### Appendix I :

Pro posed Methodology fo r Specify ing Atrazine Levels of Concern for

Protection of Plant Communiti es in Freshwater Eco systems
(90 Pages)1

1

2 

## Proposed Methodology for 3 

## Specifying Atrazine Levels of Concern 4


## for Protection of Plant Communities 5 

## in Freshwater Ecosystems 6

7

Report To: 8

Environmental Fate and Effects Division 9

Office of Pesticide Programs 10

U.S. Environmental Protection Agency 11

Washington, DC 12

13

EPA/600/R-12/019 14

15

Russell Erickson 16

Mid-Continent Ecology Division 17

National Health and Ecological Effects Laboratory 18

Office of Research and Development 19

U.S. Environmental Protection Agency 20

Duluth, MN 21

22

March 5, 2012 23

242 ACKNOWLEDGEMENTS 25

This document has been reviewed in accordance with U.S. Environmental Protection 26 Agency policy and approved for publication. Mentio n of trade names or commercial products 27 does not constitute endorsement or recommendation f or use. The author would like to 28 acknowledge the valuable reviews of drafts of this document by: 29

David Mount, U.S.EPA, Mid-Continent Ecology Divisio n 30 Dale Hoff, U.S.EPA, Mid-Continent Ecology Division 31 Mary Ann Starus, U.S.EPA, Mid-Continent Ecology Div ision 32 Glen Thursby, U.S.EPA, Atlantic Ecology Division 33 Joseph S. Meyer (retired), University of Wyoming, D epartment of Zoology and Physiology 34 35 The author would also like to acknowledge the impor tance to this document's development of his 36 numerous interactions over the last several years w ith various personnel of U.S.EPA's Office of 37 Pesticide Programs and Office of Water. 38 393

1. INTRODUCTION 40

This document describes a proposed methodology fo r setting a level of concern (LOC) 41 for atrazine in natural freshwater systems to preve nt unacceptably adverse effects on the aquatic 42 plant communities in those systems. Effects on hum ans and possible endocrine-disruption in 43 aquatic vertebrates are subjects of separate effort s, and certain implementation issues for aquatic 44 plant community atrazine risk assessment are also d escribed elsewhere. This first section defines 45 the problem being addressed and describes a general framework for setting the LOC. 46

1.1 Requirements for the LOC Methodology 47

Toxic chemical risk assessment problem definition requires defining the exposure 48 scenarios to be addressed, specifying the assessmen t endpoints of concern, and identifying 49 measures of effect for the assessment endpoints (U. S.EPA 1998). 50

This LOC methodology must address the types of atr azine exposures occurring in natural 51 ecosystems for which risk is to be assessed. Atraz ine enters natural freshwater systems primarily 52 in rainfall-driven runoff, resulting in highly vari able and episodic exposures that depend on 53 rainfall distribution, atrazine application pattern s, topography, and soil properties. Figure 1 54 provides example time-series of 55 atrazine exposures during 2010 in 56 three Missouri streams, measured as 57 part of a monitoring program being 58 conducted to satisfy risk evaluations 59 required under the 2003 interim 60 reregistration of atrazine (U.S.EPA 61 2003). These examples illustrate 62 substantial variation in exposure 63 patterns, and thus the need for the 64 LOC methodology to address the 65 relationship of effects to time, 66 including high concentrations with 67 limited durations, multiple events, 68 and prolonged, variable exposures at 69 low to moderate concentrations. The 70 top and bottom series have similar 71 average concentrations but very 72 different peaks, underscoring the 73 issue of the comparative risk of short, 74 intense exposures to more prolonged 75 exposures at lower concentrations. 76

The assessment endpoint for this LOC methodology i s the productivity and composition 77 of natural aquatic plant communities. Although atr azine has been the subject of many toxicity 78 tests on individual aquatic plant species and altho ugh such tests are often used as measures of 79 effect for aquatic plant risk assessments (e.g., So lomon et al. 1996, Giddings et al. 2000), they 80 will not be used directly for that purpose in this methodology. Rather, because atrazine has been 81 Figure 1. Examples of atrazine exposure time-serie s in natural freshwater systems.

Julian Dayμg/L Atrazine 0 50 100 150

90 120 150 180 210 0 10 20 30 0 30 60 90 MO 07 2010

MO 02 2010 MO 01 20104

the subject of many experimental aquatic ecosystem studies documenting plant community 82 responses, these will be used to provide measures o f effect and to serve as the foundation for 83 defining exposures causing effects of concern. Fig ure 2 summarizes an evaluation of such 84 studies conducted by the U.S.EPA's Office of Pestic ide Programs (OPP) Environmental Fate and 85 Effects Division (EFED) (U.S.EPA 2011). In Figure 2, each experimental ecosystem treatment 86 is characterized by the duration over which effects were assessed, the average atrazine 87 concentration over this duration, and whether there were unacceptably adverse effects on the 88 plant community. For each point on Figure 2, Appen dix B of this report provides more complete 89 exposure information, the effects designation, and a literature citation; other information on the 90 analyses of these studies can be found in U.S.EPA ( 2011). It should be emphasized that a 91 fundamental assumption in using such experimental e cosystem data is that they collectively 92 describe a relationship of effects to exposure that is relevant to the probability of effects (i.e., 93 risk) occurring in natural freshwater systems. In other words, it is assumed that natural aquatic 94 plant communities will generally react adversely if subjected to the same atrazine exposures that 95 elicited adverse effects in the experimental ecosys tem studies. This assumption is inherent in 96 any assessment that extrapolates toxicity experimen ts to the field, and the use of experimental 97 ecosystems arguably provides a better basis than do single-species toxicity tests. 98

Figure 2 illustrates three important requirements for the LOC methodology: 99 (1) Diversity among the experimental approaches pr ecluded characterizing each experimental 100 ecosystem treatment with an identical, quantitative measure of effect. Therefore, LOC 101 characterizations must rely on a binary (acceptable vs. unacceptable) characterization of effect. 102 (2) Although the exposures that resulted in adverse effects are somewhat separated from those 103 that did not cause adverse effects, substantial ove rlap exists between these two groups, especially 104 in the 10-20 μ g/L range. This variability is presumably due to t he combined effect of: 105 differences in the nature of the experimental syste ms; differences in the experimental design and 106 Figure 2. Effects of atrazine on experimental ecos ystems as a function of exposure duration and avera ge

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

Test Duration (days) 5 20 50 200

10 100Average Concentration (μg/L Atrazine)

1 10 100 1000 100005

the endpoints measured; and random variability of t he response of any given system. The 107 methodology must address how to specify an LOC with in such variability. 108 (3) The LOC methodology must address the relations hip of effects to time. This is important 109 not only because of the variability of field exposu res shown in Figure 1, but also because of the 110 different durations of the experimental ecosystem e xposures (Figure 2) and exposure variability 111 within these durations (Appendix B). Because data in Figure 2 do not provide information on 112 the relationship of the same endpoint to different exposure time-series, this time-dependence 113 issue must be addressed in the formulation of the e xtrapolation methodology discussed below. 114

1.2 General Framework for the LOC methodology 115

The key issue that this LOC methodology must addre ss is how to relate aquatic plant 116 community effects elicited in an experimental ecosy stem by a particular atrazine exposure time117 series to markedly different time-series in other e xperimental studies or natural systems. If all 118 exposures of interest had the same shape (i.e., the same exposure duration and the same relative 119 changes in concentration within that duration), the LOC could be based on the relationship of 120 effects in the experimental studies to any convenie nt measure of exposure. However, the 121 markedly different exposure shapes discussed above preclude such a simple approach, and there 122 is thus a need for a method to translate any exposu re time-series to a "common currency" that 123 integrates time and concentration into an index of the relative total severity of effects from the 124 exposure. This "effects index" serves only as a re lative measure of effect because the 125 experimental ecosystem effects define the absolute levels of concern. Text Box 1 further defines 126 and discusses this concept of an effects index. 127

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 si ngle 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 specif ied to (a) involve constant atrazine exposure over 30 d at several concentrations and (b) demonst rate that 20 μg atrazine/L constitutes an LOC based on the magnitude of effects elicited. Howeve r, this concentration-based LOC applies only to constant, 30-d exposures, whereas the exposure of i nterest is specified for this example to be a 10-d exposure at 100 μg atrazine/L. The basic ques tion is whether this more intense (5x higher) but more brief (3x shorter) exposure should be cons idered worse than the 30 d LOC concentration, provided the effects are assessed in the same manne r and over the same time period as in the original study. A very simple "effects index" for this would assum e that effects increase linearly with both concentration and time, so that the effects index c ould be the area under the exposure time-series, measured in "ppb-days" (note: this effects index de finition is provided only to illustrate the concept – the actual methodology should consider the nonlin earity of effects versus exposure) The LOC for this effects index would therefore by 600 ppb-d ays (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 i t has no inherent absolute meaning for risk except when calibrated to the experimental ecosyste m results. Its use is only for translating any exposure time-series to a common scale of compariso n, so that the LOC of 600 ppb-days can be used to judge any other exposure of interest, provi ded the exposure is for a system to which the experimental ecosystem is relevant.6

The effects index proposed for the LOC methodology will be described in Section 2. For 128 discussing the assessment framework here, it is onl y necessary to assume the existence of an 129 effects index that is suitable for comparing the re lative severity of different exposure time series. 130 Figure 3 provides a schematic of an assessment fram ework using such an effects index. 131

The process starts (Box 1) with compiling relevant experimental ecosystem data, 132 categorizing each treatment as to whether there was an effect or not and specifying the exposure 133 time-series for the treatment. This step is not a subject of this report, but rather is addressed in 134 U.S.EPA (2011). The effects index is then calculat ed (Box 2) for each experimental ecosystem 135 treatment, providing the "common currency" to compa re the severity of each exposure. The 136 relationship of the binary experimental ecosystem e ffects to this effects index is then examined 137 (Box 3) to set a level of concern for the effects i ndex (LOC EI ), based on the probability of 138 eliciting an effect (i.e., risk). 139

The LOC EI is applied to exposures in natural systems as foll ows. Exposure time-series 140 are compiled for the various exposures of interest in natural ecosystems (Box 4) and the effects 141 index for each exposure is computed (Box 5). Risk is characterized (Box 6) by dividing the 142 effects index by the LOC EI to compute the "effects exceedence factor" (EEF). The EEF indicates 143 whether the LOC is exceeded (i.e., EEF>1) and by ho w much. The EEF thus represents a risk 144 quotient approach, but this different terminology i s used here to distinguish this effects-based 145 quotient from concentration-based risk quotients co mmonly used. 146

