Appendix E: 

Proposed Methodology for Specifying Atrazine Levels of Concern for 

Protection of Plant Communities in Freshwater Ecosystems 

(90 Pages) 
1 

1 

2 

3 Proposed Methodology for 

4 Specifying Atrazine Levels of Concern 

5 for Protection of Plant Communities 

6 in Freshwater Ecosystems 

7 

8 Report To: 

9 Environmental Fate and Effects Division 

10 Office of Pesticide Programs 

11 U.S. Environmental Protection Agency 

12 Washington, DC 

13 

14 EPA/600/R-12/019 

15 

16 Russell Erickson 

17 Mid-Continent Ecology Division 

18 National Health and Ecological Effects Laboratory 

19 Office of Research and Development 

20 U.S. Environmental Protection Agency 

21 Duluth, MN 

22 

23 March 5, 2012 

24 
2

25 ACKNOWLEDGEMENTS 

26 This document has been reviewed in accordance with U.S. Environmental Protection 

27 Agency policy and approved for publication. Mention of trade names or commercial products 

28 does not constitute endorsement or recommendation for use. The author would like to 

29 acknowledge the valuable reviews of drafts of this document by: 

30 David Mount, U.S.EPA, Mid-Continent Ecology Division 

31 Dale Hoff, U.S.EPA, Mid-Continent Ecology Division 

32 Mary Ann Starus, U.S.EPA, Mid-Continent Ecology Division 

33 Glen Thursby, U.S.EPA, Atlantic Ecology Division 

34 Joseph S. Meyer (retired), University of Wyoming, Department of Zoology and Physiology 

35 

36 The author would also like to acknowledge the importance to this document's development of his 

37 numerous interactions over the last several years with various personnel of U.S.EPA's Office of 

38 Pesticide Programs and Office of Water. 

39 
3

40 1. INTRODUCTION

41 This document describes a proposed methodology for setting a level of concern (LOC) 

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

43 plant communities in those systems. Effects on humans and possible endocrine-disruption in 

44 aquatic vertebrates are subjects of separate efforts, and certain implementation issues for aquatic 

45 plant community atrazine risk assessment are also described elsewhere. This first section defines 

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

47 1.1 Requirements for the LOC Methodology 

48 Toxic chemical risk assessment problem definition requires defining the exposure 

49 scenarios to be addressed, specifying the assessment endpoints of concern, and identifying 

50 measures of effect for the assessment endpoints (U.S.EPA 1998). 

51 This LOC methodology must address the types of atrazine exposures occurring in natural 

52 ecosystems for which risk is to be assessed. Atrazine enters natural freshwater systems primarily 

53 in rainfall-driven runoff, resulting in highly variable and episodic exposures that depend on 

54 rainfall distribution, atrazine application patterns, topography, and soil properties. Figure 1 

55 provides example time-series of 

56 atrazine exposures during 2010 in 

57 three Missouri streams, measured as 

58 part of a monitoring program being 

59 conducted to satisfy risk evaluations 

60 required under the 2003 interim 

61 reregistration of atrazine (U.S.EPA 

62 2003). These examples illustrate 

63 substantial variation in exposure 

64 patterns, and thus the need for the 

65 LOC methodology to address the 

66 relationship of effects to time, 

67 including high concentrations with 

68 limited durations, multiple events, 

69 and prolonged, variable exposures at 

70 low to moderate concentrations. The 

71 top and bottom series have similar 

72 average concentrations but very 

73 different peaks, underscoring the 

74 issue of the comparative risk of short, 

75 intense exposures to more prolonged 

76 exposures at lower concentrations. 

77 The assessment endpoint for this LOC methodology is the productivity and composition 

78 of natural aquatic plant communities. Although atrazine has been the subject of many toxicity 

79 tests on individual aquatic plant species and although such tests are often used as measures of 

80 effect for aquatic plant risk assessments (e.g., Solomon et al. 1996, Giddings et al. 2000), they 

81 will not be used directly for that purpose in this methodology. Rather, because atrazine has been 

Figure 1. Examples of atrazine exposure time-series in natural 

freshwater systems. 

Julian Day

μg/L Atrazine

0

50

100

150

90 120 150 180 210

0

10

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30

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MO 07 2010

MO 02 2010

MO 01 2010
4

82 the subject of many experimental aquatic ecosystem studies documenting plant community 

83 responses, these will be used to provide measures of effect and to serve as the foundation for 

84 defining exposures causing effects of concern. Figure 2 summarizes an evaluation of such 

85 studies conducted by the U.S.EPA's Office of Pesticide Programs (OPP) Environmental Fate and 

86 Effects Division (EFED) (U.S.EPA 2011). In Figure 2, each experimental ecosystem treatment 

87 is characterized by the duration over which effects were assessed, the average atrazine 

88 concentration over this duration, and whether there were unacceptably adverse effects on the 

89 plant community. For each point on Figure 2, Appendix B of this report provides more complete 

90 exposure information, the effects designation, and a literature citation; other information on the 

91 analyses of these studies can be found in U.S.EPA (2011). It should be emphasized that a 

92 fundamental assumption in using such experimental ecosystem data is that they collectively 

93 describe a relationship of effects to exposure that is relevant to the probability of effects (i.e., 

94 risk) occurring in natural freshwater systems. In other words, it is assumed that natural aquatic 

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

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

97 any assessment that extrapolates toxicity experiments to the field, and the use of experimental 

98 ecosystems arguably provides a better basis than do single-species toxicity tests. 

99 Figure 2 illustrates three important requirements for the LOC methodology: 

100 (1) Diversity among the experimental approaches precluded characterizing each experimental 

101 ecosystem treatment with an identical, quantitative measure of effect. Therefore, LOC 

102 characterizations must rely on a binary (acceptable vs. unacceptable) characterization of effect. 

103 (2) Although the exposures that resulted in adverse effects are somewhat separated from those 

104 that did not cause adverse effects, substantial overlap exists between these two groups, especially 

105 in the 10-20 μg/L range. This variability is presumably due to the combined effect of: 

106 differences in the nature of the experimental systems; differences in the experimental design and 

Figure 2. 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
5

107 the endpoints measured; and random variability of the response of any given system. The 

108 methodology must address how to specify an LOC within such variability. 

109 (3) The LOC methodology must address the relationship of effects to time. This is important 

110 not only because of the variability of field exposures shown in Figure 1, but also because of the 

111 different durations of the experimental ecosystem exposures (Figure 2) and exposure variability 

112 within these durations (Appendix B). Because data in Figure 2 do not provide information on 

113 the relationship of the same endpoint to different exposure time-series, this time-dependence 

114 issue must be addressed in the formulation of the extrapolation methodology discussed below. 

115 1.2 General Framework for the LOC methodology 

116 The key issue that this LOC methodology must address is how to relate aquatic plant 

117 community effects elicited in an experimental ecosystem by a particular atrazine exposure time118 series to markedly different time-series in other experimental studies or natural systems. If all 

119 exposures of interest had the same shape (i.e., the same exposure duration and the same relative 

120 changes in concentration within that duration), the LOC could be based on the relationship of 

121 effects in the experimental studies to any convenient measure of exposure. However, the 

122 markedly different exposure shapes discussed above preclude such a simple approach, and there 

123 is thus a need for a method to translate any exposure time-series to a "common currency" that 

124 integrates time and concentration into an index of the relative total severity of effects from the 

125 exposure. This "effects index" serves only as a relative measure of effect because the 

126 experimental ecosystem effects define the absolute levels of concern. Text Box 1 further defines 

127 and discusses this concept of an effects index. 

Text Box 1. The nature and purpose of the "effects index".

 To further clarify the nature and purpose of the "effects index", consider a simple 

hypothetical example in which the results from a single experimental ecosystem study must be 

used to assess risk to the same ecosystem, but for an exposure with a different shape. For this 

example, the experimental ecosystem study is specified to (a) involve constant atrazine exposure 

over 30 d at several concentrations and (b) demonstrate that 20 μg atrazine/L constitutes an LOC 

based on the magnitude of effects elicited. However, this concentration-based LOC applies only to 

constant, 30-d exposures, whereas the exposure of interest is specified for this example to be a 

10-d exposure at 100 μg atrazine/L. The basic question is whether this more intense (5x higher) 

but more brief (3x shorter) exposure should be considered worse than the 30 d LOC concentration, 

provided the effects are assessed in the same manner and over the same time period as in the 

original study. 

 A very simple "effects index" for this would assume that effects increase linearly with both 

concentration and time, so that the effects index could be the area under the exposure time-series, 

measured in "ppb-days" (note: this effects index definition is provided only to illustrate the concept

– the actual methodology should consider the nonlinearity of effects versus exposure) The LOC 

for this effects index would therefore by 600 ppb-days (20 μg/L x 30 days) based on the 

experimental results. This effects index-based LOC is exceeded by the effects index value of 1000 

ppb-days (100 μg/L x 10 days) for the new exposure of interest. 

 This effects index is a relative measure in that it has no inherent absolute meaning for risk 

except when calibrated to the experimental ecosystem results. Its use is only for translating any 

exposure time-series to a common scale of comparison, so that the LOC of 600 ppb-days can be 

used to judge any other exposure of interest, provided the exposure is for a system to which the 

experimental ecosystem is relevant.
6

128 The effects index proposed for the LOC methodology will be described in Section 2. For 

129 discussing the assessment framework here, it is only necessary to assume the existence of an 

130 effects index that is suitable for comparing the relative severity of different exposure time series. 

131 Figure 3 provides a schematic of an assessment framework using such an effects index. 

132 The process starts (Box 1) with compiling relevant experimental ecosystem data, 

133 categorizing each treatment as to whether there was an effect or not and specifying the exposure 

134 time-series for the treatment. This step is not a subject of this report, but rather is addressed in 

135 U.S.EPA (2011). The effects index is then calculated (Box 2) for each experimental ecosystem 

136 treatment, providing the "common currency" to compare the severity of each exposure. The 

137 relationship of the binary experimental ecosystem effects to this effects index is then examined 

138 (Box 3) to set a level of concern for the effects index (LOCEI), based on the probability of 

139 eliciting an effect (i.e., risk). 

 The LOCEI 140 is applied to exposures in natural systems as follows. Exposure time-series 

141 are compiled for the various exposures of interest in natural ecosystems (Box 4) and the effects 

142 index for each exposure is computed (Box 5). Risk is characterized (Box 6) by dividing the 

effects index by the LOCEI 143 to compute the "effects exceedence factor" (EEF). The EEF indicates 

144 whether the LOC is exceeded (i.e., EEF>1) and by how much. The EEF thus represents a risk 

145 quotient approach, but this different terminology is used here to distinguish this effects-based 

146 quotient from concentration-based risk quotients commonly used. 

Figure 3. Assessment framework for risk of atrazine to aquatic plant communities, based on experimental 

ecosystem results and an effects index for comparing different exposure time-series. 
7

147 Risk can also be characterized by what is termed the "concentration exceedence factor" 

148 (CEF) in Box 6. This factor is based on iterative calculations to determine the multiplicative 

149 factor by which the exposure must be decreased so that the effects index exactly equals the 

LOCEI. As for the EEF, a CEF indicates whether the LOCEI 150 is exceeded and by how much, but 

151 on a concentration scale rather than an effects scale. This could have some advantage in 

152 determining remediation goals or, conversely, determining how far exposures are below levels of 

153 concern. However, this is an approximate measure for such purposes, because the CEF is 

154 premised on the same multiplicative factor applying to the entire concentration time-series. 

155 Box 7 and the associated gray arrows in Figure 3 represent a final step in the assessment 

156 framework that is not addressed in this document. It would be desirable for LOCs to be on a 

157 concentration scale rather than an effects scale so that they relate more easily and directly to 

158 exposure monitoring data. In Box 7, the relationship of EEFs to an average exposure 

159 concentration for a large number of existing exposure time series is examined to determine an 

160 LOC based on this average concentration, and which then can be applied to new exposure time161 series, for which the effects index need not be computed. Developing such a concentration162 based LOC from the effects index-based LOC is being addressed separately by EFED. 

163 Finally, it should be emphasized that the only site-specific factor intended to be addressed 

164 in this LOC methodology is the exposure time-series. The methodology is not intended to 

165 address other site-specific factors, such as physicochemical conditions and the nature of the 

166 biological community. Addressing such conditions is not feasible from a standpoint of both 

167 effort/cost and knowledge of their influence on atrazine effects. Rather, this method will be 

168 generic in that any site with the same atrazine concentration time-series will be assessed as 

169 having the same risk.

170 
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171 2. PLANT ASSEMBLAGE TOXICITY INDEX 

172 2.1 Potential Effects Indices 

173 There are various possibilities, with differing complexities, for calculating an effects 

174 index to serve in the assessment framework of Figure 3. For illustrative purposes only, Text Box 

175 1 assumed that effects increased linearly with both concentration and time, leading to an effects 

176 index of ppb-days. To actually apply this simple, linear model a priori is not justified. Rather, 

177 the effects index should consider ecotoxicological relationships. 

178 At the other extreme of complexity are community simulation models that address not 

179 only the immediate impact of atrazine on plant community primary production, but also consider 

180 the ramifications of this on plant community dynamics throughout a growing season. Earlier 

181 efforts for developing an LOC methodology considered the use of the Comprehensive Aquatic 

182 Simulation Model (CASM) (Bartell et al. 2000, Volz et al. 2007), but determined that this model 

183 was not suitable for the purposes here (U.S.EPA 2009, Erickson 2009). This model does not 

184 provide any clear, validated, substantial added-value beyond describing the immediate response 

185 of plant community growth, entails extensive data and parameterization needs that were not 

186 completely satisfied, and involves considerable uncertainty. CASM is more suited for focused 

187 site assessments, involving considerable resources for system-specific model development and 

188 application, and a completely different assessment framework. 

189 A community simulation model such as CASM applies information from atrazine toxicity 

190 tests on individual plants species to calculate the direct (primary) impact on the plant community 

191 being simulated, but then also considers the secondary (indirect) ramifications on plant 

192 community dynamics. The direct, primary impact was determined to be more important for 

193 assessing the relative impact of different atrazine exposure time-series (i.e., the purpose of the 

194 effects index) than are the secondary impacts (U.S.EPA 2009). Thus, the approach pursued here 

195 was to base the effects index just on this primary impact, avoiding various uncertainties and 

196 complexities in the community model. 

197 The need here therefore is to use the 

198 collective information from toxicity tests on 

199 individual plant species to provide a measure 

200 of direct impacts of atrazine on plant 

201 communities. To this end, past assessments 

202 of the risk of atrazine to aquatic plant 

203 communities (e.g., Solomon et al. 1996; 

204 Giddings et al. 2000) have generally 

205 summarized the results of a toxicity test as a 

206 median effect concentration (EC50), the 

207 concentration causing a 50% decrease in 

208 some measure of growth over the duration of 

209 the test. Average EC50s for each species are 

210 then used to describe a species sensitivity 

211 distribution (SSD) – the cumulative 

212 percentage of species with EC50s less than a 

Figure 4. Example of aquatic plant SSD based on data 

from Giddings et al. (2000). 

EC50 (μg/L Atrazine)

5 20 50 200 500 2000 10 100 1000

Cumulative Percentage

0

20

40

60

80

100
9

213 certain value (e.g., Figure 4). SSDs are typically applied by addressing what percentiles are 

214 exceeded by an exposure. For example, in Figure 2, an exposure of 10 μg/L would be below the 

215 EC50s of 95% of the species and an exposure of 45 μg/L would be below the EC50s of 80%. 

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

217 exposures in Figure 1: 

218 (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, the 5th 219 percentile in Figure 4 only 

220 describes the concentration at which the growth of a particular species is reduced by 50%. No 

221 information is provided on how much greater effects on this species are at higher concentrations, 

222 or how much smaller effects are at lower concentrations. For other species, no information is 

223 given other than that their EC50s are less than or greater than the LOC. Much more information 

224 regarding effects is contained within the toxicity test data, but how should it be used? 

225 (2) SSDs such as in Figure 4 also do not address the issue of time. How should effects be 

226 described for longer or shorter exposures and, especially, exposure concentrations that fluctuate? 

