steep) in Table 2. This average effect requires m ultiplying the level of toxic effect expected for a particular EC 50 and Steep by the probability of a species having that EC 50 and Steep, and doing this for all possible combina tions of EC 50 and Steep. Mathematically, this can be expressed as follows, w here the function “ tox” (the expected toxicity at exposure concentration C and for toxicity parame ters EC 50 and Steep) is multiplied by the function “dens” (the density function for the joint probability di stribution of EC 50 and Steep), and this product is then integrated across all values o f EC 50 and Steep.



# ( )

50 50 50 ( , , ) ( , ) PATI tox C EC Steep dens EC Steep dSteep dEC = ⋅ 

# ∫ ∫

Rather than trying to directly evaluate this integr al by numerical integration, it was estimated by randomly sampling 1000 pairs of EC 50 and Steep from the density function, applying the toxicity function to these 1000 random pairs, and taking the average of these 1000 toxicity values. Based on tests in which this process was repeated, 1000 p oints was sufficient to estimate PATI with a relative error of <2%.

(b) The daily PATI values were used to calculate av erage PATI values for 30, 60, 90, and 120-d averaging periods for each experimental ecosystem e xposure. For experimental ecosystem treatments shorter than the averaging period, conce ntrations of zero were used to fill in the rest of the period, so that short duration is reflected in a lower average PATI value.26

(c) For each averaging period, a binary logistic re gression was conducted as described in Section 3.2. The LOC PATI

was set to the PATI 50% estimate from this regression (50% probability of an effect).

(d) The daily PATI values and maximum running avera ge PATIs for each averaging period were computed for each of the example chemograph in Figu re 11.

(e) For each averaging period and example chemograp h, the ratio of the average PATI to the LOC PATI was calculated and is referred to here as the “Eff ects Exceedence Factor” (EEF). This measure of how much estimated effects exceeded the level of concern was used to evaluate the sensitivity of effects determinations to the averag ing period (Figure 12).

(f) Calculations were also conducted iteratively to determine the “Concentration Exceedence Factor” (CEF) – the factor by which the entire expo sure time-series would need to be decreased so that the average PATI equals the LOC PATI . This describes how much exposure exceeds the LOC on a concentration rather than an effects scale , and was also used to evaluate how the sensitivity of effects determinations to the averag ing period (Figure 12).

Figure 12. Sensitivity of effects assessments to th e PATI averaging period, based on CEF (top panel) a nd EEF (bottom panel). Chemograph # denotes example field exposure time-series in Figure 7.

Chemograph # 1 2 3 4 5 6 7 8 9 10 11 12Concentration Exceedence Factor

0.2 0.5 2 5

1 10

Default - 90d Averaging Period 30d Averaging Period 60d Averaging Period 120d Averaging Period

Chemograph # 1 2 3 4 5 6 7 8 9 10 11 12Effects Exceedence Factor

0.2 0.5 2 5

1 10

Default - 90d Averaging Period 30d Averaging Period 60d Averaging Period 120d Averaging Period27

Figure 12 illustrates how the averaging period affe cts both the EEF and the CEF. As would be expected, both measures of effect show ver y similar patterns. Relative to the proposed averaging period of 90 d, increasing the averaging period to 120 d or decreasing it to 60 d has negligible influence (<10%) on the CEFs and EEFs. In contrast, using a 30-d averaging period underestimates CEFs and EEFs, compared to the other periods, for field exposures which have substantial concentrations over a period longer tha n 30 d. As already noted, this shorter averaging period is arguably inappropriate and was only included here to illustrate the magnitude of error that would result, which is still no great er than a factor of 2 and usually much less. Based on this sensitivity analysis, a 90-d averagin g period is recommended for the methodology.

4.3.2 Sensitivity Analysis for Toxicity Information

Using the 90-d averaging period, effects assessmen ts were made for each of the example field exposures in Figure 7 using the following opt ions for toxicity information: (1) The overall distributions for logEC50 and logSt eep reported in Table 2 (default). (2) The logEC50 distributions for the four major ta xonomic groups in Table 2 (using the overall distribution for logSteep). (3) An equally weighted composite of the logEC50 di stributions for the four major taxonomic
groups. (4) The individual tests in Table 1 (using the aver age value of -0.05 for log(Steep) for tests in which this was not determined). (5) The overall distribution using genera means rat her than individual tests (Section 2.3). These assessments were conducted analogously to the protocol described above for the averaging period evaluations and are summarized in Figure 13 for options 1-3 (the results for options 4-5 also differ insubstantially from option 1). Becaus e Figure 12 already shows that the patterns of

Figure 13. Sensitivity of effects assessments to se lection of EC50 distribution, based on CEF. Chemog raph # denotes example field exposure time-series in Figur e 7.

Chemograph # 1 2 3 4 5 6 7 8 9 10 11 12Concentration Exceedence Factor

0.2 0.5 2 5

1 10 Default - Overall EC50 Distribution Composite of Taxa Distributions Green Algae Only Diatoms/Cryptomonads Only Blue-green Algae Only Vascular Plants Only28

the EEF and CEF are virtually the same, Figure 13 o nly shows the CEF results.

The effects on PATI of options 2 and 3 were alrea dy shown in Figure 7 to involve major changes compared to the default option 1, including reducing the median EC 50 by up to 40% and increasing it by up to 100%, thereby creating a 3-f old range in the median sensitivity possible for the plant assemblage. Despite these large changes in the toxic sensitivity, Figure 13 shows negligible variation in the effects assessments. T he maximum variation of the CEF from the default value was never more than 7% and averaged l ess than 4%. This small sensitivity might seem surprising, given how much the toxicity distri butions change, but this is because the experimental ecosystems, not the toxicity distribut ions, determine the level of concern. The toxicity distributions are only being used to assess the expected relative effects of different exposure times-series, and these relative effects a re similar whether the plant assemblage is sensitive or tolerant. Because this sensitivity an alysis shows such small effects of alternative compositions of the plant assemblage, the recommend ation here is to use the overall toxicity distribution in Figure 2 that was used as the defau lt for this analysis.

4.4 Contribution of Uncertainties in Toxicity Distr ibutions to Assessment Uncertainty

Although varying the taxa selection in Section 4. 2.2 had insignificant effect on results, this involved substantial changes to only one param eter of four that describes toxicity distributions – the mean log 10 (EC 50 ). The standard deviation of the log 10 (EC 50 ) varied a small amount among taxonomic groups, and the mean and sta ndard deviation of log 10 (Steep) did not vary at all. Therefore, more evaluation is needed regarding whether the combined uncertainty of all the parameters also has negligible effects on r esults.

To this end, an uncertainty assessment was conduc ted that involved (a) generating 1000 sets of toxicity parameters (means and standard dev iations for both log 10 (EC 50 ) and log 10 (Steep)), (b) determining the LOC PATI for each parameter set, and (c) determining the CE F for each example chemograph for these 1000 combinations of t oxicity parameters and LOC PATI s. The 1000 means for log 10 (EC 50 ) and log 10 (Steep) were generated by random sampling from norm al distributions with the overall means and standard e rrors of the means for these variables reported in Table 2 (overall distribution). The 1000 standa rd deviations for log 10 (EC 50 ) and log 10 (Steep) were generated by random sampling from chi-square d istributions based on the standard deviations of these variables reported in Table 2 ( overall distribution).

Figure 14 summarizes this uncertainty analysis, c omparing the 10 th and 90 th uncertainty percentiles to the median results. The lower bound varies from 85% to 95% of the median, with an average of 90%; the upper bound varies from 106% to 120% of the median, with an average of 113%. If uncertainty estimates are to be provid ed, they would need to reflect all important

sources 2 , compared to which the uncertainties in the toxici ty distributions used by PATI should be negligible.

2 An example of another source of error in the overal l methodology is the uncertainty in the log(LOC PATI ) from the logistic regression. When the best estimates of th e overall toxicity distributions are used in calcul ating PATI, the standard error for log(LOC PATI ) is 0.16 from the binary regression analysis, whic h produces a 10 th to 90 th percentile range for the CEF of 55-183% of the median. Other sources of uncertainty include exposure characteriz ations and experimental ecosystem results.29

4.5 Summary of Implementation

Based on the above sensitivity and uncertainty ana lyses, this PATI-based methodology is proposed to be implemented with (a) an overall log( EC 50 ) distribution (normal) with mean 2.12 and standard deviation 0.37, (b) an overall log(Ste ep) distribution (normal) with mean -0.05 and standard deviation of 0.18, and (c) no consideratio n of uncertainty associated with the errors in these toxicity distributions. Based on a random sa mple of these distributions (n=1000), a daily PATI value will be determined for any atrazine conc entration time-series of interest (field or experimental) and the PATI will be set to the maxim um 90-d rolling average of the PATI values for that time-series. The LOC PATI will be determined based on logistic regression of the PATIs for the experimental ecosystem exposures and the bi nary effects determinations for these exposures, setting the LOC PATI to PATI value in the regression curve associated wi th a 50% probability of effect (or some other desired sensit ivity level). The ratio of PATIs calculated for field exposures of interest to this LOC PATI (EEF) will be used to determine whether the exposures exceed a level of concern and, if desired , iterative calculations will be used to determine the concentration factor (CEF) by which t he exposure exceeds a level of concern. FORTRAN-based computer programs and associated inpu t files for this implementation have been developed and are separately available from th e author. Figure 14. Uncertainty analysis for PATI-based MEI methodology. Solid line denotes CEFs based on bes t estimates of toxicity parameter distributions used in PATI calculations. Dashed lines denote 10 th and 90 th percentiles due to uncertainty of these toxicity di stributions.

Chemograph # 1 2 3 4 5 6 7 8 9 10 11 12Concentratration Exceedence Factor

0.2 0.5 2 5

1 103031

5. REFERENCES

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SINGLE-SPECIES PLANT TOXICITY TEST REVIEW

This appendix provides a summary for each report a nd journal article reviewed for developing the compilation of EC 50 s and steepnesses for the relationship of plant spe cific growth rate (SGR) to atrazine concentration. Bold numbers in the tables or text denote values from each study selected for inclusion in the compilation.

A.1 Protocol for Application of Toxicity Test Data

A.1.1 Acceptability of Measurement Variables

(1) The preferred measurement variable for assessing at razine effects was plant biomass (dry weight, or wet weight if procedures provided consis tent removal of adhering water), but measures that are approximately proportional to bio mass (algal cell count or cell volume, duckweed frond count) were also accepted.

(2) If measures outlined in (1) were not available, O 2 evolution or 14 C fixation measurements were accepted provided that they were not significa ntly compromised by any lag in inducing effects and their relationship to SGR could be defi ned.

(3) Data based just on chlorophyll content were not us ed because the chlorophyll content per cell can change markedly in response to atrazine, leadin g to markedly different EC 50 s for chlorophyll than for actual biomass (see discussion in Section 2.2.1 in main report text). Similarly, optical density was not accepted because it also is affecte d by chlorophyll content, often being measured near a chlorophyll absorbance maximum.

A.1.2 Translating Reported Data into SGR EC 50 and Steep

The nature of the data and the level of detail prov ided in the reviewed reports/papers varied widely, requiring several different procedures for translating the reported data into the elements of the data compilation: the SGR EC 50 , a steepness for the SGR vs. atrazine concentratio n relationship, and the SGR C
. (1) Initial and final biomasses (or surrogate) were rep orted for a concentration series.

The preferred data were reported initial and final biomasses (or acceptable surrogates) for all treatment concentrations, from which SGRs would the n be computed. A regression analysis of SGR vs. atrazine concentration (C ATZ ) was then conducted, resulting in characterizing b oth the EC 50 and the steepness for the relationship based on th e basic measurements in the study. The analyses were by least-square, nonlinear regression using Version 1.2 of the software package TRAP (Toxicity Relationship Analysis Program) (U.S. EPA Mid-Continent Ecology Division, 2006), using the “logistic equation” model option a nd the log-transform option for C ATZ . (This model option uses the logistic equation to provide a sigmoidal regression function shape, but is a regression of a continuous variable, not a binary l ogistic analysis.) The defining parameters for this function are the control SGR (SGR C ), the log 10 (EC 50 ) for the SGR, and a measure of relative40

steepness (“Steep”) defined as  d(SGR/SGR C )/d(log 10 (C ATZ ))  at the EC 50 , this regression analysis thus providing all the elements for the de sired data compilation.

(2) SGRs or relative SGRs were reported for a conce ntration series.

If the author reported SGRs (based on biomass or ac ceptable biomass surrogates) for all treatment concentrations, but not the actual initia l and final biomasses, these SGRs were used directly in regression analysis as described in (A1) above to obtain the SGR C , SGR EC 50 , and steepness parameter. If the reported SGRs were rel ative (fraction of the control), the regression was conducted to obtain an EC 50 and steepness to include in the compilation, but n ot an SGR C , although in some cases the latter was specified sep arately by the author(s).