Figure 3. Assessment framework for risk of atrazin e to aquatic plant communities, based on experiment al

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

Risk can also be characterized by what is termed t he "concentration exceedence factor" 147 (CEF) in Box 6. This factor is based on iterative calculations to determine the multiplicative 148 factor by which the exposure must be decreased so t hat the effects index exactly equals the 149 LOC EI . As for the EEF, a CEF indicates whether the LOC EI is exceeded and by how much, but 150 on a concentration scale rather than an effects sca le. This could have some advantage in 151 determining remediation goals or, conversely, deter mining how far exposures are below levels of 152 concern. However, this is an approximate measure f or such purposes, because the CEF is 153 premised on the same multiplicative factor applying to the entire concentration time-series. 154

Box 7 and the associated gray arrows in Figure 3 r epresent a final step in the assessment 155 framework that is not addressed in this document. It would be desirable for LOCs to be on a 156 concentration scale rather than an effects scale so that they relate more easily and directly to 157 exposure monitoring data. In Box 7, the relationsh ip of EEFs to an average exposure 158 concentration for a large number of existing exposu re time series is examined to determine an 159 LOC based on this average concentration, and which then can be applied to new exposure time160 series, for which the effects index need not be com puted. Developing such a concentration161 based LOC from the effects index-based LOC is being addressed separately by EFED. 162

Finally, it should be emphasized that the only sit e-specific factor intended to be addressed 163 in this LOC methodology is the exposure time-series . The methodology is not intended to 164 address other site-specific factors, such as physic ochemical conditions and the nature of the 165 biological community. Addressing such conditions i s not feasible from a standpoint of both 166 effort/cost and knowledge of their influence on atr azine effects. Rather, this method will be 167 generic in that any site with the same atrazine con centration time-series will be assessed as 168 having the same risk. 169

1708

2. PLANT ASSEMBLAGE TOXICITY INDEX 171

2.1 Potential Effects Indices 172

There are various possibilities, with differing co mplexities, for calculating an effects 173 index to serve in the assessment framework of Figur e 3. For illustrative purposes only, Text Box 174 1 assumed that effects increased linearly with both concentration and time, leading to an effects 175 index of ppb-days. To actually apply this simple, linear model a priori is not justified. Rather, 176 the effects index should consider ecotoxicological relationships. 177

At the other extreme of complexity are community s imulation models that address not 178 only the immediate impact of atrazine on plant comm unity primary production, but also consider 179 the ramifications of this on plant community dynami cs throughout a growing season. Earlier 180 efforts for developing an LOC methodology considere d the use of the Comprehensive Aquatic 181 Simulation Model (CASM) (Bartell et al. 2000, Volz et al. 2007), but determined that this model 182 was not suitable for the purposes here (U.S.EPA 200 9, Erickson 2009). This model does not 183 provide any clear, validated, substantial added-val ue beyond describing the immediate response 184 of plant community growth, entails extensive data a nd parameterization needs that were not 185 completely satisfied, and involves considerable unc ertainty. CASM is more suited for focused 186 site assessments, involving considerable resources for system-specific model development and 187 application, and a completely different assessment framework. 188

A community simulation model such as CASM applies information from atrazine toxicity 189 tests on individual plants species to calculate the direct (primary) impact on the plant community 190 being simulated, but then also considers the second ary (indirect) ramifications on plant 191 community dynamics. The direct, primary impact was determined to be more important for 192 assessing the relative impact of different atrazine exposure time-series (i.e., the purpose of the 193 effects index) than are the secondary impacts (U.S. EPA 2009). Thus, the approach pursued here 194 was to base the effects index just on this primary impact, avoiding various uncertainties and 195 complexities in the community model. 196

The need here therefore is to use the 197 collective information from toxicity tests on 198 individual plant species to provide a measure 199 of direct impacts of atrazine on plant 200 communities. To this end, past assessments 201 of the risk of atrazine to aquatic plant 202 communities (e.g., Solomon et al. 1996; 203 Giddings et al. 2000) have generally 204 summarized the results of a toxicity test as a 205 median effect concentration (EC 50 ), the 206 concentration causing a 50% decrease in 207 some measure of growth over the duration of 208 the test. Average EC 50 s for each species are 209 then used to describe a species sensitivity 210 distribution (SSD) – the cumulative 211 percentage of species with EC 50 s less than a 212 Figure 4. Example of aquatic plant SSD based on dat a from Giddings et al. (2000).

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

0 20 40 60 80 1009

certain value (e.g., Figure 4). SSDs are typically applied by addressing what percentiles are 213 exceeded by an exposure. For example, in Figure 2, an exposure of 10 μ g/L would be below the 214 EC 50 s of 95% of the species and an exposure of 45 μ g/L would be below the EC 50 s of 80%. 215

However, such SSDs have major shortcomings, especi ally for addressing the types of 216 exposures in Figure 1: 217

(1) SSDs based just on EC 50 s provide limited information on the overall toxic impact to the 218 assemblage of species used for the SSD. For exampl e, the 5 th percentile in Figure 4 only 219 describes the concentration at which the growth of a particular species is reduced by 50%. No 220 information is provided on how much greater effects on this species are at higher concentrations, 221 or how much smaller effects are at lower concentrat ions. For other species, no information is 222 given other than that their EC 50 s are less than or greater than the LOC. Much more information 223 regarding effects is contained within the toxicity test data, but how should it be used? 224

(2) SSDs such as in Figure 4 also do not address th e issue of time. How should effects be 225 described for longer or shorter exposures and, espe cially, exposure concentrations that fluctuate? 226 If an LOC based on an SSD percentile is simply appl ied to the peak exposure, the exposure time227 series in the top panel in Figure 1 would be consid ered of most concern, but toxic impact would 228 probably be greater for the middle time-series and perhaps as great for the lower time-series, 229 because of the more prolonged and multiple exposure periods. How should total impact be 230 assessed over an entire time-series? 231

(3) Although the EC 50 s in Figure 2 all describe plant growth in some fas hion, growth is measured 232 in a variety of ways (final plant biomass, net chan ge in biomass, growth rate, oxygen evolution, 233 carbon fixation, plant length, cell numbers, change s in chlorophyll) and over a wide range of 234 exposure durations and conditions, such that these EC 50 s can have greatly different meaning 235 regarding actual plant sensitivity. The spread of values in the SSD might therefore be due to 236 differences among test endpoints as well as differe nces among species. Such inconsistency in 237 the meaning of EC 50 s will cause any LOC from the SSD to have uncertain meaning. 238

2.2 Definition of the Plant Assemblage Toxicity Ind ex 239

To quantify the overall effect of atrazine on an assemblage of plant species of interest, the 240 effects index proposed here is the "Plant Assemblag e Toxicity Index" (PATI). PATI is a simple 241 extension of the SSD concept that (a) considers the entire growth inhibition vs. concentration 242 curve (“toxicity relationship”) for each plant spec ies and (b) determines the average effect level 243 across all species (the "assemblage") at each conce ntration. Figure 5 illustrates this, using 244 atrazine toxicity data summarized in Appendix A. T he middle panel shows overlapping toxicity 245 relationships for 20 plant genera. In the top pane l, the EC 50 s for each genus are used to create a 246 traditional SSD – simply the cumulative percentage of the EC 50 s. For the bottom panel, the 247 average magnitude of effect across all species at e ach concentration is used to create the PATI 248 distribution. At 50 μ g/L, the average effect over all genera is 19%, pro viding the PATI value in 249 the bottom panel (arrow). Thus, rather than just p roviding the percentage of species that have an 250 EC 50 below some concentration (e.g., 50 μ g/L corresponds roughly to the 16 th percentile on the 251 SSD), PATI describes the percent reduction in plant production for the entire assemblage 252 (weighting each species equally). Although the sha pe of the PATI curve is similar to that of the 25310

traditional SSD curve, it provides more 254 information on the total impact on the plant 255 assemblage and allows more meaningful 256 comparisons between different exposure 257 concentrations. 258

However, the definition and 259 calculation of PATI illustrated in Figure 5 is 260 not yet complete because it does not address 261 the issue of time. For a time-series of daily 262 concentrations, there would need to be 263 separate calculations for each day to generate 264 a time-series of daily PATI values, using 265 toxicity endpoints relevant to this timeframe. 266 Because of the rapid recovery of growth rates 267 in toxicity tests when atrazine exposures are 268 terminated (e.g. Abou-Waly et al. 1991, 269 Desjardin et al. 2003), daily PATI values need 270 not consider residual toxicity from exposures 271 on previous days, but rather only the toxicity 272 for the current day's exposure. 273

Because the effects index is intended 274 to describe total toxic impact, the approach 275 here to address time is simply to sum the daily 276 PATI values to provide a "cumulative PATI". 277 This is illustrated in Figure 6. Concentrations 278 in the left panel are converted to daily PATI value s (middle panel), which are then summed to 279 provide the cumulative PATI values in the right pan el. The cumulative PATI can also be viewed 280 as the "area under the curve" of the daily values, this area being a measure of the total toxic 281 impact of the exposure. 282 Figure 5. Comparison of toxicity relationships for 20 plant genera (middle panel), the SSD of EC 50 s 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 ( μ g/L) 20 50 200 500 10 100 1000Percent of Species with EC50BelowDesignated Atrazine Concentration

0 20 40 60 80 100

Atrazine Concentration ( μ g/L)

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

0 20 40 60 80 100

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

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

0 20 40 60 80 100

Figure 6. Overview of PATI calculations. A concen tration time-series (left panel) is converted to ex pected instantaneous or daily reductions in plant assembla ge growth (middle panel), which is then integrated to provide a cumulative PATI value for the exposure (right pan el).