227 If an LOC based on an SSD percentile is simply applied to the peak exposure, the exposure time228 series in the top panel in Figure 1 would be considered of most concern, but toxic impact would 

229 probably be greater for the middle time-series and perhaps as great for the lower time-series, 

230 because of the more prolonged and multiple exposure periods. How should total impact be 

231 assessed over an entire time-series? 

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

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

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

235 exposure durations and conditions, such that these EC50s can have greatly different meaning 

236 regarding actual plant sensitivity. The spread of values in the SSD might therefore be due to 

237 differences among test endpoints as well as differences among species. Such inconsistency in 

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

239 2.2 Definition of the Plant Assemblage Toxicity Index 

240 To quantify the overall effect of atrazine on an assemblage of plant species of interest, the 

241 effects index proposed here is the "Plant Assemblage Toxicity Index" (PATI). PATI is a simple 

242 extension of the SSD concept that (a) considers the entire growth inhibition vs. concentration 

243 curve (“toxicity relationship”) for each plant species and (b) determines the average effect level 

244 across all species (the "assemblage") at each concentration. Figure 5 illustrates this, using 

245 atrazine toxicity data summarized in Appendix A. The middle panel shows overlapping toxicity 

246 relationships for 20 plant genera. In the top panel, the EC50s for each genus are used to create a 

247 traditional SSD – simply the cumulative percentage of the EC50s. For the bottom panel, the 

248 average magnitude of effect across all species at each concentration is used to create the PATI 

249 distribution. At 50 μg/L, the average effect over all genera is 19%, providing the PATI value in 

250 the bottom panel (arrow). Thus, rather than just providing the percentage of species that have an 

EC50 below some concentration (e.g., 50 μg/L corresponds roughly to the 16th 251 percentile on the 

252 SSD), PATI describes the percent reduction in plant production for the entire assemblage 

253 (weighting each species equally). Although the shape of the PATI curve is similar to that of the 
10

254 traditional SSD curve, it provides more 

255 information on the total impact on the plant 

256 assemblage and allows more meaningful 

257 comparisons between different exposure 

258 concentrations. 

259 However, the definition and 

260 calculation of PATI illustrated in Figure 5 is 

261 not yet complete because it does not address 

262 the issue of time. For a time-series of daily 

263 concentrations, there would need to be 

264 separate calculations for each day to generate 

265 a time-series of daily PATI values, using 

266 toxicity endpoints relevant to this timeframe. 

267 Because of the rapid recovery of growth rates 

268 in toxicity tests when atrazine exposures are 

269 terminated (e.g. Abou-Waly et al. 1991, 

270 Desjardin et al. 2003), daily PATI values need 

271 not consider residual toxicity from exposures 

272 on previous days, but rather only the toxicity 

273 for the current day's exposure. 

274 Because the effects index is intended 

275 to describe total toxic impact, the approach 

276 here to address time is simply to sum the daily 

277 PATI values to provide a "cumulative PATI". 

278 This is illustrated in Figure 6. Concentrations 

279 in the left panel are converted to daily PATI values (middle panel), which are then summed to 

280 provide the cumulative PATI values in the right panel. The cumulative PATI can also be viewed 

281 as the "area under the curve" of the daily values, this area being a measure of the total toxic 

282 impact of the exposure. 

Figure 5. Comparison of toxicity relationships for 20 

plant genera (middle panel), the SSD of EC50s for 

these genera (top panel), and the plant assemblage 

toxicity index (bottom panel, PATI = the average of

the curves in the middle panel). 

Atrazine Concentration (μg/L)

Percent of Species with EC

10 20 50 200 500 100 1000

50 Below

Designated Atrazine Concentration

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60

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100

Atrazine Concentration (μg/L)

10 20 50 200 500 100 1000

Percent Toxic Effectfor Individual Species

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1 2 3 4 5 6 7 89 101112 13 1415 16 17181920

Atrazine Concentration (μg/L)

10 20 50 200 500 100 1000

Plant Assemblage Toxicity Index

(Average Effect Across All Species)

0

20

40

60

80

100

Figure 6. Overview of PATI calculations. A concentration time-series (left panel) is converted to expected 

instantaneous or daily reductions in plant assemblage growth (middle panel), which is then integrated to provide 

a cumulative PATI value for the exposure (right panel). 

Time (d)

0 5 10 15 20

Daily PATI (%)

0

10

20

30

40

0 5 10 15 20

Atrazine Concentration (μg/L)

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100

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Cumulative PATI (%-days)

0

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11

283 The summation units of this cumulative PATI are analogous to the ppb-days discussed 

284 earlier or, more familiarly, with degree-days used to describe the total heating or cooling impact 

285 of seasonal weather. A fundamental aspect of such a summation is that a certain reduction in 

286 growth over 1 d is treated as having equal importance as: half that reduction persisting for 2 d; a 

287 quarter of that reduction persisting for 4 d; etc. Although such a general time-dependence has 

288 not been demonstrated for actual aquatic ecosystems, it has been observed to approximate well 

289 the cumulative effects on biomass in single-species toxicity tests that maintain a constant level of 

290 effects on plant growth rate during the exposure period (e.g., Shafer et al. 1994). 

291 This methodology uses a simple summation of toxic effects to provide an index for the 

292 relative toxic effects of different time-series on plant communities and deliberately does not 

293 address any further effects on plant community dynamics beyond short-term reductions in 

294 growth across the plant assemblage. As already noted, the basic PATI calculation is similar to 

295 the first step in community models such as CASM, which on each day calculates the toxic 

296 impact on the growth of various species – the fundamental difference being that PATI does not 

297 consider how this toxicity changes community composition through time. Because community 

298 dynamics are driven on each day by the same growth reductions that are incorporated into PATI, 

299 PATI does describe the primary driving force for atrazine effects on plant communities. Even if 

300 community dynamics modify the relative severity of some time-series compared to that expected 

301 based just on PATI, these would be secondary effects and are not understood well enough to be 

302 satisfactorily addressed (U.S.EPA 2009, Erickson 2009). 

303 However, this summation cannot be continued indefinitely, but rather is limited here to 

304 an "assessment period" that can reflect risk management decisions about cumulative effects. For 

305 example, if two short atrazine exposures were separated by 90 d, a 120 d assessment period 

306 would consider them cumulative whereas a 60 d assessment period would not, this shorter period 

307 instead assuming that sufficient time had passed that the second exposure should be assessed 

308 independently of the first. The shorter assessment period would also avoid assigning concern to 

309 prolonged low exposures of uncertain, minor impact. For exposures with finite durations less 

310 than the assessment period, the summation would simply stop at the exposure duration. For 

311 exposures with durations greater than the assessment period, the summation would encompass 

312 the worst part of the exposure. For this report, this limit on cumulative toxicity will be 

313 designated with a subscript denoting the length of the assessment period (e.g., PATI30d denotes a 

314 30-d assessment period). Without a subscript, PATI will refer to daily or instantaneous values, 

315 or the general PATI concept. The selection of the assessment period is addressed in Section 4. 

316 2.3 Single-Species Plant Toxicity Test Data 

317 Implementation of the PATI approach requires a compendium of the effects of atrazine 

318 on aquatic plants or statistical distributions describing these effects. Existing compendia of plant 

319 effects concentrations (ECs) (e.g., Giddings et al. 2000) have certain shortcomings regarding 

320 their applicability to risk assessment, which warranted reanalysis of existing single-species 

321 toxicity tests. This section describes: the shortcomings of concern; a new review and analysis of 

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

323 PATI and for conducting atrazine risk assessments. 

324 
12

Figure 7. Variation of plant growth effects with time 

and measurement endpoint. Top panel shows 

exponential biomass changes at the control SGR 

(solid line) and at one-half of the control SGR (dashed 

line). Bottom panel converts this to percent effect on 

biomass (solid line), on biomass increase (dashed 

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

Time (d)

0 1 2 3 4

Plant Biomass (g)

0

5

10

15

20

Time (d)

0 1 2 3 4

Percent Reduction Relative to Control

0

20

40

60

80

100

Initial Biomass

Specific Growth Rate

Biomass

Biomass Increase

Biomass for

50% Reduction in

Specific Growth Rate

Control Biomass

325 2.3.1. Issues in Interpreting and Applying Plant Toxicity Test Results 

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

327 how tests are conducted and analyzed. For microalgae, tests are usually conducted on cell 

328 suspensions under favorable (at least at test start) conditions of temperature, light, and nutrients. 

329 These tests can involve various measurement endpoints, including (a) actual biomass; (b) 

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

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

332 over which measurements are made can vary from several minutes to several weeks, and 

333 measurements might be reported at multiple times or only at the end of exposure. Biomass or 

334 biomass surrogates might be analyzed based on (a) biomass values at various times during the 

335 exposure, (b) biomass increase (growth) at 

336 various times, (c) the area under the growth 

337 time-series (AUC), and/or (d) specific growth 

rate (SGR)1338 . 

339 The meaning of an EC can be greatly 

340 affected by test duration and by whether it is 

341 based on absolute biomass, growth, or SGR. 

342 To illustrate this, Figure 7 provides a 

343 hypothetical example comparing growth when 

344 the control SGR (SGRC) is 1.0/d to when a 

345 chemical exposure reduces the SGR to half of 

346 this value. The top panel shows the actual 

347 biomass vs. time in the control compared to 

348 the chemical exposure, while the bottom panel 

349 shows the percent reduction due to chemical 

350 exposure for SGR (constant at 50%), absolute 

351 biomass, and growth (biomass increase). 

352 For growth, the treatment that is an 

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

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

355 For absolute biomass, this concentration 

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

357 these times. For other values of SGRC, more 

358 widely ranging ECs can occur. Using absolute 

359 biomass can result in particularly misleading 

360 ECs when growth rates are modest. For 

361 example, when control growth is just a 

362 doubling of biomass over the duration of the 

 

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). 
13

363 test, an EC50 for absolute biomass actually represents no growth. Such issues with endpoint 

364 definition have been noted by others (e.g., Bergtold and Dohmen 2010) and are reflected in 

365 recent OECD guidelines. 

366 Therefore, EC50s reported for absolute biomass, growth, and SGR will differ from each 

367 other, and these differences will vary with exposure duration and the SGRC. This is especially 

368 problematic when reports for toxicity tests just provide ECs, without sufficient information on 

369 absolute biomasses and/or SGRs as a function of time and concentration to calculate more 

370 consistent and meaningful measures of effect. Compendia that simply transcribe reported EC50s 

371 can be describing a wide range of different effects, and assessments based on such compendia 

372 will be ill-defined. 

373 Other factors make the meaning of reported plant ECs even less certain. As an algal 

374 suspension grows, the growth rate will decline because of nutrient depletion and self-shading. 

375 This departure from exponential growth will be most pronounced in the treatments with the 

376 highest growth rates (i.e., the control and low toxicant concentrations with little or no effect), so 

377 that the treatments with greater toxic effects might "catch up" as exposure duration increases, 

378 causing ECs for total growth to not decrease with time as much as they would without these 

379 limitations, or to even increase with time. In other words, the toxicity test actually can include 

380 stressors (nutrient/light limitations) in addition to the toxicant that can confound the effects of the 

381 toxicant. In fact, some standard plant test protocols were originally designed to assess nutrient 

382 limitations, and the durations were selected to result in nutrient depletion (e.g., Miller et al. 

383 1978). When used for toxicants, this type of study design can result in complicated growth 

384 dynamics and relationships that are difficult to interpret and apply. Tests can also have different 

385 photoperiods, which would also need to be considered in comparing ECs for growth (although 

386 ECs for SGR can be directly compared between different photoperiods). 

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

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

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

390 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 

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

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

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

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

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

initial cell density (2⋅105 cells/ml), which would have resulted at 3 d in a cell density of 3⋅108396 

397 cell/ml if a SGRC similar to that in the flow-through system had been maintained for the entire 3 

398 d. Such a cell density would have resulted in both self-shading and nutrient depletion in the 

399 control, contributing to the apparent reduced sensitivity. Increases with time for growth-based 

400 ECs are evident in other studies in the review presented later, although the opposite can also be 

401 true, indicating additional complexities. 

402 Changes in cell condition other than light and nutrient limitations might also affect ECs 

403 and their dependence on test duration. For example, chlorophyll content per cell can increase 

404 with time to compensate for reduced photosynthesis. Mayer et al. (1998) reported the 
14

405 chlorophyll content of algal cells to increase by 10-fold in response to exposure to 200 μg/L 

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

407 surrogate for plant biomass inadvisable, potentially misrepresenting toxic effects on biomass. 

408 For example, van der Heever and Grobbelaar (1996) reported effect concentrations in the same 

409 exposures to be about 2.5-fold higher when based on chlorophyll than when based on cell 

410 numbers or dry weight. Similarly, toxicants can alter cell volume and mass (e.g., van der Heever 

411 and Grobbelaar 1996), creating differences among ECs based on cell count, cell volume, and cell 

412 weight, although these differences are much smaller than those due to the influence of 

413 chlorophyll, test duration, nutrient depletion, and light limitations. 

414 Although oxygen production and radiocarbon fixation are arguably closely linked to 

415 biomass production, ECs based on these measures can also pose interpretation problems: 

416 (a) They are often done over such short durations that apparent effects might be reduced because 

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

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

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

420 prior exposure have been reported to be similar to those based on biomass measurements (e.g., 

421 Turbak et al. 1986). 

422 (b) Short-term radiocarbon fixation rates can conceivably reflect gross or net photosynthesis (or 

423 a weighted combination of the two) depending on the disposition of the radioactive carbon in the 

424 organism. Williams et al. (1996) determined that radiocarbon fixation over short periods 

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

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

427 photosynthesis. 

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

429 evolution measurements can involve extremes of oxygen concentrations that might affect 

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

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

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

433 their impacts. 

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

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

436 interpretation. Short-term values for oxygen production or radiocarbon fixation for an 

437 approximately constant mass of algae are analogous to the SGR, whereas measurements long 

438 enough for substantial growth to occur would be analogous to net cumulative growth, creating 

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

440 1986), the situation was especially complicated because carbon-14 fixation was measured only 

441 during a short period at the end of a 24-h atrazine exposure, so that the measured fixation rate 

442 reflected both effects of the toxicant on the rate of carbon fixation per cell and the cumulative 

443 differences in cell density due to the preceding exposure. 

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

445 conditions discussed above. Many macrophytes grow slowly enough so that biomass increases 
15

446 by only a few multiples during the tests. Duckweed tests show more rapid growth, but also 

447 usually do not reach biomass levels sufficient to suppress growth rates (frond crowding or 

448 nutrient depletion). However, the general issues raised above for microalgae should still be 

449 considered in the interpretation of macrophyte tests and the definition of their ECs. For example, 

450 reduced photosynthesis can result in elongation of plant shoots with little or no biomass increase, 

451 so that shoot length can be a poor surrogate for biomass changes (e.g., Fairchild et al. 1994, 

452 1998). In addition, some macrophyte tests involve rhizomes, which contain resources to 

453 temporarily support growth that might obscure toxic effects, again making length a questionable 

454 measure and even making weight problematic if only shoot biomass is measured. Furthermore, 

455 if test protocols with cuttings result in slow growth (e.g., due to the absence of rooting), 

456 variability can make it difficult to quantify toxic effects and/or make such toxic effects of 

457 uncertain relevance to the field. Finally, use of oxygen in interpreting growth of some vascular 

458 plants might be confounded by gas exchanges to aerenchyma (air channels). 

459 2.3.2. Review of Single-Species Plant Toxicity Tests 

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

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

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

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

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

465 compared among tests and describe short term effects relevant to daily PATI values. The SGR 

466 was selected as this “common currency” because it reduces the dependence of ECs on test 

467 duration and is more directly applicable to addressing effects of time variable exposure. In 

468 addition to compiling information on EC50s, there was also a need for information on the entire 

469 SGR vs. concentration curve, which is also inadequately addressed in previous compendia. 

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

471 information regarding exposure conditions and effects by the Great Lakes Environmental Center 

472 (Traverse City, MI) under support from the Office of Science and Technology of U.S.EPA’s 

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

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

475 further analyzed by U.S.EPA to compile desired information on the relationship of SGR to 

476 atrazine concentration, using the following sigmoidal relationship (logistic equation): 

477 (Equation 1)

478 for which the parameters are the SGR-based EC50, the steepness of the relationship of SGR vs. 