(3) EC

50 for the SGR was reported with or without slope.

If the author computed SGRs, but only reported an S GR-based EC 50 without SGRs for individual treatment concentrations, the author-calculated SGR EC 50 was included in the compilation. If the author also specified the type of relationship used in the EC 50 estimation and a slope for that relationship, this information was converted to the steepness parameter of the relationship used in EPA’s regressions; otherwise no steepness was co mpiled. If the author separately provided information on the SGR C , this also was included in the compilation.

(4) Multiple EC

p s for growth reported; SGR C reported.

(a) If multiple EC p s for growth over a specified duration (t) and the SGR C for that duration were reported, SGRs corresponding to these biomass-based EC p s were calculated using the equation: 

# ( ) ( ) 

# ( )

100 1 ln 1 1 C p SGR t SGR e t ⋅ = − ⋅ +

In other words, this is the value for the SGR at th e concentration causing a p% decrease in growth. The resultant SGRs (and their associated c oncentrations) were then subject to regression analysis to provide estimates for the SGR EC 50 and steepness. This provided a SGR EC 50 , a steepness, and a SGR C for the compilation.

(b) If the author did not specify multiple EC p s for growth, but did provide the biomass-based EC 50 , the type of relationship used in this EC 50 estimation, and the slope for that relationship, additional EC p s for growth (p ≤ 90%) were calculated for this author-reported curve and also converted to SGRs. These were then subject to regr ession analysis to provide estimates for the SGR EC 50 and steepness, although any confidence limits on t hese estimates would not be valid given that the data points were not independent. R ather, this was simply a mechanism to convert the the author-reported curve for biomass-based ECs to the equivalent curve for SGR-based ECs.

(c) If the smallest SGR was more than 75% of the SGR C for either of the above options, the regression analysis was not conducted because this would involve too much extrapolation to estimate the SGR EC 50 . However, the possibility of extrapolating this S GR to the SGR EC50 per (B6) below was then considered.41

(5) Multiple EC

p s for growth reported; SGR C not reported.

If multiple EC p s or an EC 50 /slope combination for algal growth were reported, but an SGR C was not reported, the process in (B4) above was still used, but using SGR C s reported for other studies on test species in the same taxonomic group. Becau se this involves using data from other experimental systems and test species, three separa te analyses were conducted using median (low-high) estimates for the SGR C of 1.35 (1.05-1.74) for green algae, 1.03 (0.80-1. 32) for diatoms, and 0.65 (0.50-0.83) for blue-green algae. The SGR EC 50 and steepness from the regression analysis using the median SGR C estimate were included in the compilation, provide d the SGR EC 50 s derived using the low and high SGR C estimates differed by no more than a factor of 2.0.

[ The low/mid/high SGR C

estimates were based on ANOVA of logSGR C s from algal studies in which SGR C was reported (see Table 1 in Section 2.2). Analyse s using Statistica (Version 8.0, StatSoft, Tulsa, O K) provided a log mean for each major algal taxonomic group (0.135 for green algae, 0.013 for diatoms, - 0.189 for blue-green algae) and a pooled standard d eviation (0.122). The low/mid/high estimates for SGR C were based on calculating the mean ± 1 std.dev. of these log values and then taking antilogarithms. Separate SGR C

values for species within a taxonomic group were n ot justified because of large withinspecies variability relative to between-species var iability, as evidenced in Table 1 and other sources (e.g., Saenz et al. 1997). ]

(6) EC

50 only for growth reported; SGR C reported.

If the EC 50 s for growth over a specified duration (t) and the SGR C for that duration were reported, this biomass-based EC 50 was equated to an SGR EC p using the following equation to determine p: 

# ( ) 

# ( ) ( ) 1 ln 0.5 1

1 C

C SGR t

SGR SGR SGR e t p ⋅ = ⋅ +

= −

When only the SGR C and this single SGR are available, no regression a nalysis is possible. Rather, this SGR EC p was extrapolated to an SGR EC 50 using the equation 2 50 10 p S

P EC EC ⋅ = ⋅ , where S is based on regression curve steepnesses fr om other studies. Because this involves using data from other experimental systems and test speci es, three estimates of the SGR EC 50 were made using low, middle, and high estimates for the steepness of 0.68, 0.95, and 1.31. The estimate for the SGR EC 50 from the middle steepness estimate was included in the compilation, but only if the estimates based on low and high ste epness differed by less than a factor of 2.0. This factor of two requirement was met if p>16 for the estimated SGR EC p .
[ An ANOVA of all the log steepness determined in all studies indicated no significant differences among species or broader taxonomic groups, so the o verall mean and standard deviation of the log steepnesses were used to set low/mid/high estim ates. ]42

(7) EC

50 only for growth reported; SGR C not reported.

When only an EC 50 for growth was reported and a study-specific SGR C was not reported, the biomass-based EC 50 was equated to SGR-based EC p s per section (B5) using low, middle, and high estimates for SGR C . Then, each of these SGR-based EC p estimates was extrapolated to SGR EC 50 estimates per section (B6) using low, middle, and high steepness estimates. The SGR EC 50 estimate based on the middle SGR C and steepness estimates was included in the compilation, provided the extremes of the estimates varied by less than a factor of 2.0. This factor of two requirement resulted in this procedur e being applicable for green algae tests of up to 2 d long, but tests could be up to 4-d long for blue-green algae and up to 3-d long for diatoms. Extrapolating EC 50 s for biomass to SGR EC 50 s were just too uncertain for tests longer than thi s.
(8) Oxygen evolution or 14 C fixation reported

(a) If the exposure and measurement periods were short enough so that biomass did not change appreciably during these periods, and if initial bi omasses were either measured or could be treated as approximately the same among treatments, oxygen evolution and radiocarbon fixation rates were treated as proportional to SGR and EC p s for these rates were treated as comparable to SGR-based EC p s. However, this also required consideration of wh ether these periods were so short that any lag in the induction of toxicity wou ld significantly perturb the measurement. Hersh and Crumpton (1989) and Millie and Hersh (198 7) reported effects on oxygen evolution that were >50% within several minutes of exposure t o atrazine concentrations that caused similar effects on biomass-based SGRs. Thus, data were acc epted provided an induction lag of 5 min would not significantly confound results.

(b) When the exposure and measurement periods were the same and biomass changed enough over the period to substantially affect estimated E C p s, oxygen evolution and radiocarbon fixation were treated as being proportional to net growth ( e SGR

⋅ t ), and ECs were converted to an SGR basis analogously to procedures described above for biomass-based ECs.

(c) If a substantial exposure period of duration “t” p receded a short measurement period, so that the treatments would start with significantly diffe rent initial biomasses for the oxygen evolution/radiocarbon fixation measurement period, these measures were treated as being proportional to SGR

⋅ e SGR

⋅ t ; i.e., the biomass accretion in the exposure perio d prior to the start of measurement is e SGR

⋅ t and the oxygen evolution/radiocarbon fixation rate is proportional to the SGR times that biomass accretion. This required co nverting ECs to an SGR-basis using approaches analogous to that described above for bi omass.
A.1.3 Issues regarding biomass surrogates and varia bility.

One uncertainty issue occurred when the biomass sur rogate was cell counts made manually using a hemocytometer or similar device. In some cases, cell density estimates were based on <100 cells counted in total for the control treatment an d just several cells for atrazine treatments with large effects. Even 100 cells represents about +/- 20% uncertainty in the cell density. Therefore, it was desired to have >200 cells counted in the co ntrol treatment in order to have reasonable discrimination between the control and treatments w ith 25-50% reduced growth. Another area of concern was frond counts for duckweed, and how clos ely such counts mirror biomass when43

growth is limited and thus might have a greater per centage of newer, small fronds. Where possible, it was desired to have at least a 4-fold increase in the number of control fronds so that the counts were not excessively dominated by new, s mall fronds. A final area of concern was macrophyte shoot tests at times when controls had n ot increased by at least 50%, especially if this was measured by shoot length, which can change disproportionately to shoot weight when photosynthesis is inhibited. No firm rules were im posed with regard to any of these concerns, because any uncertainty depends on the number of re plicates in a test, the specific times, the variability among replicates, etc. How these conce rns were evaluated are identified in the summaries for each study in Appendix A.

A.1.4 Treatment of data at multiple times

When biomasses or biomass surrogates were reported at multiple times within a test duration, analyses were conducted for each time; however, the compilation selected results from only one of these times. This time was long enough to avoid problems with uncertain measurements of biomass early in some tests (e.g., the hemocytomete r count issue discussed above), but short enough to avoid potential biases associated with de clining SGR C discussed earlier. Again, no firm rules could be adopted for this because of var ious study-specific factors and because it involved balancing uncertainties at early times wit h those at later times. The decision process regarding this are provided in the summaries for ea ch study in Appendix A.

A.2 Review Summaries

A.2.1. Algae

(1) Gala and Giesy 1990

The authors conducted a 96-h flask test of Selenastrum capricornutum growth at multiple atrazine concentrations, enumerating cell density b ased on hemocytometer cell counts. Concentrations were measured. Illumination was con tinuous at 40 μ E/m 2 /s, temperature was 24 C. They reported average SGRs over 96 h at each tr eatment concentration, which were directly used in EPA regression analyses. Data for earlier times were not reported, but authors noted the use of extra nutrients to maintain exponential grow th. Due to the duration and growth rates, cell densities would have been high enough to avoid conc erns about low numbers of individuals manually counted. Measured (Target) Concentration ( μ g/L) Author Measured SGR (1/d)
Control 1.007 64 (60) 0.773 121 (120) 0.508 261 (250) 0.244 499 (500) 0.013
EC 50 ( μ g/L) 125

(80-194) Steepness 1.07

(0.46-1.77)44

(2) van der Heever and Grobbelaar 1996

The authors conducted a 72-h flask test of Selenastrum capricornutum growth at multiple atrazine concentrations, determining biomass (dry w eight), cell density (electronic particle counter), and chlorophyll (by both spectrometry and fluorometry) at 0, 24, 48, and 72 h. Concentrations were nominal. Illumination was cont inuous at 300 μ E/m 2 /s and temperature was 23 C. The authors graphically reported relative (to control) SGRs based on all these measures. Author-reported ECs based on chlorophyll were subst antially (almost 3X) higher than for cell density and biomass, and were not used in accordanc e with the review guidelines. Relative SGRs for cell density and biomass were estimated fr om the figures, reported in the table below, and used in EPA regression analyses to determine EC 50 and steepness. The results based on dry weight were selected for use because EC 50 s were modestly higher for cell density (average LC 50 = 406 by cell density, 311 by weight) indicative of decreases in mass per cell at higher atrazine concentrations, so that using cell density would sl ightly reduce the apparent sensitivity of biomass to atrazine. The results at 1 d were selec ted for use because it was unknown whether control growth rates declined with time, given that only relative SGRs were reported, and because use of an electronic particle counter shoul d have avoided the problems with low manual cell counts at early times.

Nominal Conc ( μ g/L) Author Relative SGR, Cell Counts Author Relative SG R, Dry Weight

1d 2d 3d 1d 2d 3d
1 1.13 1.30 1.22 1.06 1.10 1.00
5 0.98 1.00 0.95 1.00 1.18 1.02
10 0.98 1.11 1.07 0.84 1.02 0.91
50 0.97 0.97 0.97 0.88 1.00 0.93
100 0.95 1.10 1.08 0.83 1.06 0.91
500 0.35 0.30 0.30 0.18 0.30 0.33
1000 0.37 0.34 0.37 0.10 0.10 0.10
5000 0.20 0.12 0.10 0.00 0.00 0.00
EC 50 ( μ g/L) 439 370 401 236 (149-376) 352 352

Steepness 0.56 0.79 0.78 1.01 (0.52-1.50) 1.44 1.14

(3) van der Heever and Grobbelaar 1997

The authors conducted a 30-min oxygen evolution ass ay for Selenastrum capricornutum exposed to multiple atrazine concentrations. Conce ntrations were nominal. Illumination was continuous at 300 μ E/m 2 /s and temperature was 23 C. Oxygen evolution rate s relative to the control were reported graphically and the values in the table below were estimated from the figure. Because of negative responses at high conc entrations, the regression in this review included a non-zero asymptote at high concentration s, but the EC 50 is still defined relative to zero oxygen evolution, not this negative asymptote, so t hat this would best reflect net production. Although there was no prior exposure before oxygen evolution measurements were made, the measurement period was long enough relative to the 5-min induction standard that these results45

were accepted. It should be noted that the results are consistent with those for a flask test by the same authors discussed above.

