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, MN 
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1. INTRODUCTION 

 This document describes proposed methodology for setting levels of concern (LOCs) for 

atrazine in natural freshwater systems to prevent unacceptably adverse effects on the aquatic 

plant communities in those systems. Effects on humans and possible effects on amphibian 

development are the subjects of separate efforts, and additional components of aquatic plant 

community atrazine risk assessment are described elsewhere. This first section defines the 

problem being addressed and describes a general strategy for setting the LOC. 

1.1 Requirements for the LOC Methodology 

 This methodology must be germane to the effects of concern and the exposures expected 

within systems to which the LOC is applied. Regarding effects, the concern is the productivity 

and composition of aquatic plant communities. Fortunately, atrazine has been the subject of 

many laboratory toxicity tests on individual aquatic 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 limitations is presented in Section 1.3. More 

details on these data and how they can be applied to LOCs are presented in Sections 2 and 3. 

 Regarding exposures, atrazine enters natural freshwater systems primarily in rainfalldriven runoff, resulting in highly variable and episodic 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 methodology is not intended to address 

site-specific factors, such as physicochemical conditions and the nature of the biological 

community. Addressing such conditions is not feasible 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-series will be assessed as having the same risk.

Figure 1. Examples of atrazine exposure time-series 

for natural freshwater systems. 

Julian Day

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1.2 Shortcomings of Existing Assessment Approaches 

 For toxicity tests on individual plant species, past assessments of the risk of atrazine to 

aquatic plant communities (e.g., Solomon et al. 1996; Giddings et al. 2000) have generally 

summarized the results of a toxicity test as a 

median effect concentration (EC50), the 

concentration causing a 50% decrease in 

some measure of growth over the duration of 

the test. Average EC50s for each species are 

then used to describe a species sensitivity 

distribution (SSD), the cumulative 

percentage of species with EC50s 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 μg/L 

would be below the EC50s of 95% of the 

species and an LOC of 45 μg/L would be 

below the EC50s of 80% of the species. 

 However, such LOCs have major shortcomings, especially for addressing the types of 

exposures in Figure 1: 

(1) SSDs based just on EC50s provide limited information on the overall toxic impact to the 

assemblage of species used for the SSD. For example, if an LOC is set to the 5th percentile in 

this distribution, it only describes the concentration 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 are at lower concentrations. For other species, 

no information is given other than that their EC50s are less than or greater than the LOC. Much 

more information regarding effects is contained within the data, but how should it be used? 

(2) SSDs also do not address the issue of time. How should effects be described for longer or 

shorter exposures and, especially, exposure concentrations that fluctuate? If the LOC is simply 

applied to the peak exposure, the exposure time-series 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 over a specified period also entails various 

problems and uncertainties. How should total impact be assessed over an entire time-series? 

(3) Although the EC50s in Figure 2 all describe plant growth in some fashion, growth is measured 

in a variety of ways (final plant biomass, net change in biomass, growth rate, oxygen evolution, 

carbon fixation, plant length, cell numbers, changes in chlorophyll) and over a wide range of 

exposure durations and conditions, such that these EC50s 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 data 

from Giddings et al. (2000). 

EC50 (μg/L Atrazine)

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differences among test methods as well as differences among species. Such inconsistency in the 

meaning of EC50s will cause any LOC from the SSD to have uncertain meaning. 

(4) Selecting the percentile in an SSD that constitutes an LOC is very problematic with regard to 

the level of protection afforded to a plant community as a whole. Does the 5th percentile in the 

SSD represent enough effect to be considered unacceptable, especially for transient exposures 

such as in Figure 1, from which plant growth might quickly recover? Is the 20th 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 experimental ecosystem studies provides an 

alternative to single-species toxicity tests that partially 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 μg/L to be of concern based on experimental ecosystem data. However, 

applying these data also entails difficulties. Treatments are generally too few and measured 

endpoints too diverse to establish quantitative measures of effect that are consistent across 

studies; rather, results are typically classified as 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 proposes a simple strategy for using data 

from both single-species toxicity tests and experimental ecosystems. This strategy consists of 

two basic steps: 

(1) More fully describe single-species plant toxicity 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 Toxicity 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 how high PATI must be to expect 

unacceptable effects on the productivity and composition of aquatic plant communities. This 

combines the greater ecological relevance of the experimental ecosystems with an index that can 

relate the severity of toxicity for the exposure time-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 PATI (=LOCPATI). 

 Implementing this methodology is also conceptually simple. PATI is calculated for an 

exposure of interest in a natural system, and compared to LOCPATI. Implementation examples 

and uncertainty issues are the subject of Section 4. 
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 An important point to emphasize is that this LOC is based on a measure of toxic effect 

(PATI) that is estimated from reported concentrations, in contrast to typical LOCs which are 

based on the concentrations themselves. For this document, only this effects-based LOCPATI will 

be addressed. However, it is also possible to use the effects-based LOCPATI 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 ongoing separate effort by EFED. 
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2. PLANT ASSEMBLAGE TOXICITY INDEX 

2.1 Definition of PATI 

 In order to quantify the overall effect of atrazine 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”) for 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 EC50s for each 

species are used to create a traditional SSD, 

which just describes the cumulative 

percentage of the EC50s. 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 μg/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 μg/L in the bottom panel (arrow). 

 Thus, rather than just providing the 

percentage of species that have an EC50 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 illustrated 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 each day to generate a time-series of daily PATI 

values. Because of the rapid recovery of growth rates after atrazine exposures (e.g. Abou-Waly 

et al. 1991, Desjardin et al. 2003), such daily PATI values need not consider residual toxicity 

from exposures on previous days, but rather only the toxicity for the current day’s exposure. 

These daily values would then be averaged over a period 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 averaging period (e.g., PATI30d 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 averaging period is addressed in Section 4. 

Figure 3. Hypothetical comparison of toxicity 

relationships for several species (middle panel), the 

SSD of EC50s 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 (μg/L)

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 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 of that reduction persisting for 4 d, etc. PATI 

assumes that such a simple summation/averaging of effects 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 processes 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 dynamics 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 any 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 compendia of the effects of atrazine on 

aquatic plants or statistical distributions describing 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 warranted reanalysis of existing single-species 

toxicity tests. This section describes the shortcomings of concern, a new review and analysis of 

toxicity data, and a new compendium of plant toxicity information more suitable for calculating 

PATI and for conducting atrazine risk assessments. 

2.2.1. Issues in Interpreting and Applying Plant Toxicity Test Results 

 ECs from plant toxicity tests can vary widely in both value and meaning depending on 

how tests are conducted and analyzed. For microalgae, 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 endpoints, including (a) actual biomass; (b) 

surrogates for biomass such as cell counts, cell volume, optical density, or chlorophyll content; 

and (c) indicators of growth such as oxygen evolution or radioactive carbon fixation. The period 

over which measurements are made can vary from several 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) biomass 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 test 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 (SGRC) is 1/d and when a chemical 

exposure reduces the SGR to half of this value. The top panel shows the actual biomass vs. time 

 

1

 The specific growth rate (SGR) =dB(t)/dt/B(t), where B is biomass and t is time. SGR is thus the fractional rate of 

change of biomass with time and has units of inverse time. If SGR is constant, the growth rate is exponential and 

B(t)=B(0)e

SGRt

. 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 mean that biomass actually increases to 2.7 times the initial 

value – only over short periods will fraction growth closely adhere to SGR (e.g., 1% growth over 0.01 d). 
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in the control compared to the chemical exposure, while the bottom panel shows the percent 

reduction in the chemical exposure relative to control for the SGR (constant at 50%), absolute 

biomass, and growth. 

For growth, the treatment that is an 

EC50 for SGR will be an EC62 at 1 d, an EC73

at 2 d, and an EC88 at 4 d if the SGRC is 1/d. 

For absolute biomass, this concentration 

would be an EC39, EC63, EC86, respectively, at 

these times. For other values of SGRC, 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 EC50 for absolute biomass 

actually represents no growth. 

 Therefore, EC50s reported for absolute 

biomass, growth, and SGR will differ from 

each other, and these differences will vary 

with exposure duration and the SGRC. 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 EC50s 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 because 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 toxicant concentrations with little or no effect), so 

that the treatments with greater toxic effects might "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 toxicant, and these other stressors can confound the 

effects of the toxicant. In fact, some standard plant 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 difficult 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 between different photoperiods). 

Figure 4. Variation of plant growth effects with time 

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 (dashed 

line), and on specific growth rate (dotted line). 

Time (d)

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 Schafer et al. (1994) provide a noteworthy example of some of these problems. In a 10-d 

test in a flow-through system in which a constant control growth rate was maintained by 

replenishing the nutrient solution and periodically cropping biomass, they reported growth-based 

EC50s to drop from 50 μg/L at 4 d to 20 μg/L at 7 d to 10 μg/L at 10 d. This is plausibly 

attributable to a constant relationship of SGR to concentration during these 10 d, so that a 

constant EC for growth rate translates into widely variant ECs for growth. These authors also 

reported an EC50 of 350 μg/L for a static, 3-d flask desk, indicating much less sensitivity 

compared both to the flow-through systems and to photosynthesis measurements made in the 

first day of these static tests. This apparent lower sensitivity is likely at least partly due to a high 

initial cell density (2⋅105 cells/ml), which would have resulted at 3 d in a cell density of 3⋅108

cell/ml if a SGRC similar to that in the flow-through system had been 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 reduced sensitivity. Increases with time for growthbased ECs are evident in other studies in the review presented later, although the opposite can 

also be true, indicating additional complexities. 

 Changes in cell condition other than light and nutrient 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 10-fold in response to exposure to 200 μg/L 

atrazine. Such changes in the chlorophyll content per cell make the use of chlorophyll as a 

surrogate for plant biomass inadvisable, likely misrepresenting toxic effects on biomass. For 

example, van der Heever and Grobbelaar (1996) reported 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 alter cell volume and mass (e.g., van der Heever 

and Grobbelaar 1996), creating differences among ECs 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 fixation 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 that apparent effects might be reduced because 

of the time it takes to fully induce the effects of a toxicant, unless there is sufficient pre-exposure 

to the toxicant before the measurements are made. Fortunately, for atrazine, effects do appear to 

be induced quickly, such that EC50s based on oxygen measurements with just several minutes 

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 conceivably 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 radiocarbon fixation over short periods 

approximates net photosynthesis for good growing conditions (which would be expected in 

toxicity tests); therefore, radiocarbon fixation will be assumed in this review to represent net 

photosynthesis. 
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(c) Although oxygen production should parallel net photosynthesis, test methods using oxygen 

evolution measurements can involve extremes of oxygen concentrations that might affect 

photosynthesis and/or respiration – either high, supersaturated levels as oxygen increases from 

initial levels, or low concentrations due to the methodology involving an initial purging of 

oxygen. Studies with such extremes will not be used in this review because of uncertainty about 

their impacts. 

(d) Even when the test is such that oxygen production or radiocarbon fixation are arguably good 

surrogates for biomass production, the time-scale of the measurements can affect their 

interpretation. Short-term values for oxygen production 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 analogous to net cumulative growth, creating 

differences in the meaning of ECs similar to that for growth vs. SGR. In one study (Larsen et al. 

1986), the situation was especially complicated because 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 fixation per cell and the cumulative 

differences in cell density due to the preceding exposure. 

 Macrophyte tests can be less susceptible to the issues of exponential growth and limiting 

conditions discussed above. Duckweed tests often show 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 linear growth during the tests. However, 

the general issues raised above for microalgae should 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 biomass 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 questionable measure and even making weight 

problematic if only shoot biomass is measured. Furthermore, 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 uncertain relevance to the field. 