Time (d) 0 5 10 15 20Daily PATI (%)

0 10 20 30 40

0 5 10 15 20Atrazine Concentration (μg/L)

0 20 40 60 80 100

0 5 10 15 20Cumulative PATI (%-days)

0 20 40 60 80 100 12011

The summation units of this cumulative PATI are an alogous to the ppb-days discussed 283 earlier or, more familiarly, with degree-days used to describe the total heating or cooling impact 284 of seasonal weather. A fundamental aspect of such a summation is that a certain reduction in 285 growth over 1 d is treated as having equal importan ce as: half that reduction persisting for 2 d; a 286 quarter of that reduction persisting for 4 d; etc. Although such a general time-dependence has 287 not been demonstrated for actual aquatic ecosystems , it has been observed to approximate well 288 the cumulative effects on biomass in single-species toxicity tests that maintain a constant level of 289 effects on plant growth rate during the exposure pe riod (e.g., Shafer et al. 1994). 290

This methodology uses a simple summation of toxic effects to provide an index for the 291 relative toxic effects of different time-series on plant communities and deliberately does not 292 address any further effects on plant community dyna mics beyond short-term reductions in 293 growth across the plant assemblage. As already not ed, the basic PATI calculation is similar to 294 the first step in community models such as CASM, wh ich on each day calculates the toxic 295 impact on the growth of various species – the funda mental difference being that PATI does not 296 consider how this toxicity changes community compos ition through time. Because community 297 dynamics are driven on each day by the same growth reductions that are incorporated into PATI, 298 PATI does describe the primary driving force for at razine effects on plant communities. Even if 299 community dynamics modify the relative severity of some time-series compared to that expected 300 based just on PATI, these would be secondary effect s and are not understood well enough to be 301 satisfactorily addressed (U.S.EPA 2009, Erickson 20 09). 302

However, this summation cannot be continued indefi nitely, but rather is limited here to 303 an "assessment period" that can reflect risk manage ment decisions about cumulative effects. For 304 example, if two short atrazine exposures were separ ated by 90 d, a 120 d assessment period 305 would consider them cumulative whereas a 60 d asses sment period would not, this shorter period 306 instead assuming that sufficient time had passed th at the second exposure should be assessed 307 independently of the first. The shorter assessment period would also avoid assigning concern to 308 prolonged low exposures of uncertain, minor impact. For exposures with finite durations less 309 than the assessment period, the summation would sim ply stop at the exposure duration. For 310 exposures with durations greater than the assessmen t period, the summation would encompass 311 the worst part of the exposure. For this report, t his limit on cumulative toxicity will be 312 designated with a subscript denoting the length of the assessment period (e.g., PATI 30d denotes a 313 30-d assessment period). Without a subscript, PATI will refer to daily or instantaneous values, 314 or the general PATI concept. The selection of the assessment period is addressed in Section 4. 315

2.3 Single-Species Plant Toxicity Test Data 316

Implementation of the PATI approach requires a co mpendium of the effects of atrazine 317 on aquatic plants or statistical distributions desc ribing these effects. Existing compendia of plant 318 effects concentrations (ECs) (e.g., Giddings et al. 2000) have certain shortcomings regarding 319 their applicability to risk assessment, which warra nted reanalysis of existing single-species 320 toxicity tests. This section describes: the shortc omings of concern; a new review and analysis of 321 toxicity data; and a new compendium of plant toxici ty information more suitable for calculating 322 PATI and for conducting atrazine risk assessments. 323

32412 Figure 7. Variation of plant growth effects with ti me and measurement endpoint. Top panel shows exponential biomass changes at the control SGR (solid line) and at one-half of the control SGR (da shed line). Bottom panel converts this to percent effec t on biomass (solid line), on biomass increase (dashed line), and on specific growth rate (dotted line).

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

0 5 10 15 20

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

0 20 40 60 80 100 Initial Biomass

Specific Growth Rate Biomass Biomass Increase Biomass for 50% Reduction in Specific Growth Rate Control Biomass 2.3.1. Issues in Interpreting and Applying Plant To xicity Test Results 325

ECs from plant toxicity tests can vary widely in both value and meaning depending on 326 how tests are conducted and analyzed. For microalg ae, tests are usually conducted on cell 327 suspensions under favorable (at least at test start ) conditions of temperature, light, and nutrients. 328 These tests can involve various measurement endpoin ts, including (a) actual biomass; (b) 329 surrogates for biomass such as cell counts, cell vo lume, optical density, or chlorophyll content; 330 and (c) indicators of growth such as oxygen evoluti on or radioactive carbon fixation. The period 331 over which measurements are made can vary from seve ral minutes to several weeks, and 332 measurements might be reported at multiple times or only at the end of exposure. Biomass or 333 biomass surrogates might be analyzed based on (a) b iomass values at various times during the 334 exposure, (b) biomass increase (growth) at 335 various times, (c) the area under the growth 336 time-series (AUC), and/or (d) specific growth 337 rate (SGR) 1

. 338

The meaning of an EC can be greatly 339 affected by test duration and by whether it is 340 based on absolute biomass, growth, or SGR. 341 To illustrate this, Figure 7 provides a 342 hypothetical example comparing growth when 343 the control SGR (SGR C ) is 1.0/d to when a 344 chemical exposure reduces the SGR to half of 345 this value. The top panel shows the actual 346 biomass vs. time in the control compared to 347 the chemical exposure, while the bottom panel 348 shows the percent reduction due to chemical 349 exposure for SGR (constant at 50%), absolute 350 biomass, and growth (biomass increase). 351

For growth, the treatment that is an 352 EC 50 for SGR will be an EC 62 at 1 d, an EC 73 353 at 2 d, and an EC 88 at 4 d if the SGR C is 1/d. 354 For absolute biomass, this concentration 355 would be an EC 39 , EC 63 , EC 86 , respectively, at 356 these times. For other values of SGR C , more 357 widely ranging ECs can occur. Using absolute 358 biomass can result in particularly misleading 359 ECs when growth rates are modest. For 360 example, when control growth is just a 361 doubling of biomass over the duration of the 362

1 The specific growth rate (SGR) =dB(t)/dt/B(t), whe re B is biomass and t is time. SGR is thus the fra ctional rate of change of biomass with time and has units of invers e time. If SGR is constant, the growth rate is exp onential and B(t)=B(0)  e SGR  t . Thus, if SGR is 1/d, this does not mean that the biomass will double in one day; rather the "compounding interest" of exponential growth will m ean that biomass actually increases to 2.7 times th e initial value – only over short periods will fraction growt h closely adhere to SGR (e.g., 1% growth over 0.01 d).13

test, an EC 50 for absolute biomass actually represents no growth . Such issues with endpoint 363 definition have been noted by others (e.g., Bergtol d and Dohmen 2010) and are reflected in 364 recent OECD guidelines. 365

Therefore, EC 50 s reported for absolute biomass, growth, and SGR wi ll differ from each 366 other, and these differences will vary with exposur e duration and the SGR C . This is especially 367 problematic when reports for toxicity tests just pr ovide ECs, without sufficient information on 368 absolute biomasses and/or SGRs as a function of tim e and concentration to calculate more 369 consistent and meaningful measures of effect. Comp endia that simply transcribe reported EC 50 s 370 can be describing a wide range of different effects , and assessments based on such compendia 371 will be ill-defined. 372

Other factors make the meaning of reported plant ECs even less certain. As an algal 373 suspension grows, the growth rate will decline beca use of nutrient depletion and self-shading. 374 This departure from exponential growth will be most pronounced in the treatments with the 375 highest growth rates (i.e., the control and low tox icant concentrations with little or no effect), so 376 that the treatments with greater toxic effects migh t "catch up" as exposure duration increases, 377 causing ECs for total growth to not decrease with t ime as much as they would without these 378 limitations, or to even increase with time. In oth er words, the toxicity test actually can include 379 stressors (nutrient/light limitations) in addition to the toxicant that can confound the effects of th e 380 toxicant. In fact, some standard plant test protoc ols were originally designed to assess nutrient 381 limitations, and the durations were selected to res ult in nutrient depletion (e.g., Miller et al. 382 1978). When used for toxicants, this type of study design can result in complicated growth 383 dynamics and relationships that are difficult to in terpret and apply. Tests can also have different 384 photoperiods, which would also need to be considere d in comparing ECs for growth (although 385 ECs for SGR can be directly compared between differ ent photoperiods). 386

Schafer et al. (1994) provide a noteworthy exampl e of some of these problems. In a 10-d 387 test in a flow-through system in which a constant c ontrol growth rate was maintained by 388 replenishing the nutrient solution and periodically cropping biomass, they reported growth-based 389 EC 50 s to drop from 50 μ g/L at 4 d to 20 μ g/L at 7 d to 10 μ g/L at 10 d. This is plausibly 390 attributable to a constant relationship of SGR to c oncentration during these 10 d, so that a 391 constant EC for growth rate translates into widely variant ECs for growth. These authors also 392 reported an EC 50 of 350 μ g/L for a static, 3-d flask test, indicating much l ess sensitivity 393 compared both to the flow-through systems and to ph otosynthesis measurements made in the 394 first day of these static tests. This apparent low er sensitivity likely is due at least partly to a h igh 395 initial cell density (2

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

⋅ 10 8 396 cell/ml if a SGR C similar to that in the flow-through system had bee n maintained for the entire 3 397 d. Such a cell density would have resulted in both self-shading and nutrient depletion in the 398 control, contributing to the apparent reduced sensi tivity. Increases with time for growth-based 399 ECs are evident in other studies in the review pres ented later, although the opposite can also be 400 true, indicating additional complexities. 401

Changes in cell condition other than light and nu trient limitations might also affect ECs 402 and their dependence on test duration. For example , chlorophyll content per cell can increase 403 with time to compensate for reduced photosynthesis. Mayer et al. (1998) reported the 40414

chlorophyll content of algal cells to increase by 1 0-fold in response to exposure to 200 μ g/L 405 atrazine. Such changes in the chlorophyll content per cell make the use of chlorophyll as a 406 surrogate for plant biomass inadvisable, potentiall y misrepresenting toxic effects on biomass. 407 For example, van der Heever and Grobbelaar (1996) r eported effect concentrations in the same 408 exposures to be about 2.5-fold higher when based on chlorophyll than when based on cell 409 numbers or dry weight. Similarly, toxicants can al ter cell volume and mass (e.g., van der Heever 410 and Grobbelaar 1996), creating differences among EC s based on cell count, cell volume, and cell 411 weight, although these differences are much smaller than those due to the influence of 412 chlorophyll, test duration, nutrient depletion, and light limitations. 413

Although oxygen production and radiocarbon fixati on are arguably closely linked to 414 biomass production, ECs based on these measures can also pose interpretation problems: 415

(a) They are often done over such short durations t hat apparent effects might be reduced because 416 of the time it takes to fully induce the effects of a toxicant, unless there is sufficient pre-exposur e 417 to the toxicant before the measurements are made. Fortunately, for atrazine, effects do appear to 418 be induced quickly, such that EC 50 s based on oxygen measurements with just several mi nutes 419 prior exposure have been reported to be similar to those based on biomass measurements (e.g., 420 Turbak et al. 1986). 421