Nominal Conc ( μ g/L) Author Relative

Oxygen Evolution
5 100
50 84
500 27
1000 0
5000 -14
10000 -25
EC 50 ( μ g/L) 223

(144-346) Steepness 0.61

(0.42-0.80)

(4) Kallqvist and Romstad 1994

The authors conducted a 72-h flask test of Selenastrum capricornutum growth at multiple atrazine concentrations, enumerating cell density u sing an electronic particle counter. Concentrations were nominal. Illumination was cont inuous at 70 μ E/m 2 /s and temperature was not reported but followed OECD standards of 23±2 C. The authors conducted a regression analysis of probit-transformed relative SGRs, reporting an SGR EC 50 of 110 μ g/L (95% cl = 99- 121) and an EC 10 of 27 μ g/L. Individual SGRs were not reported, but these two ECs allow estimating a steepness of 0.90 for the sigmoidal function used in this review.

The authors also conducted 3- to 6-d microplate exp osures of several algal species to atrazine. The duration of the test varied with species in ord er to be within the period of exponential growth. Illumination was continuous at 70 μ E/m 2 /s for green algae and 30 μ E/m 2 /s for others. For these exposures, relative SGRs for each treatme nt were reported graphically. Values estimated from the figures are provided in the foll owing table, along with EC 50 s and steepnesses estimated from regression analysis of this data. T he EC 50 for Selenastrum was higher for the microplate exposures than for the flask tests (alth ough by less than 2-fold), suggesting that the microplate exposure methodology might involve facto rs that lead to decreased apparent sensitivity (e.g., nutrient or atrazine reductions, although the former would not be expected if exponential growth was maintained). These micropla te-based numbers were still compiled for use in subsequent analyses because the Selenastrum EC 50 s was well within the reported range of results for this species from other studies; howeve r, this possible source of uncertainty was recognized in applications of these data.

Nominal
Concentration Relative SGR (% of Control)

Selenastrum capricornutum Chlamydomonas

noctigama Cyclotella

sp. Cryptomonas

pyrinoidifera Microcystis

aeruginosa Synechococcus

leopoliensis
0 100 100 100 100 100 100
3.2 95 11046

10 100 100 99 102 91
20 100
32 93 97 99 95 80
60 100 70
100 73 84 96 91 88 57
200 95 85
320 34 53 61 69 69 30
600 40 58 16
1000 12 28 17 33 13
2000 5
3200 0 7 0 0 3 0
6000
10000 0 0 0
EC 50 201

(177-227) 378

(313-456) 462

(383-556) 494

(415-587) 603

(443-820) 136

(116-159) Steepness 0.79

(0.68-0.90) 0.65

(0.53-0.77) 1.22

(0.80-1.64) 1.15

(0.85-1.45) 0.77

(0.43-1.11) 0.59

(0.52-0.66)

(5) Hoberg 1991a

The author conducted a 96-h flask test of Selenastrum capricornutum growth at multiple atrazine concentrations, enumerating cell density based on h emocytometer cell counts. The author provided a data table of cell counts at 1, 2, 3, 4 d at multiple concentrations; initial cell counts were reported to be 1  10 4 . Concentrations were measured and were stable for 4 d (concentrations were 2X higher than target due to d iluting error). Light was continuous at 450- 500 ft-c and temperature was 24-25 C. SGRs were ca lculated by EPA for each duration and concentration and used in regression analyses to es timate EC 50 and steepness. Substantial and continuing declines in control SGRs were observed, so that the growth rate over 2 d was 24% less than that over the first day. However, cell c ounts over the first day were lower than desired for good quantification and the drop in SGR could b e partly due to uncertainty in both the initial and day 1 cell counts. Therefore, day 2 values wer e selected for the data compilation.

Conc ( μ g/L) Author Cell Counts (/10 4 ) Calculated SGR (1/d) Target Measured 1d 2d 3d` 4d 1d 2d 3d 4d
0 - 10.0 33.0 71.7 105.0 2.30 1.75 1.42 1.16
32 76 5.0 9.3 49.7 101.7 1.61 1.12 1.30 1.16
63 130 2.3 5.0 31.7 27.7 0.83 0.80 1.15 0.83
120 250 1.7 4.0 1.7 2.0 0.53 0.69 0.18 0.17
240 510 0.7 2.3 2.0 1.0 <0.00 0.42 0.23 0.00
490 970 0 0 0 0 - - - -
EC 50 ( μ g/L) 109 131 (59-290) 180 161

Steepness 1.13 0.62 (0.18-1.10) 2.61 2.4247

(6) Hoberg 1993a

The author conducted a 96-h flask test of Selenastrum capricornutum growth at multiple atrazine concentrations, enumerating cell density based on h emocytometer cell counts. The author provided a data table of cell counts at 1, 2, 3, 4 d at multiple concentrations; initial cell counts were reported to be 0.3  10 4 . Concentrations were measured and were stable for 4 d. Light was continuous at 300-450 ft-c and temperature was 24 C . SGRs were calculated by EPA for each duration and concentration from these counts. The control SGR during the first day was exceptionally high (3.32/d) and dropped to more typ ical levels during subsequent days. In addition, SGRs were high during the first day even at the highest atrazine concentration (2.30/d at 450 μ g/L), and also dropped to more typical values durin g subsequent days (<0.1/d). These atypical results might represent an error in the in itial cell density, the reported value of which was atypically low and could not be verified. Thes e data were therefore not used.

(7) Caux et al. 1996

The authors conducted a 4-d microplate test of Selenastrum capricornutum growth at multiple atrazine concentrations, enumerating cell density u sing an electronic particle counter. Light was continuous at 60 μ E/m 2 /s and temperature was 24 C. The authors only prov ided a 4-d EC 50 for cell density (26 μ g/L), with no data on actual cell counts at test te rmination for atrazine treatments. No information was provided on actual treatment concentrations. However, they did report an initial cell density of 1  10 4 and a final control cell density of 1-2  10 6 , corresponding to an SGR C of 1.15-1.32/d, a relatively narrow range. Based on the midrange of the reported final control cell counts, an SGR C of 1.25/d was used for adjusting the cell density- based EC 50 to the SGR (1.08/d) that would result in half the final co ntrol density. The authors also reported a probit slope of 4.95 for the cell density vs. log 10 C relationship, which allowed calculation of other EC p s for cell density (e.g., EC 16 and EC 84 corresponding to ±1 standard deviation in probit equation) and their corresponding SGRs. Per item (B4b) in the protocol, these estimated SGRs were subject to regression analysis to estimate the SGR EC 50 and steepness. Confidence limits are not reported because this regression was not ba sed on independent data points, but on a conversion of the reported relationship for the cel l density ECs.

p (% reduction in cell counts) ECp ( μ g/L) 4-d Cell Density

(10 4 cell/ml) Estimated SGR (1/d)
0 1.50 1.25
16 16.4 1.26 1.21
50 26 0.75 1.08
84 41 0.24 0.795
EC 50 ( μ g/L) 50 Steepness 1.66

(8) Versteeg 1990

The author compared three assays of atrazine effect s on Selenastrum capricornutum growth: a 4- d flask test enumerating cell density based on hemo cytometer cell counts, 5-min 14 C fixation48

after 30-min exposure, and 30-min oxygen evolution. Light was continuous at 86 μ E/m 2 /s for the flask test, 350 μ E/m 2 /s for the 14 C fixation, and 250 μ E/m 2 /s for the oxygen evolution; temperature was 24 C. Reported EC 50 s were 50 μ g/L for 4-d cell density, 100 μ g/L for 14 C fixation, and 380 μ g/L for oxygen evolution. Data for individual treatmen ts were not reported for atrazine, but were for simazine, another triazi ne herbicide. Measurement variables (cell densities, 14 C fixation rate, oxygen evolution rate) relative to the control are provided in the following table for simazine. Simazine showed diff erences among the EC 50 s based on cell densities, 14 C fixation rate, and oxygen evolution similar to at razine. SGRs based on cell density effects were also estimated per item (B5) of the protocol, resulting in an SGR-based EC 50 similar to that for 14 C fixation. This simazine analysis also resulted i n a slope for SGR-based ECs that was included in the compilation.

Analysis of Versteeg 1990 Results for Simazine
Concentration ( μ g/L) Cell Density

(% of Control) SGR

(% of Control) 14 C Fixation Rate

(% of Control) Oxygen Evolution

(% of Control)
0 100 100 100 100
25 104
50 78 95 103
100 47 86
150 23 73 93
175 59
200 10 58
225 80
300 38 70
500 43
EC 50 ( μ g/L) 95 180 215 437 Steepness 1.58 1.50 1.19 1.26

Based on the experimental procedures and the result s for both atrazine and simazine, this study was applied as follows regarding EC 50 s: (a) Because the oxygen evolution assay involved pur ging oxygen, with uncertain effects on photosynthesis rates and sensitivity to atrazine, t hese data were not used. (b) Because the 14 C fixation assay included prior exposure, the resul ts will be used. Because of the short exposure and measurement periods, the EC 50 ( 

### 100 μ g/L ) for 14 C fixation will be treated as being equivalent to those for SGRs. (c) The smaller EC 50 for the flask test cell density is likely due to i t being for cumulative growth over 4 d. Per item (B7) in the protocol, this had too long a duration to e xtrapolate the cell density-based EC 50 to an SGR-based EC 50 given the range of estimates for the unknown SGR C and steepness. However, if the steepness for simaz ine is used, the procedure would result in the estimates for the SGR EC 50 from 95-115 μ g/L, similar to that for 14 C fixation.

(9) Larsen et al. 1986

The authors reported EC 50 s for 14 C fixation rates of several algal species, measured over 2 hours after 24 h prior exposure to atrazine. Light was c ontinuous at 400 ft-c and temperature was 24 C.49

Because the 24 h prior exposure would result in sub stantially different biomasses among treatments, this measure is not proportional to the SGR, and because fixation was not cumulative over the entire period (26 h), it is also not propo rtional to net growth. Assuming that SGR is approximately constant within each treatment, the b iomass at 24 h would be e SGR and the carbon fixation over the 2-h measurement period would be p roportional to SGR  e SGR , ignoring the small amount of growth over that 2 h and assuming that th e measured fixation over the 2 h is approximately proportional to the SGR. Given this relationship, per item (B7) of the protocol, an EC 50 for the SGR can still be calculated from this info rmation, if an SGR C and steepness can be estimated for use in the following calculations:

(a) Solve for SGR p ( p = percent reduction in SGR relative to control) corresponding to the EC 50 for 14 C fixation using the equation

0.5 C P SGR SGR P C SGR e SGR e

= ⋅ (i.e., this equation describes what the SGR would have to be so that the function SGR

⋅ e SGR is at half of its control value).

(b) Calculate p as 100

⋅ (1-SGR p /SGR C ).

(c) Use the estimated steepness for the toxicity re lationship to extrapolate the known SGR EC

p (=EC 50 for 14 C fixation) to the SGR EC 50
.

For Selenastrum capricornutum , the authors reported EC 50 s for 14 C fixation of 34-53 μ g/L (three tests, average 43). Using this average EC 50 , the procedure described above was conducted multiple times using the low, middle, and high esti mates for SGR C and steepness identified in the protocol for this review. The range of the resulta nt SGR EC 50 s was 66-114 μ g/L, narrow enough to include the median SGR EC 50 (78 μ g/L) in the data compilation. For the other specie s, the following table summarizes comparable calculations. For green algae, the same ratio (1.88) between the carbon fixation and SGR EC50s was used as for Selenastrum . For blue-green algae, the ratio used was 1.43 based on the estimates for SGR C for blue-green algae specified in the review guidelines.

Test Species 14 C EC 50 ( μ g/L) SGR EC 50

( μ g/L) Selenastrum capricornutum 43 78 Ankistrodesmus sp. 66 119 Chlamydomonas reinhardi 37 67 Scenedesmus obliquus 48 87 Chlorella vulgaris 308 557 Stigeoclonium tenue 175 317

Ulothrix subconstricta 88 159 Anabaena cylindrica 204 286

(10) Mayer et al. 1998, Selenastrum capricornutum

The authors provided an EC 10 , EC 50 , and EC 90 for SGRs from a standard ISO 8692 toxicity flask test (3 d). The actual temperature and light inten sity was not reported, but the cited test protocol50

specified 60-120 μ E/m2/s and 23±2 C. The author-reported SGR EC 50 of 

### 164 μ g/L will be used, but the multiple ECs can also be used to esti mate the steepness parameter for the sigmoidal relationship used in this review. The author also reported information on effects of light, temperature, pH, and nitrogen source on both contro l growth and toxic effects. This information indicated the SGR C for this study under standard conditions was about 

### 1.8/d , but insufficient information was available to use other toxicity inf ormation for the present analysis. This study did document a 10-fold increase in chlorophyll cont ent per cell due to atrazine exposure (200 μ g/L), which provides some of the basis for not acce pting this as a surrogate for biomass.

p (% reduction in control SGR) ECp ( μ g/L) Relative
SGR

0 1.0
10 17.2 0.90
50 164 0.50
90 688 0.10
EC 50 ( μ g/L) 164 Steepness 0.79

(11) Roberts et al. 1990

The authors conducted a 7-d flask test of Selenastrum capricornutum growth at multiple atrazine concentrations, enumerating cell density based on h emocytometer cell counts. Concentrations were nominal. Light was continuous at 2300 ft-c an d temperature was 24 C. The authors reported the number for the doublings (cell count b asis) over 3 d. This number of doublings was converted to a factor increase, which was converted to an SGR and subject to regression analysis.