2.2.2. Review of Single-Species Plant Toxicity Tests 

 The inconsistency issues among single-species toxicity test ECs discussed above have not 

been adequately addressed in past reviews of atrazine toxicity (e.g., Solomon et al. 1996; 

Giddings et al. 2000) and might distort atrazine risk assessments. There was thus a need for 

better analyses of single-species plant toxicity tests with atrazine to produce EC compendia 

which are more consistent, involving a “common currency” that can be more legitimately 

compared among tests and applied meaningfully to risk assessments. The SGR was selected as 

this “common currency” because it reduces the dependence of ECs on test duration and is 

directly applicable to addressing effects of time variable exposure. In addition, rather than 

simply compiling information on EC50s, there was also a need for information on other effect 

levels, which is also inadequately addressed in previous compendia. 

 To this end, available single-species toxicity tests with atrazine were reviewed for 

information regarding exposure conditions and effects by the Great Lakes Environmental Center 
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(Traverse City, MI) under support from the Office of Science and Technology of U.S.EPA’s 

Office of Water (EPA Contract 68-C-04-006, Work Assignment 4-34, Subtask 1-16). Journal 

articles and reports identified by this review as containing potentially useful information were 

further analyzed by U.S.EPA to compile desired information, which consisted of SGR-based 

EC50s, the steepness of the relationship of SGR vs. atrazine concentration, and the control SGR 

(SGRC). The steepness is described here by the parameter “Steep,” which is defined in 

Appendix A as part of the general mathematical formulation 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 the compilation of SGR EC50, Steep, and 

SGRC. Table 1 provides the final compilation. 

 Although not included in the compilation because they were conducted in estuarine water 

near 10 ppt salinity, studies on Myriophyllum spicatum and Potamogeton perfoliatus by Kemp et 

al. (1985) and Jones et al. (1986) are consistent with the vascular plant results in Table 1. For 

both these species, oxygen production-based reductions in photosynthesis (Kemp et al. 1985) 

indicated EC50s to be near or below 50 μg/L in the first two weeks of exposure (although some 

lessening of these effects was apparent in the ensuing two weeks). For Potamogeton perfoliatus, 

radiocarbon fixation-based reductions in photosynthesis (Jones et al. 1986) indicated the EC50 to 

be between 50 μg/L and 100 μg/L. 

2.3 Statistical Analysis of Toxicity Data 

 The SGR EC50 data in Table 1 were log10 transformed and subject to an analysis of 

variance using the general linear model (GLM) procedure of Statistica (Version 8.0, StatSoft, 

Tulsa, OK, USA). A nested ANOVA showed no significant 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 result being treated equally regardless of the 

number of tests within a genus. This analysis showed significant differences among the 

taxonomicgroups, with the mean log10(EC50) being 2.09 for green algae, 2.35 for 

diatoms/cryptomonads, 2.42 for blue-green algae, and 1.93 for vascular plants (Table 2). These 

log values correspond, respectively, to median EC50s of 123, 224, 263, and 85 μg/L. Standard 

errors on these mean log10(EC50)s varied from 0.07 to 0.12 (Table 2), depending on the number 

of observations on each group. It should be further noted that the values for Chromalveolata and 

Cyanobacteria were based on only a few studies, underscoring 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 from 0.29 to 0.35 and the pooled value being 

0.33 (Table 2). The overall, unweighted mean and standard deviation of all log10(EC50)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 individual observations produced similar values 

for the overall mean and standard deviation – 2.07 and 0.35. 

 The steepness parameter (Steep) data in Table 1 were also log10 transformed and subject 

to ANOVA. The ANOVAs showed no significance differences either between genera or the 

broader taxonomic groups. The within-group means ranged 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 log10(Steep) 
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Table 1. Compiled data regarding atrazine toxicity to aquatic plants. All data pertain to the specific growth rate 

(SGR) of the plant. Compilation includes the EC50 for the SGR, a steepness measure for a fitted logistic 

relationship of SGR to atrazine concentration (Steep=-d(SGR/SGRC)/d(log10(CATZ)) at the EC50), and the control 

SGR (SGRC) under the test conditions. Italicized EC50s denote values whose estimation required information 

on SGRC and/or steepness from other studies. 

Genus SGR EC50 (μg/L) Steep SGRC (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, cryptomonads) 

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 1981 
14

(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 individual observations resulted in a very 

similar log mean (-0.08) and standard deviation (0.16). A correlation analysis also showed no 

significant correlation between log10(EC50) and log10(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 log10(EC50) 

and log10(Steep) summarized in Table 2, which 

was estimated by generating 1000 randomly 

selected pairs of log10(EC50) and log10(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 relationships because of the finite amount 

of data. This uncertainty is reflected in the standard errors for the distribution means reported in 

Table 2, as well as uncertainty in the standard deviations 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 (μg/L)

10 100 1000 20 50 200 500

Plant Assemblage Toxicity Index

0

20

40

60

80

100

Table 2. Summary statistics for SGR-based toxicity data from Table 1. 

Taxonomic 

Group 

log(EC50) 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 0.03 
15

how PATI based on the overall distribution in Table 2 would vary by changing the mean of the 

log10(EC50)s and the standard deviation of the log10(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 represent 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 contrasts PATI relationships based (a) on the 

overall distributions of log10(EC50) and log10(Steep) in Table 2, (b) the separate distributions in 

Table 2 for the four major taxa, and (c) a composite distribution based on equal representation of 

the four major taxa. Again as expected, the selected 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 Section 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 (μg/L)

10 100 1000 20 50 200 500

Plant 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 EC50s (dashed-dotted 

lines) and to 95% confidence limits for the 

standard deviation of the EC50s (dashed lines). 

Atrazine Concentration (μg/L)

10 100 1000 20 50 200 500

Plant Assemblage Toxicity Index

0

20

40

60

80

100
16
17

3. USING EXPERIMENTAL ECOSYSTEM DATA TO SPECIFY THE LOC 

3.1 Experimental Ecosystem Data Set 

 For atrazine, an extensive set of experimental ecosystem 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 exposures such as those in Figure 1. However, 

they can be used to decide what level of PATI should 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 exposure and the relationship of these PATI 

values to observed effects in these ecosystems is used to specify LOCPATI. Then, PATI values 

are calculated for the field exposures and compared to this LOCPATI. One consequence of this is 

that LOCPATI reflects the types and levels of effects considered unacceptable in these 

experimental systems, rather than simply making a judgement 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 exposures. 

. A fundamental assumption in using these experimental 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 systems. In other words, it is assumed that natural 

aquatic plant communities will generally react adversely if subjected to the same exposures that 

elicited adverse effects in the experimental ecosystem studies. This assumption is inherent in 

any assessment that extrapolates toxicity experiments 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 data was the subject of a separate effort 

by the EFED, who provided a data compilation for the LOC methodology development 

described here (personal communication, Brian Anderson, 2010). Appendix B provides the 

portions of this data compilation relevant to the analysis here, including an exposure profile, an 

effects determination, and a literature citation for each experimental ecosystem atrazine 

treatment. These data are summarized in Figure 8, where each experimental ecosystem 

treatment is characterized by the duration over which 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 developing a PATI-based LOC. It does not 

address the fact that the exposure concentrations decline over the test duration to various degrees 

among these different data; these declines are documented in Appendix B. This decline further 

complicates application of such data to the types of 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 need to be addressed in deriving a PATIbased LOC. First, diversity among the experimental approaches and the typically small number 

of treatments precluded characterizing each experimental ecosystem treatment with a 

comparable, quantitative measure of effect. Therefore, LOC specification must rely on a binary 

(acceptable vs. unacceptable) characterization of effect. Second, although the exposures that 
18

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 μg/L range. This is 

presumably due to the combined effect of (a) different sensitivities of the experimental systems 

to atrazine, (b) random variability in system response to atrazine, and (c) differences among test 

methodologies affecting how well atrazine effects can be detected. The next section will 

consider how both the binary nature of the experimental ecosystem data and this 

variability/overlap issue should be addressed in specifying a PATI-based LOC. 

3.2 Quantification of PATI-Based LOC from Binary Data 

 To introduce the analysis of the binary experimental 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 that 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 which 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 probability of mortality at each concentration, this 

relationship being the same in the left and right panels because both panels represent the same 

information and analysis. 

 Because probit analysis uses the binary response of the individual organism as the basic 

observation, it is actually more directly related to the right panel of Figure 9 than to the left. 

Furthermore, if individual organisms all have different exposures, the presentation format of the 

left panel cannot be used (i.e., no groups of replicate organisms upon which to compute fraction 

survival), but a plot such as in the right panel can still be done and probit analysis is still 

appropriate. For example, if the offsets for the points in the right panel of Figure 9 actually 

Figure 8. Effects of atrazine on experimental ecosystems as a function of exposure duration and average 

concentration. Closed triangles denote adverse effects, open triangles no effects. 

Test Duration (days)

5 20 50 200 10 100

Average Concentration (

μg/L Atrazine)

1

10

100

1000

10000
19

represented different concentrations, probit analysis could still be applied even without replicate 

points at the same concentration. 

 The experimental ecosystem data provide an analysis situation analogous to the survival 

data in the right panel of Figure 9. Figure 10 replots the experimental ecosystem data from 

Figure 8 as binary effects (1 if there is an effect, 0 if there isn’t) vs. a PATI90d 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 than 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 data analysis. For a hypothetical toxicity test, left panel shows 

fraction (of 10 organisms) which died at each exposure concentration while right panel plots individual organism 

response as 0 (if survived) and 1 (if dead). Lines denote probit relationship for probability of death. 

Concentration

0.1 1 10 0.2 0.5 2 5

Probability of Mortality

0.0

0.2

0.4

0.6

0.8

1.0

Concentration

0.1 1 10 0.2 0.5 2 5

0.0

0.2

0.4

0.6

0.8

1.0

Figure 10. Experimental ecosystem data plotted as effect/no effect versus PATI90d, fitted to a logistic relationship 

for the probability of an effect versus PATI, this probability being 50% when PATI90d equals ≈2. 

Plant Assemblage Toxicity Index

0.1 1 10 100 0.2 0.5 2 5 20 50

Probability of Adverse Effect

0.0

0.2

0.4

0.6

0.8

1.0

LOCPATI=2
20

The example values here are only intended to demonstrate how the experimental ecosystem data 

can be used to derive an LOCPATI. 

 Although there is a clear increase in the probability of effects as PATI90d increases in 

Figure 10, there also is considerable overlap between effects and no effects with respect to 

PATI90d, especially in the 1 to 5 range for PATI90d. This variability/overlap issue was already 

noted regarding Figure 8, and should be viewed here in terms of any particular PATI90d value 

having a probability of eliciting an effect across the variety of experimental ecosystem studies 

used here. That there is a probability, rather than a certainty, of having an adverse effect at any 

PATI90d value is again indicative of sensitivity differences among the systems and/or various 

experimental uncertainties. Across all PATI90d values, there would be an underlying relationship 

for this probability, illustrated by the curve on Figure 10. 

 This probability relationship can be quantified using 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 to 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 ecosystem set as a function of PATI90d, this logistic 

probability expression can be formulated as: 

(Equation 1)

where P is the probability (percent scale) of an adverse effect at a PATI90d value, PATI50% is the 

PATI90d value at which P=0.5 (50% chance of an effect over the range of experimental 

ecosystems), and S is a steepness parameter (>0) for the relationship. 