479 atrazine concentration ("Steep"), and the control SGR (SGRC). Appendix A further discusses this 

480 equation and its use in the analyses, as well as (a) guidelines and procedures used in the EPA 

481 evaluations of toxicity tests and (b) a summary of each toxicity test reviewed. Table 1 provides 

482 the compilation of SGR EC50, Steep, and SGRC from these analyses. 

483 

4 log log ( ) 10 10 50 ( ) ( ) 1

C

ATZ Steep C EC

SGR SGR

e

⋅ ⋅ −

=

+
16

484 

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 parameter 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. Appendix A provides more details on these data and analyses. 

Genus SGR EC50 (μg/L) Steep SGRC (d-1) Reference 

CHLOROPHYTA (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 
17

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

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

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

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

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

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

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

492 be between 50 μg/L and 100 μg/L. 

493 2.4 Statistical Distribution of Toxicity Relationship Parameters

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

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

496 StatSoft, Tulsa, OK, USA). A nested ANOVA showed no significant differences between 

497 genera within the larger taxonomic groups identified in Table 1, so the analysis was simplified to 

498 a one-way ANOVA on these taxonomic groups, with each test result being treated equally 

499 regardless of the number of tests within a species or genus. This analysis indicated significant 

500 differences among the taxonomic groups, with the mean log10(EC50) being 2.09 for green algae, 

501 2.35 for diatoms/cryptomonads, 2.42 for blue-green algae, and 1.93 for vascular plants (Table 2). 

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

503 However, it should be noted that these taxonomic differences are uncertain due to the limited 

504 amount of data for some of the taxa – the standard errors of these mean log10(EC50)s varied from 

505 0.07 to 0.12 (Table 2), depending on the number of observations for each group, and their 95% 

506 confidence limits overlapped. The within-group variability did not differ significantly between 

507 the taxonomic groups, with the within-group standard deviation ranging from 0.29 to 0.35 (Table 

508 2) and the pooled value being 0.33. The overall, unweighted mean and standard deviation of all 

509 log10(EC50)s were 2.12 and 0.37 (this higher standard deviation being reflective of the intergroup 

510 variability). Basing the analysis on genus means rather than individual tests produced similar 

511 values for the overall mean (2.07) and standard deviation (0.35) of log10(EC50)s. 

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

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

Table 2. Summary statistics for SGR-based toxicity relationships from Table 1 (based on individual tests within 

designated taxonomic group). 

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 
18

514 broader taxonomic groups. The within-group means ranged from -0.03 for the green algae and 

515 diatoms to -0.11 for the blue-green algae, with an overall mean of -0.05 (Table 2). The steepness 

516 distribution is therefore described here based simply on this overall mean for log10(Steep) 

517 (corresponding to a median value for Steep of 0.89) and the overall observed standard deviation 

518 (0.18) (Table 2). Using genus means rather than individual observations resulted in a very 

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

520 significant correlation between log10(EC50) and log10(Steep), so these parameters will be treated 

521 independently in any analyses. 

522 2.5 Uncertainty of PATI Relationships 

523 The toxicity data analyses here provide the basis for computing an overall measure of 

524 toxic impact on an assemblage of plant species (i.e., PATI) as a function of concentration. 

525 However, this does involve some issues regarding data selection and processing that will be 

526 relevant to uncertainty analyses presented in Section 4 of this document. 

527 One issue is whether PATI should be calculated directly from the individual tests in 

528 Table 1 (using the overall median steepness for any test without a measured steepness) or be 

529 based on the overall distributions of log10(EC50) and log10(Steep) summarized in Table 2. For the 

530 individual tests, calculating PATI is simply a matter of averaging the toxicity relationships across 

531 all the tests. For the summary distributions, calculating PATI requires multiplying the level of 

532 toxic effect expected for a particular EC50 and Steep by the probability density for that 

533 combination of EC50 and Steep, and doing this for all possible combinations of EC50 and Steep. 

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

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

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

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

538 PATI tox C EC Steep dens EC Steep dSteep dEC = ⋅ ∫ ∫( ( , , ) ( , ) 50 50 50 ) (Equation 2)

539 Rather than evaluating this by numerical integration, it was estimated by randomly sampling 

540 10000 pairs of EC50 and Steep from the density function (assumed to be bivariate log normal 

541 with means and standard deviations as in Table 2), applying the toxic relationship function (Eq. 

542 1) to these random pairs, and taking the mean 

543 of these toxicity values. Based on repeated 

544 tests of this process, 10000 points were 

545 sufficient to evaluate this integral with a 

546 relative error of <0.5%. 

547 Figure 8 provides a comparison of 

548 these two calculations methods, showing a 

549 negligible difference for concentrations 

550 >10 μg/L, with the difference growing to 

551 about 30% at 2 μg/L and a PATI value of 

552 ca. 1. This calculation method issue would 

553 thus appear not to be a significant uncertainty 

554 source, but its impact on risk characterization 

555 will be examined in Section 4. 

Figure 8. PATI relationships based on the toxicity

relationships for individual tests (dashed line) versus 

based on the overall summary distribution of the 

relationship parameters EC50 and Steep (solid line). 

Atrazine Concentration (μg/L)

2 5 20 50 200 500 10 100 1000

Plant Assemblage Toxicity Index (%)

2

5

20

50

1

10

100
19

556 Another issue is the uncertainty associated with PATI relationships because of the finite 

557 number of toxicity relationships used in its formulation. This uncertainty is reflected in the 

558 standard errors for the means of the toxicity relationship parameters (log10(EC50), log10(Steep)) 

559 reported in Table 2, as well as the uncertainty 

560 in the parameter standard deviations. Figure 

561 9 shows how PATI based on the overall 

562 distribution in Table 2 would vary by 

563 changing the mean and standard deviations of 

564 the parameters to their lower and upper 95% 

565 confidence limits. At most concentrations, 

566 the largest effects are for the uncertainties in 

567 the mean log10(EC50), but the other 

568 uncertainties become substantial at lower 

569 concentrations, with the uncertainty in PATI 

570 due to the mean log10(Steep) reaching a factor 

571 of approximately 2.0 at 2 μg/L and a PATI 

572 value of ca. 1. The impact of this uncertainty 

573 on risk characterizations will also be 

574 considered in Section 4. 

575 A third issue is that the PATI relationships in Figures 8 and 9 represent an assemblage of 

576 plant species and tests defined by the available test data, but different assemblages are possible 

577 by selecting or weighting particular taxa. 

578 Figure 10 contrasts PATI relationships based 

579 (a) on the overall distributions of log10(EC50) 

580 and log10(Steep) in Table 2, (b) the separate 

581 distributions in Table 2 for the four major 

582 taxa, and (c) a composite distribution based on 

583 equal weighting of the four major taxa (in 

584 contrast to the overall distribution, which is 

585 unweighted across all tests regardless of the 

586 major taxon). The PATI values for the overall 

587 distribution and the composite distribution 

588 have negligible differences, but the PATI 

589 relationships for the major taxa can differ 

590 substantially from each other due to the 

591 apparent differences in their relative 

592 sensitivities. 

593 Because vascular plants have the lowest estimated mean log10(EC50) (i.e, the greatest 

594 average sensitivity), they have the highest PATI values in Figure 10. At 2, 5, and 10 μg/L 

595 atrazine, the estimated PATI values are, respectively, 2.2-, 1.9-, and 1.7-fold larger than for the 

596 overall distribution. Only 5.5 μg atrazine/L is needed to reach a PATI value of 5%, versus 10 

597 μg/L for the overall distribution. 

Figure 10. Comparison of PATI based on the overall 

toxicity distribution (solid line) to distributions for 

green algae(short dash), diatoms(long dash), bluegreen algae(dash-dotted), vascular plants(dash-double 

dotted), and a composite of the four taxa (dotted).

Atrazine Concentration (μg/L)

2 5 20 50 200 500 10 100 1000

Plant Assemblage Toxicity Index (%)

2

5

20

50

1

10

100

Figure 9. Comparison of best estimate of overall PATI 

relationship (solid line), to 95% confidence limits for 

the mean for logEC50 (short dash) and logSteep (dashdotted) and standard deviations for logEC50 (long 

dash) and logSteep (dash-double dotted). 

Atrazine Concentration (μg/L)

2 5 20 50 200 500 10 100 1000

Plant Assemblage Toxicity Index (%)

2

5

20

50

1

10

100
20

598 Even greater differences occur for the diatom/cryptophyte group, which has markedly 

599 lower PATI values at low atrazine concentrations because of a combination of a larger-than600 average mean and a smaller-than-average standard deviation for log10(EC50). At 2, 5, and 10 

601 μg/L atrazine, the estimated PATI values are, respectively, 4.4-, 3.6-, and 3.1-fold smaller than 

602 for the overall distribution. Almost 25 μg/L atrazine is needed to reach a PATI value of 5%, 

603 versus 10 μg/L for the overall distribution. 

604 The effects of these plant assemblage differences on risk characterization also will be 

605 examined in Section 4. However, it should be noted here that, because PATI is intended to serve 

606 as a relative index of the effects of different exposure concentrations, the slopes of the 

607 relationships in Figure 10, not the absolute PATI values, will determine how risk 

608 characterizations depend on the taxonomy of the assemblage. Although the estimated PATI 

609 values for the vascular and diatom groups differ by nearly an order of magnitude at low atrazine 

610 concentrations, the log slopes in Figure 10 are not very different from each other (e.g., the 

611 relative changes in PATI from 10 to 20 μg atrazine/L are 1.9, 2.1, and 2.4 for the vascular plant, 

612 overall, and diatom distributions, respectively). Thus, it should be anticipated that the analyses 

613 in Section 4 will show limited sensitivity of risk characterizations to assemblage taxonomy. 

614 
21

615 3. USING EXPERIMENTAL ECOSYSTEM DATA TO SPECIFY THE LOC FOR PATI 

616 Using the experimental ecosystem data to determine an LOC for the cumulative PATI 

617 involves relating a binary response (yes/no effect for each experimental ecosystem treatment) to 

618 a quantitative measure for the severity of the exposure (cumulative PATI). Before presenting 

619 this process, it would be useful to first discuss a similar but more familiar analysis. 

620 Mortality in a toxicity test also involves a binary response – an individual organism either 

621 dies or not. Mortality data is often plotted as the fraction of a group of organisms that died (by 

622 an observation time) vs. the concentration to which the group was exposed, shown in the left 

623 panel of Figure 11. However, such data can also be plotted based on the response of each 

624 individual organism (0 if alive, 1 if dead), shown on the right panel of Figure 11, in which offsets 

625 are used to show points that actually have the same concentrations. Probit analysis is a common 

626 method applied to such data to generate a sigmoidal relationship for the probability of mortality 

627 at each concentration, this relationship being the same in the left and right panels because both 

628 panels represent the same information and analysis.

629 Because probit analysis uses the binary response of the individual organisms as the basic 

630 observation, it is actually more directly related to the right panel of Figure 11 than to the left. 

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

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

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

634 appropriate. For example, if the offsets for the points in the right panel of Figure 11 actually 

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

636 points at the same concentration. 

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

638 data in the right panel of Figure 11. Figure 12 replots the experimental ecosystem data from 

639 Figure 2 as binary effects (1 if there is an effect, 0 if there is not) vs. a PATI60d value. (For the 

640 purpose of this example, the overall distribution of toxicity values in Table 2 was used as the 

641 basis for PATI, along with the 60-d assessment period. The basis for these choices is addressed 

642 in Section 4.) 

Figure 11. Probit analysis as an example of binary data analysis. For a hypothetical toxicity test, the left panel 

shows the fraction (of 10 organisms) which died at each concentration while the 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
22

643 Although there is a clear increase in the probability of effects as PATI60d increases in 

644 Figure 12, there also is considerable overlap between effects and no effects with respect to 

645 PATI60d, especially in the 100 to 200 range for PATI60d. This variability/overlap issue was 

646 already noted regarding Figure 2, and should be viewed here in terms of any particular PATI60d

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

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

649 at any PATI60d value is again indicative of sensitivity differences among the systems and/or 

650 various experimental uncertainties. Across all PATI60d values, there would be an underlying 

651 relationship for this probability, illustrated by the curve on Figure 12. 

652 This probability relationship can be quantified using probit or similar binary analyses. 

653 Field et al. (1999, 2002) applied binary analysis to sediment toxicity assessments of a similar 

654 nature (i.e., relating binary effect data to an exposure concentration), but rather than the Gaussian 

655 distribution-based relationship of probit analysis, they applied a similar, but simpler, probability 

656 relationship based on the logistic equation. For describing the probability of effects in the 

657 experimental ecosystem set as a function of PATI60d, this logistic probability expression can be 

658 formulated as: 

659 (Equation 3)

660 where P is the probability (percent scale) of an adverse effect at a PATI60d value, PATI50% is the 

661 PATI60d value at which P=0.5 (50% chance of an effect over the range of experimental 

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

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

664 appropriate for analyzing the data in Figure 12 because it does not reflect certain errors in the 

665 statistical analysis regarding whether an experimental ecosystem treatment is concluded to have 

( ) 50% ( ) 10 50% 10 60

60

log log

1 1

1 1 10 d

d

S

PATI S PATI PATI

PATI

P

⋅ −

= =

+ +

Figure 12. Experimental ecosystem data plotted as effect/no effect versus PATI90d, fitted to a logistic relationship 

for the probability of an effect versus PATI. 

60-d Cumulative Plant Assemblage Toxicity Index (%-days)

2 5 20 50 200 500 2000 5000 10 100 1000

Probability of Adverse Effect

0.0

0.2

0.4

0.6

0.8

1.0

LOCPATI=132
23

666 an adverse effect. Most importantly, Type I error (the probability of concluding a treatment has 

667 an effect when it actually does not) is typically set at 0.05. This means that, although the actual 

668 probability of an adverse effect approaches zero as PATI approaches zero per Equation 1, the 

669 probability of stating that there is an effect does not approach zero, but rather approaches 0.05. 

670 Type II error (the probability of concluding a treatment does not have an effect when it actually 

671 does) will also affect the curve, but it is not possible to adjust for this without more detailed 

672 information on the statistical power of the various tests. However, because Type II error will go 

673 to zero as concentration increases, it will not affect the upper asymptote of the curve like Type I 

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

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

676 logistic model with a lower asymptote of 0.05, modifying Equation 2 as follows: 

677 (Equation 4)

678 where Pdata refers to the probability of a data point being stated to have an effect, in contrast to P 

679 being the actual probability of having an effect. 

680 Using Equation 4, a maximum likelihood analysis was conducted on the data in Figure 10 

681 to generate estimates for the equation parameters, PATI50% and S. Using these parameter 

682 estimates, the curve in Figure 12 was calculated, but using Equation 3 rather than Equation 4 so 

683 the curve shows the actual estimated P, not Pdata. Once estimated, this curve provides a basis for 

684 making risk management decisions regarding what PATI value is considered an LOC. For 

685 example, for Figure 12, a risk management decision to use P=0.5 would result in an LOCPATI60d

686 of 132 %-days. 

687 This LOCPATI60d of 132 %-days represents substantial reductions in growth rate for this 

688 plant assemblage for short exposures (e.g., 44% for a three day exposure), but progressively 

689 smaller effects for longer exposures (e.g., 10% for two weeks, 5% for four weeks). However, it 

690 is important to remember these percentages do not define the level of protection; rather, it is 

691 the experimental ecosystem results that define the effects of concern! PATI is only being used 

692 to describe the relative effects of different exposure time-series. It is the experimental ecosystem 

693 effects that define the effects of concern and what level of PATI for the selected assemblage of 

694 toxicity data correlates with these effects. It is not being assumed that a certain value for PATI 

695 has inherent significance, so it is not appropriate considering (under the assessment framework 

696 being used here) whether reducing growth rate by 44% for three days is too restrictive or not 

697 restrictive enough. This PATI-based methodology assumes only that the relative effects of 

698 concentration and time on PATI are useful for extrapolating between different exposure time699 series for the experimental and natural plant communities being assessed. 