Nominal Concentration ( μ g/L) Number of Doublings Relative Growth

(Factor increase) Calculated SGR (1/d)
0 7.13 140 1.65

50 6.64 100 1.53
100 5.08 33.8 1.17
150 4.10 17.2 0.95
EC 50 ( μ g/L) 163 Steepness 1.22

(12) Parrish, 1978

The author conducted 5-d flask tests of Selenastrum capricornutum and Microcystis aeruginosa growth at multiple atrazine concentrations, enumera ting cell density based on hemocytometer cell counts. Concentrations were nominal. Light w as continuous at 400 ft-c and temperature was 24 C. The author provided a data table of cell counts at 3 and 5 d at multiple concentrations; initial cell counts were 2  10 4 for Selenastrum and 5  10 4 for Microcystis . SGRs were calculated from the counts for each duration and concentration

.51

Results for Selenastrum are in the following table. Because there was not a substantial decline in the SGR C and results agreed between the two durations, the 5-d results were selected for use.

Conc ( μ g/L) (nominal) Author Cell Counts (/10 4 ) Calculated SGR (1/d)
3d 5d 3d 5d
0 55.8 249.6 1.110 0.965
32 50.6 207.3 1.077 0.928
54 34.5 130.3 0.949 0.835
90 14.6 28.2 0.663 0.529
150 8.9 8.9 0.498 0.300
250 0.7 0.7 <0 <0
EC 50 115 101 (79-130) Steepness 1.47 1.61 (0.67-2.55)

Results for Microcystis are in the following table. Control growth actual ly increased later in the test and EC 50 s were similar for both durations, so the 5-d resul ts were selected for use.

Conc ( μ g/L) (nominal) Author Cell Counts (/10 4 ) Calculated SGR (1/d)
3d 5d 3d 5d
0 14.3 77.1 0.350 0.547
65 13.2 71.6 0.324 0.532
108 12.9 26.1 0.316 0.330
180 6.5 21.5 0.087 0.292
300 5.1 9.6 0.007 0.130
500 4.7 4.0 0.000 0.000
EC 50 154 164 (95-285) Steepness 4.2 1.25 (0.24-2.46)

(13) Turbak et al. 1986, Selenastrum capricornutum

The authors reported an EC 50 of 

### 70 μ g/L based on a 30-min oxygen evolution assay with Selenastrum capricornutum , with no additional information to determine the s teepness of the relationship. The actual temperature and light int ensity was not reported, but the test protocol specified 400 ft-c and 24 C. The methods descriptio n did indicate that there was some exposure prior to oxygen measurements, and 30 min is long en ough not to be greatly perturbed by induction lags of several minutes. Therefore, this EC 50 based on rate of oxygen evolution was accepted as informative of an SGR EC 50 . They also reported a 59 μ g/L SGR EC 50 based on a 2- 3 week bottle test. Because of the length of this test and the lack of specifics regarding it, this EC 50 was not used, but this result does not contradict the EC 50 based on oxygen evolution.52

(14) Radetski et al. 1995 The authors reported a 72-h EC 50 of 118 μ g/L for Selenastrum capricornutum based on cell counts (Coulter counter) in a semistatic microplate well test. The actual temperature and light intensity was not reported, but the cited test prot ocol specified 60-120 μ E/m2/s and 23±2 C. They also reported an initial cell count of 2  10 4 and a final control cell count of 6.6  10 6

, corresponding to an SGR C of 

### 1.93/d . At the reported EC 50 , the final cell count would thus have been 3.3  10 6 , equivalent to an SGR of 1.70, corresponding to a 12% reduction from the control value (i.e., the growth EC 50 is an SGR EC 12 ). Per protocol item (B6) , this is too long of an extrapolation to estimate an SGR EC 50 given the uncertainty in the steepness of the rela tionship, so an SGR EC 50 was not computed. However, the SGR C was used in the compilation.

(15) Abou-Waly et al. 1991

The authors conducted 7-d flask tests of Selenastrum capricornutum and Anabaena flos-aquae aeruginosa growth at multiple atrazine concentrations, measur ing weights and chlorophyll concentrations. Concentrations were nominal. The authors reported SGRs for multiple durations and concentrations, but only for chlorophyll measur ements. Therefore, these data were not used in accordance with item (A3) of the protocol. Reported chlorophyll-based growt h rates and EC 50 s had a complex relationships to time and exposure concentration, thereby substantiating concerns about using chlorophyll measurements. For Anabaena , transferring organisms to control media after the end of the exposure test sh owed rapid recovery of growth rates.

(16) Hughes et al. 1988, Hughes 1986

The authors conducted 5-d flask tests of the growth of two algal species, Anabaena flos-aquae and Navicula pulliculosa , at multiple atrazine concentrations, enumerating cell density by electronic particle counting. Concentrations were not measured. Light was continuous, and light intensity/temperatures were 200 ft-c/24 C for Anaba ena and 400 ft-c/20 C for Navicula. The author provided data tables of algal cell densities at 3 and 5 d. SGRs were calculated for each duration and concentration from these counts, based on the reported initial algal cell densities of 2  10 4 cells/ml.

The following table provides results for Anabaena flos-aquae . Because no significant effects of duration are evident on either control growth rates or the EC 50 , the 5-d results were selected for further use.

Conc ( μ g/L) (nominal) Author Cell Counts (/10 4 ) Calculated SGR (1/d)
3d 5d 3d 5d
0 23.4 88.0 0.82 0.76

100 16.9 68.4 0.71 0.71
200 16.1 47.5 0.69 0.63
400 8.4 24.7 0.48 0.50
800 6.7 10.2 0.40 0.33
1600 3.9 5.6 0.22 0.2153 3200 4.5 5.5 0.27 0.20
EC 50 736 706 (440-1131) Steepness 0.48 0.59 (0.35-0.83)

The following table provides results for Navicula pullculosa . Because control growth was maintained or even increased through 5 d, the 5-d r esults were selected for further use.

Conc ( μ g/L) (nominal) Author Cell Counts (/10 4 ) Calculated SGR (1/d)
3d 5d 3d 5d
0 26.2 347 0.86 1.03

100 9.4 132 0.53 0.84
200 6.0 29.3 0.37 0.54
400 3.6 7.7 0.20 0.27
800 2.3 2.8 0.05 0.07
1600 1.9 1.9 0.00 0.00
3200 2.1 1.8
EC 50 153 217 (189-248) Steepness 0.80 1.08 (0.87-1.29)

(17) Fairchild et al. 1994, 1998

The authors assessed the effects of four herbicides on plant growth using 4-d tests with six algal species. Concentrations were not measured in expos ure chambers, but the stock concentrations were verified. Because chlorophyll was used to qua ntify algal biomass, these data were not used here per item (A3) of the protocol.

(18) Fairchild et al. 1995, 1997

The authors conducted 4-d tests of Selenastrum capricornutum at multiple atrazine concentrations (as well as 15 other herbicides). C oncentrations were not measured. Because chlorophyll was used to quantify Selenastrum biomass, these data were not used here per item (A3) of the protocol.

(19) Burrell et al. 1985

The authors conducted an 11-d flask tests of the gr owth of Chlorella vulgaris and Ankistrodesmus braunii at multiple atrazine concentrations, enumerating ce ll density based on optical density and hemocytometer cell counts. Con centrations were not measured. Illumination was continuous at 30 μ E/m 2 /s and temperature was 24 C. Initial cell densitie s were 1  10 5 and exponential cell growth was reported to be maintain ed for the test duration, culminating in a final54

cell density of 1.7  10 6 (SGR C =0.26/d) in the Chlorella test and 3.8  10 6 (SGR C =0.33/d) in the Ankistrodesmus test. The authors graphically reported the percen t reduction in the final cell density at each atrazine concentration, which were estimated from the figure and reported in the table below. Based on the final cell densities in the control and the test durations, these percent reductions in cell density were converted to SGRs a t each atrazine concentration and subject to regression analyses to determine the SGR EC 50 and steepness. Although this test was longer than would typically be used for this compilation, the SGR C were low enough (at least in part due to low light intensities) that total cell densi ties were not so high as to confound results or to doubt the authors’ statement that exponential growt h was maintained. However, because these SGR C s were so low they were not used for estimating SGR C s for other studies.

Ankistrodesmus Chlorella

Nominal
Atrazine Conc ( μ g/L) % Reduction

in Growth SGR

(1/d) Nominal

Atrazine Conc ( μ g/L) % Reduction

in Growth SGR

(1/d)

Control 0 .331 Control 0 .258
40 19 .312 10 27 .229
60 49 .269 30 55 .185
70 66 .232 50 67 .157

100 81 .180 70 72 .142
100 75 .131

EC 50 ( μ g/L) 104 (83-131) EC 50 ( μ g/L) 91 (70-118) Steepness 1.41 (0.56-2.36) Steepness 0.47 (0.32-0.63)

(20) Kirby and Sheahan 1994

The authors conducted a 4-d flask test of the growt h of Scenedesmus subspicatus at multiple atrazine concentrations; concentrations were measur ed. Illumination was continuous at 3500 lux and temperature was 25 C. The authors only reporte d EC 50 s based on final biomass, without any information on specific treatments, growth rates, e tc. Initial cell density was 1

⋅ 10 4 cell/ml and growth was quantified by spectrophotometric absorba nce calibrated to cell density. The EC 50 based on final cell density was 21 μ g/L. Because only an EC 50 was reported and an SGR C was not reported, estimation of the SGR EC 50 would be per item (B7) of the protocol, but this was not done because the extrapolation would be too great ( the extrapolated value would be 80 μ g/L with a range of 50 to 150 μ g/L). In addition, this study used optical density near the chlorophyll a maximum, and so would not be used per the review gu idelines.

(21) Millie and Hersh 1987

The authors determined oxygen evolution rates in an electrode chamber for three geographical races of Cyclotella meneghiana exposed to different atrazine concentrations (unme asured). Illumination was at 300 μ E/m 2 /s and temperature was 25 C. The authors graphical ly reported the percent inhibition of oxygen evolution rate relativ e to controls at each concentration, and these55

percentages were determined from the graph and subj ect to regression analysis to determine oxygen evolution EC 50 and steepness. Because these were based on a shor t-term (1 min) oxygen evolution and because there was prior exposure to e ach atrazine concentration of several minutes before oxygen evolution was measured, ECs from thes e oxygen evolution rates were accepted as being comparable to SGR ECs.

Nominal
Atrazine Conc ( μ g/L) Oxygen Evolution Rate - % of Control

Minnesota Race Arizona Race Iowa Race

1 94 92
6 95 85

31 80 77
64 89 58 62
95 78 51 54
143 71 39 40
213 53 31 34
277 40 25 21
338 32 15 22

EC 50 ( μ g/L) 225

(202-251) 100

(86-116) 114

(93-141) Steepness 1.00

(0.79-1.20) 0.67

(0.56-0.79 0.65

(0.49-0.81)

(22) Hersh and Crumpton 1989

The authors determined oxygen evolution rates in an electrode chamber of a commercial strain of Chlamydomonas reinhardii and of three isolates of Chlorella sp. obtained from an uncontaminated natural system exposed to different atrazine concentrations (unmeasured). Illumination was at 300 μ E/m 2 /s and temperature was 25 C. Only the EC 50 for the reduction in oxygen evolution rates relative to control were rep orted (no data on actual oxygen evolution vs. concentration), but because these were based on a s hort-term (1 min) oxygen evolution and because there was prior exposure to each atrazine c oncentration of several minutes before oxygen evoluation was measured, these oxygen evolua tion EC 50 s were accepted as being comparable to SGR EC 50 s. For Chlamydomonas , the EC50 was 

### 45 μ g/L and for Chlorella it averaged 

### 37 μ g/L across the three isolates (range=36-41).

(23) Stratton 1981, 1984

The author measured 14 C fixation over 3 h and cell growth rate (by optica l density) over 12-14 d for five algal species exposed to various atrazine and atrazine metabolite concentrations. Concentrations were unmeasured. For the 14 C fixation tests, light intensity was 7000 lux and temperature was 20 C; these were not specified for the growth test, but presumably were the same because these were also the culture conditions . For the growth tests, data other than EC 50 s at the end of the test were not provided, except fo r A. inaequalis , and this showed nonexponential growth throughout the last 10 d of the test and indicated the EC50 was lower at 4-5 d56

than later in the text, although the plotted data w ere insufficient to quantify this. In addition, optical density was measured at wavelengths with su bstantial chlorophyll absorption for at least three of the species. For these reasons, the ECs f rom the long growth test were not used, and only the 14 C fixation EC50s were compiled:

Anabaena

inaequalis Anabaena

cylindrica Anabaena

variabilis Chlorella

pyrenoidosa Scenedesmus

quadricauda

14 C fixation
EC 50 ( μ g/L) 280 470 70 480 300

(24) Schafer et al. 1994

The authors conducted a 10-d test of the growth of Chlamydomonas reinhardi in a flow-through apparatus that maintained exponential cell growth, and reported EC 50 s and EC 10 s for growth at 4, 7, and 10 d. Concentrations were measured. The li ght intensity was 7000 lux with a 14/10 photoperiod and the temperature was 24 C. Informat ion was also provided to allow estimation of the SGR C to be 1.06/d, but no additional information on act ual or relative cell counts at different concentrations and times, etc. was given. These EC s were reported to be for growth (not growth rate) and to be derived per OECD method 201, so pre sumably were based on “area under the curve” (AUC). They thus do not represent the diffe rence between the biomass at the stated time and the biomass at test start, but rather the sum o f these differences across the whole time interval (and thus a measure of the average increas e). Because this system maintained an exponential growth and because the SGR C is known, the EC 50 s can be used to estimate SGRs for those concentrations, as summarized in the followin g table. The magnitudes of these estimated effects on the SGR are insufficient to support a re gression analysis to estimate the SGR EC 50 and steepness (due to the large extrapolation from 16% effect to 50% effect). However, per item (B6) in the protocol, this SGR EC 16 of 51 μ g/L can be extrapolated to an estimate of 

### 141 μ g/L for the SGR EC50.

Concentration ( μ g/L) Duration (d) for which

concentration is AUC EC 50 SGR (1/d)

Control N/A 1.060
10.2 10 0.99
21 7 0.96
51 4 0.89

The authors also conducted 3-d flask tests of the g rowth of Chlamydomonas reinhardii and Scenedesumus subspicatus at different atrazine concentrations, measuring ce ll densities at 1, 2, and 3 d with an electronic particle counter. Illum ination was continuous at 8000 lux and the temperature was 20 C. The authors reported 3-d EC 50 s and EC 10 s from these tests, but without any other effect information (e.g., actual or relat ive cell counts at different concentrations and times, growth rates). Because of high initial cell densities (2

⋅ 10 5 cell/ml) that would have led to growth-inhibiting densities based on the SGR C from the flow-through test, the growth EC 50 for Chlamydomonas (350 μ g/L) cannot be converted to information on an SGR E C. For Scenedesmus , initial cell densities were low enough (5

⋅ 10 4 cell/ml) to make converting the57

growth EC 50 (72 μ g/L) reasonable; however, this would follow item (B 7) of the protocol, and the duration of the test is too long for this extrapola tion given uncertainties in both SGR C and steepness.

(25) Faust et al. 1993

The authors conducted 1-d tests of Chlorella fusca growth at multiple atrazine concentrations. This was a synchronized culture of 1 generation per day, in which a cell grows during the light period (14 h) and releases a set of daughter cells in the subsequent dark period (10 h); cell counts were by Coulter counter. The SGR C for cell number would be ln(# of daughter cells) f or the control treatment, but this number was not reported . This number can be as low as 4 (SGR C =1.4/d), but in a related paper by Altenburger et a l. (1990), a value of 12 was indicated (SGR C =2.5/d). The authors reported a probit equation fo r cell reproduction over 24 hour. The points on this probit equation corresponding to -2, -1, 0, 1, and 2 probit units from the median were calculated to provide EC p s for cell “reproduction” (table below). Then, two sets of SGR estimates corresponding to these EC p s were calculated based on the two alternatives for the SGR C , and regression analyses were conducted on each of these sets of SGRs. The resultant SGR EC50 estimates did not differ markedly (table b elow), so the average of these were included in the data compilation.

Concentration ( μ g/L) Percent of Control

Reproduction SGR (1/d)

Control 100 1.4 2.4
2.45 97.5 1.381 2.377
6.1 84 1.272 2.243
15.1 50 0.927 1.794
37.2 16 0.398 0.957
92 2.5 0.074 0.224
EC 50 ( μ g/L) 22 29 Steepness 1.08 1.06

(26) Geyer et al. 1985

The authors conducted 4-d flask tests of Scenedesmus subspicatus growth at multiple atrazine concentrations. The AUC EC 50 was reported to be 110 μ g/L, but other information (effects at higher concentrations, control SGR) were not report ed. This test does not meet the protocols stated earlier for extrapolating such an EC 50 to one for the SGR.

(27) Zagorc-Koncan 1996

The author determined the net production of oxygen over 24 h (by liberated gas via Warburgtype apparatus) and increased biomass as measured b y chlorophyll over 72 h of Scenedesmus subspicatus exposed to multiple atrazine concentrations. Ligh t was continuous at 800 lux and temperature was 20 C. As noted in the protocol, ch lorophyll is not an acceptable surrogate for biomass. Regarding oxygen evolution, the authors r eported an EC50 of 25 μ g/L, but because of58

the lengthy incubation this should be proportional to net biomass gain and not directly related to effects on SGR. To convert to an SGR-basis require s estimating SGRs based on the oxygen production and assumptions regarding SGR C . Such estimates based on the range of SGR C for green algae observed in other studies are included in the table below and subject to regression analysis. Variation in the assumed SGR C did not cause great variation in the estimated SGR EC50; because of the low temperature and light inte nsity, the compilation used the value from the lowest SGR C value.

Nominal
Atrazine Conc ( μ g/L) Estimated SGR (1/d)

SGR C =1.05 SGR C =1.35 SGR C =1.74

Control 1.050 1.350 1.740

0.1 1.038 1.336 1.724
1.0 1.004 1.297 1.681
5.0 0.926 1.208 1.580
10 0.896 1.173 1.54
50 0.431 0.604 0.86

EC 50 ( μ g/L) 39

(27-56) 44 51

Steepness 0.73

(0.45-1.01) 0.72 0.70

(28) Tang et al. 1997

The authors conducted 28 d tests with several algal species. Growth was measured based on chlorophyll measurements and optical density near t he chlorophyll a maximum. Due to both the length and the type of measurement, these data were not used.

(29) Gramlich and Frans 1064

The authors conducted a 5-d flask test with Chlorella pyrenoidosa at several atrazine concentrations. Because biomass was measured by op tical density and because initial values for biomass were not given, useful results for the comp ilation could not be obtained from this study.

(30) Stratton and Giles 1990

The authors examined the effect of volume and initi al cell density on the toxicity of atrazine to Chlorella pyrenoidosa , measured by radiocarbon uptake over 24 h. Althou gh these experiments demonstrated inhibition relative to the control and did include some treatments with approximately 50% inhibition, only one concentratio n was tested, absolute fixation rates were not tested, and a variety of processes might be aff ecting the observed inhibition. This precluded applying these data to the data compilation of inte rest here.59

(31) Boger and Schlue 1976

The authors evaluated photosynthesis based on oxyge n evolution rate after several days of exposure to atrazine and the recovery of photosynth esis upon transfer of exposed algae to clean medium and control algae to contaminated medium. H owever, only one concentration was tested and results could not be related to the effe ct concentrations desired in this review.

(32) University of Mississippi 1991

The authors evaluated growth of Selenastrum capricornutum (4 d) at multiple atrazine concentrations. This test involved methodological and performance problems that precluded its use, especially for determining SGR-based ECs. Chl orophyll measurements were made, but were erratic in addition to being not accepted in t he protocol used here. Both cell densities and weights were also measured, but no initial cell den sity was specified, final densities were based on inadequate numbers of cells, and many of the mea surements of final weight were negative. Atrazine effects were evident at 100 μ g/L, but the next lower and higher concentration wa s 10- fold different (10 and 1000 μ g/L) , precluding any good characterization of dose -response.

A.2.2 Vascular Plants

(1) Hughes et al. 1988, Hughes 1986 The authors conducted a 5-d test with the duckweed, Lemna gibba , at multiple atrazine concentrations, assessing growth by frond count. C oncentrations were not measured. Light was at 500 ft-c and temperature was 25 C. The authors p rovided data tables of duckweed frond counts at 3 and 5 d. SGRs were calculated for each duration and concentration from these counts, based on an initial frond count of 16. The following table summarizes observations and the estimated SGRs. Because control growth was les s than a factor of two at 3 d, the 5-d results were selected for further use.

Conc ( μ g/L) (nominal) Average Frond
Counts SGR (1/d)

3d 5d 3d 5d
0 29.0 49.3 0.198 0.225
100 27.0 40.0 0.174 0.183
200 19.7 29.7 0.069 0.124
400 16.3 21.7 0.006 0.061
800 16.0 16.3 0.000 0.004
1600 1.9 1.9
3200 2.1 1.8
EC 50 169 224 (151-332) Steepness 2.17 1.14 (0.43-1.85)60

(2) Hoberg 2007

The author conducted growth tests with isolated sho ots of Elodea canadensis at multiple atrazine concentrations and at zero, dim (500 lux), and opti mal (6000 lux) light levels (only the higher light level is appropriate for this review). Conce ntrations were measured and temperature was 20-25C. Data tables were provided for individual s hoot lengths at 0 and 14 d and individual shoot dry weights at 14 d for multiple concentratio ns. Only dry weight is considered here (shoot lengths were a poor surrogate for growth because su bstantial shoot elongation was observed in low light and at high atrazine concentrations were no growth in weight was observed). This requires having an estimate of the initial dry weig ht, which the author reported for a separate initial sample of shoots as being 0.1346 g/shoot. It was assumed that this weight applied to the average initial shoot length (8.3 cm/shoot) so that the initial weight per cm 0.0162 g/cm. This factor was used to estimate the initial weights for each replicate tanks based on the initial shoot lengths within that tank, allowing SGRs to be compu ted for each tank. The following table lists the reported final weights, the estimated initial w eights, and the resultant shoot weight SGRs, along with the EC50 and steepness parameter estimat ed by regression analysis. This regression analysis is relatively uncertain because the lowest treatment concentration corresponds to an EC68, leaving an absence of data at low to moderate effect. However, the estimated steepness is similar to others reported for this species (Table XX) so the EC50 estimate was still deemed acceptable for us.

Measured Concentration ( μ g/L) Estimated Initial Average

Shoot Weight (g dwt) Reported Final Average Shoot Weight (g dwt) Shoot Weight SGR (1/d) 0 0.133,0.120,0.129,0.121 0.420,0.415,0.420,0.471 0 .082,0.089,0.084,0.097 464 0.126,0.131,0.141,0.129 0.166,0.218,0.225,0.178 0.020,0.036,0.034,0.023 853 0.137,0.139,0.136,0.153 0.213,0.179,0.184,0.185 0.031,0.018,0.022,0.009 1761 0.131,0.133,0.149,0.136 0.128,0.166,0.214,0.12 6 -0.001,0.016,0.026,-0.005 Regression EC 50 ( μ g/L) 204 (59-600) Regression Steepness 0.52 (0.15-0.98)

(3) Hoberg 1991b

The author conducted a 7-d test of Lemna gibba growth at multiple atrazine concentrations; concentrations were measured. Light was continuous and temperature was 24 C. The author provided a data table of frond counts at 3, 6, and 7 d at multiple concentrations; initial frond counts were 15. SGRs were calculated for each dura tion and concentration from the counts and regression analyses were conducted on these SGRs. Because of the absence of growth on day 7, the 6-d values were compiled.

Measured Atrazine Concentration (μg/L) Average Frond Counts SGR (1/d)
3d 6d 7d 3d 6d 7d
0 34.0 78.0 80.7 0.273 0.275 0.240
15 32.0 84.0 85.3 0.253 0.287 0.24861 28 31.0 78.0 77.0 0.242 0.275 0.234
57 33.0 68.0 68.3 0.263 0.252 0.217
120 28.3 52.0 51.3 0.212 0.207 0.176
220 21.7 34.0 31.3 0.123 0.136 0.105
390 19.0 19.7 19.3 0.079 0.045 0.036
EC 50 230 202 (174-234) 189

Steepness 1.14 1.24 (0.85-1.62) 1.24

(4) Hoberg 1993b

The author conducted a 14-d test of Lemna gibba growth at multiple atrazine concentrations. Concentrations were measured. Light was at 400 ft- c and temperature at 24 C. The author provided a data table of frond counts at 3, 6, 9, 1 2, and 14 d and dry weight at 14 d. Initial frond counts were 15. Initial dry weight was unreported but it was assumed for this analysis that the initial dry weight per frond was equal to that in t he control at the end (=110 mg/529=0.208 mg/frond), so that the initial dry weight would be 3.12 mg. SGRs were calculated for each duration and concentration from the counts and dry weights and regression analyses were conducted on these SGRs. Based on frond count, som e reduction in control growth rate occurred after 9 d, but did not appreciably affect estimated SGR EC 50 s. For the 14-d data, dry weights resulted in an EC 50 29% lower than that based on frond count. This is likely attributable to the lower dry weight/frond at higher atrazine concentra tions (i.e., smaller fronds due to atrazine effects), but also could be contributed to by overe stimation of the initial dry weight if control fronds at the end were on average larger than those at the beginning. This illustrates a possible weakness in the use of frond counts for duckweed te sts, but also a weakness in most tests regarding measuring initial weights. Due to it bei ng a direct measure of biomass rather than an indicator, the dry weight-based results were compil ed.