 Although P is the underlying probability of an actual adverse effect, this equation is not 

appropriate for analyzing the data in Figure 10 because it does not reflect certain errors in the 

statistical analysis regarding whether an experimental 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 set 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 treatment does not have an effect when it actually 

does) will also affect the curve, but it is not possible 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 affect the upper asymptote of the curve like Type I 

error affects the lower asymptote, and thus will not affect the basic sigmoidal shape of the curve 

being fitted. The binary regression used in the LOC methodology will therefore use a logistic 

model with a lower asymptote of 0.05, modifying Equation 1 as follows: 

(Equation 2)

( ) 50% ( ) 10 50% 10 90

90

log log

1 1

1 1 10 d

d

S

PATI S PATI PATI

PATI

P

−

= =

+ +

( )

( )

50%

50%

1 0.05

1

S

PATI

PATI

data S

PATI

PATI

P

+ ⋅

=

+
21

where Pdata refers to the probability of a data point being stated 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, PATI50% and S. Using these parameter 

estimates, the curve in Figure 10 was calculated, using Equation 1 so the curve shows the actual 

estimated P, not Pdata. Once estimated, this curve provides a basis for making risk management 

decisions regarding what PATI value is considered an LOC. For example, in the example of 

Figure 10, a risk management decision to use P=0.5 would result in an LOCPATI of about 2. 

It should be noted here, that although LOCPATI=2 might seem to be a low value (2% 

reduction in average growth rate across the plant assemblage), this is an average value across all 

species for 90 d. For the days with more intense exposure during this period, the average effects 

on the assemblage would be much greater than 2%, and even greater for the sensitive species 

within the assemblage. Over 4 d, growth could be reduced by nearly 50% for the entire 

assemblage and completely suppressed for many species, while still satisfying this average. For 

the shortest exposure in Figure 1, there could be almost complete cessation of growth while still 

satisfying PATI90d=2. Furthermore, just a 2% reduction in growth every day over several weeks 

can “compound” to a substantial change in assemblage biomass and composition. 

However, it also important to remember that LOCPATI is a relative index that is used here 

to compare experimental ecosystem results and to apply them to other exposure time-series. It is 

the experimental ecosystem results that define the magnitude of effects that are of concern, not 

LOCPATI. Unless LOCPATI is so extreme as to invalidate any utility in comparing different 

exposure time-series or to raise questions about the validity of the experimental ecosystem data, 

its value is not an appropriate basis for addressing the level of protection being provided. 
22
23

4. IMPLEMENTATION OF PATI-BASED RISK ASSESSMENT METHODOLOGY 

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 parameterization and performance evaluations will 

involve a larger subset (Figure 11) of the chemographs supplied to this effort by EFED. Of these 

chemographs, numbers 1, 3, 5, 7, 9, and 12 are as measured in various monitoring efforts from 

which EFED compiled examples. The remaining chemographs 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 exposures for evaluating the methodology.

Figure 11. Example atrazine chemographs used in methodology 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 50 100 150 200 250 300 350

Concentration (

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 11
24

4.2 Parameterization Issues for PATI-based LOCs

Implementing a PATI-based methodology requires specifying (a) the toxicity distributions (EC50

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 considered inconsequential by PATI, 

this period needs to be long enough to encompass (a) exposures of significance to establishing 

LOCPATI from the experimental ecosystems (Figure 8) and (b) effects expected from seasonal 

field exposures (Figure 11). However, it should not 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 in the example chemographs of Figure 11. It 

also encompasses the duration of all but a few of the experimental ecosystems in Figure 8; most 

importantly, it includes all the no-effect treatments – not doing so would underestimate how 

much exposure can occur without causing effects. The few experimental ecosystem treatments 

with durations longer than this are of no consequence to setting the LOCPATI, because they 

simply verify significant effects for high PATI values. 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 averaging period and the consequences of other 

choices, sensitivity analyses below will address how risk determinations would differ for a 

longer (120-d) and a shorter (60-d) averaging period, this shorter period still encompassing 

almost all the experimental ecosystem overlap region and almost all significant exposures in the 

example chemographs. A 30-d averaging period is also included in this sensitivity analysis to 

document the impact of a period that is arguably too short, in that it is less than the duration of a 

substantial percentage of the experimental ecosystems treatments without effects and poorly 

covers periods of substantial exposure in the example 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 information, which were already discussed to some 

degree in Section 2.4: 

(a) Should PATI calculations be directly based on the discrete data points in Table 1, or should 

the methodology follow the typical assessment practice of using the data to estimate sensitivity 

distributions (Table 2), and basing calculations on such distributions? 

(b) Should the methodology be weighted in some manner for taxonomic groups, or follow 

standard practice (e.g., typical SSDs) of not adjusting 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 point of the distributions based on all the 

available, individual toxicity observations (the “overall” distributions of log10(EC50) and 

log10(Steep) summarized in Table 2). Sensitivity analyses 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 distributions, (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 regarding 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 distributions specified in Table 2, effects 

assessments were made for each of the example chemographs in Figure 11, using averaging 

periods of 30, 60, 90, and 120 d. These assessments proceeded as follows: 

(a) The daily PATI values for each experimental ecosystem exposure were calculated. As 

illustrated in Figure 3, this involves computing, for each daily exposure concentration C, an 

average effect across a set of species whose toxicity relationships are summarized by the 

distributions of log10(EC50) and log10(Steep) in Table 2. This average effect requires multiplying 

the level of toxic effect expected for a particular EC50 and Steep by the probability of a species 

having that EC50 and Steep, and doing this for all possible combinations of EC50 and Steep. 

Mathematically, this can be expressed as follows, where the function “tox” (the expected toxicity 

at exposure concentration C and for toxicity parameters EC50 and Steep) is multiplied by the 

function“dens” (the density function for the joint probability distribution of EC50 and Steep), and 

this product is then integrated across all values of EC50 and Steep. 

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

Rather than trying to directly evaluate this integral by numerical integration, it was estimated by 

randomly sampling 1000 pairs of EC50 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 points was sufficient to estimate PATI with a 

relative error of <2%. 

(b) The daily PATI values were used to calculate average PATI values for 30, 60, 90, and 120-d 

averaging periods for each experimental ecosystem exposure. For experimental ecosystem 

treatments shorter than the averaging period, concentrations 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 regression was conducted as described in Section 

3.2. The LOCPATI was set to the PATI50% estimate from this regression (50% probability of an 

effect). 

(d) The daily PATI values and maximum running average PATIs for each averaging period were 

computed for each of the example chemograph in Figure 11. 

(e) For each averaging period and example chemograph, the ratio of the average PATI to the 

LOCPATI was calculated and is referred to here as the “Effects 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 averaging period (Figure 12). 

(f) Calculations were also conducted iteratively to determine the “Concentration Exceedence 

Factor” (CEF) – the factor by which the entire exposure time-series would need to be decreased 

so that the average PATI equals the LOCPATI. 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 averaging period (Figure 12). 

Figure 12. Sensitivity of effects assessments to the PATI averaging period, based on CEF (top panel) and 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 12

Concentration 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 12

Effects Exceedence Factor

0.2

0.5

2

5

1

10

Default - 90d Averaging Period

30d Averaging Period

60d Averaging Period

120d Averaging Period 
27

Figure 12 illustrates how the averaging period affects both the EEF and the CEF. As 

would be expected, both measures of effect show very 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 than 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 greater than a factor of 2 and usually much less. 

Based on this sensitivity analysis, a 90-d averaging period is recommended for the methodology. 

4.3.2 Sensitivity Analysis for Toxicity Information

 Using the 90-d averaging period, effects assessments were made for each of the example 

field exposures in Figure 7 using the following options for toxicity information: 

(1) The overall distributions for logEC50 and logSteep reported in Table 2 (default). 

(2) The logEC50 distributions for the four major taxonomic groups in Table 2 (using the overall 

distribution for logSteep). 

(3) An equally weighted composite of the logEC50 distributions for the four major taxonomic 

groups. 

(4) The individual tests in Table 1 (using the average value of -0.05 for log(Steep) for tests in 

which this was not determined). 

(5) The overall distribution using genera means rather 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). Because Figure 12 already shows that the patterns of 

Figure 13. Sensitivity of effects assessments to selection of EC50 distribution, based on CEF. Chemograph # 

denotes example field exposure time-series in Figure 7. 

Chemograph #

1 2 3 4 5 6 7 8 9 10 11 12

Concentration 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 Only
28

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

 The effects on PATI of options 2 and 3 were already shown in Figure 7 to involve major 

changes compared to the default option 1, including reducing the median EC50 by up to 40% and 

increasing it by up to 100%, thereby creating a 3-fold 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. The maximum variation of the CEF from the 

default value was never more than 7% and averaged less than 4%. This small sensitivity might 

seem surprising, given how much the toxicity distributions change, but this is because the 

experimental ecosystems, not the toxicity distributions, 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 are similar whether the plant assemblage is 

sensitive or tolerant. Because this sensitivity analysis shows such small effects of alternative 

compositions of the plant assemblage, the recommendation here is to use the overall toxicity 

distribution in Figure 2 that was used as the default for this analysis. 

4.4 Contribution of Uncertainties in Toxicity Distributions 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 parameter of four that describes toxicity 

distributions – the mean log10(EC50). The standard deviation of the log10(EC50) varied a small 

amount among taxonomic groups, and the mean and standard deviation of log10(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 results. 

 To this end, an uncertainty assessment was conducted that involved (a) generating 1000 

sets of toxicity parameters (means and standard deviations for both log10(EC50) and log10(Steep)), 

(b) determining the LOCPATI for each parameter set, and (c) determining the CEF for each 

example chemograph for these 1000 combinations of toxicity parameters and LOCPATIs. The 

1000 means for log10(EC50) and log10(Steep) were generated by random sampling from normal 

distributions with the overall means and standard errors of the means for these variables reported 

in Table 2 (overall distribution). The 1000 standard deviations for log10(EC50) and log10(Steep) 

were generated by random sampling from chi-square distributions based on the standard 

deviations of these variables reported in Table 2 (overall distribution). 

 Figure 14 summarizes this uncertainty analysis, comparing the 10th and 90th 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 provided, they would need to reflect all important 

sources

2

, compared to which the uncertainties in the toxicity distributions used by PATI should 

be negligible. 

 

2An example of another source of error in the overall methodology is the uncertainty in the log(LOCPATI) from the 

logistic regression. When the best estimates of the overall toxicity distributions are used in calculating PATI, the 

standard error for log(LOCPATI) is 0.16 from the binary regression analysis, which produces a 10th to 90th percentile 

range for the CEF of 55-183% of the median. Other sources of uncertainty include exposure characterizations and 

experimental ecosystem results. 
29

4.5 Summary of Implementation 

 Based on the above sensitivity and uncertainty analyses, this PATI-based methodology is 

proposed to be implemented with (a) an overall log(EC50) distribution (normal) with mean 2.12 

and standard deviation 0.37, (b) an overall log(Steep) distribution (normal) with mean -0.05 and 

standard deviation of 0.18, and (c) no consideration of uncertainty associated with the errors in 

these toxicity distributions. Based on a random sample of these distributions (n=1000), a daily 

PATI value will be determined for any atrazine concentration time-series of interest (field or 

experimental) and the PATI will be set to the maximum 90-d rolling average of the PATI values 

for that time-series. The LOCPATI will be determined based on logistic regression of the PATIs 

for the experimental ecosystem exposures and the binary effects determinations for these 

exposures, setting the LOCPATI to PATI value in the regression curve associated with a 50% 

probability of effect (or some other desired sensitivity level). The ratio of PATIs calculated for 

field exposures of interest to this LOCPATI (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 the exposure exceeds a level of concern. 