700 

( )

( )

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24

701 4. IMPLEMENTATION OF PATI-BASED RISK ASSESSMENT METHODOLOGY 

702 4.1 Example Field Exposure Time-Series 

703 Figure 1 provided three example field exposure time-series (chemographs) for use in the 

704 problem definition. In this section, method parameterization and performance evaluations will 

705 involve a larger set (Figure 13) of chemographs from the 2010 monitoring program to provide a 

706 greater diversity of exposures for evaluating the methodology. EEFs for all chemographs will be 

707 presented, but because uncertainties are most important for EEFs near 1.0, summary statistics for 

708 sensitivity and uncertainty analyses below are based only on sites with 0.3<EEF<3.0. 

Figure 13. Example atrazine chemographs used in methodology evaluations. 

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25

709 4.2 Parameterization Issues for PATI-based LOCs

710 Implementing a PATI-based methodology requires specifying (a) the toxicity relationship 

711 parameters (EC50s and Steeps) to use in daily PATI calculations and (b) the assessment period 

712 over which to evaluate cumulative PATI. 

713 4.2.1 Assessment Period – Issues and Options 

714 Because exposure outside the assessment period is considered inconsequential by PATI, 

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

LOCPATI 716 from the experimental ecosystems (Figure 2) and (b) effects expected from seasonal 

717 field exposures (Figure 13). However, it should not be any longer than necessary, in order to 

718 avoid uncertain inferences regarding (a) cumulative effects of low concentrations and (b) widely 

719 separated exposures that are independent regarding ecological effects. 

720 A 60-d assessment period was chosen as a provisional focus for consideration because it 

721 would include all or almost all periods of significant exposure in the example chemographs of 

722 Figure 13 and also encompasses the duration of all but a few of the experimental ecosystems in 

723 Figure 8. A few additional considerations regarding this period relative to the experimental 

724 ecosystem treatments should be noted: 

725 (1) It is just slightly shorter than the longest experimental ecosystem treatment with no effect. If 

726 the assessment period was significantly shorter than treatments with no effect, this would under727 represent how substantial exposures could be without causing effects and thus be too restrictive. 

728 (2) For those treatments with effects, a shorter period would also be too restrictive by assuming 

729 that less exposure was needed to elicit effects than actually was involved (e.g., an effect observed 

730 over a 60-d exposure would be assumed to require less exposure than actually was required). 

731 This consideration does not pertain to the few experimental ecosystems with extremely long 

732 durations, because they simply verify significant effects for high PATI values. For the LOC, the 

733 important treatments with effects are those whose exposures near to those without effects. 

734 (3) That 60 d is longer than many experimental ecosystem treatments with effects is not an issue, 

735 provided the effects from these shorter exposures would still be considered unacceptable from 

736 the perspective of this longer assessment period (e.g., if a 30-d exposure showing effects had 

737 been monitored for another 30 d without exposure, the effects during the first 30 d would be 

738 considered unacceptable despite any recovery that occurred during the second 30 d). 

739 To evaluate the suitability of 60 d as the assessment period, compared to possible 

740 alternative choices, sensitivity analyses below will address how risk characterizations would 

741 differ for assessment periods from 30-d to 120-d. A 30-d assessment period is included in this 

742 sensitivity analysis to document the impact of a period that is arguably too short, in that it is less

743 than the duration of a substantial percentage of the experimental ecosystems treatments that 

744 discriminate effects and no effects, and also inadequately covers periods of substantial exposure 

745 in the example chemographs. 

746 
26

747 4.2.2 Toxicity Relationship Parameters – Issues and Options 

748 The review and analysis of single-species toxicity test data in Sections 2.2 and 2.3 

749 provide the basis for specifying toxicity relationships for PATI calculations, but there are options 

750 and uncertainties in applying this information, which were already discussed to some extent in 

751 Section 2.4: 

752 (a) Should PATI calculations be directly based on the discrete estimates for the toxicity 

753 relationship parameters (EC50 and Steep) in Table 1, or should the methodology follow the 

754 typical assessment practice of using the data to estimate sensitivity distributions (Table 2), and 

755 basing assessments on such distributions? 

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

757 standard practice (e.g., typical SSDs) of not adjusting for the relative representation of different 

758 taxa in the available data? 

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

760 tests? 

761 The strategy here was to use, as a default reference, distributions based on all the 

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

763 log10(Steep) summarized in Table 2). Sensitivity analyses were conducted to determine how 

764 substantially risk characterizations varied for alternatives from this default, including (a) the use 

765 of discrete parameter estimates in Table 1 instead of these default distributions (as was done for 

766 Figure 8), (b) different weightings of the major taxonomic groups (such as in Figure 10), and (c) 

767 basing distributions on genus means instead of individual test results. Based on this sensitivity 

768 analysis, decisions can be made regarding how these issues should be addressed in the final 

769 methodology. 

770 4.3 Sensitivity Analyses for PATI-Based LOCs 

771 4.3.1 Sensitivity Analysis for Assessment Period 

772 Using the overall (default) toxicity parameter distributions specified in Table 2, effects 

773 assessments were made for each of the example chemographs in Figure 13, using assessment 

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

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

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

777 average effect across a set of toxicity relationships. Because the toxicity relationship parameters 

778 are represented by distributions, this calculation was conducted as described in Section 2.5. 

779 (b) The daily PATI values were used to calculate cumulative PATI values for 30-, 60-, 90-, and 

780 120-d assessment periods for each experimental ecosystem treatment. When the exposure 

781 duration exceeded the assessment period, the contiguous period of exposure resulting in the 

782 highest cumulative PATI value was used. 
27

783 (c) For each assessment period, a binary logistic regression was conducted as described in 

Section 3.2. The LOCPATI 784 was set to the PATI50% estimate from this regression (50% probability 

785 of an effect). 

786 (d) Daily PATI values were computed for each of the example chemograph in Figure 11. 

787 Cumulative PATI values for each assessment period were calculated for the contiguous period of 

788 exposure resulting in the highest value. 

789 (e) For each assessment period and example chemograph, risk was characterized by calculating 

790 the EEF and CEF (see Figure 3 and associated text for definition of these terms). 

791 Figure 14 illustrates how the assessment period affects risk characterization, as 

792 represented by the EEF. (CEFs showed patterns very close to the EEFs and are not included 

793 here.) Relative to the proposed assessment period of 60 d, increasing the assessment period to 90 

794 or 120 d resulted in small increases in the EEF, except for one site (MO 02) for which the 

795 increases were 28-29%. For the other sites, EEFs increased by an average of 5.6% (range 2.7%-

796 11.0%) for the 90 d assessment period and 8.6% (range 1.6-17.4%) for the 120 d assessment 

797 period. In contrast, using a 30-d assessment period reduced the EEF, relative to 60 d, by a mean 

798 of 24% (range 2-40%), the larger reductions being associated with sites with substantial 

799 exposures for more than 30 d. Using such a short averaging period poorly addresses 

800 experimental ecosystem treatment effects, but more importantly assumes that major portions of 

801 many field exposures should be ignored. 

802 4.3.2 Sensitivity Analysis for Toxicity Information

803 Using the 60-d assessment period, risk characterizations were made for each of the 

804 example field exposures in Figure 13 using the following options for toxicity information: 

Figure 14. Sensitivity of risk characterization to assessment period length, based on effects exceedence factors at 

20 selected sites monitored in 2010. 

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28

805 (1) The overall distributions for log10(EC50) and log10(Steep) reported in Table 2 (default). 

806 (2) The individual log10(EC50) distributions for the four major taxonomic groups in Table 2 

807 (using the overall distribution for log10(Steep)). 

808 (3) An equal-weighted composite of the log10(EC50) distributions for the four taxonomic groups. 

809 (4) The individual tests in Table 1 (using the average value of -0.05 for log10(Steep) for tests in 

810 which this was not determined). 

811 (5) The overall distribution using genera means rather than individual tests (Section 2.3). 

812 These evaluations were conducted in accord with the protocol described above for the 

813 assessment period evaluations and are summarized in Figure 15. For most options (green algae, 

814 bluegreen algae, individual tests, composite taxa, genus means), the EEF deviations from the 

815 default option were generally negligible, averaging <3.5% and never exceeding 13%. For the 

816 diatom distribution (the least sensitive group at low atrazine concentrations per Figure 10), EEFs 

817 usually are lower than for the default option – averaging 14% lower and ranging from 37% lower 

818 to 22% higher. For the vascular plant distribution (the most sensitive group), EEFs usually are 

819 higher than for the default option – averaging 12% higher and ranging from 3% lower to 33% 

820 higher. Given the magnitude of the differences in mean log10(EC50), these differences are rather 

821 small, and also are not statistically significant given the uncertainties in the toxicity data. 

822 This small sensitivity of EEFs to changes in the toxicity information used in PATI might 

823 seem surprising given the large sensitivity of PATI itself to these changes (Figure 10), but this is 

824 because the experimental ecosystems, not the toxicity distributions, determine the level of 

Figure 15. Sensitivity of risk characterization to selection of toxicity data, based on effects exceedence factors at 

20 selected sites monitored in 2010 

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29

825 concern. PATI is only being used to assess the relative effects between different exposure times826 series, and these relative effects are similar whether the plant assemblage is sensitive or tolerant. 

827 As noted in Section 2.5, these relative effects are related to the slopes in Figure 10, which differ 

828 little among the various taxonomic assemblage definitions compared to the large variation in the 

829 absolute PATI values. From another perspective, using a more sensitive set of toxicity data will 

830 result in higher PATI values for both the experimental ecosystem treatments and the field 

831 exposures, so that the net effect of taxonomy on the EEFs is much less than that on PATI itself. 

832 However, there are still some effects of taxonomy on EEFs because PATI is not linear 

833 with concentration. The smaller slopes in Figure 10 for the vascular plants than the diatoms 

834 mean that the lower atrazine concentrations will contribute relatively more to the vascular plant835 based PATI than the diatom-based PATI. And because periods of relatively lower concentration 

836 are more prevalent in most field exposures than in most experimental ecosystem treatments, this 

837 results in slightly higher EEFs for the vascular plant-based PATI than the diatom-based PATI. 

838 However, these differences are small for any field exposure with an EEF near 1.0 and thus have 

839 negligible effect on risk characterizations (Figure 15) despite the substantial differences in 

840 absolute PATI values. 

841 Because this sensitivity analysis shows such small effects from even extreme choices for 

842 the taxonomic composition of the plant assemblage and because of the statistical uncertainties of 

843 these effects, the recommendation here is to use the overall toxicity distribution in Table 2 that 

844 was used as the default for this analysis. Using all the data, rather than a subset, is also more in 

845 keeping with how aquatic risk assessments generally reflect a broad assemblage of organisms. 

846 4.4 Contribution of Toxicity Distribution Uncertainty to Overall Assessment Uncertainty 

847 Although varying the assemblage taxonomy in Section 4.3.2 did not affect risk 

848 characterizations enough to support using something other than the overall parameter 

849 distributions, this does not mean that uncertainty in these distributions is negligible. More 

850 evaluation was needed of the uncertainty of EEFs as a function of the uncertainties of all the 

851 parameters for the toxicity relationships used to calculate PATI. 

852 To this end, an uncertainty assessment was conducted that involved (a) generating 10000 

853 sets of toxicity parameter distributions (means and standard deviations for both log10(EC50) and 

log10(Steep)), (b) determining the LOCPATI 854 for each parameter distribution set, and (c) 

855 determining the EEF for each example chemograph for each parameter distribution set. The 

856 means of the 10000 distributions for log10(EC50) and log10(Steep) were generated by random 

857 sampling from normal distributions with the overall distribution means and standard errors for 

858 these parameters in Table 2. The standard deviations of the 10000 distributions for log10(EC50) 

859 and log10(Steep) were generated by random sampling from chi-square distributions based on the 

860 overall distribution standard deviations for these parameters in Table 2, using a degree of 

861 freedom based on the number of data in Table 1. Due to the observed lack of correlation 

862 between EC50 and Steep, the sampling for these two parameteers was done independently. 

 Figure 16 summarizes this uncertainty analysis, comparing the 10th and 90th 863 uncertainty 

864 percentiles to the median results. The lower bound for the EEF varies from 85% to 98% of the 
30

865 median among the chemographs, with an average of 95%, while the upper bound varies from 

866 102% to 120% of the median, with an average of 107%. 

867 Although this demonstrates that uncertainties in the toxicity data used to parameterize 

868 PATI result in very little uncertainty in the final risk characterizations, this is only one 

869 component of the uncertainty for the total methodology. If uncertainty estimates are to be 

provided, they would need to reflect all important sources

2

870 , compared to which these 

871 uncertainties for the toxicity distributions used by PATI should be relatively minor. 

872 

873 

 

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(PATI50%) 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 the characterization of field 

exposures and of experimental ecosystem effects. 

Figure 16. Uncertainty analysis for risk characterizations due to uncertainties in toxicity distributions used to 

parameterize PATI. Solid line denotes EEFs based on best estimates of toxicity parameter distributions. Dashed 

lines denote 10th and 90th percentiles due to uncertainty of these distributions. 

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31

Figure 17. Relationship of PATI to atrazine concentration. 

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874 5. SUMMARY AND RECOMMENDATIONS REGARDING LOC METHODOLOGY 

875 As noted in Section 1, this LOC methodology starts with experimental ecosystem studies 

876 regarding effects of atrazine on aquatic plant communities. Each experimental ecosystem 

877 treatment must be characterized regarding (a) whether there is an unacceptably adverse effect 

878 and (b) the atrazine concentration time series. This characterization was provided in U.S.EPA 

879 (2011) and summarized in Appendix B. The basic problem addressed here is the issue of 

880 comparing effects across different exposure time series, both among the experimental 

881 ecosystems and between the experimental ecosystems and exposures of interest in natural 

882 systems. This is done with an effects index that specifies the relative toxic severity of different 

883 time-series. The proposal here is that this index be the 60-d cumulative PATI value. This index 

884 is applied as follows: 

885 (1) Based on available toxicity tests with individual aquatic plant species, relationships of SGR 

886 versus atrazine concentration are developed and used to specify statistical distributions for the 

887 relationship parameters (EC50, Steep). For this report, the tests were described using a logistic 

888 relationship of SGR versus log atrazine concentration, and the distributional recommendations 

889 were for the log10(EC50) to have a mean 2.12 and standard deviation 0.37 and for the log10(Steep) 

890 to have a mean of -0.05 and a standard deviation of 0.18, based on an unweighted analysis across 

891 all tests. Although some differences among major taxa were indicated, alternative distributions 

892 using different taxonomic weightings had small and uncertain effects on assessment results. The 

893 distributions recommended here merit additional evaluation regarding the toxicity test data set 

894 used and the distributional shape and composition. 

895 (2) The relationship of daily PATI values to atrazine concentration should be developed for the 

896 assemblage of species described by the distributions for the toxicity relationship parameters 

897 (EC50, Steep). This requires integrating the expected toxic response across the joint distribution 

898 of the parameters; this integration is best done by randomly selecting a large number (e.g., 

899 10000) of EC50/Steep pairs from these distributions, determining the toxicity relationship for 

900 each parameter pair, and averaging across all these relationships (note: this numerical method for 

901 integrating across the distributions need only be done once and then applied to all subsequent 

902 PATI calculations). For the distributions specified here, this results in the following relationship 
32

903 of daily PATI values to atrazine concentration (Figure 17): 

904 (3) Based on this relationship of daily PATI to atrazine concentration, a cumulative PATI value 

905 (=the sum of the daily PATI values) is calculated for each experimental ecosystem exposure to 

906 provide a measure for the total relative toxic impact of that exposure. This cumulative PATI 

907 value must be limited to a time frame (assessment period) consistent with risk management goals 

908 and the experimental ecosystem data, for which a provisional period of 60 d is proposed here. 

909 The binary effects determinations for each exposure are plotted against the cumulative PATI60d

910 values, and a regression is performed to describe the probability of effect versus PATI. For the 

911 daily PATI relationship and the experimental ecosystem dataset used here, this results in the 

912 relationship already shown in Figure 12 and repeated in Figure 18: 

913 The above relationship describes the probability of effect versus PATI60d, using the logistic 

914 equation, with equation parameters log10(EC50)=132 %-days and a steepness=2.03. If the EC50 is 

the designated level of concern, the LOCPATI 915 is thus 132 %-days for a 60 d assessment period. 