(b) Short-term radiocarbon fixation rates can conce ivably reflect gross or net photosynthesis (or 422 a weighted combination of the two) depending on the disposition of the radioactive carbon in the 423 organism. Williams et al. (1996) determined that r adiocarbon fixation over short periods 424 approximates net photosynthesis for good growing co nditions (which would be expected in 425 toxicity tests); therefore, radiocarbon fixation wi ll be assumed in this review to represent net 426 photosynthesis. 427

(c) Although oxygen production should parallel net photosynthesis, test methods using oxygen 428 evolution measurements can involve extremes of oxyg en concentrations that might affect 429 photosynthesis and/or respiration – either high, su persaturated levels as oxygen increases from 430 initial levels, or low concentrations due to the me thodology involving an initial purging of 431 oxygen. Studies with such extremes will not be use d in this review because of uncertainty about 432 their impacts. 433

(d) Even when the test is such that oxygen producti on or radiocarbon fixation are arguably good 434 surrogates for biomass production, the time-scale o f the measurements can affect their 435 interpretation. Short-term values for oxygen produ ction or radiocarbon fixation for an 436 approximately constant mass of algae are analogous to the SGR, whereas measurements long 437 enough for substantial growth to occur would be ana logous to net cumulative growth, creating 438 differences in the meaning of ECs similar to that f or growth vs. SGR. In one study (Larsen et al. 439 1986), the situation was especially complicated bec ause carbon-14 fixation was measured only 440 during a short period at the end of a 24-h atrazine exposure, so that the measured fixation rate 441 reflected both effects of the toxicant on the rate of carbon fixa tion per cell and the cumulative 442 differences in cell density due to the preceding ex posure. 443

Macrophyte tests can be less susceptible to the i ssues of exponential growth and limiting 444 conditions discussed above. Many macrophytes grow slowly enough so that biomass increases 44515

by only a few multiples during the tests. Duckweed tests show more rapid growth, but also 446 usually do not reach biomass levels sufficient to s uppress growth rates (frond crowding or 447 nutrient depletion). However, the general issues r aised above for microalgae should still be 448 considered in the interpretation of macrophyte test s and the definition of their ECs. For example, 449 reduced photosynthesis can result in elongation of plant shoots with little or no biomass increase, 450 so that shoot length can be a poor surrogate for bi omass changes (e.g., Fairchild et al. 1994, 451 1998). In addition, some macrophyte tests involve rhizomes, which contain resources to 452 temporarily support growth that might obscure toxic effects, again making length a questionable 453 measure and even making weight problematic if only shoot biomass is measured. Furthermore, 454 if test protocols with cuttings result in slow grow th (e.g., due to the absence of rooting), 455 variability can make it difficult to quantify toxic effects and/or make such toxic effects of 456 uncertain relevance to the field. Finally, use of oxygen in interpreting growth of some vascular 457 plants might be confounded by gas exchanges to aere nchyma (air channels). 458

2.3.2. Review of Single-Species Plant Toxicity Test s 459

The inconsistency issues among single-species tox icity test ECs discussed above have not 460 been adequately addressed in past reviews of atrazi ne toxicity (e.g., Solomon et al. 1996; 461 Giddings et al. 2000) and might distort atrazine ri sk assessments. There was thus a need for 462 better analyses of single-species plant toxicity te sts with atrazine to produce EC compendia 463 which are more consistent, providing a “common curr ency” that can be more legitimately 464 compared among tests and describe short term effect s relevant to daily PATI values. The SGR 465 was selected as this “common currency” because it r educes the dependence of ECs on test 466 duration and is more directly applicable to address ing effects of time variable exposure. In 467 addition to compiling information on EC 50 s, there was also a need for information on the ent ire 468 SGR vs. concentration curve, which is also inadequa tely addressed in previous compendia. 469

To this end, available single-species toxicity te sts with atrazine were reviewed for 470 information regarding exposure conditions and effec ts by the Great Lakes Environmental Center 471 (Traverse City, MI) under support from the Office o f Science and Technology of U.S.EPA’s 472 Office of Water (EPA Contract 68-C-04-006, Work Ass ignment 4-34, Subtask 1-16). Journal 473 articles and reports identified by this review as c ontaining potentially useful information were 474 further analyzed by U.S.EPA to compile desired info rmation on the relationship of SGR to 475 atrazine concentration, using the following sigmoid al relationship (logistic equation): 476

(Equation 1) 477

for which the parameters are the SGR-based EC 50 , the steepness of the relationship of SGR vs. 478 atrazine concentration (" Steep "), and the control SGR ( SGR C ). Appendix A further discusses this 479 equation and its use in the analyses, as well as (a ) guidelines and procedures used in the EPA 480 evaluations of toxicity tests and (b) a summary of each toxicity test reviewed. Table 1 provides 481 the compilation of SGR EC 50 , Steep, and SGR C from these analyses. 482

483 ( ) ( ) 

### ( ) 10 10 50 4 log log 

### 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 specif ic growth rate (SGR) of the plant. Compilation includes the EC 50 for the SGR, a steepness parameter for a fitted lo gistic relationship of SGR to atrazine concentration (Stee p=-d(SGR/SGR C )/d(log 10 (C ATZ )) at the EC 50 ), and the control SGR (SGR C ) under the test conditions. Italicized EC50s deno te values whose estimation required information on SGR C and/or steepness from other studies. Appendix A p rovides more details on these data and analyses.

Genus SGR EC 50 ( μ g/L) Steep SGR C (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, cryptomona ds) Cryptomonas 494 1.15 Kallqvist and Romstad 1994

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

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

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

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

Lemna 202 1.24 0.24 Hoberg 1991B 93 1.33 0.25 Hoberg 1993B 49 1.71 0.23 Hoberg 1993C
115 0.42 0.21 Fairchild et al. 1998 224 1.14 0.21 Hughes et al 1988 95 Kirby and Sheehan 1994 90 1.18 0.40 Desjardin 2003 Myriophyllum <150 0.02 Fairchild et al. 1998 Najas sp. 15 1.67 Fairchild et al. 1998 Potamogeton 63 0.69 Forney and Davis 1981
Vallisneria 141 0.40 Forney and Davis 198117

Although not included in the compilation because t hey were conducted in estuarine water 485 near 10 ppt salinity, studies on Myriophyllum spicatum and Potamogeton perfoliatu s by Kemp et 486 al. (1985) and Jones et al. (1986) are consistent w ith the vascular plant results in Table 1. For 487 both these species, oxygen production-based reducti ons in photosynthesis (Kemp et al. 1985) 488 indicated EC50s to be near or below 50 μ g/L in the first two weeks of exposure (although so me 489 lessening of these effects was apparent in the ensu ing two weeks). For Potamogeton perfoliatus

, 490 radiocarbon fixation-based reductions in photosynth esis (Jones et al. 1986) indicated the EC 50 to 491 be between 50 μ g/L and 100 μ g/L. 492

2.4 Statistical Distribution of Toxicity Relationsh ip Parameters 493

The SGR EC 50 data in Table 1 were log 10 transformed and subject to an analysis of 494 variance (ANOVA) using the general linear model (GL M) procedure of Statistica (Version 8.0, 495 StatSoft, Tulsa, OK, USA). A nested ANOVA showed n o significant differences between 496 genera within the larger taxonomic groups identifie d in Table 1, so the analysis was simplified to 497 a one-way ANOVA on these taxonomic groups, with eac h test result being treated equally 498 regardless of the number of tests within a species or genus. This analysis indicated significant 499 differences among the taxonomic groups, with the me an log 10 (EC 50 ) being 2.09 for green algae, 500 2.35 for diatoms/cryptomonads, 2.42 for blue-green algae, and 1.93 for vascular plants (Table 2). 501 These log values correspond, respectively, to media n EC 50 s of 123, 224, 263, and 85 μ g/L. 502 However, it should be noted that these taxonomic di fferences are uncertain due to the limited 503 amount of data for some of the taxa – the standard errors of these mean log 10 (EC 50 )s varied from 504 0.07 to 0.12 (Table 2), depending on the number of observations for each group, and their 95% 505 confidence limits overlapped. The within-group var iability did not differ significantly between 506 the taxonomic groups, with the within-group standar d deviation ranging from 0.29 to 0.35 (Table 507 2) and the pooled value being 0.33. The overall, u nweighted mean and standard deviation of all 508 log 10 (EC 50 )s were 2.12 and 0.37 (this higher standard deviati on being reflective of the intergroup 509 variability). Basing the analysis on genus means r ather than individual tests produced similar 510 values for the overall mean (2.07) and standard dev iation (0.35) of log 10 (EC 50 )s. 511

The steepness parameter (Steep) data in Table 1 we re also log 10 transformed and subject 512 to ANOVA. The ANOVAs showed no significance differ ences either between genera or the 513

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

designated taxonomic group).

Taxonomic Group log(EC 50 ) log(Steep)

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

Diatoms/Cryptomonads 2.35 0.29 0.12 -0.03 0.12 0.05

Blue-green Algae 2.42 0.35 0.12 -0.12 0.15 0.07

Vascular Plants 1.93 0.34 0.09 -0.07 0.23 0.06

Overall 2.12 0.37 0.06 -0.05 0.18 0.0318

broader taxonomic groups. The within-group means r anged from -0.03 for the green algae and 514 diatoms to -0.11 for the blue-green algae, with an overall mean of -0.05 (Table 2). The steepness 515 distribution is therefore described here based simp ly on this overall mean for log 10 (Steep) 516 (corresponding to a median value for Steep of 0.89) and the overall observed standard deviation 517 (0.18) (Table 2). Using genus means rather than in dividual observations resulted in a very 518 similar log mean (-0.08) and standard deviation (0. 16). A correlation analysis also showed no 519 significant correlation between log 10 (EC 50 ) and log 10 (Steep), so these parameters will be treated 520 independently in any analyses. 521

2.5 Uncertainty of PATI Relationships 522

The toxicity data analyses here provide the basis for computing an overall measure of 523 toxic impact on an assemblage of plant species (i.e ., PATI) as a function of concentration. 524 However, this does involve some issues regarding da ta selection and processing that will be 525 relevant to uncertainty analyses presented in Secti on 4 of this document. 526