FORTRAN-based computer programs and associated input files for this implementation have 

been developed and are separately available from the author. 

Figure 14. Uncertainty analysis for PATI-based MEI methodology. Solid line denotes CEFs based on best 

estimates of toxicity parameter distributions used in PATI calculations. Dashed lines denote 10th and 90th

percentiles due to uncertainty of these toxicity distributions. 

Chemograph #

1 2 3 4 5 6 7 8 9 10 11 12

Concentratration Exceedence Factor

0.2

0.5

2

5

1

10
30
31

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39

APPENDIX A 

SINGLE-SPECIES PLANT TOXICITY TEST REVIEW

 This appendix provides a summary for each report and journal article reviewed for 

developing the compilation of EC50s and steepnesses for the relationship of plant specific 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 atrazine effects was plant biomass (dry 

weight, or wet weight if procedures provided consistent removal of adhering water), but 

measures that are approximately proportional to biomass (algal cell count or cell volume, 

duckweed frond count) were also accepted. 

(2) If measures outlined in (1) were not available, O2 evolution or 14C fixation measurements 

were accepted provided that they were not significantly compromised by any lag in inducing 

effects and their relationship to SGR could be defined. 

(3) Data based just on chlorophyll content were not used because the chlorophyll content per cell 

can change markedly in response to atrazine, leading to markedly different EC50s 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 affected by chlorophyll content, often being measured 

near a chlorophyll absorbance maximum. 

A.1.2 Translating Reported Data into SGR EC50 and Steep 

The nature of the data and the level of detail provided 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 EC50, a steepness for the SGR vs. atrazine concentration 

relationship, and the SGRC. 

(1) Initial and final biomasses (or surrogate) were reported for a concentration series. 

The preferred data were reported initial and final biomasses (or acceptable surrogates) for all 

treatment concentrations, from which SGRs would then be computed. A regression analysis of 

SGR vs. atrazine concentration (CATZ) was then conducted, resulting in characterizing both the 

EC50 and the steepness for the relationship based on the 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 and the log-transform option for CATZ. (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 logistic analysis.) The defining parameters for 

this function are the control SGR (SGRC), the log10(EC50) for the SGR, and a measure of relative 
40

steepness (“Steep”) defined as d(SGR/SGRC)/d(log10(CATZ)) at the EC50, this regression 

analysis thus providing all the elements for the desired data compilation. 

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

If the author reported SGRs (based on biomass or acceptable biomass surrogates) for all 

treatment concentrations, but not the actual initial and final biomasses, these SGRs were used 

directly in regression analysis as described in (A1) above to obtain the SGRC, SGR EC50, and 

steepness parameter. If the reported SGRs were relative (fraction of the control), the regression 

was conducted to obtain an EC50 and steepness to include in the compilation, but not an SGRC, 

although in some cases the latter was specified separately by the author(s). 

(3) EC50 for the SGR was reported with or without slope. 

If the author computed SGRs, but only reported an SGR-based EC50 without SGRs for individual 

treatment concentrations, the author-calculated SGR EC50 was included in the compilation. If 

the author also specified the type of relationship used in the EC50 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 compiled. If the author separately provided 

information on the SGRC, this also was included in the compilation. 

(4) Multiple ECps for growth reported; SGRC reported. 

(a) If multiple ECps for growth over a specified duration (t) and the SGRC for that duration were 

reported, SGRs corresponding to these biomass-based ECps 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 the concentration causing a p% decrease in 

growth. The resultant SGRs (and their associated concentrations) were then subject to regression 

analysis to provide estimates for the SGR EC50 and steepness. This provided a SGR EC50, a 

steepness, and a SGRC for the compilation. 

(b) If the author did not specify multiple ECps for growth, but did provide the biomass-based 

EC50, the type of relationship used in this EC50 estimation, and the slope for that relationship, 

additional ECps for growth (p≤90%) were calculated for this author-reported curve and also 

converted to SGRs. These were then subject to regression analysis to provide estimates for the 

SGR EC50 and steepness, although any confidence limits on these estimates would not be valid 

given that the data points were not independent. Rather, 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 SGRC for either of the above options, the 

regression analysis was not conducted because this would involve too much extrapolation to 

estimate the SGR EC50. However, the possibility of extrapolating this SGR to the SGR EC50 per 

(B6) below was then considered. 
41

(5) Multiple ECps for growth reported; SGRC not reported. 

If multiple ECps or an EC50/slope combination for algal growth were reported, but an SGRC was 

not reported, the process in (B4) above was still used, but using SGRCs reported for other studies 

on test species in the same taxonomic group. Because this involves using data from other 

experimental systems and test species, three separate analyses were conducted using median 

(low-high) estimates for the SGRC 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 EC50 and steepness from the 

regression analysis using the median SGRC estimate were included in the compilation, provided 

the SGR EC50s derived using the low and high SGRC estimates differed by no more than a factor 

of 2.0. 

[The low/mid/high SGRC estimates were based on ANOVA of logSGRCs from algal studies in which SGRC

was reported (see Table 1 in Section 2.2). Analyses using Statistica (Version 8.0, StatSoft, Tulsa, OK) 

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 deviation (0.122). The low/mid/high estimates for 

SGRC were based on calculating the mean ± 1 std.dev. of these log values and then taking antilogarithms. 

Separate SGRC values for species within a taxonomic group were not justified because of large withinspecies variability relative to between-species variability, as evidenced in Table 1 and other sources (e.g., 

Saenz et al. 1997).] 

(6) EC50 only for growth reported; SGRC reported.

If the EC50s for growth over a specified duration (t) and the SGRC for that duration were 

reported, this biomass-based EC50 was equated to an SGR ECp 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 SGRC and this single SGR are available, no regression analysis is possible. 

Rather, this SGR ECp was extrapolated to an SGR EC50 using the equation 2

50 10 p S EC ECP

⋅

= ⋅ , 

where S is based on regression curve steepnesses from other studies. Because this involves using 

data from other experimental systems and test species, three estimates of the SGR EC50 were 

made using low, middle, and high estimates for the steepness of 0.68, 0.95, and 1.31. The 

estimate for the SGR EC50 from the middle steepness estimate was included in the compilation, 

but only if the estimates based on low and high steepness differed by less than a factor of 2.0. 

This factor of two requirement was met if p>16 for the estimated SGR ECp. 

[An ANOVA of all the log steepness determined in all studies indicated no significant differences 

among species or broader taxonomic groups, so the overall mean and standard deviation of the 

log steepnesses were used to set low/mid/high estimates.] 
42

(7) EC50 only for growth reported; SGRC not reported. 

When only an EC50 for growth was reported and a study-specific SGRC was not reported, the 

biomass-based EC50 was equated to SGR-based ECps per section (B5) using low, middle, and 

high estimates for SGRC. Then, each of these SGR-based ECp estimates was extrapolated to 

SGR EC50 estimates per section (B6) using low, middle, and high steepness estimates. The SGR 

EC50 estimate based on the middle SGRC 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 procedure 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 EC50s for biomass to SGR EC50s were just too uncertain for tests longer than this. 

(8) Oxygen evolution or 14C 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 biomasses 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 ECps for these rates were treated as comparable to 

SGR-based ECps. However, this also required consideration of whether these periods were so 

short that any lag in the induction of toxicity would significantly perturb the measurement. 

Hersh and Crumpton (1989) and Millie and Hersh (1987) reported effects on oxygen evolution 

that were >50% within several minutes of exposure to atrazine concentrations that caused similar 

effects on biomass-based SGRs. Thus, data were accepted 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 ECps, 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” preceded a short measurement period, so that 

the treatments would start with significantly different 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 period 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 converting ECs to an SGR-basis using 

approaches analogous to that described above for biomass. 

A.1.3 Issues regarding biomass surrogates and variability. 

One uncertainty issue occurred when the biomass surrogate 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 and 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 control treatment in order to have reasonable 

discrimination between the control and treatments with 25-50% reduced growth. Another area of 

concern was frond counts for duckweed, and how closely such counts mirror biomass when 
43

growth is limited and thus might have a greater percentage 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, small fronds. A final area of concern was 

macrophyte shoot tests at times when controls had not 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 imposed with regard to any of these concerns, 

because any uncertainty depends on the number of replicates in a test, the specific times, the 

variability among replicates, etc. How these concerns 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 hemocytometer count issue discussed above), but short 

enough to avoid potential biases associated with declining SGRC discussed earlier. Again, no 

firm rules could be adopted for this because of various study-specific factors and because it 

involved balancing uncertainties at early times with those at later times. The decision process 

regarding this are provided in the summaries for each 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 based on hemocytometer cell counts. 

Concentrations were measured. Illumination was continuous at 40 μE/m2/s, temperature was 24 

C. They reported average SGRs over 96 h at each treatment 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 growth. Due to the duration and growth rates, cell 

densities would have been high enough to avoid concerns about low numbers of individuals 

manually counted. 

Measured (Target) 

Concentration (μg/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 

EC50 (μg/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 weight), cell density (electronic particle 

counter), and chlorophyll (by both spectrometry and fluorometry) at 0, 24, 48, and 72 h. 

Concentrations were nominal. Illumination was continuous at 300 μE/m2

/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 substantially (almost 3X) higher than for cell 

density and biomass, and were not used in accordance with the review guidelines. Relative 

SGRs for cell density and biomass were estimated from the figures, reported in the table below, 

and used in EPA regression analyses to determine EC50 and steepness. The results based on dry 

weight were selected for use because EC50s were modestly higher for cell density (average LC50

= 406 by cell density, 311 by weight) indicative of decreases in mass per cell at higher atrazine 

concentrations, so that using cell density would slightly reduce the apparent sensitivity of 

biomass to atrazine. The results at 1 d were selected 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 should have avoided the problems with low manual 

cell counts at early times. 

Nominal 

Conc (μg/L)

Author Relative SGR, Cell Counts Author Relative SGR, 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 

EC50 (μg/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 assay for Selenastrum capricornutum

exposed to multiple atrazine concentrations. Concentrations were nominal. Illumination was 

continuous at 300 μE/m2

/s and temperature was 23 C. Oxygen evolution rates 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 concentrations, the regression in this review 

included a non-zero asymptote at high concentrations, but the EC50 is still defined relative to zero 

oxygen evolution, not this negative asymptote, so that 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 results 
45

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 (μg/L)

Author Relative 

Oxygen Evolution 

5 100 

50 84 

500 27 

1000 0 

5000 -14 

10000 -25 

EC50 (μg/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 using an electronic particle counter. 

Concentrations were nominal. Illumination was continuous at 70 μE/m2/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 EC50 of 110 μg/L (95% cl = 99-

121) and an EC10 of 27 μg/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 exposures of several algal species to atrazine. 

The duration of the test varied with species in order to be within the period of exponential 

growth. Illumination was continuous at 70 μE/m2

/s for green algae and 30 μE/m2/s for others. 

For these exposures, relative SGRs for each treatment were reported graphically. Values 

estimated from the figures are provided in the following table, along with EC50s and steepnesses 

estimated from regression analysis of this data. The EC50 for Selenastrum was higher for the 

microplate exposures than for the flask tests (although by less than 2-fold), suggesting that the 

microplate exposure methodology might involve factors 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 microplate-based numbers were still compiled for 

use in subsequent analyses because the Selenastrum EC50s was well within the reported range of 

results for this species from other studies; however, 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 95 110 
46

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 

EC50 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 hemocytometer 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 1104. Concentrations were measured and were stable for 4 d 

(concentrations were 2X higher than target due to diluting error). Light was continuous at 450-

500 ft-c and temperature was 24-25 C. SGRs were calculated by EPA for each duration and 

concentration and used in regression analyses to estimate EC50 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 counts over the first day were lower than desired 

for good quantification and the drop in SGR could be partly due to uncertainty in both the initial 

and day 1 cell counts. Therefore, day 2 values were selected for the data compilation. 