916 These particular values are contingent on the toxicity data set used for PATI, the experimental 

917 ecosystem dataset, and a risk management decision regarding what probability of effect is of 

918 concern, and thus would change if any of these factors is modified. 

919 (4) This level of concern for PATI is applied to environmental data by calculating the cumulative 

920 PATI for each environmental exposure time-series of interest. The effects exceedence factor 

(EEF) (=ratio of PATI60ds calculated for field exposures of interest to the LOCPATI 921 ) is used to 

922 determine whether the exposures exceed a level of concern. If desired, iterative calculations can 

923 be used to determine the concentration exceedence factor (CEF) by which the exposure exceeds 

924 a level of concern. FORTRAN-based computer programs and associated input files for this 

925 implementation have been developed and are separately available from the author. PATI-based 

926 EEFs for a suitable set of field exposures can be used to develop a concentration-based LOC to 

927 apply to future exposures without needing to make actual PATI-based calculations, and this is a 

928 subject of a separate effort. 

929 

Figure 18. Experimental ecosystem data plotted as effect/no effect versus PATI90d, fitted to a logistic relationship 

for the probability of an effect versus PATI. 

60-d Cumulative Plant Assemblage Toxicity Index (%-days)

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33

930 6. REFERENCES 

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1213 
42

1214 
43

1215 APPENDIX A 

1216 SINGLE-SPECIES PLANT TOXICITY TEST REVIEW

1217 This appendix provides a summary for each report and journal article reviewed for 

1218 developing the compilation of EC50s and steepness values for the relationship of plant specific 

1219 growth rate (SGR) to atrazine concentration. Bold numbers in the tables or text denote values 

1220 from each study selected for inclusion in the compilation. 

1221 A.1 Protocol for Application of Toxicity Test Data 

1222 A.1.1 Acceptability of measurement variables 

1223 (1) The preferred measurement variable for assessing atrazine effects was plant biomass (dry 

1224 weight, or wet weight if procedures provided consistent removal of adhering water), but 

1225 measures that are approximately proportional to biomass (algal cell count or cell volume, 

1226 duckweed frond count) were also accepted. 

(2) If measures outlined in (1) were not available, O2 evolution or 14 1227 C fixation measurements 

1228 were accepted provided that they were not significantly compromised by any lag in inducing 

1229 effects and their relationship to SGR could be defined. 

1230 (3) Data based just on chlorophyll content were not used because the chlorophyll content per cell 

1231 can change markedly in response to atrazine, leading to markedly different EC50s for chlorophyll 

1232 than for actual biomass (see discussion in Section 2.2.1 in main report text). Similarly, optical 

1233 density was not accepted because it also is affected by chlorophyll content, often being measured 

1234 near a chlorophyll absorbance maximum. 

1235 A.1.2 Translating reported data into SGR EC50 and steepness parameter values 

1236 The nature of the data and the level of detail provided in the reviewed reports/papers varied 

1237 widely, requiring several different procedures for translating the reported data into the elements 

1238 of the data compilation: the SGR EC50, a steepness for the SGR vs. atrazine concentration 

1239 relationship, and the SGRC. 

1240 A.1.2.1 Initial and final biomasses (or surrogate) were reported for a concentration series. 

1241 The preferred data were reported initial and final biomasses (or acceptable surrogates) for all 

1242 treatment concentrations, from which SGRs would then be computed. A regression analysis of 

1243 SGR vs. atrazine concentration (CATZ) was then conducted, resulting in characterizing both the 

1244 EC50 and the steepness for the relationship based on the basic measurements in the study. The 

1245 analyses were by least-square, nonlinear regression using Version 1.2 of the software package 

1246 TRAP (Toxicity Relationship Analysis Program) (U.S.EPA Mid-Continent Ecology Division, 

1247 Duluth, MN, http://www.epa.gov/medatwrk/Prods_Pubs/trap.htm), using the “logistic equation” 

1248 model option and the log-transform option for CATZ. This model option uses the logistic 

1249 equation to provide a sigmoidal regression function shape, but is a regression of a continuous 

1250 variable, not binary logistic analysis: 
44

4 log log ( ) 10 10 50 ( ) ( ) 1

C

ATZ Steep C EC

SGR SGR

e

⋅ ⋅ −

=

+

1251 

1252 The defining parameters for this function are the control SGR (SGRC), the log10(EC50) for the 

1253 SGR, and a measure of relative steepness (“Steep”) defined as d(SGR/SGRC)/d(log10(CATZ)) at 

1254 the EC50. 

1255 A.1.2.2 SGRs or relative SGRs were reported for a concentration series. 

1256 If the author reported SGRs (based on biomass or acceptable biomass surrogates) for all 

1257 treatment concentrations, but not the actual initial and final biomasses, these SGRs were used 

1258 directly in regression analysis as described in (A1) above to obtain the SGRC, SGR EC50, and 

1259 steepness parameter. If the reported SGRs were relative (fraction of the control), the regression 

1260 was conducted to obtain an EC50 and steepness to include in the compilation, but not an SGRC, 

1261 although in some cases the latter was specified separately by the author(s). 

1262 A.1.2.3 EC50 for the SGR was reported with or without slope. 

1263 If the author computed SGRs, but only reported an SGR-based EC50 without SGRs for individual 

1264 treatment concentrations, the author-calculated SGR EC50 was included in the compilation. If 

1265 the author also specified the type of relationship used in the EC50 estimation and a slope for that 

1266 relationship, this information was converted to the steepness parameter of the relationship used 

1267 in EPA’s regressions; otherwise no steepness was compiled. If the author separately provided 

1268 information on the SGRC, this also was included in the compilation. 

1269 A.1.2.4 Multiple ECps for growth reported; SGRC reported. 

1270 (a) If multiple ECps for growth over a specified duration (t) and the SGRC for that duration were 

1271 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

⋅

1272 = − ⋅ +

1273 In other words, this is the value for the SGR at the concentration causing a p% decrease in 

1274 growth. The resultant SGRs (and their associated concentrations) were then subject to regression 

1275 analysis to provide estimates for the SGR EC50 and steepness. This provided a SGR EC50, a 

1276 steepness, and a SGRC for the compilation. 

1277 (b) If the author did not specify multiple ECps for growth, but did provide the growth-based 

1278 EC50, the type of relationship used in this EC50 estimation, and the slope for that relationship, 

1279 additional ECps for growth (p≤90%) were calculated for this author-reported curve and also 

1280 converted to SGRs. These were then subject to regression analysis to provide estimates for the 

1281 SGR EC50 and steepness, although any confidence limits on these estimates would not be valid 

1282 given that the data points were not independent. Rather, this was simply a mechanism to convert 

1283 the the author-reported curve for biomass-based ECs to the equivalent curve for SGR-based ECs. 
45

1284 (c) If the smallest SGR was more than 75% of the SGRC for either of the above options, the 

1285 regression analysis was not conducted because this would involve too much extrapolation to 

1286 estimate the SGR EC50. However, the possibility of extrapolating this SGR to the SGR EC50 per 

1287 A.1.2.6 below was then considered. 

1288 A.1.2.5 Multiple ECps for growth reported; SGRC not reported. 

1289 If multiple ECps or an EC50/slope combination for algal growth were reported, but an SGRC was 

1290 not reported, the process in A.1.2.4 above was still used, but using SGRCs reported for other 

1291 studies on test species in the same taxonomic group. Because this involves using data from other 

1292 experimental systems and test species, three separate analyses were conducted using median 

1293 (low-high) estimates for the SGRC of 1.35 (1.05-1.74) for green algae, 1.03 (0.80-1.32) for 

1294 diatoms, and 0.65 (0.50-0.83) for blue-green algae. The SGR EC50 and steepness from the 

1295 regression analysis using the median SGRC estimate were included in the compilation, provided 

1296 the SGR EC50s derived using the low and high SGRC estimates differed by no more than a factor 

1297 of 2.0. 

1298 [The low/mid/high SGRC estimates were based on ANOVA of logSGRCs from algal studies in which SGRC

1299 was reported (see Table 1 in Section 2.2). Analyses using Statistica (Version 8.0, StatSoft, Tulsa, OK) 

1300 provided a log mean for each major algal taxonomic group (0.135 for green algae, 0.013 for diatoms, -

1301 0.189 for blue-green algae) and a pooled standard deviation (0.122). The low/mid/high estimates for 

1302 SGRC were based on calculating the mean ± 1 std.dev. of these log values and then taking antilogarithms. 

1303 Separate SGRC values for species within a taxonomic group were not justified because of large within1304 species variability relative to between-species variability, as evidenced in Table 1 and other sources (e.g., 

1305 Saenz et al. 1997).] 

1306 A.1.2.6 EC50 only for growth reported; SGRC reported.

1307 If the EC50s for growth over a specified duration (t) and the SGRC for that duration were 

1308 reported, this biomass-based EC50 was equated to an SGR ECp using the following equation to 

1309 determine p: 

( ( ))

( )

1

ln 0.5 1

1

C

C

SGR t

SGR

SGR

SGR e

t

p

⋅

= ⋅ +

= −

1310 

1311 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

⋅

1312 = ⋅ , 

1313 where S is based on regression curve steepnesses from other studies. Because this involves using 

1314 data from other experimental systems and test species, three estimates of the SGR EC50 were 

1315 made using low, middle, and high estimates for the steepness of 0.68, 0.95, and 1.31. The 

1316 estimate for the SGR EC50 from the middle steepness estimate was included in the compilation, 

1317 but only if the estimates based on low and high steepness differed by less than a factor of 2. This 

1318 factor of 2 requirement was met if p>16 for the estimated SGR ECp. 
46

1319 [An ANOVA of all the log steepness determined in all studies indicated no significant differences 

1320 among species or broader taxonomic groups, so the overall mean and standard deviation of the 

1321 log steepness were used to set low/mid/high estimates.] 

1322 A.1.2.7 EC50 only for growth reported; SGRC not reported. 

1323 When only an EC50 for growth was reported and a study-specific SGRC was not reported, the 

1324 biomass-based EC50 was equated to SGR-based ECps per section A.1.2.5 using low, middle, and 

1325 high estimates for SGRC. Then, each of these SGR-based ECp estimates was extrapolated to 

1326 SGR EC50 estimates per section A.1.2.6 using low, middle, and high steepness estimates. The 

1327 SGR EC50 estimate based on the middle SGRC and steepness estimates was included in the 

1328 compilation, provided the extremes of the estimates varied by less than a factor of 2. This factor 

1329 of 2 requirement resulted in this procedure being applicable for green algae tests of up to 2 d 

1330 long, but tests could be up to 4-d long for blue-green algae and up to 3-d long for diatoms. 

1331 Extrapolating EC50s for net growth to SGR EC50s were just too uncertain for tests longer than 

1332 this. 

A.1.2.8 Oxygen evolution or 14 1333 C fixation reported 

1334 (a) If the exposure and measurement periods were short enough so that biomass did not change 

1335 appreciably during these periods, and if initial biomasses were either measured or could be 

1336 treated as approximately the same among treatments, oxygen evolution and radiocarbon fixation 

1337 rates were treated as proportional to SGR and ECps for these rates were treated as comparable to 

1338 SGR-based ECps. However, this also required consideration of whether these periods were so 

1339 short that any lag in the induction of toxicity would significantly perturb the measurement. 

1340 Hersh and Crumpton (1989) and Millie and Hersh (1987) reported effects on oxygen evolution 

1341 that were >50% within several minutes of exposure to atrazine concentrations that caused similar 

1342 effects on biomass-based SGRs. Thus, data were accepted provided an induction lag of 5 min 

1343 would not significantly confound results. 

1344 (b) When the exposure and measurement periods were the same and biomass changed enough 

1345 over the period to substantially affect estimated ECps, oxygen evolution and radiocarbon fixation 

were treated as being proportional to net growth (e

SGR⋅t

1346 ), and ECs were converted to an SGR 

1347 basis analogously to procedures described above for biomass-based ECs. 

1348 (c) If a substantial exposure period of duration “t” preceded a short measurement period, so that 

1349 the treatments would start with significantly different initial biomasses for the oxygen 

1350 evolution/radiocarbon fixation measurement period, these measures were treated as being 

proportional to SGR⋅e

SGR⋅t

1351 ; i.e., the biomass accretion in the exposure period prior to the start of 

measurement is e

SGR⋅t

1352 and the oxygen evolution/radiocarbon fixation rate is proportional to the 

1353 SGR times that biomass accretion. This required converting ECs to an SGR-basis using 

1354 approaches analogous to that described above for biomass. 

1355 A.1.3 Issues regarding biomass surrogates and variability. 

1356 One uncertainty issue occurred when the biomass surrogate was cell counts made manually using 

1357 a hemocytometer or similar device. In some cases, cell density estimates were based on <100 
47

1358 cells counted in total for the control treatment and just several cells for atrazine treatments with 

1359 large effects. Even 100 cells represents about +/-20% uncertainty in the cell density. Therefore, 

1360 it was desired to have >200 cells counted in the control treatment in order to have reasonable 

1361 discrimination between the control and treatments with 25-50% reduced growth. Another area of 

1362 concern was frond counts for duckweed, and how closely such counts mirror biomass when 

1363 growth is limited and thus might have a greater percentage of newer, small fronds. Where 

1364 possible, it was desired to have at least a 4-fold increase in the number of control fronds so that 

1365 the counts were not excessively dominated by new, small fronds. A final area of concern was 

1366 macrophyte shoot tests at times when controls had not increased by at least 50%, especially if 

1367 this was measured by shoot length, which can change disproportionately to shoot weight when 

1368 photosynthesis is inhibited. No firm rules were imposed with regard to any of these concerns, 

1369 because any uncertainty depends on the number of replicates in a test, the specific times, the 

1370 variability among replicates, etc. How these concerns are addressed in the summaries for each 

1371 study in Appendix A. 

1372 A.1.4 Treatment of data at multiple times 

1373 When biomasses or biomass surrogates were reported at multiple times within a test's duration, 

1374 analyses were conducted for each time; however, the compilation selected results from only one 

1375 of these times. The time was selected to be long enough to avoid problems with uncertain 

1376 measurements of biomass early in some tests (e.g., the hemocytometer count issue discussed 

1377 above), but short enough to avoid potential biases associated with declining SGRC discussed 

1378 earlier. Again, no firm rules could be adopted for this because of various study-specific factors 

1379 and because it involved balancing uncertainties at early times with those at later times. The 

1380 decision process regarding this is provided in the summaries for each study below. 

1381 A.2 Review Summaries 

1382 

1383 A.2.1 Algae 

1384 

1385 (1) Gala and Giesy 1990 

1386 

1387 The authors conducted a 96-h flask test of Selenastrum capricornutum growth at multiple 

1388 atrazine concentrations, enumerating cell density based on hemocytometer cell counts. 

Concentrations were measured. Illumination was continuous at 40 μE/m2

1389 /s, temperature was 24 

1390 C. They reported average SGRs over 96 h at each treatment concentration, which were directly 

1391 used in EPA regression analyses. Data for earlier times were not reported, but authors noted the 

1392 use of extra nutrients to maintain exponential growth. Due to the duration and growth rates, cell 

1393 densities would have been high enough to avoid concerns about low numbers of individuals 

1394 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 
48

499 (500) 0.013 

EC50 (μg/L) 125 

(80-194) 

Steepness 1.07 

(0.46-1.77) 

1395 

1396 (2) van der Heever and Grobbelaar 1996

1397 

1398 The authors conducted a 72-h flask test of Selenastrum capricornutum growth at multiple 

1399 atrazine concentrations, determining biomass (dry weight), cell density (electronic particle 

1400 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

1401 /s and temperature was 

1402 23 C. The authors graphically reported relative (to control) SGRs based on all these measures. 