One issue is whether PATI should be calculated dir ectly from the individual tests in 527 Table 1 (using the overall median steepness for any test without a measured steepness) or be 528 based on the overall distributions of log 10 (EC 50 ) and log 10 (Steep) summarized in Table 2. For the 529 individual tests, calculating PATI is simply a matt er of averaging the toxicity relationships across 530 all the tests. For the summary distributions, calc ulating PATI requires multiplying the level of 531 toxic effect expected for a particular EC 50 and Steep by the probability density for that 532 combination of EC 50 and Steep, and doing this for all possible combina tions of EC 50 and Steep. 533 Mathematically, this can be expressed as follows, w here the function “ tox” (the expected toxicity 534 at exposure concentration C and for toxicity parame ters EC 50 and Steep) is multiplied by the 535 function “dens” (the density function for the joint probability di stribution of EC 50 and Steep), and 536 this product is then integrated across all values o f EC 50 and Steep. 537 

# ( )

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

# ∫ ∫ (Equation 2) 538 Rather than evaluating this by numerical integratio n, it was estimated by randomly sampling 539 10000 pairs of EC 50 and Steep from the density function (assumed to be bivariate log normal 540 with means and standard deviations as in Table 2), applying the toxic relationship function (Eq. 541 1) to these random pairs, and taking the mean 542 of these toxicity values. Based on repeated 543 tests of this process, 10000 points were 544 sufficient to evaluate this integral with a 545 relative error of <0.5%. 546

Figure 8 provides a comparison of 547 these two calculations methods, showing a 548 negligible difference for concentrations 549 >10 μg/L, with the difference growing to 550 about 30% at 2 μg/L and a PATI value of 551 ca. 1. This calculation method issue would 552 thus appear not to be a significant uncertainty 553 source, but its impact on risk characterization 554 will be examined in Section 4. 555 Figure 8. PATI relationships based on the toxicity relationships for individual tests (dashed line) ve rsus based on the overall summary distribution of the relationship parameters EC50 and Steep (solid line)

.

Atrazine Concentration ( μ g/L) 2 5 20 50 200 500

10 100 1000Plant Assemblage Toxicity Index (%)

2 5 20 50

1 10 10019

Another issue is the uncertainty associated with PATI relationships because of the finite 556 number of toxicity relationships used in its formul ation. This uncertainty is reflected in the 557 standard errors for the means of the toxicity relat ionship parameters (log 10 (EC50), log 10 (Steep)) 558 reported in Table 2, as well as the uncertainty 559 in the parameter standard deviations. Figure 560 9 shows how PATI based on the overall 561 distribution in Table 2 would vary by 562 changing the mean and standard deviations of 563 the parameters to their lower and upper 95% 564 confidence limits. At most concentrations, 565 the largest effects are for the uncertainties in 566 the mean log 10 (EC50), but the other 567 uncertainties become substantial at lower 568 concentrations, with the uncertainty in PATI 569 due to the mean log 10 (Steep) reaching a factor 570 of approximately 2.0 at 2 μg/L and a PATI 571 value of ca. 1. The impact of this uncertainty 572 on risk characterizations will also be 573 considered in Section 4. 574

A third issue is that the PATI relationships in F igures 8 and 9 represent an assemblage of 575 plant species and tests defined by the available te st data, but different assemblages are possible 576 by selecting or weighting particular taxa. 577 Figure 10 contrasts PATI relationships based 578 (a) on the overall distributions of log 10 (EC 50 ) 579 and log 10 (Steep) in Table 2, (b) the separate 580 distributions in Table 2 for the four major 581 taxa, and (c) a composite distribution based on 582 equal weighting of the four major taxa (in 583 contrast to the overall distribution, which is 584 unweighted across all tests regardless of the 585 major taxon). The PATI values for the overall 586 distribution and the composite distribution 587 have negligible differences, but the PATI 588 relationships for the major taxa can differ 589 substantially from each other due to the 590 apparent differences in their relative 591 sensitivities. 592

Because vascular plants have the lowest estimated mean log 10 (EC 50 ) (i.e, the greatest 593 average sensitivity), they have the highest PATI va lues in Figure 10. At 2, 5, and 10 μg/L 594 atrazine, the estimated PATI values are, respective ly, 2.2-, 1.9-, and 1.7-fold larger than for the 595 overall distribution. Only 5.5 μg atrazine/L is ne eded to reach a PATI value of 5%, versus 10 596 μg/L for the overall distribution. 597 Figure 10. Comparison of PATI based on the overall toxicity distribution (solid line) to distributions forgreen algae(short dash), diatoms(long dash), bluegreen algae(dash-dotted), vascular plants(dash-doub le dotted), and a composite of the four taxa (dotted).

Atrazine Concentration ( μ g/L) 2 5 20 50 200 500

10 100 1000Plant Assemblage Toxicity Index (%)

2 5 20 50

1 10 100 Figure 9. Comparison of best estimate of overall PA TI relationship (solid line), to 95% confidence limits for the mean for logEC 50 (short dash) and logSteep (dashdotted) and standard deviations for logEC 50 (long dash) and logSteep (dash-double dotted).

Atrazine Concentration ( μ g/L) 2 5 20 50 200 500

10 100 1000Plant Assemblage Toxicity Index (%)

2 5 20 50

1 10 10020

Even greater differences occur for the diatom/cryp tophyte group, which has markedly 598 lower PATI values at low atrazine concentrations be cause of a combination of a larger-than599 average mean and a smaller-than-average standard de viation for log 10 (EC 50 ). At 2, 5, and 10 600 μg/L atrazine, the estimated PATI values are, respe ctively, 4.4-, 3.6-, and 3.1-fold smaller than 601 for the overall distribution. Almost 25 μg/L atraz ine is needed to reach a PATI value of 5%, 602 versus 10 μg/L for the overall distribution. 603

The effects of these plant assemblage differences on risk characterization also will be 604 examined in Section 4. However, it should be noted here that, because PATI is intended to serve 605 as a relative index of the effects of different exp osure concentrations, the slopes of the 606 relationships in Figure 10, not the absolute PATI v alues, will determine how risk 607 characterizations depend on the taxonomy of the ass emblage. Although the estimated PATI 608 values for the vascular and diatom groups differ by nearly an order of magnitude at low atrazine 609 concentrations, the log slopes in Figure 10 are not very different from each other (e.g., the 610 relative changes in PATI from 10 to 20 μg atrazine/ L are 1.9, 2.1, and 2.4 for the vascular plant, 611 overall, and diatom distributions, respectively). Thus, it should be anticipated that the analyses 612 in Section 4 will show limited sensitivity of risk characterizations to assemblage taxonomy. 613

61421

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

Using the experimental ecosystem data to determine an LOC for the cumulative PATI 616 involves relating a binary response (yes/no effect for each experimental ecosystem treatment) to 617 a quantitative measure for the severity of the expo sure (cumulative PATI). Before presenting 618 this process, it would be useful to first discuss a similar but more familiar analysis. 619 Mortality in a toxicity test also involves a binar y response – an individual organism either 620 dies or not. Mortality data is often plotted as th e fraction of a group of organisms that died (by 621 an observation time) vs. the concentration to which the group was exposed, shown in the left 622 panel of Figure 11. However, such data can also be plotted based on the response of each 623 individual organism (0 if alive, 1 if dead), shown on the right panel of Figure 11, in which offsets 624 are used to show points that actually have the same concentrations. Probit analysis is a common 625 method applied to such data to generate a sigmoidal relationship for the probability of mortality 626 at each concentration, this relationship being the same in the left and right panels because both 627 panels represent the same information and analysis. 628

Because probit analysis uses the binary response o f the individual organisms as the basic 629 observation, it is actually more directly related t o the right panel of Figure 11 than to the left. 630 Furthermore, if individual organisms all have diffe rent exposures, the presentation format of the 631 left panel cannot be used (i.e., there are no group s of replicate organisms upon which to compute 632 fraction survival), but a plot such as in the right panel can still be done and probit analysis is sti ll 633 appropriate. For example, if the offsets for the p oints in the right panel of Figure 11 actually 634 represented different concentrations, probit analys is could still be applied even without replicate 635 points at the same concentration. 636 The experimental ecosystem data provide an analys is situation analogous to the survival 637 data in the right panel of Figure 11. Figure 12 re plots the experimental ecosystem data from 638 Figure 2 as binary effects (1 if there is an effect , 0 if there is not) vs. a PATI 60d value. (For the 639 purpose of this example, the overall distribution o f toxicity values in Table 2 was used as the 640 basis for PATI, along with the 60-d assessment peri od. The basis for these choices is addressed 641 in Section 4.) 642 Figure 11. Probit analysis as an example of binary data analysis. For a hypothetical toxicity test, t he left panel

shows the fraction (of 10 organisms) which died at each concentration while the right panel plots indi vidual organism response as 0 (if survived) and 1 (if dead ). Lines denote probit relationship for probabilit y of death.

Concentration 0.2 0.5 2 5 0.1 1 10Probability of Mortality

0.0 0.2 0.4 0.6 0.8 1.0

Concentration 0.2 0.5 2 5 0.1 1 10 0.0 0.2 0.4 0.6 0.8 1.022

Although there is a clear increase in the probabi lity of effects as PATI 60d increases in 643 Figure 12, there also is considerable overlap betwe en effects and no effects with respect to 644 PATI 60d , especially in the 100 to 200 range for PATI 60d . This variability/overlap issue was 645 already noted regarding Figure 2, and should be vie wed here in terms of any particular PATI 60d 646 value having a probability of eliciting an effect a cross the variety of experimental ecosystem 647 studies used here. That there is a probability, ra ther than a certainty, of having an adverse effect 648 at any PATI 60d value is again indicative of sensitivity differenc es among the systems and/or 649 various experimental uncertainties. Across all PAT I 60d values, there would be an underlying 650 relationship for this probability, illustrated by t he curve on Figure 12. 651

This probability relationship can be quantified us ing probit or similar binary analyses. 652 Field et al. (1999, 2002) applied binary analysis t o sediment toxicity assessments of a similar 653 nature (i.e., relating binary effect data to an exp osure concentration), but rather than the Gaussian 654 distribution-based relationship of probit analysis, they applied a similar, but simpler, probability 655 relationship based on the logistic equation. For d escribing the probability of effects in the 656 experimental ecosystem set as a function of PATI 60d , this logistic probability expression can be 657 formulated as: 658

(Equation 3) 659

where P is the probability (percent scale) of an ad verse effect at a PATI 60d value, PATI 50% is the 660 PATI 60d value at which P=0.5 (50% chance of an effect over the range of experimental 661 ecosystems), and S is a steepness parameter (>0) fo r the relationship. 662

Although P is the underlying probability of an act ual adverse effect, this equation is not 663 appropriate for analyzing the data in Figure 12 bec ause it does not reflect certain errors in the 664 statistical analysis regarding whether an experimen tal ecosystem treatment is concluded to have 665 

# ( ) ( ) 10 50% 10 60 50%
60 log log 1 1

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

⋅ − = = + + Figure 12. Experimental ecosystem data plotted as e ffect/no effect versus PATI 90d , fitted to a logistic relationship for the probability of an effect versus PATI.