Conc (μg/L) Author Cell Counts (/104) 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 - - - - 

EC50

(μg/L) 

109 131 

(59-290) 

180 161 

Steepness 1.13 0.62 

(0.18-1.10)

2.61 2.42 
47

(6) Hoberg 1993a 

The author conducted a 96-h flask test of Selenastrum capricornutum growth at multiple atrazine 

concentrations, enumerating cell density based on hemocytometer 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 0.3104. 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 typical levels during subsequent days. In 

addition, SGRs were high during the first day even at the highest atrazine concentration (2.30/d 

at 450 μg/L), and also dropped to more typical values during subsequent days (<0.1/d). These 

atypical results might represent an error in the initial cell density, the reported value of which 

was atypically low and could not be verified. These 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 using an electronic particle counter. Light was 

continuous at 60 μE/m2/s and temperature was 24 C. The authors only provided a 4-d EC50 for 

cell density (26 μg/L), with no data on actual cell counts at test termination for atrazine 

treatments. No information was provided on actual treatment concentrations. However, they did 

report an initial cell density of 1104 and a final control cell density of 1-2106, corresponding to 

an SGRC of 1.15-1.32/d, a relatively narrow range. Based on the midrange of the reported final 

control cell counts, an SGRC of 1.25/d was used for adjusting the cell density-based EC50 to the 

SGR (1.08/d) that would result in half the final control density. The authors also reported a 

probit slope of 4.95 for the cell density vs. log10C relationship, which allowed calculation of 

other ECps for cell density (e.g., EC16 and EC84 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 EC50 and steepness. Confidence limits 

are not reported because this regression was not based on independent data points, but on a 

conversion of the reported relationship for the cell density ECs. 

p 

(% reduction in cell counts)

ECp 

(μg/L) 

4-d Cell Density 

(104 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 

EC50 (μg/L) 50 

Steepness 1.66 

(8) Versteeg 1990

The author compared three assays of atrazine effects on Selenastrum capricornutum growth: a 4-

d flask test enumerating cell density based on hemocytometer cell counts, 5-min 14C fixation 
48

after 30-min exposure, and 30-min oxygen evolution. Light was continuous at 86 μE/m2/s for 

the flask test, 350 μE/m2/s for the 14C fixation, and 250 μE/m2/s for the oxygen evolution; 

temperature was 24 C. Reported EC50s were 50 μg/L for 4-d cell density, 100 μg/L for 14C 

fixation, and 380 μg/L for oxygen evolution. Data for individual treatments were not reported 

for atrazine, but were for simazine, another triazine herbicide. Measurement variables (cell 

densities, 14C fixation rate, oxygen evolution rate) relative to the control are provided in the 

following table for simazine. Simazine showed differences among the EC50s based on cell 

densities, 14C fixation rate, and oxygen evolution similar to atrazine. SGRs based on cell density 

effects were also estimated per item (B5) of the protocol, resulting in an SGR-based EC50 similar 

to that for 14C fixation. This simazine analysis also resulted in a slope for SGR-based ECs that 

was included in the compilation. 

Analysis of Versteeg 1990 Results for Simazine 

Concentration 

(μg/L) 

Cell Density 

(% of Control) 

SGR 

(% of Control) 

14C 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 

EC50 (μg/L) 95 180 215 437 

Steepness 1.58 1.50 1.19 1.26 

Based on the experimental procedures and the results for both atrazine and simazine, this study 

was applied as follows regarding EC50s: 

(a) Because the oxygen evolution assay involved purging oxygen, with uncertain effects on 

photosynthesis rates and sensitivity to atrazine, these data were not used. 

(b) Because the 14C fixation assay included prior exposure, the results will be used. Because of 

the short exposure and measurement periods, the EC50 (100 μg/L) for 14C fixation will be 

treated as being equivalent to those for SGRs. 

(c) The smaller EC50 for the flask test cell density is likely due to it being for cumulative growth 

over 4 d. Per item (B7) in the protocol, this had too long a duration to extrapolate the cell 

density-based EC50 to an SGR-based EC50 given the range of estimates for the unknown SGRC

and steepness. However, if the steepness for simazine is used, the procedure would result in the 

estimates for the SGR EC50 from 95-115 μg/L, similar to that for 14C fixation. 

(9) Larsen et al. 1986

The authors reported EC50s for 14C fixation rates of several algal species, measured over 2 hours 

after 24 h prior exposure to atrazine. Light was continuous at 400 ft-c and temperature was 24 C. 
49

Because the 24 h prior exposure would result in substantially 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 proportional to net growth. Assuming that SGR is 

approximately constant within each treatment, the biomass at 24 h would be eSGR and the carbon 

fixation over the 2-h measurement period would be proportional to SGRe

SGR, ignoring the small 

amount of growth over that 2 h and assuming that the measured fixation over the 2 h is 

approximately proportional to the SGR. Given this relationship, per item (B7) of the protocol, 

an EC50 for the SGR can still be calculated from this information, if an SGRC and steepness can 

be estimated for use in the following calculations:

(a) Solve for SGRp (p = percent reduction in SGR relative to control) 

corresponding to the EC50 for 14C fixation using the equation 

0.5 P C 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-SGRp/SGRC). 

(c) Use the estimated steepness for the toxicity relationship to extrapolate the 

known SGR ECp (=EC50 for 14C fixation) to the SGR EC50. 

For Selenastrum capricornutum, the authors reported EC50s for 14C fixation of 34-53 μg/L (three 

tests, average 43). Using this average EC50, the procedure described above was conducted 

multiple times using the low, middle, and high estimates for SGRC and steepness identified in the 

protocol for this review. The range of the resultant SGR EC50s was 66-114 μg/L, narrow enough 

to include the median SGR EC50 (78 μg/L) in the data compilation. For the other species, 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 SGRC for blue-green algae specified in the 

review guidelines. 

Test Species 14C EC50

(μg/L) 

SGR EC50

(μg/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 EC10, EC50, and EC90 for SGRs from a standard ISO 8692 toxicity flask 

test (3 d). The actual temperature and light intensity was not reported, but the cited test protocol 
50

specified 60-120 μE/m2/s and 23±2 C. The author-reported SGR EC50 of 164 μg/L will be 

used, but the multiple ECs can also be used to estimate 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 control growth and toxic effects. This information 

indicated the SGRC for this study under standard conditions was about 1.8/d, but insufficient 

information was available to use other toxicity information for the present analysis. This study 

did document a 10-fold increase in chlorophyll content per cell due to atrazine exposure (200 

μg/L), which provides some of the basis for not accepting this as a surrogate for biomass. 

p 

(% reduction in control 

SGR) 

ECp 

(μg/L) 

Relative 

SGR 

0 1.0 

10 17.2 0.90 

50 164 0.50 

90 688 0.10 

EC50 (μg/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 hemocytometer cell counts. Concentrations 

were nominal. Light was continuous at 2300 ft-c and temperature was 24 C. The authors 

reported the number for the doublings (cell count basis) 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 

(μg/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 

EC50 (μg/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, enumerating cell density based on hemocytometer 

cell counts. Concentrations were nominal. Light was 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 2104for Selenastrum and 5104 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 SGRC and results agreed between the two durations, the 5-d results were selected for use. 

Conc (μg/L) 

(nominal) 

Author Cell Counts 

(/104) 

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 

EC50 115 101 

(79-130) 

Steepness 1.47 1.61 

(0.67-2.55)

Results for Microcystis are in the following table. Control growth actually increased later in the 

test and EC50s were similar for both durations, so the 5-d results were selected for use. 

Conc (μg/L) 

(nominal) 

Author Cell Counts 

(/104) 

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 

EC50 154 164 

(95-285) 

Steepness 4.2 1.25 

(0.24-2.46)

(13) Turbak et al. 1986, Selenastrum capricornutum

The authors reported an EC50 of 70 μg/L based on a 30-min oxygen evolution assay with 

Selenastrum capricornutum, with no additional information to determine the steepness of the 

relationship. The actual temperature and light intensity was not reported, but the test protocol 

specified 400 ft-c and 24 C. The methods description did indicate that there was some exposure 

prior to oxygen measurements, and 30 min is long enough not to be greatly perturbed by 

induction lags of several minutes. Therefore, this EC50 based on rate of oxygen evolution was 

accepted as informative of an SGR EC50. They also reported a 59 μg/L SGR EC50 based on a 2-

3 week bottle test. Because of the length of this test and the lack of specifics regarding it, this 

EC50 was not used, but this result does not contradict the EC50 based on oxygen evolution. 
52

(14) Radetski et al. 1995

The authors reported a 72-h EC50 of 118 μg/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 protocol specified 60-120 μE/m2/s and 23±2 C. 

They also reported an initial cell count of 2104 and a final control cell count of 6.6106, 

corresponding to an SGRC of 1.93/d. At the reported EC50, the final cell count would thus have 

been 3.3106

, equivalent to an SGR of 1.70, corresponding to a 12% reduction from the control 

value (i.e., the growth EC50 is an SGR EC12). Per protocol item (B6), this is too long of an 

extrapolation to estimate an SGR EC50 given the uncertainty in the steepness of the relationship, 

so an SGR EC50 was not computed. However, the SGRC 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, measuring weights and chlorophyll 

concentrations. Concentrations were nominal. The authors reported SGRs for multiple durations 

and concentrations, but only for chlorophyll measurements. Therefore, these data were not used 

in accordance with item (A3) of the protocol. Reported chlorophyll-based growth rates and 

EC50s 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 showed 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 Anabaena 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 

2104 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 EC50, the 5-d results were selected for 

further use. 

Conc (μg/L) 

(nominal) 

Author Cell Counts 

(/104) 

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 0.21 
53

3200 4.5 5.5 0.27 0.20 

EC50 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 results were selected for further use. 

Conc (μg/L) 

(nominal) 

Author Cell Counts 

(/104) 

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 

EC50 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 exposure chambers, but the stock concentrations 

were verified. Because chlorophyll was used to quantify 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). Concentrations 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 growth of Chlorella vulgaris and 

Ankistrodesmus braunii at multiple atrazine concentrations, enumerating cell density based on 

optical density and hemocytometer cell counts. Concentrations were not measured. Illumination 

was continuous at 30 μE/m2

/s and temperature was 24 C. Initial cell densities were 1105 and 

exponential cell growth was reported to be maintained for the test duration, culminating in a final 
54

cell density of 1.7106 (SGRC=0.26/d) in the Chlorella test and 3.8106 (SGRC=0.33/d) in the 

Ankistrodesmus test. The authors graphically reported the percent 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 at each atrazine concentration and subject to 

regression analyses to determine the SGR EC50 and steepness. Although this test was longer 

than would typically be used for this compilation, the SGRC were low enough (at least in part 

due to low light intensities) that total cell densities were not so high as to confound results or to 

doubt the authors’ statement that exponential growth was maintained. However, because these 

SGRCs were so low they were not used for estimating SGRCs for other studies. 