1403 Author-reported ECs based on chlorophyll were substantially (almost 3X) higher than for cell 

1404 density and biomass, and were not used in accordance with the review guidelines. Relative 

1405 SGRs for cell density and biomass were estimated from the figures, reported in the table below, 

1406 and used in EPA regression analyses to determine EC50 and steepness. The results based on dry 

1407 weight were selected for use because EC50s were modestly higher for cell density (average LC50

1408 = 406 by cell density, 311 by weight) indicative of decreases in mass per cell at higher atrazine 

1409 concentrations, so that using cell density would slightly reduce the apparent sensitivity of 

1410 biomass to atrazine. The results at 1 d were selected for use because it was unknown whether 

1411 control growth rates declined with time, given that only relative SGRs were reported, and 

1412 because use of an electronic particle counter should have avoided the problems with low manual 

1413 cell counts at early times. 

1414 

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 

1415 

1416 (3) van der Heever and Grobbelaar 1997

1417 

1418 The authors conducted a 30-min oxygen evolution assay for Selenastrum capricornutum

1419 exposed to multiple atrazine concentrations. Concentrations were nominal. Illumination was 

continuous at 300 μE/m21420 /s and temperature was 23 C. Oxygen evolution rates relative to the 
49

1421 control were reported graphically and the values in the table below were estimated from the 

1422 figure. Because of negative responses at high concentrations, the regression in this review 

1423 included a non-zero asymptote at high concentrations, but the EC50 is still defined relative to zero 

1424 oxygen evolution, not this negative asymptote, so that this would best reflect net production. 

1425 Although there was no prior exposure before oxygen evolution measurements were made, the 

1426 measurement period was long enough relative to the 5-min induction standard that these results 

1427 were accepted. It should be noted that the results are consistent with those for a flask test by the 

1428 same authors discussed above. 

1429 

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) 

1430 

1431 (4) Kallqvist and Romstad 1994

1432 

1433 The authors conducted a 72-h flask test of Selenastrum capricornutum growth at multiple 

1434 atrazine concentrations, enumerating cell density using an electronic particle counter. 

Concentrations were nominal. Illumination was continuous at 70 μE/m21435 /s and temperature was 

1436 not reported but followed OECD standards of 23±2 C. The authors conducted a regression 

1437 analysis of probit-transformed relative SGRs, reporting an SGR EC50 of 110 μg/L (95% cl = 99-

1438 121) and an EC10 of 27 μg/L. Individual SGRs were not reported, but these two ECs allow 

1439 estimating a steepness of 0.90 for the sigmoidal function used in this review. 

1440 

1441 The authors also conducted 3- to 6-d microplate exposures of several algal species to atrazine. 

1442 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/m21443 /s for others. 

1444 For these exposures, relative SGRs for each treatment were reported graphically. Values 

1445 estimated from the figures are provided in the following table, along with EC50s and steepnesses 

1446 estimated from regression analysis of this data. The EC50 for Selenastrum was higher for the 

1447 microplate exposures than for the flask tests (although by less than 2-fold), suggesting that the 

1448 microplate exposure methodology might involve factors that lead to decreased apparent 

1449 sensitivity (e.g., nutrient or atrazine reductions, although the former would not be expected if 

1450 exponential growth was maintained). These microplate-based numbers were still compiled for 

1451 use in subsequent analyses because the Selenastrum EC50s was well within the reported range of 

1452 results for this species from other studies; however, this possible source of uncertainty was 

1453 recognized in applications of these data. 
50

1454 

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 

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) 

1455 

1456 (5) Hoberg 1991a

1457 

1458 The author conducted a 96-h flask test of Selenastrum capricornutum growth at multiple atrazine 

1459 concentrations, enumerating cell density based on hemocytometer cell counts. The author 

1460 provided a data table of cell counts at 1, 2, 3, 4 d at multiple concentrations; initial cell counts 

were reported to be 11041461 . Concentrations were measured and were stable for 4 d 

1462 (concentrations were 2X higher than target due to diluting error). Light was continuous at 450-

1463 500 ft-c and temperature was 24-25 C. SGRs were calculated by EPA for each duration and 

1464 concentration and used in regression analyses to estimate EC50 and steepness. Substantial and 

1465 continuing declines in control SGRs were observed, so that the growth rate over 2 d was 24% 

1466 less than that over the first day. However, cell counts over the first day were lower than desired 

1467 for good quantification and the drop in SGR could be partly due to uncertainty in both the initial 

1468 and day 1 cell counts. Therefore, day 2 values were selected for the data compilation. 

1469 

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 
51

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 

1470 

1471 (6) Hoberg 1993a 

1472 

1473 The author conducted a 96-h flask test of Selenastrum capricornutum growth at multiple atrazine 

1474 concentrations, enumerating cell density based on hemocytometer cell counts. The author 

1475 provided a data table of cell counts at 1, 2, 3, 4 d at multiple concentrations; initial cell counts 

were reported to be 0.31041476 . Concentrations were measured and were stable for 4 d. Light was 

1477 continuous at 300-450 ft-c and temperature was 24 C. SGRs were calculated by EPA for each 

1478 duration and concentration from these counts. The control SGR during the first day was 

1479 exceptionally high (3.32/d) and dropped to more typical levels during subsequent days. In 

1480 addition, SGRs were high during the first day even at the highest atrazine concentration (2.30/d 

1481 at 450 μg/L), and also dropped to more typical values during subsequent days (<0.1/d). These 

1482 atypical results might represent an error in the initial cell density, the reported value of which 

1483 was atypically low and could not be verified. These data were therefore not used. 

1484 

1485 (7) Caux et al. 1996

1486 

1487 The authors conducted a 4-d microplate test of Selenastrum capricornutum growth at multiple 

1488 atrazine concentrations, enumerating cell density using an electronic particle counter. Light was 

continuous at 60 μE/m21489 /s and temperature was 24 C. The authors only provided a 4-d EC50 for 

1490 cell density (26 μg/L), with no data on actual cell counts at test termination for atrazine 

1491 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 1-21061492 , corresponding to 

1493 an SGRC of 1.15-1.32/d, a relatively narrow range. Based on the midrange of the reported final 

1494 control cell counts, an SGRC of 1.25/d was used for adjusting the cell density-based EC50 to the 

1495 SGR (1.08/d) that would result in half the final control density. The authors also reported a 

1496 probit slope of 4.95 for the cell density vs. log10C relationship, which allowed calculation of 

1497 other ECps for cell density (e.g., EC16 and EC84 corresponding to ±1 standard deviation in probit 

1498 equation) and their corresponding SGRs. Per item A.1.2.4(b) in the protocol, these estimated 

1499 SGRs were subject to regression analysis to estimate the SGR EC50 and steepness. Confidence 

1500 limits are not reported because this regression was not based on independent data points, but on a 

1501 conversion of the reported relationship for the cell density ECs. 

1502 

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 
52

EC50 (μg/L) 50 

Steepness 1.66 

1503 

1504 (8) Versteeg 1990

1505 

1506 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 14 1507 C fixation 

after 30-min exposure, and 30-min oxygen evolution. Light was continuous at 86 μE/m21508 /s for 

the flask test, 350 μE/m2/s for the 14C fixation, and 250 μE/m21509 /s for the oxygen evolution; 

temperature was 24 C. Reported EC50s were 50 μg/L for 4-d cell density, 100 μg/L for 14 1510 C 

1511 fixation, and 380 μg/L for oxygen evolution. Data for individual treatments were not reported 

1512 for atrazine, but were for simazine, another triazine herbicide. Measurement variables (cell 

densities, 14 1513 C fixation rate, oxygen evolution rate) relative to the control are provided in the 

1514 following table for simazine. Simazine showed differences among the EC50s based on cell 

densities, 14 1515 C fixation rate, and oxygen evolution similar to atrazine. SGRs based on cell density 

1516 effects were also estimated per item A.1.2.5 of the protocol, resulting in an SGR-based EC50

similar to that for 14 1517 C fixation. This simazine analysis also resulted in a slope for SGR-based 

1518 ECs that was included in the compilation. 

1519 

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 

1520 

1521 Based on the experimental procedures and the results for both atrazine and simazine, this study 

1522 was applied as follows regarding EC50s: 

1523 (a) Because the oxygen evolution assay involved purging oxygen, with uncertain effects on 

1524 photosynthesis rates and sensitivity to atrazine, these data were not used. 

(b) Because the 14 1525 C fixation assay included prior exposure, the results will be used. Because of 

the short exposure and measurement periods, the EC50 (100 μg/L) for 14 1526 C fixation will be 

1527 treated as being equivalent to those for SGRs. 

1528 (c) The smaller EC50 for the flask test cell density is likely due to it being for cumulative growth 

1529 over 4 d. Per item A.1.2.7 in the protocol, this had too long a duration to extrapolate the cell 

1530 density-based EC50 to an SGR-based EC50 given the range of estimates for the unknown SGRC
53

1531 and steepness. However, if the steepness for simazine was used, the procedure would result in 

the estimates for the SGR EC50 from 95-115 μg/L, consistent with that for 14 1532 C fixation. 

1533 

1534 (9) Larsen et al. 1986

1535 

The authors reported EC50s for 14 1536 C fixation rates of several algal species, measured over 2 h after 

1537 24 h prior exposure to atrazine. Light was continuous at 400 ft-c and temperature was 24 C. 

1538 Because the 24-h prior exposure would result in substantially different biomasses among 

1539 treatments, this measure is not proportional to the SGR, and because fixation was not cumulative 

1540 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 1541 and the carbon 

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

SGR 1542 , ignoring the small 

1543 amount of growth over that 2 h and assuming that the measured fixation over the 2 h is 

1544 approximately proportional to the SGR. Given this relationship, per item A.1.2.7 of the 

1545 protocol, an EC50 for the SGR can still be calculated from this information, if an SGRC and 

1546 steepness can be estimated for use in the following calculations: 

1547 

1548 (a) Solve for SGRp (p = percent reduction in SGR relative to control) 

corresponding to the EC50 for 14 1549 C fixation using the equation 

0.5 P C SGR SGR

P C 1550 SGR e SGR e = ⋅ (i.e., this equation describes what the SGR would 

have to be so that the function SGR⋅e

SGR 1551 is at half of its control value). 

1552 

1553 (b) Calculate p as 100⋅(1-SGRp/SGRC). 

1554 

1555 (c) Use the estimated steepness for the toxicity relationship to extrapolate the 

known SGR ECp (=EC50 for 14 1556 C fixation) to the SGR EC50. 

1557 

For Selenastrum capricornutum, the authors reported EC50s for 14 1558 C fixation of 34-53 μg/L (three 

1559 tests, average 43). Using this average EC50, the procedure described above was conducted 

1560 multiple times using the low, middle, and high estimates for SGRC and steepness identified in the 

1561 protocol for this review. The range of the resultant SGR EC50s was 66-114 μg/L, narrow enough 

1562 to include the median SGR EC50 (78 μg/L) in the data compilation. For the other species, the 

1563 following table summarizes comparable calculations. For green algae, the same ratio (1.88) 

1564 between the carbon fixation and SGR EC50s was used as for Selenastrum. For blue-green algae, 

1565 the ratio used was 1.43 based on the estimates for SGRC for blue-green algae specified in the 

1566 review guidelines. 

1567 

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 
54

Ulothrix subconstricta 88 159 

Anabaena cylindrica 204 286 

1568 

1569 (10) Mayer et al. 1998

1570 

1571 The authors provided an EC10, EC50, and EC90 for SGRs from a standard ISO 8692 toxicity flask 

1572 test (3 d) with Selenastrum capricornutum. The actual temperature and light intensity was not 

1573 reported, but the cited test protocol specified 60-120 μE/m2/s and 23±2 C. The author-reported 

1574 SGR EC50 of 164 μg/L will be used, but the multiple ECs can also be used to estimate the 

1575 steepness parameter for the sigmoidal relationship used in this review. The author also reported 

1576 information on effects of light, temperature, pH, and nitrogen source on both control growth and 

1577 toxic effects. This information indicated the SGRC for this study under standard conditions was 

1578 about 1.8/d, but insufficient information was available to use other toxicity information for the 

1579 present analysis. This study did document a 10-fold increase in chlorophyll content per cell due 

1580 to atrazine exposure (200 μg/L), which provides some of the basis for not accepting this as a 

1581 surrogate for biomass. 

1582 

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 

1583 

1584 (11) Roberts et al. 1990

1585 

1586 The authors conducted a 7-d flask test of Selenastrum capricornutum growth at multiple atrazine 

1587 concentrations, enumerating cell density based on hemocytometer cell counts. Concentrations 

1588 were nominal. Light was continuous at 2300 ft-c and temperature was 24 C. The authors 

1589 reported the number for the doublings (cell count basis) over 3 d. This number of doublings was 

1590 converted to a factor increase, which was converted to an SGR and subject to regression 

1591 analysis. 

1592 

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 

1593 
55

1594 (12) Parrish, 1978

1595 

1596 The author conducted 5-d flask tests of Selenastrum capricornutum and Microcystis aeruginosa

1597 growth at multiple atrazine concentrations, enumerating cell density based on hemocytometer 

1598 cell counts. Concentrations were nominal. Light was continuous at 400 ft-c and temperature 

1599 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 51041600 for Microcystis. SGRs were calculated 

1601 from the counts for each duration and concentration. Results for Selenastrum are in the 

1602 following table. Because there was not a substantial decline in the SGRC and results agreed 

1603 between the two durations, the 5-d results were selected for use. 

1604 

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)

1605 

1606 Results for Microcystis are in the following table. Control growth actually increased later in the 

1607 test and EC50s were similar for both durations, so the 5-d results were selected for use. 

1608 

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)

1609 

1610 (13) Turbak et al. 1986 

1611 
56

1612 The authors reported an EC50 of 70 μg/L based on a 30-min oxygen evolution assay with 

1613 Selenastrum capricornutum, with no additional information to determine the steepness of the 

1614 relationship. The actual temperature and light intensity was not reported, but the test protocol 

1615 specified 400 ft-c and 24 C. The methods description did indicate that there was some exposure 

1616 prior to oxygen measurements, and 30 min is long enough not to be greatly perturbed by 

1617 induction lags of several minutes. Therefore, this EC50 based on rate of oxygen evolution was 

1618 accepted as informative of an SGR EC50. They also reported a 59 μg/L SGR EC50 based on a 2-

1619 3 week bottle test. Because of the length of this test and the lack of specifics regarding it, this 

1620 EC50 was not used, but this result does not contradict the EC50 based on oxygen evolution. 

1621 

1622 (14) Radetski et al. 1995

1623 

1624 The authors reported a 72-h EC50 of 118 μg/L for Selenastrum capricornutum based on cell 

1625 counts (Coulter counter) in a semistatic microplate well test. The actual temperature and light 

1626 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

1627 , 

1628 corresponding to an SGRC of 1.93/d. At the reported EC50, the final cell count would thus have 

been 3.31061629 , equivalent to an SGR of 1.70, corresponding to a 12% reduction from the control 

1630 value (i.e., the growth EC50 is an SGR EC12). Per protocol item A.1.2.6, this is too long of an 

1631 extrapolation to estimate an SGR EC50 given the uncertainty in the steepness of the relationship, 

1632 so an SGR EC50 was not computed. However, the SGRC was used in the compilation. 

1633 

1634 (15) Abou-Waly et al. 1991

1635 

1636 The authors conducted 7-d flask tests of Selenastrum capricornutum and Anabaena flos-aquae 

1637 aeruginosa growth at multiple atrazine concentrations, measuring weights and chlorophyll 

1638 concentrations. Concentrations were nominal. The authors reported SGRs for multiple durations 

1639 and concentrations, but only for chlorophyll measurements. Therefore, these data were not used 

1640 in accordance with item (A3) of the protocol. Reported chlorophyll-based growth rates and 

1641 EC50s had complex relationships to time and exposure concentration, thereby substantiating 

1642 concerns about using chlorophyll measurements. For Anabaena, transferring organisms to 

1643 control media after the end of the exposure test showed rapid recovery of growth rates. 

1644 

1645 (16) Hughes et al. 1988, Hughes 1986

1646 

1647 The authors conducted 5-d flask tests of the growth of two algal species, Anabaena flos-aquae

1648 and Navicula pelliculosa, at multiple atrazine concentrations, enumerating cell density by 

1649 electronic particle counting. Concentrations were not measured. Light was continuous, and light 

1650 intensity/temperatures were 200 ft-c/24 C for Anabaena and 400 ft-c/20 C for Navicula. The 

1651 author provided data tables of algal cell densities at 3 and 5 d. SGRs were calculated for each 

1652 duration and concentration from these counts, based on the reported initial algal cell densities of 

21041653 cells/ml. 