### 60-d Cumulative Plant Assemblage Toxicity Index (%- days) 2 5 20 50 200 500 2000 5000

10 100 1000Probability of Adverse Effect

0.0 0.2 0.4 0.6 0.8 1.0



### LOC PATI 

### =13223

an adverse effect. Most importantly, Type I error (the probability of concluding a treatment has 666 an effect when it actually does not) is typically s et at 0.05. This means that, although the actual 667 probability of an adverse effect approaches zero as PATI approaches zero per Equation 1, the 668 probability of stating that there is an effect does not approach zero, but rather approaches 0.05. 669 Type II error (the probability of concluding a trea tment does not have an effect when it actually 670 does) will also affect the curve, but it is not pos sible to adjust for this without more detailed 671 information on the statistical power of the various tests. However, because Type II error will go 672 to zero as concentration increases, it will not aff ect the upper asymptote of the curve like Type I 673 error affects the lower asymptote, and thus will no t overtly affect the basic sigmoidal shape of 674 the curve being fitted. The binary regression used in the LOC methodology will therefore use a 675 logistic model with a lower asymptote of 0.05, modi fying Equation 2 as follows: 676

(Equation 4) 677

where P data refers to the probability of a data point being st ated to have an effect, in contrast to P 678 being the actual probability of having an effect. 679

Using Equation 4, a maximum likelihood analysis was conducted on the data in Figure 10 680 to generate estimates for the equation parameters, PATI 50% and S. Using these parameter 681 estimates, the curve in Figure 12 was calculated, b ut using Equation 3 rather than Equation 4 so 682 the curve shows the actual estimated P, not P data . Once estimated, this curve provides a basis for 683 making risk management decisions regarding what PAT I value is considered an LOC. For 684 example, for Figure 12, a risk management decision to use P=0.5 would result in an LOC PATI60d 685 of 132 %-days. 686

This LOC PATI60d of 132 %-days represents substantial reductions in growth rate for this 687 plant assemblage for short exposures (e.g., 44% for a three day exposure), but progressively 688 smaller effects for longer exposures (e.g., 10% for two weeks, 5% for four weeks). However, it 689 is important to remember these percentages do not d efine the level of protection; rather, it is 690 the experimental ecosystem results that define the effects of concern! PATI is only being used 691 to describe the relative effects of different exposure time-series. It is the experimental ecosystem 692 effects that define the effects of concern and what level of PATI for the selected assemblage of 693 toxicity data correlates with these effects. It is not being assumed that a certain value for PATI 694 has inherent significance, so it is not appropriate considering (under the assessment framework 695 being used here) whether reducing growth rate by 44 % for three days is too restrictive or not 696 restrictive enough. This PATI-based methodology as sumes only that the relative effects of 697 concentration and time on PATI are useful for extra polating between different exposure time698 series for the experimental and natural plant commu nities being assessed. 699

700 

## ( ) ( )

50%
60

50%
60 1 0.05

1 d

d S PATI PATI
data S

PATI PATI P + ⋅

= +24

4. IMPLEMENTATION OF PATI-BASED RISK ASSESSMENT MET HODOLOGY 701

4.1 Example Field Exposure Time-Series 702

Figure 1 provided three example field exposure time -series (chemographs) for use in the 703 problem definition. In this section, method parame terization and performance evaluations will 704 involve a larger set (Figure 13) of chemographs fro m the 2010 monitoring program to provide a 705 greater diversity of exposures for evaluating the m ethodology. EEFs for all chemographs will be 706 presented, but because uncertainties are most impor tant for EEFs near 1.0, summary statistics for 707 sensitivity and uncertainty analyses below are base d only on sites with 0.3<EEF<3.0. 708

Figure 13. Example atrazine chemographs used in met hodology evaluations.

60 90 120 150 180 210 240 0 10 20 30 40 50

60 90 120 150 180 210 240 0 10 20 30 40 50

60 90 120 150 180 210 240 0 20 40 60 80 100

60 90 120 150 180 210 240 0 10 20 30 40 50 60 90 120 150 180 210 240 0 10 20 30 40 50

60 90 120 150 180 210 240 0 10 20 30 40 50

60 90 120 150 180 210 240 0 4 8 12 16 20

60 90 120 150 180 210 240 0 20 40 60 80 100 60 90 120 150 180 210 240 0 10 20 30 40 50

IA 04 2010 60 90 120 150 180 210 240 0 20 40 60 80 100

60 90 120 150 180 210 240 0 4 8 12 16 20

60 90 120 150 180 210 240 0 4 8 12 16 20

60 90 120 150 180 210 240 0 20 40 60 80 100

60 90 120 150 180 210 240 0 10 20 30 40 50

60 90 120 150 180 210 240 0 20 40 60 80 100

60 90 120 150 180 210 240 0 10 20 30 40 50

60 90 120 150 180 210 240 0 4 8 12 16 20

60 90 120 150 180 210 240 0 40 80 120 160 200

60 90 120 150 180 210 240 0 20 40 60 80 100

60 90 120 150 180 210 240 0 10 20 30 40 50

60 90 120 150 180 210 240Concentration (μg/L Atrazine)

0 10 20 30 40 50

60 90 120 150 180 210 240 0 20 40 60 80 100

60 90 120 150 180 210 240 0 10 20 30 40 50

60 90 120 150 180 210 240 0 20 40 60 80 100

60 90 120 150 180 210 240 0 10 20 30 40 50

60 90 120 150 180 210 240 0 10 20 30 40 50

60 90 120 150 180 210 240 0 10 20 30 40 50

60 90 120 150 180 210 240 0 10 20 30 40 50

60 90 120 150 180 210 240 0 20 40 60 80 100

60 90 120 150 180 210 240 0 40 80 120 160 200

60 90 120 150 180 210 240 0 10 20 30 40 50

Julian Day

60 90 120 150 180 210 240 0 4 8 12 16 20 MO 08 2010 IA 05 2010 IA 03 2010

MO 01D 2010 MO 01 2010

MO 07 2010 NE 04 2010 OH 05 2010 NE 09 2010 MO 05B 2010 LA 04 2010 KS 02 2010 IN 12 2010 IL 16 2010

MO 04a 2010 MO 06 2010

IL 14 2010 IL 12 2010 IL 10 2010 IL 15 2010

MO 05 2010 MO 02 2010 MO 01C 2010 LA 05 2010 IL 11 2010 IL 13 2010

KS 03 2010 IL 17 2010 KS 01 2010

NE 05 2010 OH 06 201025

4.2 Parameterization Issues for PATI-based LOCs 709

Implementing a PATI-based methodology requires spe cifying (a) the toxicity relationship 710 parameters (EC 50 s and Steeps) to use in daily PATI calculations and (b) the assessment period 711 over which to evaluate cumulative PATI. 712

4.2.1 Assessment Period – Issues and Options 713

Because exposure outside the assessment period is considered inconsequential by PATI, 714 this period needs to be long enough to encompass (a ) exposures of significance to establishing 715 LOC PATI from the experimental ecosystems (Figure 2) and (b ) effects expected from seasonal 716 field exposures (Figure 13). However, it should no t be any longer than necessary, in order to 717 avoid uncertain inferences regarding (a) cumulative effects of low concentrations and (b) widely 718 separated exposures that are independent regarding ecological effects. 719

A 60-d assessment period was chosen as a provision al focus for consideration because it 720 would include all or almost all periods of signific ant exposure in the example chemographs of 721 Figure 13 and also encompasses the duration of all but a few of the experimental ecosystems in 722 Figure 8. A few additional considerations regardin g this period relative to the experimental 723 ecosystem treatments should be noted: 724

(1) It is just slightly shorter than the longest ex perimental ecosystem treatment with no effect. If 725 the assessment period was significantly shorter tha n treatments with no effect, this would under726 represent how substantial exposures could be withou t causing effects and thus be too restrictive. 727

(2) For those treatments with effects, a shorter pe riod would also be too restrictive by assuming 728 that less exposure was needed to elicit effects tha n actually was involved (e.g., an effect observed 729 over a 60-d exposure would be assumed to require le ss exposure than actually was required). 730 This consideration does not pertain to the few expe rimental ecosystems with extremely long 731 durations, because they simply verify significant e ffects for high PATI values. For the LOC, the 732 important treatments with effects are those whose e xposures near to those without effects. 733

(3) That 60 d is longer than many experimental ecos ystem treatments with effects is not an issue, 734 provided the effects from these shorter exposures w ould still be considered unacceptable from 735 the perspective of this longer assessment period (e .g., if a 30-d exposure showing effects had 736 been monitored for another 30 d without exposure, t he effects during the first 30 d would be 737 considered unacceptable despite any recovery that o ccurred during the second 30 d). 738

To evaluate the suitability of 60 d as the assessm ent period, compared to possible 739 alternative choices, sensitivity analyses below wil l address how risk characterizations would 740 differ for assessment periods from 30-d to 120-d. A 30-d assessment period is included in this 741 sensitivity analysis to document the impact of a pe riod that is arguably too short, in that it is less 742 than the duration of a substantial percentage of th e experimental ecosystems treatments that 743 discriminate effects and no effects, and also inade quately covers periods of substantial exposure 744 in the example chemographs. 745

74626

4.2.2 Toxicity Relationship Parameters – Issues and Options 747

The review and analysis of single-species toxicity test data in Sections 2.2 and 2.3 748 provide the basis for specifying toxicity relations hips for PATI calculations, but there are options 749 and uncertainties in applying this information, whi ch were already discussed to some extent in 750 Section 2.4: 751

(a) Should PATI calculations be directly based on t he discrete estimates for the toxicity 752 relationship parameters (EC 50 and Steep) in Table 1, or should the methodology f ollow the 753 typical assessment practice of using the data to es timate sensitivity distributions (Table 2), and 754 basing assessments on such distributions? 755

(b) Should the methodology be weighted in some mann er for taxonomic groups, or follow 756 standard practice (e.g., typical SSDs) of not adjus ting for the relative representation of different 757 taxa in the available data? 758

(c) Should calculations be based on average results for each species or genus, or on individual 759 tests? 760