Measured
Atrazine
Concen. Average Frond Count Avg dwt (mg) Frond Count SGR (1/d) Dwt
SGR
3d 6d 9d 12d 14d 14d 3d 6d 9d 12d 14d 14d
0 37.0 99.0 255 424 529 110 .301 .314 .315 .278 .25 4 .254
3.4 35.3 91.0 244 426 440 96 .285 .300 .310 .279 .2 41 .245
7.2 36.0 89.0 253 475 470 117 .292 .297 .313 .288 . 246 .259 17 36.3 76.0 202 334 364 77 ,295 .270 .289 .259 .22 8 .229
47 32.3 71.7 163 303 310 17 .256 .261 .265 .250 .21 6 .222
92 26.7 45.0 79 117 117 16 .192 .183 .185 .171 .147 .116
240 20.7 25.7 35 36 43 5 .107 .090 .094 .073 .075 . 036
EC 50 156 133 130 129 134 93 (72-120) Steepness 0.87 0.85 0.85 1.09 0.90 1.33 (.58-2.07)62

(5) Hoberg 1993c

The author conducted a 14-d test of Lemna gibba growth at multiple atrazine concentrations. Concentrations were measured. Light was continuous at 450-500 ft-c and temperature was 25 C. The author provided a data table of frond counts at 3, 6, 9, 12, and 14 d and dry weight at 14 d. Initial frond counts were 15. Initial dry weight w as unreported but it was assumed for this analysis that the initial dry weight per frond was equal to that in the control at the end, resulting in a estimated initial dry weight of 3.7 mg. SGRs were calculated for each duration and concentration from the counts and dry weights and r egression analyses were conducted on these SGRs. As for Hoberg 1993b, dry weight-based SGRs s howed a lower EC 50 and higher steepness than frond count-basis, and were selected for the c ompilation.

Measured
Atrazine
Concen Average Frond Count Avg dwt (mg) Frond Count

SGR (1/d) Dwt
SGR
3d 6d 9d 12d 14d 14d 3d 6d 9d 12d 14d 14d
0 37.2 88.7 191 277 356 88 0.303 0.296 0.283 0.243 0.226 0.226
0.53 37.3 84.7 187 257 364 82 0.304 0.288 0.280 0.237 0.228 0.221
1.3 37.0 85.7 185 241 327 94 0.301 0.290 0.278 0.231 0.220 0.231
3.0 36.7 89.7 178 284 298 90 0.298 0.298 0.275 0.245 0.214 0.228
8.3 34.3 83.3 162 278 321 72 0.276 0.286 0.264 0.243 0.219 0.212
18 32.3 71.0 136 204 258 58 0.255 0.259 0.245 0.218 0.203 0.197
44 26.0 46.3 81 132 147 24 0.183 0.188 0.187 0.181 0.163 0.134
100 20.3 26.7 35 48 53 4.2 0.101 0.096 0.094 0.097 0.090 0.009
EC 50 61 63 67 82 81 49 (42-58) Steepness 0.78 0.95 0.91 0.099 0.96 1.71 (.82-2.60)

(6) Desjardin et al., 2003

The authors conducted tests on Lemna gibba growth at multiple atrazine concentrations and for multiple durations (1-14 d) followed by examination of recovery. Concentrations were measured. Temperature was 24-25 C and light intens ity 4250-5750 lux. Rapid recovery was demonstrated, but the analyses here are concerned w ith effects during the exposure period. Furthermore, this analysis will be restricted to a 7-d test, because both the longer tests (9-14 d) produced less than a 20% reduction in the SGR and t he 1-3 d tests provided uncertain results due to the short duration and limited concentration ran ge. The authors provided data at day 2, 4, and 7 d and dry weight at 7 d at multiple concentration s. Initial frond counts were 15 at day -1 and were 20-21 at the start of exposure (this 1 d perio d of growth was done to identify/discard chambers that showed little or no growth; despite t his precaution, one control replicate had poor enough growth to be excluded as an outlier). The i nitial dry weight was estimated to be 2.8 mg based on the average dry weight/frond in the no-eff ect concentrations at the end of the exposure. SGRs were calculated for each duration and concentr ation from the counts and dry weights.63

Measured
Atrazine
Concen Average Frond Count Avg dwt (mg) Frond Count

SGR (1/d) Dwt
SGR
2d 4d 7d 7d 2d 4d 7d 7d
0.0 40 76 321 37.1 0.347 0.334 0.397 0.381
4.7 42 93 349 46.0 0.347 0.372 0.402 0.405
9.4 41 96 340 46.2 0.359 0.392 0.405 0.412
19.0 41 95 294 38.1 0.359 0.390 0.384 0.385
38.0 43 88 262 30.8 0.383 0.370 0.368 0.354
77.0 32 60 121 12.0 0.235 0.275 0.257 0.220
157 31 47 61 5.7 0.195 0.201 0.152 0.106
EC 50 159 165 116 90 (75-108) Steepness 1.09 1.05 1.06 1.18 (.75-1.62)

(7) Fairchild et al. 1994, 1998

The authors assessed the effects of four herbicides on plant growth using 4-d tests with Lemna minor and 14-d tests with Ceratophyllum dermersum

, Elodea canadensis

, Myriophyllum heterophyllum , and Najas sp. Temperature was 25 C and light was 60 μ E/m 2 /sConcentrations were not measured in exposure chambers, but the sto ck concentrations were verified. The 1994 report provided detailed biomass measurements absen t in the 1998 journal article.

Lemna Initial frond counts were 12 in each replicate and final frond counts are listed in the following table. The limited duration resulted in limited growth (barely 2-fold in the control) that makes these results rather uncertain, particul arly based on frond counts.

Nominal
Atrazine Conc ( μ g/L) Final frond counts

in replicates SGRs

(1/d)

0 34,26,23 0.260,0.193,0.163 37.5 25,25,19 0.184,0.115,0.163 75 19,20,15 0.128,0.056,0.101 150 15,17,20 0.087,0.128,0.092 300 16,18,22 0.101,0.152,0.110 600 12,14,14 0.000,0.038,0.026

EC 50 ( μ g/L) 114 (34-390) Steepness 0.42 (0.06-0.79)

Najas: Replicates were created by placing natural pond sed iments from Najas beds in beakers, from which plants germinated. Plants were grown fo r approximately 2 weeks to approximately 3 cm in height, at which time the 14-d chemical expos ure began. After the exposure, plants were sieved and wet weights were determined. Initial we t weights were not determined, but based on64

the similarity in the average weights in the highes t three treatments (following table) it was assumed that these treatments had zero net growth a nd SGRs were estimated based on an initial wet weight of 69.5 mg, the overall average final we ight of these treatments. Given the number of replicates with lower final weights, the initial weights obviously varied considerably across replicates, but by basing SGR on the mean weight ac ross replicates, this variability is reduced enough to produce a clear dose-response. To the ex tent that the highest three treatments did not have zero net growth the estimated EC50 will be bia sed, but substantial bias would be unlikely because (a) if substantial positive growth was occu rring a concentration effect should be evident and (b) if substantial negative growth was occurrin g this would imply a high initial weight incompatible with the information on control growth (i.e. a disproportionate amount of control growth in the two weeks prior to exposure compared to the 2 weeks of exposure).

Nominal
Atrazine Conc ( μ g/L) Final wwt

for replicates (mg) Final mean wwt

for treatment (mg) SGRs

(1/d)

Control 306,111,122 180 0.068 Solvent Control 285,168,57 170 0.064 8.4 66,170,185 140 0.050 18.8 164,68,57 96 0.023 37.5 57,91,55 68 -0.001 75 65,7,137 70 +0.001 150 49,75,90 71 +0.002

EC 50 ( μ g/L) 14.5 (12.3-17.2) Steepness 1.67 (1.00-2.33)

Ceratophyllum: The authors provided wet weights for each replica te at 0, 7, and 14 d, allowing calculation of SGRs and regression analysis of thes e SGRs to determine the EC 50 and steepness of the SGR vs concentration relationship. There wa s nearly a doubling of weight in the controls over the 14-d, allowing sufficient growth so that e ffects were apparent and could be quantified.

Nominal
Atrazine Conc ( μ g/L) Initial wwt

for replicates (mg) Final (14 d) wwt

for replicates (mg) SGR

for replicates (1/d) Control 1578,1202,1730 2292,2409,2735 0.027,0.050,0 .033 Solvent Control 1310,1746,1622 2010,2965,2477 0.031 ,0.038,0.030 18.8 1209,937,1232 1476,1262,1798 0.014,0.021,0.027 37.5 1960,1777,1089 2281,2076,1378 0.011,0.011,0.01 7 75 2649,1062,2420 2410,1078,2434 -0.007,0.001,0.000 150 1362,1322,1482 1454,1446,1415 0.005,0.006,-0.00 3 300 1166,1516,878 1102,1563,1023 -0.004,0.002,0.010

EC 50 ( μ g/L) 24 (14-42)65 Steepness 0.81 (0.12-1.50)

Myriophyllum: The authors provided wet weights for each replica te at 0, 7, and 14 d, allowing calculation of the SGR for each replicate. However , the growth in controls and in NOECs was too small and variable for good quantification of e ffects on SGR. At day 14 (table below), the weight gain of individual replicates varied from -4 -16% (average 8%) in the control, 1-31% (13%) in the solvent control, from 11-16% (15%) at 37.5 μ g/L, and 2-26% (15%) at 75 μ g/L. In addition, at day 7, the weight gains were 12-17% (1 5%) in the controls, 25-33% (28%) in the solvent controls, 13-16% (13%) at 37.5 μ g/L, and 6-21% (11%) at 75 μ g/L. These data illustrate not just a small amount of growth and great variabi lity relative to the average net growth, but also no or negative growth in most replicates durin g the second week, which the authors also noted in other experiments. In addition, there is an inconsistency between the 7- and 14-d data in that the 14-d data show no difference among the con trols and the two lowest concentrations, whereas the 7-d data indicate better growth in the solvent controls relative to the control without solvent and the two lowest concentrations. Althoug h there are clear effects at 150 μ g/L and above, there is not a good reference against which to quantify effects on the SGR. This underscores the requirement in the protocol that co ntrol growth be large and consistent enough to quantify ECs with reasonable precision. The most that can be inferred from this test is that 37.5 and 75 μ g/L are apparently NOECs and the SGR EC 50 is probably ≈ <150 μ g/L.

Nominal
Atrazine Conc ( μ g/L) Initial wwt

for replicates (mg) Final (14 d) wwt

for replicates (mg) SGR

for replicates (1/d) SGR

for treatment (1/d) Control 3330,4547,3200 3696,4379,3712 0.007,-0.003, 0.011 0.005 Solvent Control 3137,3767,3817 3184,3981,5017 0.001 ,0.004,0.020 0.008 37.5 2600,3077,3084 3021,3402,3603 0.011,0.007,0.01 1 0.010 75 3046,2872,4122 3895,3382,4197 0.018,0.012,0.001 0.010 150 3262,3854,4414 3782,3726,4454 0.011,-0.002,0.00 1 0.003 300 3559,3039,2756 3359,2074,2829 -0.004,-0.027,0.0 02 -0.010 600 2812,3748,3341 1877,3363,2992 -0.029,-0.008,-0. 008 -0.015

EC 50 ( μ g/L) < ≈ 150 Steepness

Elodea: The authors provided both wet weights for each re plicate at 0, 7, and 14 d, allowing calculation of the SGR for each replicate. However , as for Myriophyllum , the control growth was very small, averaging only about 15% over the t wo weeks. Although, this growth was not as variable as for Myriophyllum , it still is a questionable reference against whic h to quantify effects on SGRs. In addition, the lowest treatment concent ration produced no growth on average, and negative growth became progressively greater at hig her concentrations, so that ECs for SGR could not be quantified even if the controls were g ood references for quantifying the SGR. The most that can be inferred from this test is that th e SGR EC 50 is <38 μ g/L, although even this might be confounded by the low control growth.66 Nominal
Atrazine Conc ( μ g/L) Initial wwt

for replicates (mg) Final (14 d) wwt

for replicates (mg) SGR

for replicates (1/d) SGR

for treatment

Control 4820,5564,6866 5949,6345,7802 0.015,0.009,0 .009 0.014 Solvent Control 5554,5672,6624 6336,6140,7016 0.009 ,0.006,0.004 0.008 37.5 7146,3370,5500 7258,3232,5556 0.001,-0.003,0.0 01 0.001 75 6028,5477,6477 5435,5178,6478 -0.007,-0.004,0.00 0 -0.002 150 4941,4929,4992 4778,4851,5554 -0.002,-0.001,0.0 07 -0.002 300 6080,5937,5398 5575,5543,5087 -0.006,-0.005,-0. 004 -0.004 600 6902,7160,6200 3960,6302,5605 -0.040,-0.009,-0. 007 -0.018

EC 50 ( μ g/L) <37.5 Steepness

(8) Fairchild et al. 1995, 1997

The authors conducted 4-d tests of Lemna minor growth at multiple atrazine concentrations (as well as 15 other herbicides). Concentrations were not measured. For Lemna , the reported EC 50 of 153 μ g/L was based on growth (frond count basis), and in sufficient information was provided to convert this to a growth rate basis. Based on a control growth rate of 0.21/d for identical methodology used above by Fairchild et al. (1994, 1 998) this EC 50 would correspond to an EC 82
. Because this extrapolation was greater than allowed in the protocol, this data just indicate that the SGR EC 50 is <153 μ g/L, which does not contradict the results of Fairc hild et al. (1994, 1998).