1654 

1655 The following table provides results for Anabaena flos-aquae. Because no significant effects of 

1656 duration are evident on either control growth rates or the EC50, the 5-d results were selected for 

1657 further use. 
57

1658 

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 

3200 4.5 5.5 0.27 0.20 

EC50 736 706 

(440-1131)

Steepness 0.48 0.59 

(0.35-0.83)

1659 

1660 The following table provides results for Navicula pellculosa. Because control growth was 

1661 maintained or even increased through 5 d, the 5-d results were selected for further use. 

1662 

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)

1663 

1664 (17) Fairchild et al. 1994, 1998

1665 

1666 The authors assessed the effects of four herbicides on plant growth using 4-d tests with six algal 

1667 species. Concentrations were not measured in exposure chambers, but the stock concentrations 

1668 were verified. Because chlorophyll was used to quantify algal biomass, these data were not used 

1669 here per item (A3) of the protocol. 

1670 

1671 (18) Fairchild et al. 1995, 1997

1672 

1673 The authors conducted 4-d tests of Selenastrum capricornutum at multiple atrazine 

1674 concentrations (as well as 15 other herbicides). Concentrations were not measured. Because 
58

1675 chlorophyll was used to quantify Selenastrum biomass, these data were not used here per item 

1676 (A3) of the protocol. 

1677 

1678 (19) Burrell et al. 1985

1679 

1680 The authors conducted an 11-d flask tests of the growth of Chlorella vulgaris and 

1681 Ankistrodesmus braunii at multiple atrazine concentrations, enumerating cell density based on 

1682 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 11051683 and 

1684 exponential cell growth was reported to be maintained for the test duration, culminating in a final 

cell density of 1.7106 (SGRC=0.26/d) in the Chlorella test and 3.81061685 (SGRC=0.33/d) in the 

1686 Ankistrodesmus test. The authors graphically reported the percent reduction in the final cell 

1687 density at each atrazine concentration, which were estimated from the figure and reported in the 

1688 table below. Based on the final cell densities in the control and the test durations, these percent 

1689 reductions in cell density were converted to SGRs at each atrazine concentration and subject to 

1690 regression analyses to determine the SGR EC50 and steepness. Although this test was longer 

1691 than would typically be used for this compilation, the SGRC were low enough (at least in part 

1692 due to low light intensities) that total cell densities were not so high as to confound results or to 

1693 doubt the authors’ statement that exponential growth was maintained. However, because these 

1694 SGRCs were so low they were not used for estimating SGRCs for other studies. 

1695 

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) 

1696 

1697 (20) Kirby and Sheahan 1994

1698 

1699 The authors conducted a 4-d flask test of the growth of Scenedesmus subspicatus at multiple 

1700 atrazine concentrations; concentrations were measured. Illumination was continuous at 3500 lux 

1701 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⋅1041702 cell/ml and 

1703 growth was quantified by spectrophotometric absorbance calibrated to cell density. The EC50

1704 based on final cell density was 21 μg/L. Because only an EC50 was reported and an SGRC was 

1705 not reported, estimation of the SGR EC50 would be per item A.1.2.7 of the protocol, but this was 
59

1706 not done because the extrapolation would be too great (the extrapolated value would be 80 μg/L 

1707 with a range of 50 to 150 μg/L). In addition, this study used optical density near the chlorophyll 

1708 a maximum, and so would not be used per the review guidelines. 

1709 

1710 (21) Millie and Hersh 1987

1711 

1712 The authors determined oxygen evolution rates in an electrode chamber for three geographical 

1713 races of Cyclotella meneghiana exposed to different atrazine concentrations (unmeasured). 

Illumination was at 300 μE/m21714 /s and temperature was 25 C. The authors graphically reported the 

1715 percent inhibition of oxygen evolution rate relative to controls at each concentration, and these 

1716 percentages were determined from the graph and subject to regression analysis to determine 

1717 oxygen evolution EC50 and steepness. Because these were based on a short-term (1 min) oxygen 

1718 evolution and because there was prior exposure to each atrazine concentration of several minutes 

1719 before oxygen evolution was measured, ECs from these oxygen evolution rates were accepted as 

1720 being comparable to SGR ECs. 

1721 

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) 

1722 

1723 (22) Hersh and Crumpton 1989

1724 

1725 The authors determined oxygen evolution rates in an electrode chamber of a commercial strain of 

1726 Chlamydomonas reinhardii and of three isolates of Chlorella sp. obtained from an 

1727 uncontaminated natural system exposed to different atrazine concentrations (unmeasured). 

Illumination was at 300 μE/m2

1728 /s and temperature was 25 C. Only the EC50 for the reduction in 

1729 oxygen evolution rates relative to control were reported (no data on actual oxygen evolution vs. 

1730 concentration), but because these were based on a short-term (1 min) oxygen evolution and 

1731 because there was prior exposure to each atrazine concentration of several minutes before 

1732 oxygen evolution was measured, these oxygen evolution EC50s were accepted as being 

1733 comparable to SGR EC50s. For Chlamydomonas, the EC50 was 45 μg/L and for Chlorella it 

1734 averaged 37 μg/L across the three isolates (range=36-41). 
60

1735 

1736 (23) Stratton 1981, 1984

1737 

The author measured 14 1738 C fixation over 3 h and cell growth rate (by optical density) over 12-14 d 

1739 for five algal species exposed to various atrazine and atrazine metabolite concentrations. 

Concentrations were unmeasured. For the 14 1740 C fixation tests, light intensity was 7000 lux and 

1741 temperature was 20 C; these were not specified for the growth test, but presumably were the 

1742 same because these were also the culture conditions. For the growth tests, data other than EC50s 

1743 at the end of the test were not provided, except for A. inaequalis, and this showed non1744 exponential growth throughout the last 10 d of the test and indicated the EC50 was lower at 4-5 d 

1745 than later in the text, although the plotted data were insufficient to quantify this. In addition, 

1746 optical density was measured at wavelengths with substantial chlorophyll absorption for at least 

1747 three of the species. For these reasons, the ECs from the long growth test were not used, and 

only the 14 1748 C fixation EC50s were compiled: 

1749 

 Anabaena 

inaequalis 

Anabaena 

cylindrica 

Anabaena 

variabilis 

Chlorella 

pyrenoidosa 

Scenedesmus 

quadricauda 

14C fixation 

EC50 (μg/L) 

280 470 70 480 300 

1750 

1751 (24) Schafer et al. 1994 

1752 

1753 The authors conducted a 10-d test of the growth of Chlamydomonas reinhardi in a flow-through 

1754 apparatus that maintained exponential cell growth, and reported EC50s and EC10s for growth at 4, 

1755 7, and 10 d. Concentrations were measured. The light intensity was 7000 lux with a 14/10 

1756 photoperiod and the temperature was 24 C. Information was also provided to allow estimation of 

1757 the SGRC to be 1.06/d, but no additional information on actual or relative cell counts at different 

1758 concentrations and times, etc. was given. These ECs were reported to be for growth (not growth 

1759 rate) and to be derived per OECD method 201, so presumably were based on “area under the 

1760 curve” (AUC). They thus do not represent the difference between the biomass at the stated time 

1761 and the biomass at test start, but rather the sum of these differences across the whole time 

1762 interval (and thus a measure of the average increase). Because this system maintained an 

1763 exponential growth and because the SGRC is known, the EC50s can be used to estimate SGRs for 

1764 those concentrations, as summarized in the following table. The magnitudes of these estimated 

1765 effects on the SGR are insufficient to support a regression analysis to estimate the SGR EC50 and 

1766 steepness (due to the large extrapolation from 16% effect to 50% effect). However, per item 

1767 A.1.2.6 in the protocol, this SGR EC16 of 51 μg/L can be extrapolated to an estimate of 141 

1768 μg/L for the SGR EC50. 

1769 

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 
61

1770 

1771 The authors also conducted 3-d flask tests of the growth of Chlamydomonas reinhardii and 

1772 Scenedesumus subspicatus at different atrazine concentrations, measuring cell densities at 1, 2, 

1773 and 3 d with an electronic particle counter. Illumination was continuous at 8000 lux and the 

1774 temperature was 20 C. The authors reported 3-d EC50s and EC10s from these tests, but without 

1775 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⋅1051776 cell/ml) that would have led to 

1777 growth-inhibiting densities based on the SGRC from the flow-through test, the growth EC50 for 

1778 Chlamydomonas (350 μg/L) cannot be converted to information on an SGR EC. For 

Scenedesmus, initial cell densities were low enough (5⋅1041779 cell/ml) to make converting the 

1780 growth EC50 (72 μg/L) reasonable; however, this would follow item A.1.2.7 of the protocol, and 

1781 the duration of the test is too long for this extrapolation given uncertainties in both SGRC and 

1782 steepness. 

1783 

1784 (25) Faust et al. 1993 

1785 

1786 The authors conducted 1-d tests of Chlorella fusca growth at multiple atrazine concentrations. 

1787 This was a synchronized culture of 1 generation per day, in which a cell grows during the light 

1788 period (14 h) and releases a set of daughter cells in the subsequent dark period (10 h); cell counts 

1789 were by Coulter counter. The SGRC for cell number would be ln(# of daughter cells) for the 

1790 control treatment, but this number was not reported. This number can be as low as 4 

1791 (SGRC=1.4/d), but in a related paper by Altenburger et al. (1990), a value of 12 was indicated 

1792 (SGRC=2.5/d). The authors reported a probit equation for cell reproduction over 24 h. The 

1793 points on this probit equation corresponding to -2, -1, 0, 1, and 2 probit units from the median 

1794 were calculated to provide ECps for cell “reproduction” (table below). Then, two sets of SGR 

1795 estimates corresponding to these ECps were calculated based on the two alternatives for the 

1796 SGRC, and regression analyses were conducted on each of these sets of SGRs. The resultant 

1797 SGR EC50 estimates did not differ markedly (table below), so the average of these were 

1798 included in the data compilation. 

1799 

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 

1800 

1801 (26) Geyer et al. 1985 

1802 

1803 The authors conducted 4-d flask tests of Scenedesmus subspicatus growth at multiple atrazine 

1804 concentrations. The AUC EC50 was reported to be 110 μg/L, but other information (effects at 
62

1805 higher concentrations, control SGR) were not reported. This test does not meet the protocols 

1806 stated earlier for extrapolating such an EC50 to one for the SGR. 

1807 

1808 (27) Zagorc-Koncan 1996 

1809 

1810 The author determined the net production of oxygen over 24 h (by liberated gas via Warburg1811 type apparatus) and increased biomass as measured by chlorophyll over 72 h of Scenedesmus 

1812 subspicatus exposed to multiple atrazine concentrations. Light was continuous at 800 lux and 

1813 temperature was 20 C. As noted in the protocol, chlorophyll is not an acceptable surrogate for 

1814 biomass. Regarding oxygen evolution, the authors reported an EC50 of 25 μg/L, but because of 

1815 the lengthy incubation this should be proportional to net biomass gain and not directly related to 

1816 effects on SGR. To convert to an SGR-basis requires estimating SGRs based on the oxygen 

1817 production and assumptions regarding SGRC. Such estimates based on the range of SGRC for 

1818 green algae observed in other studies are included in the table below and subject to regression 

1819 analysis. Variation in the assumed SGRC did not cause great variation in the estimated SGR

1820 EC50; because of the low temperature and light intensity, the compilation used the value from 

1821 the lowest SGRC value. 

1822 

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 

1823 

1824 (28) Tang et al. 1997 

1825 

1826 The authors conducted 28 d tests with several algal species. Growth was measured based on 

1827 chlorophyll measurements and optical density near the chlorophyll a maximum. Due to both the 

1828 length and the type of measurement, these data were not used. 

1829 

1830 (29) Gramlich and Frans 1964 

1831 

1832 The authors conducted a 5-d flask test with Chlorella pyrenoidosa at several atrazine 

1833 concentrations. Because biomass was measured by optical density and because initial values for 

1834 biomass were not given, useful results for the compilation could not be obtained from this study. 

1835 
63

1836 (30) Stratton and Giles 1990 

1837 

1838 The authors examined the effect of volume and initial cell density on the toxicity of atrazine to 

1839 Chlorella pyrenoidosa, measured by radiocarbon uptake over 24 h. Although these experiments 

1840 demonstrated inhibition relative to the control and did include some treatments with 

1841 approximately 50% inhibition, only one concentration was tested, absolute fixation rates were 

1842 not tested, and a variety of processes might be affecting the observed inhibition. This precluded 

1843 applying these data to the data compilation of interest here. 

1844 

1845 

1846 (31) Boger and Schlue 1976 

1847 

1848 The authors evaluated photosynthesis based on oxygen evolution rate after several days of 

1849 exposure to atrazine and the recovery of photosynthesis upon transfer of exposed algae to clean 

1850 medium and control algae to contaminated medium. However, only one concentration was 

1851 tested and results could not be related to the effect concentrations desired in this review. 

1852 

1853 (32) University of Mississippi 1991 

1854 

1855 The authors evaluated growth of Selenastrum capricornutum (4 d) at multiple atrazine 

1856 concentrations. This test involved methodological and performance problems that precluded its 

1857 use, especially for determining SGR-based ECs. Chlorophyll measurements were made, but 

1858 were erratic in addition to being not accepted in the protocol used here. Both cell densities and 

1859 weights were also measured, but no initial cell density was specified, final densities were based 

1860 on inadequate numbers of cells, and many of the measurements of final weight were negative. 

1861 Atrazine effects were evident at 100 μg/L, but the next lower and higher concentration was 10-

1862 fold different (10 and 1000 μg/L) , precluding any good characterization of dose-response. 

1863 

1864 A.2.2 Vascular plants 

1865 

1866 (1) Hughes et al. 1988, Hughes 1986

1867 The authors conducted a 5-d test with the duckweed, Lemna gibba, at multiple atrazine 

1868 concentrations, assessing growth by frond count. Concentrations were not measured. Light was 

1869 at 500 ft-c and temperature was 25 C. The authors provided data tables of duckweed frond 

1870 counts at 3 and 5 d. SGRs were calculated for each duration and concentration from these 

1871 counts, based on an initial frond count of 16. The following table summarizes observations and 

1872 the estimated SGRs. Because control growth was less than a factor of two at 3 d, the 5-d results 

1873 were selected for further use. 

1874 

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 
64

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)

1875 

1876 

1877 (2) Hoberg 2007

1878 

1879 The author conducted growth tests with isolated shoots of Elodea canadensis at multiple atrazine 

1880 concentrations and at zero, dim (500 lux), and optimal (6000 lux) light levels (only the higher 

1881 light level is appropriate for this review). Concentrations were measured and temperature was 

1882 20-25C. Data tables were provided for individual shoot lengths at 0 and 14 d and individual 

1883 shoot dry weights at 14 d for multiple concentrations. Only dry weight is considered here (shoot 

1884 lengths were a poor surrogate for growth because substantial shoot elongation was observed in 

1885 low light and at high atrazine concentrations were no growth in weight was observed). This 

1886 requires having an estimate of the initial dry weight, which the author reported for a separate 

1887 initial sample of shoots as being 0.1346 g/shoot. It was assumed that this weight applied to the 

1888 average initial shoot length (8.3 cm/shoot) so that the initial weight per cm 0.0162 g/cm. This 

1889 factor was used to estimate the initial weights for each replicate tanks based on the initial shoot 

1890 lengths within that tank, allowing SGRs to be computed for each tank. The following table lists 

1891 the reported final weights, the estimated initial weights, and the resultant shoot weight SGRs, 

1892 along with the EC50 and steepness parameter estimated by regression analysis. This regression 

1893 analysis is relatively uncertain because the lowest treatment concentration corresponds to an 

1894 EC68, leaving an absence of data at low to moderate effect. However, the estimated steepness is 

1895 similar to others reported for this species (Table XX) so the EC50 estimate was still deemed 

1896 acceptable for us. 