Ankistrodesmus Chlorella 

Nominal 

Atrazine Conc 

(μg/L) 

% Reduction 

in Growth 

SGR 

(1/d) 

Nominal 

Atrazine Conc 

(μg/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 

EC50 (μg/L) 104 

(83-131) 

EC50 (μg/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 growth of Scenedesmus subspicatus at multiple 

atrazine concentrations; concentrations were measured. Illumination was continuous at 3500 lux 

and temperature was 25 C. The authors only reported EC50s based on final biomass, without any 

information on specific treatments, growth rates, etc. Initial cell density was 1⋅104 cell/ml and 

growth was quantified by spectrophotometric absorbance calibrated to cell density. The EC50

based on final cell density was 21 μg/L. Because only an EC50 was reported and an SGRC was 

not reported, estimation of the SGR EC50 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 μg/L with 

a range of 50 to 150 μg/L). In addition, this study used optical density near the chlorophyll a 

maximum, and so would not be used per the review guidelines. 

(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 (unmeasured). 

Illumination was at 300 μE/m2

/s and temperature was 25 C. The authors graphically reported the 

percent inhibition of oxygen evolution rate relative to controls at each concentration, and these 
55

percentages were determined from the graph and subject to regression analysis to determine 

oxygen evolution EC50 and steepness. Because these were based on a short-term (1 min) oxygen 

evolution and because there was prior exposure to each atrazine concentration of several minutes 

before oxygen evolution was measured, ECs from these oxygen evolution rates were accepted as 

being comparable to SGR ECs. 

Nominal 

Atrazine Conc 

(μg/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 

EC50 (μg/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 μE/m2

/s and temperature was 25 C. Only the EC50 for the reduction in 

oxygen evolution rates relative to control were reported (no data on actual oxygen evolution vs. 

concentration), but because these were based on a short-term (1 min) oxygen evolution and 

because there was prior exposure to each atrazine concentration of several minutes before 

oxygen evoluation was measured, these oxygen evoluation EC50s were accepted as being 

comparable to SGR EC50s. For Chlamydomonas, the EC50 was 45 μg/L and for Chlorella it 

averaged 37 μg/L across the three isolates (range=36-41). 

(23) Stratton 1981, 1984

The author measured 14C fixation over 3 h and cell growth rate (by optical density) over 12-14 d 

for five algal species exposed to various atrazine and atrazine metabolite concentrations. 

Concentrations were unmeasured. For the 14C 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 EC50s 

at the end of the test were not provided, except for A. inaequalis, and this showed nonexponential growth throughout the last 10 d of the test and indicated the EC50 was lower at 4-5 d 
56

than later in the text, although the plotted data were insufficient to quantify this. In addition, 

optical density was measured at wavelengths with substantial chlorophyll absorption for at least 

three of the species. For these reasons, the ECs from the long growth test were not used, and 

only the 14C fixation EC50s were compiled: 

 Anabaena 

inaequalis 

Anabaena 

cylindrica 

Anabaena 

variabilis 

Chlorella 

pyrenoidosa 

Scenedesmus 

quadricauda 

14C fixation 

EC50 (μg/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 EC50s and EC10s for growth at 4, 

7, and 10 d. Concentrations were measured. The light intensity was 7000 lux with a 14/10 

photoperiod and the temperature was 24 C. Information was also provided to allow estimation of 

the SGRC to be 1.06/d, but no additional information on actual or relative cell counts at different 

concentrations and times, etc. was given. These ECs were reported to be for growth (not growth 

rate) and to be derived per OECD method 201, so presumably were based on “area under the 

curve” (AUC). They thus do not represent the difference between the biomass at the stated time 

and the biomass at test start, but rather the sum of these differences across the whole time 

interval (and thus a measure of the average increase). Because this system maintained an 

exponential growth and because the SGRC is known, the EC50s can be used to estimate SGRs for 

those concentrations, as summarized in the following table. The magnitudes of these estimated 

effects on the SGR are insufficient to support a regression analysis to estimate the SGR EC50 and 

steepness (due to the large extrapolation from 16% effect to 50% effect). However, per item 

(B6) in the protocol, this SGR EC16 of 51 μg/L can be extrapolated to an estimate of 141 μg/L

for the SGR EC50. 

Concentration 

(μg/L) 

Duration (d) for which 

concentration is AUC EC50

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 growth of Chlamydomonas reinhardii and 

Scenedesumus subspicatus at different atrazine concentrations, measuring cell densities at 1, 2, 

and 3 d with an electronic particle counter. Illumination was continuous at 8000 lux and the 

temperature was 20 C. The authors reported 3-d EC50s and EC10s from these tests, but without 

any other effect information (e.g., actual or relative cell counts at different concentrations and 

times, growth rates). Because of high initial cell densities (2⋅105

 cell/ml) that would have led to 

growth-inhibiting densities based on the SGRC from the flow-through test, the growth EC50 for 

Chlamydomonas (350 μg/L) cannot be converted to information on an SGR EC. For 

Scenedesmus, initial cell densities were low enough (5⋅104

 cell/ml) to make converting the 
57

growth EC50 (72 μg/L) reasonable; however, this would follow item (B7) of the protocol, and the 

duration of the test is too long for this extrapolation given uncertainties in both SGRC 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 SGRC for cell number would be ln(# of daughter cells) for the 

control treatment, but this number was not reported. This number can be as low as 4 

(SGRC=1.4/d), but in a related paper by Altenburger et al. (1990), a value of 12 was indicated 

(SGRC=2.5/d). The authors reported a probit equation for 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 ECps for cell “reproduction” (table below). Then, two sets of SGR 

estimates corresponding to these ECps were calculated based on the two alternatives for the 

SGRC, and regression analyses were conducted on each of these sets of SGRs. The resultant 

SGR EC50 estimates did not differ markedly (table below), so the average of these were 

included in the data compilation. 

Concentration 

(μg/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 

EC50 (μg/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 EC50 was reported to be 110 μg/L, but other information (effects at 

higher concentrations, control SGR) were not reported. This test does not meet the protocols 

stated earlier for extrapolating such an EC50 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 by chlorophyll over 72 h of Scenedesmus 

subspicatus exposed to multiple atrazine concentrations. Light was continuous at 800 lux and 

temperature was 20 C. As noted in the protocol, chlorophyll is not an acceptable surrogate for 

biomass. Regarding oxygen evolution, the authors reported an EC50 of 25 μg/L, but because of 
58

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 requires estimating SGRs based on the oxygen 

production and assumptions regarding SGRC. Such estimates based on the range of SGRC for 

green algae observed in other studies are included in the table below and subject to regression 

analysis. Variation in the assumed SGRC did not cause great variation in the estimated SGR

EC50; because of the low temperature and light intensity, the compilation used the value from 

the lowest SGRC value. 

Nominal 

Atrazine Conc 

(μg/L) 

Estimated SGR (1/d) 

SGRC=1.05 SGRC=1.35 SGRC=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 

 

EC50 (μg/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 the 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 optical density and because initial values for 

biomass were not given, useful results for the compilation could not be obtained from this study. 

(30) Stratton and Giles 1990 

The authors examined the effect of volume and initial cell density on the toxicity of atrazine to 

Chlorella pyrenoidosa, measured by radiocarbon uptake over 24 h. Although these experiments 

demonstrated inhibition relative to the control and did include some treatments with 

approximately 50% inhibition, only one concentration was tested, absolute fixation rates were 

not tested, and a variety of processes might be affecting the observed inhibition. This precluded 

applying these data to the data compilation of interest here. 
59

(31) Boger and Schlue 1976 

The authors evaluated photosynthesis based on oxygen evolution rate after several days of 

exposure to atrazine and the recovery of photosynthesis upon transfer of exposed algae to clean 

medium and control algae to contaminated medium. However, only one concentration was 

tested and results could not be related to the effect 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. Chlorophyll measurements were made, but 

were erratic in addition to being not accepted in the protocol used here. Both cell densities and 

weights were also measured, but no initial cell density was specified, final densities were based 

on inadequate numbers of cells, and many of the measurements of final weight were negative. 

Atrazine effects were evident at 100 μg/L, but the next lower and higher concentration was 10-

fold different (10 and 1000 μg/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. Concentrations were not measured. Light was 

at 500 ft-c and temperature was 25 C. The authors provided 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 less than a factor of two at 3 d, the 5-d results 

were selected for further use. 

Conc (μg/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 

EC50 169 224 

(151-332) 

Steepness 2.17 1.14 

(0.43-1.85)
60

(2) Hoberg 2007

The author conducted growth tests with isolated shoots of Elodea canadensis at multiple atrazine 

concentrations and at zero, dim (500 lux), and optimal (6000 lux) light levels (only the higher 

light level is appropriate for this review). Concentrations were measured and temperature was 

20-25C. Data tables were provided for individual shoot lengths at 0 and 14 d and individual 

shoot dry weights at 14 d for multiple concentrations. Only dry weight is considered here (shoot 

lengths were a poor surrogate for growth because substantial 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 weight, 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 computed for each tank. The following table lists 

the reported final weights, the estimated initial weights, and the resultant shoot weight SGRs, 

along with the EC50 and steepness parameter estimated 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 

(μg/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 0.128,0.166,0.214,0.126 -0.001,0.016,0.026,-0.005 

Regression EC50 (μg/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 duration 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 0.248 
61

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 

EC50 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, 12, 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 the 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, some reduction in control growth rate occurred 

after 9 d, but did not appreciably affect estimated SGR EC50s. For the 14-d data, dry weights 

resulted in an EC50 29% lower than that based on frond count. This is likely attributable to the 

lower dry weight/frond at higher atrazine concentrations (i.e., smaller fronds due to atrazine 

effects), but also could be contributed to by overestimation 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 tests, but also a weakness in most tests 

regarding measuring initial weights. Due to it being a direct measure of biomass rather than an 

indicator, the dry weight-based results were compiled. 

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 .254 .254 

3.4 35.3 91.0 244 426 440 96 .285 .300 .310 .279 .241 .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 .228 .229 

47 32.3 71.7 163 303 310 17 .256 .261 .265 .250 .216 .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 

EC50 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 was 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 regression analyses were conducted on these 

SGRs. As for Hoberg 1993b, dry weight-based SGRs showed a lower EC50 and higher steepness 

than frond count-basis, and were selected for the compilation. 

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 

EC50 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 intensity 4250-5750 lux. Rapid recovery was 

demonstrated, but the analyses here are concerned with 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 the 1-3 d tests provided uncertain results due 

to the short duration and limited concentration range. The authors provided data at day 2, 4, and 

7 d and dry weight at 7 d at multiple concentrations. Initial frond counts were 15 at day -1 and 

were 20-21 at the start of exposure (this 1 d period of growth was done to identify/discard 

chambers that showed little or no growth; despite this precaution, one control replicate had poor 

enough growth to be excluded as an outlier). The initial dry weight was estimated to be 2.8 mg 

based on the average dry weight/frond in the no-effect concentrations at the end of the exposure. 

SGRs were calculated for each duration and concentration 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 

EC50 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 μE/m2

/sConcentrations 

were not measured in exposure chambers, but the stock concentrations were verified. The 1994 

report provided detailed biomass measurements absent 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, particularly based on frond counts. 

Nominal 

Atrazine Conc 

(μg/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

EC50 (μg/L) 114 

(34-390) 

Steepness 0.42 

(0.06-0.79) 

Najas: Replicates were created by placing natural pond sediments from Najas beds in beakers, 

from which plants germinated. Plants were grown for approximately 2 weeks to approximately 3 

cm in height, at which time the 14-d chemical exposure began. After the exposure, plants were 

sieved and wet weights were determined. Initial wet weights were not determined, but based on 
64

the similarity in the average weights in the highest three treatments (following table) it was 

assumed that these treatments had zero net growth and SGRs were estimated based on an initial 

wet weight of 69.5 mg, the overall average final weight 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 across replicates, this variability is reduced 

enough to produce a clear dose-response. To the extent that the highest three treatments did not 

have zero net growth the estimated EC50 will be biased, but substantial bias would be unlikely 

because (a) if substantial positive growth was occurring a concentration effect should be evident 

and (b) if substantial negative growth was occurring 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 

(μg/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 

EC50 (μg/L) 14.5 

(12.3-17.2) 

Steepness 1.67 

(1.00-2.33) 

Ceratophyllum: The authors provided wet weights for each replicate at 0, 7, and 14 d, allowing 

calculation of SGRs and regression analysis of these SGRs to determine the EC50 and steepness 

of the SGR vs concentration relationship. There was nearly a doubling of weight in the controls 

over the 14-d, allowing sufficient growth so that effects were apparent and could be quantified. 