(9) Kirby and Sheahan 1994

The authors conducted a 10-d test of the growth of Lemna minor at multiple atrazine concentrations; concentrations were measured. Temp erature was 25 C and light intensity was 3500 lux. The authors only reported EC50s based on final biomass, without any information on specific treatments, growth rates, etc. The initia l biomass was 10 fronds and growth was quantified by chlorophyll, frond count, and fresh w eight, with the respective EC 50 s being 56, 60, and 62 μ g/L. Using the average SGR C from other studies with Lemna (0.27/d, range 0.21- 0.38/d), the EC 50 for frond count would correspond to an EC 25 for SGR. Using the average steepness for SGR vs. concentration from other stud ies with Lemna (1.0 for frond count increase, 1.4 for weight increase), the SGR EC 50 would then be 

### 105 μ g/L based on frond count and 

### 95 μ g/L based on weight.

(10) University of Mississippi 1991

The authors evaluated growth of Lemna gibba (14 d), and Elodea canadensis (10 d) at multiple atrazine concentrations. These assays entailed met hodological and performance problems that precluded their use, especially for determining SGR -based ECs. Chlorophyll measurements were erratic in addition to being not accepted in the pr otocol used here. For Lemna , both frond counts and weights were measured, but frond counts indicat ed poor control growth (an SGR of 0.1/d, compared to 0.2-0.4/d in other studies), no initial weights were given, and final weights had poor precision. For Elodea , final dry weights did show a substantial effect o f atrazine, but initial67

weights were not given, so that growth could not be assessed either as a rate or an absolute amount. For both species, atrazine effects were ev ident at 100 μ g/L, but the next lower and higher concentration was 10-fold different (10 and 1000 μ g/L) , precluding any good characterization of dose-response.

(11) Forney and Davis 1981; Davis 1980, Forney 1980

The authors evaluated growth of Elodea canadensis

, Myriophyllum spicatum

, Potamogeton perfoliatus , and Vallisneria americana in exposures of 3-9 weeks to multiple atrazine concentrations. Depending on the experime nt and test species, light varied from 3 to 170 μ E/m 2 /s (14/10 h photoperiod) and temperature was 20-30 C. Unfortunately, most of the evaluations were of shoot length increase, which as discussed above is a questionable surrogate for growth. In three instances, useful information regarding the SGR EC 50 could be obtained: For Potamogeton , in one experiment, dry weight was measured in add ition to shoot length. However, the nature of the weight measurements was unclear (gross weight vs. growth, how much of plant included) and the authors noted that food reserves in the tuber used to sprout Potamogeton would partially mask herbicide effects, so that th ese weight measurements would overestimate ECs. This experiment also showed atra zine-dependent mortality at concentrations of 32 μ g/L and above. The following table shows the avera ge dry weight of plants (at death or end of test for survivors), the percent survival, a nd the product of dry weight and survival as an estimate of live biomass at the end of the study. For issues regarding weight effects already noted, this product might still underestimate bioma ss production, but was considered adequately informative of atrazine effects on the SGR of a pop ulation of this plant. A regression analysis was thus conducted on this product and used for the compilation.

Nominal
Atrazine Conc ( μ g/L) % of Control

Dry Weight % Survival % of Control
Biomass

0 100 100 100

10 86 100 86

32 86 73 63

100 74 62 46
320 55 0 0

EC 50 ( μ g/L) 63 Steepness 0.69

For Vallisneria , leaf length was measured and was used as a surrog ate for growth because it would be less susceptible than shoot length to elon gation with little or no weight increase. Even with this acceptance, most data could not be used b ecause the authors noted that effects of atrazine were not evident early in the experiments, likely due to food reserves in the tubers, and that some experiments had light intensities high en ough to inhibit leaf growth in favor of tuber and lateral shoot development. Thus, analysis here was restricted to latter part of one test which the authors reported as being most informative abou t atrazine effects. The following table provides the percentage increase in leaf length dur ing the last week of this experiment, which should be approximately proportional to the SGR. I n another experiment with insufficient data68

for analysis here, there was information on the rat io of plant weight to leaf length as a function of atrazine, which did indicate some thinning of the l eaves due to atrazine. The following table includes those ratios, which provided a basis for e stimating weight based on leaf length (only three measured values – so interpolated value used for 32 μ g/L and possible extrapolated values for 1000 μ g/L). This resulted in a decrease in the SGR EC50 of about 28%.

Nominal
Atrazine Conc ( μ g/L) % Increase in

Leaf Length
in Week 6 Dry Weight/

Leaf Length (fraction of control) Estimated
% Increase in Weight
0 14.3 1.00 14.3
32 9.8 0.97 9.5
100 10.2 0.94 9.6
320 5.9 0.82 4.8

1000 3.6 0.7-0.8 2.5-2.9

EC 50 ( μ g/L) 195 140-141 Steepness 0.36 0.39-0.41

For Elodea , in one experiment dry weight increase was measure d. The following table provides these data. Because initial and final dry weights weren’t provided, SGRs cannot be calculated, but the slow growth rates of these plan ts should make the net increase proportional to SGR. Because of the widely space concentrations, t he estimated parameters are uncertain, but clearly indicate the SGR EC50 to be less than 100 μ g/L.

Nominal
Atrazine Conc ( μ g/L) Average Increase

in Plant Dry Wt. (mg)
0 37

10 28

100 17

1000 11

EC 50 ( μ g/L) 65 Steepness 0.28

(10) Hinman 1989

The author tested the effects of atrazine on both r oot and shoot growth of Hydrilla verticillata in both water and sediment exposures (14 d). Concentr ations were nominal, light was 40-50 μ E/m 2 /s, and temperature was 25 C. Both shoot and root growth was monitored by increase in length. Increases in shoot length are subject to q uestions about elongation without increasing weight, but this is not true for root growth, which should still be an indicator of atrazine effects on primary production. The following table compare s the data on root and shoot growth for the water-based exposures. Shoot lengths do indicate a higher threshold for effects, but then a steeper decline, with the EC50 being about 80% high er than for root length.69 Nominal
Atrazine Conc ( μ g/L) Shoot Length