The strategy here was to use, as a default referen ce, distributions based on all the 761 available, individual toxicity observations (i.e., the “overall” distributions of log 10 (EC 50 ) and 762 log 10 (Steep) summarized in Table 2). Sensitivity analys es were conducted to determine how 763 substantially risk characterizations varied for alt ernatives from this default, including (a) the use 764 of discrete parameter estimates in Table 1 instead of these default distributions (as was done for 765 Figure 8), (b) different weightings of the major ta xonomic groups (such as in Figure 10), and (c) 766 basing distributions on genus means instead of indi vidual test results. Based on this sensitivity 767 analysis, decisions can be made regarding how these issues should be addressed in the final 768 methodology. 769

4.3 Sensitivity Analyses for PATI-Based LOCs 770

4.3.1 Sensitivity Analysis for Assessment Period 771

Using the overall (default) toxicity parameter dis tributions specified in Table 2, effects 772 assessments were made for each of the example chemo graphs in Figure 13, using assessment 773 periods of 30, 60, 90, and 120 d. These assessment s proceeded as follows: 774

(a) The daily PATI values for each experimental eco system treatment were calculated. As 775 illustrated in Figure 3, this involves computing, f or each daily exposure concentration, an 776 average effect across a set of toxicity relationshi ps. Because the toxicity relationship parameters 777 are represented by distributions, this calculation was conducted as described in Section 2.5. 778

(b) The daily PATI values were used to calculate cu mulative PATI values for 30-, 60-, 90-, and 779 120-d assessment periods for each experimental ecos ystem treatment. When the exposure 780 duration exceeded the assessment period, the contig uous period of exposure resulting in the 781 highest cumulative PATI value was used. 78227

(c) For each assessment period, a binary logistic r egression was conducted as described in 783 Section 3.2. The LOC PATI was set to the PATI 50% estimate from this regression (50% probability 784 of an effect). 785

(d) Daily PATI values were computed for each of the example chemograph in Figure 11. 786 Cumulative PATI values for each assessment period w ere calculated for the contiguous period of 787 exposure resulting in the highest value. 788

(e) For each assessment period and example chemogra ph, risk was characterized by calculating 789 the EEF and CEF (see Figure 3 and associated text f or definition of these terms). 790

Figure 14 illustrates how the assessment period aff ects risk characterization, as 791 represented by the EEF. (CEFs showed patterns very close to the EEFs and are not included 792 here.) Relative to the proposed assessment period of 60 d, increasing the assessment period to 90 793 or 120 d resulted in small increases in the EEF, ex cept for one site (MO 02) for which the 794 increases were 28-29%. For the other sites, EEFs i ncreased by an average of 5.6% (range 2.7%- 795 11.0%) for the 90 d assessment period and 8.6% (ran ge 1.6-17.4%) for the 120 d assessment 796 period. In contrast, using a 30-d assessment perio d reduced the EEF, relative to 60 d, by a mean 797 of 24% (range 2-40%), the larger reductions being a ssociated with sites with substantial 798 exposures for more than 30 d. Using such a short a veraging period poorly addresses 799 experimental ecosystem treatment effects, but more importantly assumes that major portions of 800 many field exposures should be ignored. 801

4.3.2 Sensitivity Analysis for Toxicity Information 802

Using the 60-d assessment period, risk characteriz ations were made for each of the 803 example field exposures in Figure 13 using the foll owing options for toxicity information: 804 Figure 14. Sensitivity of risk characterization to assessment period length, based on effects exceeden ce factors at

20 selected sites monitored in 2010.

IA 03 IA 05

I L 1 1
I L 1 3 IL 15 IL 17 KS 0 1 KS 0 3 LA 05 MO 01C MO 0 2 MO 0 5 MO 0 6 MO 0 8 NE 05 OH 06

I A 0 4

I L 1 0 IL 12 IL 14

I L 1 6 IN 1 2 KS 02 LA 04 MO 0 1 MO 0 1 D MO 04a MO 05B MO 07 NE 04 OH 0 5 NE 0 9Effects Exceedence Factor

0.2 0.5 2 5

0.1 1 10 30 d Assessment Period
90 d Assessment Period
120 d Assessment Period
60 d Assessment Period28

(1) The overall distributions for log 10 (EC 50 ) and log 10 (Steep) reported in Table 2 (default). 805 (2) The individual log 10 (EC 50 ) distributions for the four major taxonomic groups in Table 2 806 (using the overall distribution for log 10 (Steep)). 807 (3) An equal-weighted composite of the log 10 (EC 50 ) distributions for the four taxonomic groups. 808 (4) The individual tests in Table 1 (using the aver age value of -0.05 for log 10 (Steep) for tests in 809 which this was not determined). 810 (5) The overall distribution using genera means rat her than individual tests (Section 2.3). 811

These evaluations were conducted in accord with the protocol described above for the 812 assessment period evaluations and are summarized in Figure 15. For most options (green algae, 813 bluegreen algae, individual tests, composite taxa, genus means), the EEF deviations from the 814 default option were generally negligible, averaging <3.5% and never exceeding 13%. For the 815 diatom distribution (the least sensitive group at l ow atrazine concentrations per Figure 10), EEFs 816 usually are lower than for the default option – ave raging 14% lower and ranging from 37% lower 817 to 22% higher. For the vascular plant distribution (the most sensitive group), EEFs usually are 818 higher than for the default option – averaging 12% higher and ranging from 3% lower to 33% 819 higher. Given the magnitude of the differences in mean log 10 (EC 50 ), these differences are rather 820 small, and also are not statistically significant g iven the uncertainties in the toxicity data. 821

This small sensitivity of EEFs to changes in the toxicity information used in PATI might 822 seem surprising given the large sensitivity of PATI itself to these changes (Figure 10), but this is 823 because the experimental ecosystems, not the toxici ty distributions, determine the level of 824

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

20 selected sites monitored in 2010

I A 0 3

I A 0 5

I L 11

I L 1 3 IL 15

I L 17 KS 01 KS 03 LA 0 5
M O 01C MO 0 2 MO 0 5 M O 06 MO 0 8 NE 05 OH 06

I A 0 4 IL 10

I L 1 2 IL 1 4

I L 1 6

I N 1 2 K S 02 L A 04 MO 01 MO 01D MO 0 4 a MO 05B MO 0 7 NE 0 4 OH 05 NE 0 9Effects Exceedence Factor

0.2 0.5 2 5

0.1 1 10 Green Algae
Diatoms Bluegreen Algae
Vascular Plants Composite Taxa
Individual Tests
Genus Means
Oveerall Distribution29

concern. PATI is only being used to assess the relative effects between d ifferent exposure times825 series, and these relative effects are similar whet her the plant assemblage is sensitive or tolerant. 826 As noted in Section 2.5, these relative effects are related to the slopes in Figure 10, which differ 827 little among the various taxonomic assemblage defin itions compared to the large variation in the 828 absolute PATI values. From another perspective, us ing a more sensitive set of toxicity data will 829 result in higher PATI values for both the experimental ecosystem treatments and the fiel d 830 exposures, so that the net effect of taxonomy on th e EEFs is much less than that on PATI itself. 831

However, there are still some effects of taxonomy on EEFs because PATI is not linear 832 with concentration. The smaller slopes in Figure 1 0 for the vascular plants than the diatoms 833 mean that the lower atrazine concentrations will co ntribute relatively more to the vascular plant834 based PATI than the diatom-based PATI. And because periods of relatively lower concentration 835 are more prevalent in most field exposures than in most experimental ecosystem treatments, this 836 results in slightly higher EEFs for the vascular pl ant-based PATI than the diatom-based PATI. 837 However, these differences are small for any field exposure with an EEF near 1.0 and thus have 838 negligible effect on risk characterizations (Figure 15) despite the substantial differences in 839 absolute PATI values. 840

Because this sensitivity analysis shows such small effects from even extreme choices for 841 the taxonomic composition of the plant assemblage a nd because of the statistical uncertainties of 842 these effects, the recommendation here is to use th e overall toxicity distribution in Table 2 that 843 was used as the default for this analysis. Using a ll the data, rather than a subset, is also more in 844 keeping with how aquatic risk assessments generally reflect a broad assemblage of organisms. 845

4.4 Contribution of Toxicity Distribution Uncertain ty to Overall Assessment Uncertainty 846

Although varying the assemblage taxonomy in Secti on 4.3.2 did not affect risk 847 characterizations enough to support using something other than the overall parameter 848 distributions, this does not mean that uncertainty in these distributions is negligible. More 849 evaluation was needed of the uncertainty of EEFs as a function of the uncertainties of all the 850 parameters for the toxicity relationships used to c alculate PATI. 851

To this end, an uncertainty assessment was conduc ted that involved (a) generating 10000 852 sets of toxicity parameter distributions (means and standard deviations for both log 10 (EC 50 ) and 853 log 10 (Steep)), (b) determining the LOC PATI for each parameter distribution set, and (c) 854 determining the EEF for each example chemograph for each parameter distribution set. The 855 means of the 10000 distributions for log 10 (EC 50 ) and log 10 (Steep) were generated by random 856 sampling from normal distributions with the overall distribution means and standard errors for 857 these parameters in Table 2. The standard deviatio ns of the 10000 distributions for log 10 (EC 50 ) 858 and log 10 (Steep) were generated by random sampling from chi- square distributions based on the 859 overall distribution standard deviations for these parameters in Table 2, using a degree of 860 freedom based on the number of data in Table 1. Du e to the observed lack of correlation 861 between EC 50 and Steep, the sampling for these two parameteers was done independently. 862

Figure 16 summarizes this uncertainty analysis, co mparing the 10 th and 90 th uncertainty 863 percentiles to the median results. The lower bound for the EEF varies from 85% to 98% of the 86430

median among the chemographs, with an average of 95 %, while the upper bound varies from 865 102% to 120% of the median, with an average of 107%

. 866

Although this demonstrates that uncertainties in t he toxicity data used to parameterize 867 PATI result in very little uncertainty in the final risk characterizations, this is only one 868 component of the uncertainty for the total methodol ogy. If uncertainty estimates are to be 869 provided, they would need to reflect all important sources 2 , compared to which these 870 uncertainties for the toxicity distributions used b y PATI should be relatively minor. 871 872 873

2 An example of another source of error in the overal l methodology is the uncertainty in the log(LOC PATI ) from the logistic regression. When the best estimates of th e overall toxicity distributions are used in calcul ating PATI, the standard error for log(PATI 50% ) is 0.16 from the binary regression analysis, whic h produces a 10 th to 90 th percentile range for the CEF of 55-183% of the median. Other sources of uncertainty include the characterization of field exposures and of experimental ecosystem effects. Figure 16. Uncertainty analysis for risk character izations due to uncertainties in toxicity distribut ions used to parameterize PATI. Solid line denotes EEFs based o n best estimates of toxicity parameter distribution s. Dashed lines denote 10 th and 90 th percentiles due to uncertainty of these distributi ons.