1897 

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) 

1898 

1899 (3) Hoberg 1991b

1900 
65

1901 The author conducted a 7-d test of Lemna gibba growth at multiple atrazine concentrations; 

1902 concentrations were measured. Light was continuous and temperature was 24 C. The author 

1903 provided a data table of frond counts at 3, 6, and 7 d at multiple concentrations; initial frond 

1904 counts were 15. SGRs were calculated for each duration and concentration from the counts and 

1905 regression analyses were conducted on these SGRs. Because of the absence of growth on day 7, 

1906 the 6-d values were compiled. 

1907 

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 

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 

1908 

1909 (4) Hoberg 1993b

1910 

1911 The author conducted a 14-d test of Lemna gibba growth at multiple atrazine concentrations. 

1912 Concentrations were measured. Light was at 400 ft-c and temperature at 24 C. The author 

1913 provided a data table of frond counts at 3, 6, 9, 12, and 14 d and dry weight at 14 d. Initial frond 

1914 counts were 15. Initial dry weight was unreported but it was assumed for this analysis that the 

1915 initial dry weight per frond was equal to that in the control at the end (=110 mg/529=0.208 

1916 mg/frond), so that the initial dry weight would be 3.12 mg. SGRs were calculated for each 

1917 duration and concentration from the counts and dry weights and regression analyses were 

1918 conducted on these SGRs. Based on frond count, some reduction in control growth rate occurred 

1919 after 9 d, but did not appreciably affect estimated SGR EC50s. For the 14-d data, dry weights 

1920 resulted in an EC50 29% lower than that based on frond count. This is likely attributable to the 

1921 lower dry weight/frond at higher atrazine concentrations (i.e., smaller fronds due to atrazine 

1922 effects), but also could be contributed to by overestimation of the initial dry weight if control 

1923 fronds at the end were on average larger than those at the beginning. This illustrates a possible 

1924 weakness in the use of frond counts for duckweed tests, but also a weakness in most tests 

1925 regarding measuring initial weights. Due to it being a direct measure of biomass rather than an 

1926 indicator, the dry weight-based results were compiled. 

1927 

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 
66

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)

1928 

1929 

1930 (5) Hoberg 1993c

1931 

1932 The author conducted a 14-d test of Lemna gibba growth at multiple atrazine concentrations. 

1933 Concentrations were measured. Light was continuous at 450-500 ft-c and temperature was 25 C. 

1934 The author provided a data table of frond counts at 3, 6, 9, 12, and 14 d and dry weight at 14 d. 

1935 Initial frond counts were 15. Initial dry weight was unreported but it was assumed for this 

1936 analysis that the initial dry weight per frond was equal to that in the control at the end, resulting 

1937 in a estimated initial dry weight of 3.7 mg. SGRs were calculated for each duration and 

1938 concentration from the counts and dry weights and regression analyses were conducted on these 

1939 SGRs. As for Hoberg 1993b, dry weight-based SGRs showed a lower EC50 and higher steepness 

1940 than frond count-basis, and were selected for the compilation. 

1941 

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)

1942 

1943 (6) Desjardin et al., 2003

1944 

1945 The authors conducted tests on Lemna gibba growth at multiple atrazine concentrations and for

1946 multiple durations (1-14 d) followed by examination of recovery. Concentrations were 

1947 measured. Temperature was 24-25 C and light intensity 4250-5750 lux. Rapid recovery was 

1948 demonstrated, but the analyses here are concerned with effects during the exposure period. 
67

1949 Furthermore, this analysis will be restricted to a 7-d test, because both the longer tests (9-14 d) 

1950 produced less than a 20% reduction in the SGR and the 1-3 d tests provided uncertain results due 

1951 to the short duration and limited concentration range. The authors provided data at day 2, 4, and 

1952 7 d and dry weight at 7 d at multiple concentrations. Initial frond counts were 15 at day -1 and 

1953 were 20-21 at the start of exposure (this 1 d period of growth was done to identify/discard 

1954 chambers that showed little or no growth; despite this precaution, one control replicate had poor 

1955 enough growth to be excluded as an outlier). The initial dry weight was estimated to be 2.8 mg 

1956 based on the average dry weight/frond in the no-effect concentrations at the end of the exposure. 

1957 SGRs were calculated for each duration and concentration from the counts and dry weights. 

1958 

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) 

1959 

1960 (7) Fairchild et al. 1994, 1998

1961 

1962 The authors assessed the effects of four herbicides on plant growth using 4-d tests with Lemna 

1963 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

1964 /sConcentrations 

1965 were not measured in exposure chambers, but the stock concentrations were verified. The 1994 

1966 report provided detailed biomass measurements absent in the 1998 journal article. 

1967 

1968 Lemna Initial frond counts were 12 in each replicate and final frond counts are listed in the 

1969 following table. The limited duration resulted in limited growth (barely 2-fold in the control) 

1970 that makes these results rather uncertain, particularly based on frond counts. 

1971 

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
68

600 12,14,14 0.000,0.038,0.026

EC50 (μg/L) 114 

(34-390) 

Steepness 0.42 

(0.06-0.79) 

1972 

1973 Najas: Replicates were created by placing natural pond sediments from Najas beds in beakers, 

1974 from which plants germinated. Plants were grown for approximately 2 weeks to approximately 3 

1975 cm in height, at which time the 14-d chemical exposure began. After the exposure, plants were 

1976 sieved and wet weights were determined. Initial wet weights were not determined, but based on 

1977 the similarity in the average weights in the highest three treatments (following table) it was 

1978 assumed that these treatments had zero net growth and SGRs were estimated based on an initial 

1979 wet weight of 69.5 mg, the overall average final weight of these treatments. Given the number 

1980 of replicates with lower final weights, the initial weights obviously varied considerably across 

1981 replicates, but by basing SGR on the mean weight across replicates, this variability is reduced 

1982 enough to produce a clear dose-response. To the extent that the highest three treatments did not 

1983 have zero net growth the estimated EC50 will be biased, but substantial bias would be unlikely 

1984 because (a) if substantial positive growth was occurring a concentration effect should be evident 

1985 and (b) if substantial negative growth was occurring this would imply a high initial weight 

1986 incompatible with the information on control growth (i.e. a disproportionate amount of control 

1987 growth in the two weeks prior to exposure compared to the 2 weeks of exposure). 

1988 

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) 

1989 

1990 Ceratophyllum: The authors provided wet weights for each replicate at 0, 7, and 14 d, allowing 

1991 calculation of SGRs and regression analysis of these SGRs to determine the EC50 and steepness 

1992 of the SGR vs concentration relationship. There was nearly a doubling of weight in the controls 

1993 over the 14-d, allowing sufficient growth so that effects were apparent and could be quantified. 

1994 

1995 

1996 

1997 
69

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) 

Steepness 0.81 

(0.12-1.50) 

1998 

1999 Myriophyllum: The authors provided wet weights for each replicate at 0, 7, and 14 d, allowing 

2000 calculation of the SGR for each replicate. However, the growth in controls and in NOECs was 

2001 too small and variable for good quantification of effects on SGR. At day 14 (table below), the 

2002 weight gain of individual replicates varied from -4-16% (average 8%) in the control, 1-31% 

2003 (13%) in the solvent control, from 11-16% (15%) at 37.5 μg/L, and 2-26% (15%) at 75 μg/L. In 

2004 addition, at day 7, the weight gains were 12-17% (15%) in the controls, 25-33% (28%) in the 

2005 solvent controls, 13-16% (13%) at 37.5 μg/L, and 6-21% (11%) at 75 μg/L. These data illustrate 

2006 not just a small amount of growth and great variability relative to the average net growth, but 

2007 also no or negative growth in most replicates during the second week, which the authors also 

2008 noted in other experiments. In addition, there is an inconsistency between the 7- and 14-d data in 

2009 that the 14-d data show no difference among the controls and the two lowest concentrations, 

2010 whereas the 7-d data indicate better growth in the solvent controls relative to the control without 

2011 solvent and the two lowest concentrations. Although there are clear effects at 150 μg/L and 

2012 above, there is not a good reference against which to quantify effects on the SGR. This 

2013 underscores the requirement in the protocol that control growth be large and consistent enough to 

2014 quantify ECs with reasonable precision. The most that can be inferred from this test is that 37.5 

2015 and 75μg/L are apparently NOECs and the SGR EC50 is probably ≈<150 μg/L. 

2016 

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 
70

Steepness 

2017 

2018 Elodea: The authors provided both wet weights for each replicate at 0, 7, and 14 d, allowing 

2019 calculation of the SGR for each replicate. However, as for Myriophyllum, the control growth 

2020 was very small, averaging only about 15% over the two weeks. Although, this growth was not as 

2021 variable as for Myriophyllum, it still is a questionable reference against which to quantify effects 

2022 on SGRs. In addition, the lowest treatment concentration produced no growth on average, and 

2023 negative growth became progressively greater at higher concentrations, so that ECs for SGR 

2024 could not be quantified even if the controls were good references for quantifying the SGR. The 

2025 most that can be inferred from this test is that the SGR EC50 is <38 μg/L, although even this 

2026 might be confounded by the low control growth. 

2027 

2028 

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 

2029 

2030 (8) Fairchild et al. 1995, 1997

2031 

2032 The authors conducted 4-d tests of Lemna minor growth at multiple atrazine concentrations (as 

2033 well as 15 other herbicides). Concentrations were not measured. For Lemna, the reported EC50

2034 of 153 μg/L was based on growth (frond count basis), and insufficient information was provided 

2035 to convert this to a growth rate basis. Based on a control growth rate of 0.21/d for identical 

2036 methodology used above by Fairchild et al. (1994, 1998) this EC50 would correspond to an EC82. 

2037 Because this extrapolation was greater than allowed in the protocol, this data just indicate that 

2038 the SGR EC50 is <153 μg/L, which does not contradict the results of Fairchild et al. (1994, 1998). 

2039 

2040 (9) Kirby and Sheahan 1994

2041 

2042 The authors conducted a 10-d test of the growth of Lemna minor at multiple atrazine 

2043 concentrations; concentrations were measured. Temperature was 25 C and light intensity was 

2044 3500 lux. The authors only reported EC50s based on final biomass, without any information on 

2045 specific treatments, growth rates, etc. The initial biomass was 10 fronds and growth was 

2046 quantified by chlorophyll, frond count, and fresh weight, with the respective EC50s being 56, 60, 

2047 and 62 μg/L. Using the average SGRC from other studies with Lemna (0.27/d, range 0.21-

2048 0.38/d), the EC50 for frond count would correspond to an EC25 for SGR. Using the average 
71

2049 steepness for SGR vs. concentration from other studies with Lemna (1.0 for frond count increase, 

2050 1.4 for weight increase), the SGR EC50 would then be 105 μg/L based on frond count and 95 

2051 μg/L based on weight. 

2052 

2053 (10) University of Mississippi 1991 

2054 

2055 The authors evaluated growth of Lemna gibba (14 d), and Elodea canadensis (10 d) at multiple 

2056 atrazine concentrations. These assays entailed methodological and performance problems that 

2057 precluded their use, especially for determining SGR-based ECs. Chlorophyll measurements were 

2058 erratic in addition to being not accepted in the protocol used here. For Lemna, both frond counts 

2059 and weights were measured, but frond counts indicated poor control growth (an SGR of 0.1/d, 

2060 compared to 0.2-0.4/d in other studies), no initial weights were given, and final weights had poor 

2061 precision. For Elodea, final dry weights did show a substantial effect of atrazine, but initial 

2062 weights were not given, so that growth could not be assessed either as a rate or an absolute 

2063 amount. For both species, atrazine effects were evident at 100 μg/L, but the next lower and 

2064 higher concentration was 10-fold different (10 and 1000 μg/L) , precluding any good 

2065 characterization of dose-response. 

2066 

2067 (11) Forney and Davis 1981; Davis 1980, Forney 1980

2068 

2069 The authors evaluated growth of Elodea canadensis, Myriophyllum spicatum, Potamogeton 

2070 perfoliatus, and Vallisneria americana in exposures of 3-9 weeks to multiple atrazine 

2071 concentrations. Depending on the experiment and test species, light varied from 3 to 170 

μE/m2

2072 /s (14/10 h photoperiod) and temperature was 20-30 C. Unfortunately, most of the 

2073 evaluations were of shoot length increase, which as discussed above is a questionable surrogate 

2074 for growth. In three instances, useful information regarding the SGR EC50 could be obtained: 

2075 

2076 For Potamogeton, in one experiment, dry weight was measured in addition to shoot length. 

2077 However, the nature of the weight measurements was unclear (gross weight vs. growth, how 

2078 much of plant included) and the authors noted that food reserves in the tuber used to sprout 

2079 Potamogeton would partially mask herbicide effects, so that these weight measurements would 

2080 overestimate ECs. This experiment also showed atrazine-dependent mortality at concentrations 

2081 of 32 μg/L and above. The following table shows the average dry weight of plants (at death or 

2082 end of test for survivors), the percent survival, and the product of dry weight and survival as an 

2083 estimate of live biomass at the end of the study. For issues regarding weight effects already 

2084 noted, this product might still underestimate biomass production, but was considered adequately 

2085 informative of atrazine effects on the SGR of a population of this plant. A regression analysis 

2086 was thus conducted on this product and used for the compilation. 

2087 

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 
72

100 74 62 46 

320 55 0 0 

EC50 (μg/L) 63 

Steepness 0.69 

2088 

2089 For Vallisneria, leaf length was measured and was used as a surrogate for growth because it 

2090 would be less susceptible than shoot length to elongation with little or no weight increase. Even 

2091 with this acceptance, most data could not be used because the authors noted that effects of 

2092 atrazine were not evident early in the experiments, likely due to food reserves in the tubers, and 

2093 that some experiments had light intensities high enough to inhibit leaf growth in favor of tuber 

2094 and lateral shoot development. Thus, analysis here was restricted to the latter part of one test 

2095 that the authors reported as being most informative about atrazine effects. The following table 

2096 provides the percentage increase in leaf length during the last week of this experiment, which 

2097 should be approximately proportional to the SGR. In another experiment with insufficient data 

2098 for analysis here, there was information on the ratio of plant weight to leaf length as a function of 

2099 atrazine, which did indicate some thinning of the leaves due to atrazine. The following table 

2100 includes those ratios, which provided a basis for estimating weight based on leaf length (only 

2101 three measured values – so interpolated value used for 32 μg/L and possible extrapolated values 

2102 for 1000 μg/L). This resulted in a decrease in the SGR EC50 of about 28%. 

2103 

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 

2104 

2105 For Elodea, in one experiment dry weight increase was measured. The following table provides 

2106 these data. Because initial and final dry weights weren’t provided, SGRs cannot be calculated, 

2107 but the slow growth rates of these plants should make the net increase proportional to SGR. 

2108 Because of the widely space concentrations, the estimated parameters are uncertain, but clearly 

2109 indicate the SGR EC50 to be less than 100 μg/L. 

2110 

Nominal 

Atrazine Conc 

(μg/L) 

Average Increase 

in Plant Dry Wt. 

(mg) 

0 37 

10 28 

100 17 

1000 11 
73

EC50 (μg/L) 65 

Steepness 0.28 

2111 

2112 (10) Hinman 1989 

2113 

2114 The author tested the effects of atrazine on both root and shoot growth of Hydrilla verticillata in 

2115 both water and sediment exposures (14 d). Concentrations were nominal, light was 40-50 

μE/m22116 /s, and temperature was 25 C. Both shoot and root growth was monitored by increase in 

2117 length. Increases in shoot length are subject to questions about elongation without increasing 

2118 weight, but this is not true for root growth, which should still be an indicator of atrazine effects 

2119 on primary production. The following table compares the data on root and shoot growth for the 

2120 water-based exposures. Shoot lengths do indicate a higher threshold for effects, but then a 

2121 steeper decline, with the EC50 being about 80% higher than for root length. 

2122 

2123 

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 

2124 

2125 
74

2126 
75 

APPENDIX B. 

EXPERIMENTAL ECOSYSTEM DATA 

2130 
76

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 
77

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 
78

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 
79

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 
80

2135 

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   
81

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 
82

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 
83

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 
84

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 
85

2140 

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 
86

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 
87

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 
88

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    
89

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 
90

2145