Increase (% of Control) Root Length

Increase (% of Control)
0 100 100
16 97 98

80 127 71
160 83 25
800 5 25

1600 5 8

EC 50 ( μ g/L) 222 118 Steepness 2.26 0.670APPENDIX B.EXPERIMENTAL ECOSYSTEM DATA725Table B1. Summary of experimental ecosystem studies used in development of PATILOC. ID# identifies treatment and crossreferences exposure time-series provided in Table B2. Effect is binary (yes/no) regarding whether substantial impact on plantcommunity occurred.ID #Duration(d)Initial Conc.(μg/L Atrazine)SignificantEffect?Reference1 365 500 Y Carney 1983; Kettle et al. 1987; deNoyelles et al. 1989; deNoyelles et al. 19942 365 20 YCarney 1983; Kettle et al. 1987; deNoyelles et al.1989; deNoyelles et al. 1994,deNoyelles & Kettle 1980, Dewey 19863 63 500 Y deNoyelles et al. 1982; deNoyelles et al. 19894 365 100 Y deNoyelles et al. 1989 Carney 19835 340 200 Y deNoyelles et al. 1989 Carney 19837 56 80 Y Hamilton et al. 19878 56 140 Y Hamilton et al. 19879 96 100 Y Hamilton et al. 198810 96 100 Y Herman et al. 1986; Hamilton et al. 198913 53 430 Y Stay et al. 198514 53 820 Y Stay et al. 198515 53 3980 Y Stay et al. 198517 7 100 Y Brockway et al. 198418 12 500 Y Brockway et al. 198419 12 5000 Y Brockway et al. 198422 15 15 Y Detenback et al. 199623 43 25 Y Detenback et al. 199624 32 50 Y Detenback et al. 199625 17 79 Y Detenback et al. 199626 14 100 Y Hamala and Kollig 198527 30 1000 Y Johnson 198628 21 10 Y Kosinski 1984; Kosinski and Merkle 198429 21 1000 Y Kosinski 1984; Kosinski and Merkle 198430 21 10000 Y Kosinski 1984; Kosinski and Merkle 198431 12 24 Y Krieger et al. 198832 12 134 Y Krieger et al. 198833 7 10000 Y Moorhead and Kosinski 198673Table B1 (continued).ID # Duration(d)Initial Conc.(μg/L Atrazine)SignificantEffect?Reference34 21 337 Y Pratt et al. 198835 42 204 Y Stay et al. 198936 42 500 Y Stay et al. 198937 42 1000 Y Stay et al. 198938 42 5000 Y Stay et al. 198939 55 50 Y Brockway et al. 198440 15 100 Y Brockway et al. 198441 360 100 Y deNoyelles et al. 198942 360 200 Y deNoyelles et al. 198944 21 100 Y Kosinski 1984; Kosinski and Merkle 198445 7 100 Y Moorhead and Kosinski 198646 7 1000 Y Moorhead and Kosinski 198647 53 53 Y Stay et al. 198548 53 84 Y Stay et al. 198549 53 170 Y Stay et al. 198550 42 100 Y Stay et al. 198951 12 50 Y Brockway et al. 198452 63 20 Y deNoyelles et al. 1982; deNoyelles et al. 198953 30 10 N Johnson 198654 30 100 N Johnson 198658 18 1 Y Lampert et al 198958b 42 0.1 Y Lampert et al 198959 21 32 Y Pratt et al. 198860 21 110 Y Pratt et al. 198861 42 20 N Stay et al. 198962 35 5 N van den Brink et al. 199563 7 0.5 N Brockway et al. 198464 7 5 N Brockway et al. 198465 29 0.5 N Brockway et al. 198466 70 5 N Brockway et al. 198474Table B1 (continued).ID # Duration(d)Initial Conc.(μg/L Atrazine)SignificantEffect?Reference67 14 5 N Gruessner and Watzin 199668 20 1 N Gustavson and Wängberg 199569 20 20 N Gustavson and Wängberg 199570 20 10 N Gustavson and Wängberg 199571 28 2 N Jurgensen and Hoagland 199072 28 30 N Jurgensen and Hoagland 199073 28 100 N Jurgensen and Hoagland 199075 30 25 N Lynch et al. 198576 21 3.2 N Pratt et al. 198877 21 10 N Pratt et al. 198878 30 25 Y Rohr and Crumrine, 200579 28 117 Y Rohr et al., 200880 36 6.4 N Relyea, 200981 173 84 Y Knauert et al., 2008; Knauert et al., 200982 23 10 YBerard et al. 1999a, Berard et al. 1999b, Berard and Benninghoff 2001, Sequinet al. 2001b, Leboulanger et al. 200183 40 30 N Seguin et al. 2001a84 40 2 N Seguin et al. 2001a85 40 30 Y Seguin et al. 2001b86 40 2 Y Seguin et al. 2001b87 25 30 Y Seguin et al. 200288 7 148 Y Downing et al. 200489 7 24.3 Y Downing et al. 200490 25 207 N Boone and James (2003)95 51 20 N Diana et al. (2000)96 51 196 Y Diana et al. (2000)97 51 2036 Y Diana et al. (2000)98 42 25 N McGregor et al. (2008)99 42 50 N McGregor et al. (2008)100 42 100 Y McGregor et al. (2008)101 42 250 Y McGregor et al. (2008)75Table B2. Atrazine exposure time-series for experimental ecosystem treatments, with ID# as specifiedin Table B1.ID #1 ID#2 ID#3 ID#4 ID#5 ID#7 ID#8 ID#9Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)0 500 0 20.0 0 500 0 100 0 200 1 80 1 140 1 10010 525 10 16.0 2 490 10 90 20 190 3 79 56 110 5 11720 490 20 16.0 25 465 20 85 40 120 5 78 14 10840 350 40 16.0 30 453 40 90 60 160 7 78 20 10770 490 70 15.0 55 390 70 80 70 140 9 77 24 87100 400 100 12.0 63 360 100 75 80 150 11 76 34 105130 400 130 14.0 130 70 105 120 13 76 37 142180 375 180 15.0 180 70 130 120 15 75 42 148285 250 285 7.0 285 35 160 110 17 75 54 132330 200 330 5.0 330 30 190 140 19 74 68 115365 160 365 4.0 365 25 220 120 21 73 96 53250 100 23 73290 90 25 72340 50 27 7129 7131 7033 7035 6937 6939 6841 6743 6745 6647 6649 6551 6553 6455 6476Table B2, Page 2.ID #10 ID#13 ID#14 ID#15 ID#17 ID#18 ID#19 ID#22Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)1 100 0 430 0 820 0 3980 0 100 0 500 0 5000 1 15.05 117 21 264 21 505 21 1890 1 100 1 498 1 4979 2 13.614 108 46 223 46 443 46 1390 2 99 2 496 2 4958 3 12.920 107 53 198 53 417 53 1540 3 99 3 494 3 4937 4 12.324 87 4 98 4 492 4 4917 5 11.734 105 5 98 5 490 5 4896 6 11.137 142 6 98 6 488 6 4876 7 10.642 148 7 97 7 486 7 4855 8 10.154 132 8 484 8 4835 9 9.668 115 9 481 9 4815 10 9.196 53 10 479 10 4794 11 8.711 477 11 4774 12 8.312 475 12 4754 13 7.914 7.515 7.17710Table B2, Page 3.ID #23 ID#24 ID#25 ID#26 ID#27 ID#28 ID#29 ID#30Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)1 25.1 1 50 1 79 0 100 0 1000 1 10.0 1 1000 1 100003 21.6 3 43 2 72 14 100 2 992 21 10.0 2 648 2 64845 19.6 5 39 3 68 4 983 3 522 3 52217 17.7 7 35 4 65 6 975 4 420 4 42059 16.1 9 32 5 62 8 967 5 339 5 338611 14.6 11 29 6 59 10 959 6 273 6 272613 13.2 13 26 7 56 12 951 7 220 7 219515 11.9 15 24 8 53 14 943 8 177 8 176817 10.8 17 21 9 51 16 935 9 142 9 142419 9.8 19 19 10 48 18 927 10 115 10 114621 8.9 21 18 11 46 20 919 11 92 11 92323 8.0 23 16 12 44 22 912 12 74 12 74325 7.3 25 14 13 42 24 904 13 60 13 59927 6.6 27 13 14 40 26 897 14 48 14 48229 6.0 29 12 15 38 28 889 15 39 15 38831 5.4 31 11 16 36 30 882 16 31 16 31333 4.9 17 34 17 25 17 25235 4.4 18 20 18 20337 4.0 19 16 19 16339 3.6 20 13 20 13141 3.3 21 11 21 10643 3.078Table B2, Page 4.ID #31 ID#32 ID#33 ID#34 ID#35 ID#36 ID#37 ID#38Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)0 24.0 0 134 0 10000 0 337 1 204 1 492 1 961 1 492912 24.0 12 134 1 9958 21 337 3 199 3 474 3 931 3 48062 9916 5 196 5 463 5 918 5 47583 9875 7 193 7 452 7 907 7 47104 9833 9 190 9 441 9 895 9 46625 9792 11 187 11 430 11 883 11 46156 9751 13 184 13 420 13 872 13 45697 9710 15 181 15 410 15 860 15 452317 178 17 400 17 849 17 447719 175 19 390 19 838 19 443221 172 21 381 21 827 21 438823 169 23 372 23 816 23 434425 167 25 363 25 806 25 430027 164 27 354 27 795 27 425729 161 29 346 29 785 29 421431 159 31 337 31 775 31 417133 156 33 329 33 765 33 412935 154 35 321 35 755 35 408837 151 37 314 37 745 37 404739 149 39 306 39 735 39 400641 146 41 299 41 726 41 396679Table B2, Page 5.ID #39 ID#40 ID#41 ID#42 ID#44 ID#45 ID#46 ID#47Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)0 50 0 100 0 100 0 200 1 100 1 100 1 1000 0 5255 50 15 100 180 70 180 140 2 65 2 99 2 992 21 48360 25 360 50 3 52 3 99 3 988 46 414 42 4 98 4 983 53 345 34 5 98 5 9796 27 6 98 6 9757 22 7 97 7 9718 189 1410 1211 912 713 614 515 416 317 318 219 220 180Table B2, Page 6.ID #48 ID#49 ID#50 ID#51 ID#52 ID#53 ID#54 ID#58Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)0 84 0 169 1 100 0 50 1 20.0 0 10.0 0 100 0 1.021 63 21 114 3 97 1 50 2 19.5 2 9.9 2 99 1 1.046 60 46 95 5 96 2 50 25 18.0 4 9.8 4 98 2 1.053 51 53 98 7 94 3 49 30 17.0 6 9.8 6 98 3 1.09 92 4 49 55 15.0 8 9.7 8 97 4 1.011 91 5 49 63 14.5 10 9.6 10 96 5 1.013 89 6 49 12 9.5 12 95 6 1.015 88 7 49 14 9.4 14 94 7 1.017 86 8 48 16 9.3 16 94 8 1.019 85 9 48 18 9.3 18 93 9 1.021 83 10 48 20 9.2 20 92 10 1.023 82 11 48 22 9.1 22 91 11 1.025 80 12 48 24 9.0 24 90 12 1.027 79 26 9.0 26 90 13 0.929 78 28 8.9 28 89 14 0.931 76 30 8.8 30 88 15 0.933 75 16 0.935 74 17 0.937 72 18 0.939 7141 7081Table B2, Page 7.ID #58b ID#59 ID#60 ID#61 ID#62 ID#63 ID#64 ID#65Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)0 0.1 0 32 0 110 1 17.7 0 5.0 1 0.5 1 5.0 0 0.542 0.1 10 32 10 110 3 17.4 35 5.0 2 0.5 2 5.0 29 0.521 32 21 110 5 17.1 3 0.5 3 4.97 16.9 4 0.5 4 4.99 16.7 5 0.5 5 4.911 16.5 6 0.5 6 4.913 16.3 7 0.5 7 4.915 16.117 15.919 15.721 15.523 15.325 15.127 14.929 14.731 14.533 14.335 14.137 14.039 13.841 13.68215Table B2, Page 8.ID #66 ID#67 ID#68 ID#69 ID#70 ID#71 ID#72 ID#73Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)0 5.0 1 4.7 1 1.0 1 20.0 1 10.0 1 2.0 1 30 1 10070 5.0 5 3.6 2 1.0 2 19.8 2 9.9 2 1.6 2 23 2 7810 1.2 3 1.0 3 19.7 3 9.9 3 0.0 3 0 3 014 1.2 4 1.0 4 19.7 4 9.8 4 0.0 4 0 4 05 1.0 5 19.6 5 9.8 5 0.0 5 0 5 06 1.0 6 19.5 6 9.8 6 0.0 6 0 6 07 1.0 7 19.4 7 9.7 7 0.0 7 0 7 08 1.0 8 19.3 8 9.7 8 0.0 8 0 8 09 1.0 9 19.3 9 9.6 9 0.0 9 0 9 010 1.0 10 19.2 10 9.6 10 0.0 10 0 10 011 0.9 11 19.1 11 9.5 11 0.0 11 0 11 012 0.9 12 19.0 12 9.5 12 0.0 12 0 12 013 0.9 13 18.9 13 9.5 13 0.0 13 0 13 014 0.9 14 18.9 14 9.4 14 2.0 14 30 14 10015 0.9 15 18.8 15 9.4 15 1.6 15 23 15 7816 0.9 16 18.7 16 9.3 16 0.0 16 0 16 017 0.9 17 18.6 17 9.3 17 0.0 17 0 17 018 0.9 18 18.5 18 9.3 18 0.0 18 0 18 019 0.9 19 18.5 19 9.2 19 0.0 19 0 19 020 0.9 20 18.4 20 9.2 20 0.0 20 0 20 021 0.0 21 0 21 022 0.0 22 0 22 023 0.0 23 0 23 024 0.0 24 0 24 025 0.0 25 0 25 026 0.0 26 0 26 027 0.0 27 0 27 028 0.0 28 0 28 083Table B2, Page 9.ID #75 ID#76 ID#77 ID#78 ID#79 ID#80 ID#81 ID#82Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)0 25.0 0 3.2 0 10.0 1 25.0 1 117 1 6.4 1 84 1 10.030 25.0 21 3.2 21 10.0 2 24.8 2 116 3 6.3 7 80 2 9.93 24.7 3 116 5 6.3 13 77 3 9.94 24.6 4 115 7 6.2 19 74 4 9.85 24.5 5 115 9 6.2 25 78 5 9.86 24.4 6 114 11 6.1 31 75 6 9.87 24.3 7 114 13 6.1 37 72 7 9.78 24.2 8 113 15 6.0 43 69 8 9.79 24.1 9 113 17 6.0 49 66 9 9.610 24.0 10 112 19 5.9 55 64 10 9.611 23.9 11 112 21 5.9 61 61 11 9.512 23.8 12 111 23 5.8 67 59 12 9.513 23.7 13 111 25 5.8 73 57 13 9.514 23.6 14 110 27 5.7 79 55 14 9.415 48.5 15 110 29 5.7 85 53 15 9.416 48.3 16 109 31 5.6 91 51 16 9.317 48.1 17 109 33 5.6 97 49 17 9.318 47.9 18 109 35 5.5 103 47 18 9.319 47.7 19 108 109 45 19 9.220 47.5 20 108 115 43 20 9.221 47.3 21 107 121 42 21 9.222 47.1 22 107 127 40 22 9.223 46.9 23 106 133 39 23 9.224 46.7 24 106 139 3725 46.5 25 105 145 3626 46.3 26 105 151 3427 46.1 27 105 157 3328 45.9 28 104 163 3229 45.7 169 3130 45.584Table B2, Page 10.ID #83 ID#84 ID#85 ID#86 ID#87 ID#87 ID#89 ID#90Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)1 30 1 2.0 1 30 1 2.0 1 30 1 148 1 24.3 1 2073 30 3 2.0 3 30 3 2.0 2 30 2 127 2 18.3 3 1705 29 5 2.0 5 29 5 2.0 3 30 3 120 3 20.7 5 1487 29 7 1.9 7 29 7 1.9 4 30 4 112 4 19.6 7 1309 29 9 1.9 9 29 9 1.9 5 29 5 105 5 18.6 9 11411 29 11 1.9 11 29 11 1.9 6 29 6 98 6 17.6 11 9913 28 13 1.9 13 28 13 1.9 7 29 7 88 7 15.4 13 8715 28 15 1.9 15 28 15 1.9 8 29 15 7617 28 17 1.9 17 28 17 1.9 9 29 17 6719 28 19 1.8 19 28 19 1.8 10 29 19 5821 28 21 1.8 21 28 21 1.8 11 29 21 5123 27 23 1.8 23 27 23 1.8 12 29 23 4525 27 25 1.8 25 27 25 1.8 13 28 25 3927 27 27 1.8 27 27 27 1.8 14 28 27 3429 27 29 1.8 29 27 29 1.8 15 28 29 3031 26 31 1.8 31 26 31 1.8 16 28 31 2633 26 33 1.7 33 26 33 1.7 17 28 33 2335 26 35 1.7 35 26 35 1.7 18 28 35 2037 26 37 1.7 37 26 37 1.7 19 28 37 1839 26 39 1.7 39 26 39 1.7 20 28 39 1521 28 41 1422 27 43 1223 27 45 1024 27 47 925 27 49 851 753 655 585Table B2, Page 11.ID #95 ID#96 ID#97 ID#98 ID#99 ID#100 ID#101Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)1 20.1 1 196 1 2036 1 24.5 1 50 1 104 1 2483 19.5 3 193 3 1986 42 24.5 42 50 42 104 42 2485 19.0 5 191 5 19547 18.6 7 189 7 19229 18.2 9 188 9 189011 17.8 11 186 11 185913 17.4 13 184 13 182915 17.0 15 183 15 179917 16.6 17 181 17 176919 16.3 19 180 19 174021 15.9 21 178 21 171223 15.6 23 176 23 168425 15.2 25 175 25 165627 14.9 27 173 27 162929 14.6 29 172 29 160331 14.2 31 170 31 157633 13.9 33 169 33 155135 13.6 35 167 35 152537 13.3 37 166 37 150039 13.0 39 164 39 147641 12.7 41 163 41 145243 12.4 43 161 43 142845 12.2 45 160 45 140447 11.9 47 158 47 138149 11.6 49 157 49 135951 11.4 51 155 51 13378620