Nominal 

Atrazine Conc 

(μg/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 0.011,0.011,0.017 

75 2649,1062,2420 2410,1078,2434 -0.007,0.001,0.000

150 1362,1322,1482 1454,1446,1415 0.005,0.006,-0.003 

300 1166,1516,878 1102,1563,1023 -0.004,0.002,0.010

EC50 (μg/L) 24 

(14-42) 
65

Steepness 0.81 

(0.12-1.50) 

Myriophyllum: The authors provided wet weights for each replicate 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 effects 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 μg/L, and 2-26% (15%) at 75 μg/L. In 

addition, at day 7, the weight gains were 12-17% (15%) in the controls, 25-33% (28%) in the 

solvent controls, 13-16% (13%) at 37.5 μg/L, and 6-21% (11%) at 75 μg/L. These data illustrate 

not just a small amount of growth and great variability relative to the average net growth, but 

also no or negative growth in most replicates during 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 controls 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. Although there are clear effects at 150 μg/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 control 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 75μg/L are apparently NOECs and the SGR EC50 is probably ≈<150 μg/L. 

Nominal 

Atrazine Conc 

(μg/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 0.011,0.007,0.011 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,-0.002,0.001 0.003 

300 3559,3039,2756 3359,2074,2829 -0.004,-0.027,0.002 -0.010 

600 2812,3748,3341 1877,3363,2992 -0.029,-0.008,-0.008 -0.015 

EC50 (μg/L) <≈150 

Steepness 

Elodea: The authors provided both wet weights for each replicate 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 two weeks. Although, this growth was not as 

variable as for Myriophyllum, it still is a questionable reference against which to quantify effects 

on SGRs. In addition, the lowest treatment concentration produced no growth on average, and 

negative growth became progressively greater at higher concentrations, so that ECs for SGR 

could not be quantified even if the controls were good references for quantifying the SGR. The 

most that can be inferred from this test is that the SGR EC50 is <38 μg/L, although even this 

might be confounded by the low control growth. 
66

Nominal 

Atrazine Conc 

(μg/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,-0.003,0.001 0.001 

75 6028,5477,6477 5435,5178,6478 -0.007,-0.004,0.000 -0.002 

150 4941,4929,4992 4778,4851,5554 -0.002,-0.001,0.007 -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 

EC50 (μg/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 EC50

of 153 μg/L was based on growth (frond count basis), and insufficient 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, 1998) this EC50 would correspond to an EC82. 

Because this extrapolation was greater than allowed in the protocol, this data just indicate that 

the SGR EC50 is <153 μg/L, which does not contradict the results of Fairchild 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. Temperature 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 initial biomass was 10 fronds and growth was 

quantified by chlorophyll, frond count, and fresh weight, with the respective EC50s being 56, 60, 

and 62 μg/L. Using the average SGRC from other studies with Lemna (0.27/d, range 0.21-

0.38/d), the EC50 for frond count would correspond to an EC25 for SGR. Using the average 

steepness for SGR vs. concentration from other studies with Lemna (1.0 for frond count increase, 

1.4 for weight increase), the SGR EC50 would then be 105 μg/L based on frond count and 95 

μg/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 methodological 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 protocol used here. For Lemna, both frond counts 

and weights were measured, but frond counts indicated 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 of atrazine, but initial 
67

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 evident at 100 μg/L, but the next lower and 

higher concentration was 10-fold different (10 and 1000 μg/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 experiment and test species, light varied from 3 to 

170 μE/m2/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 EC50 could be obtained: 

For Potamogeton, in one experiment, dry weight was measured in addition 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 these weight measurements would 

overestimate ECs. This experiment also showed atrazine-dependent mortality at concentrations 

of 32 μg/L and above. The following table shows the average dry weight of plants (at death or 

end of test for survivors), the percent survival, and 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 biomass production, but was considered adequately 

informative of atrazine effects on the SGR of a population of this plant. A regression analysis 

was thus conducted on this product and used for the compilation. 

Nominal 

Atrazine Conc 

(μg/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 

EC50 (μg/L) 63 

Steepness 0.69 

For Vallisneria, leaf length was measured and was used as a surrogate for growth because it 

would be less susceptible than shoot length to elongation with little or no weight increase. Even 

with this acceptance, most data could not be used because 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 enough 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 about atrazine effects. The following table 

provides the percentage increase in leaf length during the last week of this experiment, which 

should be approximately proportional to the SGR. In another experiment with insufficient data 
68

for analysis here, there was information on the ratio of plant weight to leaf length as a function of 

atrazine, which did indicate some thinning of the leaves due to atrazine. The following table 

includes those ratios, which provided a basis for estimating weight based on leaf length (only 

three measured values – so interpolated value used for 32 μg/L and possible extrapolated values 

for 1000 μg/L). This resulted in a decrease in the SGR EC50 of about 28%. 

Nominal 

Atrazine Conc 

(μg/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 

EC50 (μg/L) 195 140-141 

Steepness 0.36 0.39-0.41 

 For Elodea, in one experiment dry weight increase was measured. 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 plants should make the net increase proportional to 

SGR. Because of the widely space concentrations, the estimated parameters are uncertain, but 

clearly indicate the SGR EC50 to be less than 100 μg/L. 

Nominal 

Atrazine Conc 

(μg/L) 

Average Increase 

in Plant Dry Wt. 

(mg) 

0 37 

10 28 

100 17 

1000 11 

EC50 (μg/L) 65 

Steepness 0.28 

(10) Hinman 1989 

The author tested the effects of atrazine on both root and shoot growth of Hydrilla verticillata in 

both water and sediment exposures (14 d). Concentrations were nominal, light was 40-50 

μE/m2/s, and temperature was 25 C. Both shoot and root growth was monitored by increase in 

length. Increases in shoot length are subject to questions 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 compares 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% higher than for root length. 
69

Nominal 

Atrazine Conc 

(μg/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 

EC50 (μg/L) 222 118 

Steepness 2.26 0.6 
70
APPENDIX B. 

EXPERIMENTAL ECOSYSTEM DATA 
72

5 

Table B1. Summary of experimental ecosystem studies used in development of PATILOC. ID# identifies treatm

references exposure time-series provided in Table B2. Effect is binary (yes/no) regarding whether substantial icommunity occurred. 

ID # Duration (d) Initial Conc. (μg/L Atrazine) Significant Effect? Reference 

1 365 500 Y Carney 1983; Kettle et al. 1987; deNoyelles et al. 1989; deNo2 365 20 Y Carney 1983; Kettle et al. 1987; deNoyelles et al. 1989; deNo

deNoyelles & Kettle 1980, Dewey 1986 

3 63 500 Y deNoyelles et al. 1982; deNoyelles et al. 1989 4 365 100 Y deNoyelles et al. 1989 Carney 1983 5 340 200 Y deNoyelles et al. 1989 Carney 1983 7 56 80 Y Hamilton et al. 1987 8 56 140 Y Hamilton et al. 1987 9 96 100 Y Hamilton et al. 1988 

10 96 100 Y Herman et al. 1986; Hamilton et al. 1989 13 53 430 Y Stay et al. 1985 14 53 820 Y Stay et al. 1985 15 53 3980 Y Stay et al. 1985 17 7 100 Y Brockway et al. 1984 18 12 500 Y Brockway et al. 1984 19 12 5000 Y Brockway et al. 1984 22 15 15 Y Detenback et al. 1996 23 43 25 Y Detenback et al. 1996 24 32 50 Y Detenback et al. 1996 25 17 79 Y Detenback et al. 1996 26 14 100 Y Hamala and Kollig 1985 27 30 1000 Y Johnson 1986 28 21 10 Y Kosinski 1984; Kosinski and Merkle 1984 29 21 1000 Y Kosinski 1984; Kosinski and Merkle 1984 30 21 10000 Y Kosinski 1984; Kosinski and Merkle 1984 31 12 24 Y Krieger et al. 1988 32 12 134 Y Krieger et al. 1988 33 7 10000 Y Moorhead and Kosinski 1986 
73

Table B1 (continued). 

ID # Duration 

(d) 

Initial Conc. 

(μg/L Atrazine) 

Significant 

Effect? 

Reference 

34 21 337 Y Pratt et al. 1988 35 42 204 Y Stay et al. 1989 36 42 500 Y Stay et al. 1989 37 42 1000 Y Stay et al. 1989 38 42 5000 Y Stay et al. 1989 39 55 50 Y Brockway et al. 1984 40 15 100 Y Brockway et al. 1984 41 360 100 Y deNoyelles et al. 1989 42 360 200 Y deNoyelles et al. 1989 44 21 100 Y Kosinski 1984; Kosinski and Merkle 198445 7 100 Y Moorhead and Kosinski 1986 46 7 1000 Y Moorhead and Kosinski 1986 47 53 53 Y Stay et al. 1985 48 53 84 Y Stay et al. 1985 49 53 170 Y Stay et al. 1985 50 42 100 Y Stay et al. 1989 51 12 50 Y Brockway et al. 1984 52 63 20 Y deNoyelles et al. 1982; deNoyelles et al. 1989 53 30 10 N Johnson 1986 54 30 100 N Johnson 1986 58 18 1 Y Lampert et al 1989 

58b 42 0.1 Y Lampert et al 1989 

59 21 32 Y Pratt et al. 1988 60 21 110 Y Pratt et al. 1988 61 42 20 N Stay et al. 1989 62 35 5 N van den Brink et al. 1995 63 7 0.5 N Brockway et al. 1984 64 7 5 N Brockway et al. 1984 65 29 0.5 N Brockway et al. 1984 66 70 5 N Brockway et al. 1984 
74

Table B1 (continued). 

ID # Duration 

(d) 

Initial Conc. 

(μg/L Atrazine) 

Significant 

Effect? 

Reference 

67 14 5 N Gruessner and Watzin 1996 68 20 1 N Gustavson and Wängberg 1995 69 20 20 N Gustavson and Wängberg 1995 70 20 10 N Gustavson and Wängberg 1995 71 28 2 N Jurgensen and Hoagland 1990 72 28 30 N Jurgensen and Hoagland 1990 73 28 100 N Jurgensen and Hoagland 1990 75 30 25 N Lynch et al. 1985 76 21 3.2 N Pratt et al. 1988 77 21 10 N Pratt et al. 1988 78 30 25 Y Rohr and Crumrine, 2005 79 28 117 Y Rohr et al., 2008 80 36 6.4 N Relyea, 2009 81 173 84 Y Knauert et al., 2008; Knauert et al., 2009 82 23 10 Y Berard et al. 1999a, Berard et al. 1999b, Berard and Benning

et al. 2001b, Leboulanger et al. 2001 

83 40 30 N Seguin et al. 2001a 84 40 2 N Seguin et al. 2001a 85 40 30 Y Seguin et al. 2001b 86 40 2 Y Seguin et al. 2001b 87 25 30 Y Seguin et al. 2002 88 7 148 Y Downing et al. 2004 89 7 24.3 Y Downing et al. 2004 90 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) 
75

Table B2. Atrazine exposure time-series for experimental ecosystem treatments, with ID# as specified in Table 

ID #1 ID#2 ID#3 ID#4 ID#5 ID#7 ID#8 

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 

10 525 10 16.0 2 490 10 90 20 190 3 79 56 110 20 490 20 16.0 25 465 20 85 40 120 5 78 40 350 40 16.0 30 453 40 90 60 160 7 78 70 490 70 15.0 55 390 70 80 70 140 9 77 

100 400 100 12.0 63 360 100 75 80 150 11 76 130 400 130 14.0 130 70 105 120 13 76 180 375 180 15.0 180 70 130 120 15 75 285 250 285 7.0 285 35 160 110 17 75 330 200 330 5.0 330 30 190 140 19 74 365 160 365 4.0 365 25 220 120 21 73 

 250 100 23 73  290 90 25 72  340 50 27 71  29 71  31 70  33 70  35 69  37 69  39 68  41 67  43 67  45 66  47 66  49 65  51 65  53 64  55 64 
76

Table B2, Page 2. 

ID #10 ID#13 ID#14 ID#15 ID#17 ID#18 ID#19 

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 5 117 21 264 21 505 21 1890 1 100 1 498 1 4979 

14 108 46 223 46 443 46 1390 2 99 2 496 2 4958 20 107 53 198 53 417 53 1540 3 99 3 494 3 4937 24 87 4 98 4 492 4 4917 34 105 5 98 5 490 5 4896 37 142 6 98 6 488 6 4876 42 148 7 97 7 486 7 4855 54 132 8 484 8 4835 68 115 9 481 9 4815 96 53 10 479 10 4794 

 11 477 11 4774  12 475 12 4754   
77

10 

Table B2, Page 3. 

ID #23 ID#24 ID#25 ID#26 ID#27 ID#28 ID#29 

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 3 21.6 3 43 2 72 14 100 2 992 21 10.0 2 648 5 19.6 5 39 3 68 4 983 3 522 7 17.7 7 35 4 65 6 975 4 420 9 16.1 9 32 5 62 8 967 5 339 

11 14.6 11 29 6 59 10 959 6 273 13 13.2 13 26 7 56 12 951 7 220 15 11.9 15 24 8 53 14 943 8 177 17 10.8 17 21 9 51 16 935 9 142 19 9.8 19 19 10 48 18 927 10 115 21 8.9 21 18 11 46 20 919 11 92 23 8.0 23 16 12 44 22 912 12 74 25 7.3 25 14 13 42 24 904 13 60 27 6.6 27 13 14 40 26 897 14 48 29 6.0 29 12 15 38 28 889 15 39 31 5.4 31 11 16 36 30 882 16 31 33 4.9 17 34 17 25 35 4.4 18 20 37 4.0 19 16 39 3.6 20 13 41 3.3 21 11 43 3.0 
78

Table B2, Page 4. 

ID #31 ID#32 ID#33 ID#34 ID#35 ID#36 ID#37 

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 

12 24.0 12 134 1 9958 21 337 3 199 3 474 3 931 

 2 9916 5 196 5 463 5 918  3 9875 7 193 7 452 7 907  4 9833 9 190 9 441 9 895  5 9792 11 187 11 430 11 883  6 9751 13 184 13 420 13 872  7 9710 15 181 15 410 15 860  17 178 17 400 17 849  19 175 19 390 19 838  21 172 21 381 21 827  23 169 23 372 23 816  25 167 25 363 25 806  27 164 27 354 27 795  29 161 29 346 29 785  31 159 31 337 31 775  33 156 33 329 33 765  35 154 35 321 35 755  37 151 37 314 37 745  39 149 39 306 39 735  41 146 41 299 41 726 
79

Table B2, Page 5. 

ID #39 ID#40 ID#41 ID#42 ID#44 ID#45 ID#46 

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 

55 50 15 100 180 70 180 140 2 65 2 99 2 992 

 360 25 360 50 3 52 3 99 3 988  4 42 4 98 4 983  5 34 5 98 5 979  6 27 6 98 6 975  7 22 7 97 7 971  8 18  9 14  10 12  11 9  12 7  13 6  14 5  15 4  16 3  17 3  18 2  19 2  20 1 
80

Table B2, Page 6. 

ID #48 ID#49 ID#50 ID#51 ID#52 ID#53 ID#54 

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 

21 63 21 114 3 97 1 50 2 19.5 2 9.9 2 99 46 60 46 95 5 96 2 50 25 18.0 4 9.8 4 98 53 51 53 98 7 94 3 49 30 17.0 6 9.8 6 98 

 9 92 4 49 55 15.0 8 9.7 8 97  11 91 5 49 63 14.5 10 9.6 10 96  13 89 6 49 12 9.5 12 95  15 88 7 49 14 9.4 14 94  17 86 8 48 16 9.3 16 94  19 85 9 48 18 9.3 18 93  21 83 10 48 20 9.2 20 92  23 82 11 48 22 9.1 22 91  25 80 12 48 24 9.0 24 90  27 79 26 9.0 26 90  29 78 28 8.9 28 89  31 76 30 8.8 30 88  33 75  35 74  37 72  39 71  41 70 
81

Table B2, Page 7. 

ID #58b ID#59 ID#60 ID#61 ID#62 ID#63 ID#64 

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 

42 0.1 10 32 10 110 3 17.4 35 5.0 2 0.5 2 5.0 

 21 32 21 110 5 17.1 3 0.5 3 4.9  7 16.9 4 0.5 4 4.9  9 16.7 5 0.5 5 4.9  11 16.5 6 0.5 6 4.9  13 16.3 7 0.5 7 4.9  15 16.1  17 15.9  19 15.7  21 15.5  23 15.3  25 15.1  27 14.9  29 14.7  31 14.5  33 14.3  35 14.1  37 14.0  39 13.8  41 13.6 
82

15 

Table B2, Page 8. 

ID #66 ID#67 ID#68 ID#69 ID#70 ID#71 ID#72 

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 

70 5.0 5 3.6 2 1.0 2 19.8 2 9.9 2 1.6 2 23 

 10 1.2 3 1.0 3 19.7 3 9.9 3 0.0 3 0  14 1.2 4 1.0 4 19.7 4 9.8 4 0.0 4 0  5 1.0 5 19.6 5 9.8 5 0.0 5 0  6 1.0 6 19.5 6 9.8 6 0.0 6 0  7 1.0 7 19.4 7 9.7 7 0.0 7 0  8 1.0 8 19.3 8 9.7 8 0.0 8 0  9 1.0 9 19.3 9 9.6 9 0.0 9 0  10 1.0 10 19.2 10 9.6 10 0.0 10 0  11 0.9 11 19.1 11 9.5 11 0.0 11 0  12 0.9 12 19.0 12 9.5 12 0.0 12 0  13 0.9 13 18.9 13 9.5 13 0.0 13 0  14 0.9 14 18.9 14 9.4 14 2.0 14 30  15 0.9 15 18.8 15 9.4 15 1.6 15 23  16 0.9 16 18.7 16 9.3 16 0.0 16 0  17 0.9 17 18.6 17 9.3 17 0.0 17 0  18 0.9 18 18.5 18 9.3 18 0.0 18 0  19 0.9 19 18.5 19 9.2 19 0.0 19 0  20 0.9 20 18.4 20 9.2 20 0.0 20 0  21 0.0 21 0  22 0.0 22 0  23 0.0 23 0  24 0.0 24 0  25 0.0 25 0  26 0.0 26 0  27 0.0 27 0  28 0.0 28 0 
83

Table B2, Page 9. 

ID #75 ID#76 ID#77 ID#78 ID#79 ID#80 ID#81 

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 

30 25.0 21 3.2 21 10.0 2 24.8 2 116 3 6.3 7 80 

 3 24.7 3 116 5 6.3 13 77  4 24.6 4 115 7 6.2 19 74  5 24.5 5 115 9 6.2 25 78  6 24.4 6 114 11 6.1 31 75  7 24.3 7 114 13 6.1 37 72  8 24.2 8 113 15 6.0 43 69  9 24.1 9 113 17 6.0 49 66  10 24.0 10 112 19 5.9 55 64  11 23.9 11 112 21 5.9 61 61  12 23.8 12 111 23 5.8 67 59  13 23.7 13 111 25 5.8 73 57  14 23.6 14 110 27 5.7 79 55  15 48.5 15 110 29 5.7 85 53  16 48.3 16 109 31 5.6 91 51  17 48.1 17 109 33 5.6 97 49  18 47.9 18 109 35 5.5 103 47  19 47.7 19 108 109 45  20 47.5 20 108 115 43  21 47.3 21 107 121 42  22 47.1 22 107 127 40  23 46.9 23 106 133 39  24 46.7 24 106 139 37  25 46.5 25 105 145 36  26 46.3 26 105 151 34  27 46.1 27 105 157 33  28 45.9 28 104 163 32  29 45.7 169 31  30 45.5 
84

Table B2, Page 10. 

ID #83 ID#84 ID#85 ID#86 ID#87 ID#87 ID#89 

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 3 30 3 2.0 3 30 3 2.0 2 30 2 127 2 18.3 5 29 5 2.0 5 29 5 2.0 3 30 3 120 3 20.7 7 29 7 1.9 7 29 7 1.9 4 30 4 112 4 19.6 9 29 9 1.9 9 29 9 1.9 5 29 5 105 5 18.6 

11 29 11 1.9 11 29 11 1.9 6 29 6 98 6 17.6 13 28 13 1.9 13 28 13 1.9 7 29 7 88 7 15.4 15 28 15 1.9 15 28 15 1.9 8 29 17 28 17 1.9 17 28 17 1.9 9 29 19 28 19 1.8 19 28 19 1.8 10 29 21 28 21 1.8 21 28 21 1.8 11 29 23 27 23 1.8 23 27 23 1.8 12 29 25 27 25 1.8 25 27 25 1.8 13 28 27 27 27 1.8 27 27 27 1.8 14 28 29 27 29 1.8 29 27 29 1.8 15 28 31 26 31 1.8 31 26 31 1.8 16 28 33 26 33 1.7 33 26 33 1.7 17 28 35 26 35 1.7 35 26 35 1.7 18 28 37 26 37 1.7 37 26 37 1.7 19 28 39 26 39 1.7 39 26 39 1.7 20 28 

 21 28  22 27  23 27  24 27  25 27    
85

Table B2, Page 11. 

ID #95 ID#96 ID#97 ID#98 ID#99 ID#100 ID#101 

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 20.1 1 196 1 2036 1 24.5 1 50 1 104 1 248 3 19.5 3 193 3 1986 42 24.5 42 50 42 104 42 248 5 19.0 5 191 5 1954 7 18.6 7 189 7 1922 9 18.2 9 188 9 1890 

11 17.8 11 186 11 1859 13 17.4 13 184 13 1829 15 17.0 15 183 15 1799 17 16.6 17 181 17 1769 19 16.3 19 180 19 1740 21 15.9 21 178 21 1712 23 15.6 23 176 23 1684 25 15.2 25 175 25 1656 27 14.9 27 173 27 1629 29 14.6 29 172 29 1603 31 14.2 31 170 31 1576 33 13.9 33 169 33 1551 35 13.6 35 167 35 1525 37 13.3 37 166 37 1500 39 13.0 39 164 39 1476 41 12.7 41 163 41 1452 43 12.4 43 161 43 1428 45 12.2 45 160 45 1404 47 11.9 47 158 47 1381 49 11.6 49 157 49 1359 51 11.4 51 155 51 1337 
86

20