IA 0 3 IA 0 5

I L 11 IL 13 IL 15 IL 17 KS 01 KS 03 LA 05
M O 0 1 C MO 02 MO 05 MO 0 6 MO 0 8 NE 0 5 O H 06 IA 04 IL 10 IL 1 2 IL 1 4

I L 16

I N 1 2 K S 0 2 LA 04 MO 0 1
M O 01 D MO 04a
M O 0 5 B MO 07 N E 0 4 O H 05 NE 09Effects Exceedence Factor

0.2 0.5 2 5

0.1 1 1031

Figure 17. Relationship of PATI to atrazine concent ration.

Atrazine Concentration ( μ g/L) 2 5 20 50 200 500

10 100 1000Plant Assemblage Toxicity Index (%)

0 20 40 60 80 100 5. SUMMARY AND RECOMMENDATIONS REGARDING LOC METHOD OLOGY 874

As noted in Section 1, this LOC methodology starts with experimental ecosystem studies 875 regarding effects of atrazine on aquatic plant comm unities. Each experimental ecosystem 876 treatment must be characterized regarding (a) wheth er there is an unacceptably adverse effect 877 and (b) the atrazine concentration time series. Th is characterization was provided in U.S.EPA 878 (2011) and summarized in Appendix B. The basic pro blem addressed here is the issue of 879 comparing effects across different exposure time se ries, both among the experimental 880 ecosystems and between the experimental ecosystems and exposures of interest in natural 881 systems. This is done with an effects index that s pecifies the relative toxic severity of different 882 time-series. The proposal here is that this index be the 60-d cumulative PATI value. This index 883 is applied as follows: 884

(1) Based on available toxicity tests with individu al aquatic plant species, relationships of SGR 885 versus atrazine concentration are developed and use d to specify statistical distributions for the 886 relationship parameters (EC 50 , Steep). For this report, the tests were describe d using a logistic 887 relationship of SGR versus log atrazine concentrati on, and the distributional recommendations 888 were for the log 10 (EC 50 ) to have a mean 2.12 and standard deviation 0.37 a nd for the log 10 (Steep) 889 to have a mean of -0.05 and a standard deviation of 0.18, based on an unweighted analysis across 890 all tests. Although some differences among major t axa were indicated, alternative distributions 891 using different taxonomic weightings had small and uncertain effects on assessment results. The 892 distributions recommended here merit additional eva luation regarding the toxicity test data set 893 used and the distributional shape and composition. 894

(2) The relationship of daily PATI values to atrazi ne concentration should be developed for the 895 assemblage of species described by the distribution s for the toxicity relationship parameters 896 (EC 50 , Steep). This requires integrating the expected t oxic response across the joint distribution 897 of the parameters; this integration is best done by randomly selecting a large number (e.g., 898 10000) of EC50/Steep pairs from these distributions , determining the toxicity relationship for 899 each parameter pair, and averaging across all these relationships (note: this numerical method for 900 integrating across the distributions need only be d one once and then applied to all subsequent 901 PATI calculations). For the distributions specifie d here, this results in the following relationship 90232

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

(3) Based on this relationship of daily PATI to atr azine concentration, a cumulative PATI value 904 (=the sum of the daily PATI values) is calculated f or each experimental ecosystem exposure to 905 provide a measure for the total relative toxic impa ct of that exposure. This cumulative PATI 906 value must be limited to a time frame (assessment p eriod) consistent with risk management goals 907 and the experimental ecosystem data, for which a pr ovisional period of 60 d is proposed here. 908 The binary effects determinations for each exposure are plotted against the cumulative PATI 60d 909 values, and a regression is performed to describe t he probability of effect versus PATI. For the 910 daily PATI relationship and the experimental ecosys tem dataset used here, this results in the 911 relationship already shown in Figure 12 and repeate d in Figure 18: 912

The above relationship describes the probability of effect versus PATI 60d , using the logistic 913 equation, with equation parameters log 10 (EC 50 )=132 %-days and a steepness=2.03. If the EC 50 is 914 the designated level of concern, the LOC PATI is thus 132 %-days for a 60 d assessment period. 915 These particular values are contingent on the toxic ity data set used for PATI, the experimental 916 ecosystem dataset, and a risk management decision r egarding what probability of effect is of 917 concern, and thus would change if any of these fact ors is modified. 918

(4) This level of concern for PATI is applied to en vironmental data by calculating the cumulative 919 PATI for each environmental exposure time-series of interest. The effects exceedence factor 920 (EEF) (=ratio of PATI 60d s calculated for field exposures of interest to the LOC PATI ) is used to 921 determine whether the exposures exceed a level of c oncern. If desired, iterative calculations can 922 be used to determine the concentration exceedence f actor (CEF) by which the exposure exceeds 923 a level of concern. FORTRAN-based computer program s and associated input files for this 924 implementation have been developed and are separate ly available from the author. PATI-based 925 EEFs for a suitable set of field exposures can be u sed to develop a concentration-based LOC to 926 apply to future exposures without needing to make a ctual PATI-based calculations, and this is a 927 subject of a separate effort. 928

929 Figure 18. Experimental ecosystem data plotted as e ffect/no effect versus PATI 90d , fitted to a logistic relationship

for the probability of an effect versus PATI.

60-d Cumulative Plant Assemblage Toxicity Index (%- days) 2 5 20 50 200 500 2000 5000

10 100 1000Probability of Adverse Effect

0.0 0.2 0.4 0.6 0.8 1.0

LOC PATI =13233

6. REFERENCES 930

Abou-Waly H, Abou-Setta MM, Nigg HN, Mallory LL. 19 91. Growth response of freshwater 931 algae, Anabaena flos-aquae and Selenastrum capricornutum to atrazine and hexazinone 932 herbicides. Bull Environ Contam Toxicol 46:223-229. 933

Bartell SM, Campbell KR, Lovelock CM, Nair SK, Shaw JL. 2000. Characterizing aquatic 934 ecological risk from pesticides using a diquat dibr omide case study. III. Ecological Process 935 Models. Environ. Toxicol. Chem. 19(5):1441-1453. 936

Berard A, Benninghoff C. 2001. Pollution-induced co mmunity tolerance (PICT) and seasonal 937 variations in the sensitivity of phytoplankton to a trazine in nanocosms. Chemosphere 45:427- 938 437. 939

Berard A, Leboulanger C, Pelte T. 1999. Tolerance o f Oscillatoria limnetica (Lemmermann) to 940 atrazine in natural phytoplankton populations and i n pure culture: Influence of season and 941 temperature. Arch Environ Contam Toxicol 37:472-479

. 942

Berard A, Pelte T, Druart J. 1999. Seasonal variati ons in the sensitivity of Lake Geneva 943 phytoplankton community structure to atrazine. Arch Hydrobiol 145:277-295. 944

Boger P, Schlue U. 1976. Long-term effects of herbi cides on the photosynthetic apparatus I. 945 Influence of diuron, triazines and pyridazinones. W eed Res 16:149-154. 946

Boone MD, James SM. 2003. Interactions of an insect icide, herbicide, and natural stressors in 947 amphibian community mesocosms. Ecol Appl 13:829-841. 948

Brockway DL, Smith PD, Stancil FE. 1984. Fate and e ffects of atrazine on small aquatic 949 microcosms. Bull. Environ. Contam. Toxicol 32:345-3 53. 950

Burrell RE, Inniss WE, Mayfield CI. 1985. Detection and analysis of interactions between 951 atrazine and sodium pentachlorophenate with single and multiple algal-bacterial populations. 952 Arch Environ Contam Toxicol 14:167-177. 953

Carney EC. 1983. The effects of atrazine and grass carp on freshwater communities. Thesis. 954 University of Kansas, Lawrence, Kansas, USA. 955

Caux P-Y, Menard L, Kent RA. 1996. Comparative stud y on the effects of MCPA, butylate, 956 atrazine, and cyanazine on Selenastrum capricornutum . Environ Poll 92:219-225. 957

Davis DE. 1980. Effects of herbicides on submerged seed plants. Completion Report, Project A958 067-Al-A, Office of Water Research and Technology, Washington, DC, USA. NTIS-PB81- 959 103103. 960

Desjardin D, Krueger HO, Kendall TZ. 2003. Atrazine technical: A 14-Day static-renewal 961 toxicity test with duckweed ( Lemna gibba G3) including a recovery phase. Report 528A-131A. 962 Wildlife International, Ltd., Easton, MD, USA. 96334

deNoyelles F, Dewey SL, Huggins DG, Kettle WD. 1994 . Aquatic mesocosms in ecological 964 effects testing: Detecting direct and indirect effe cts of pesticides. In: Aquatic mesocosm studies 965 in ecological risk assessment. Graney, RL, Kennedy JH, Rodgers JH (eds.). Lewis Publ., Boca 966 Raton, FL, USA. pp. 577-603. 967

deNoyelles F, Kettle WD. 1980. Herbicides in Kansas waters - evaluations of effects of 968 agricultural runoff and aquatic weed control on aqu atic food chains. Contribution Number 219, 969 Kansas Water Resources Research Institute, Universi ty of Kansas, Lawrence, Kansas, USA. 970

deNoyelles F, Kettle WD. 1983. Site studies to dete rmine the extent and potential impact of 971 herbicide contamination in Kansas waters. Contribut ion Number 239, Kansas Water Resources 972 Research Institute, University of Kansas, Lawrence, Kansas, USA. 973

deNoyelles F, Kettle WD. 1985. Experimental ponds f or evaluating bioassay predictions. In: 974 Validation and predictability of laboratory methods for assessing the fate and effects of 975 contaminants in aquatic ecosystems. Boyle, TP (ed.) . ASTM STP 865, American Society for 976 Testing and Materials, Philadelphia, PA, USA. pp. 9 1-103. 977

deNoyelles F, Kettle WD, Fromm CH, Moffett MF, Dewe y SL. 1989. Use of experimental 978 ponds to assess the effects of a pesticide on the a quatic environment. In: Using mesocosms to 979 assess the aquatic ecological risk of pesticides: T heory and practice. Voshell, JR (ed.). Misc. 980 Publ. No. 75. Entomological Society of America, Lan ham, MD, USA. 981

deNoyelles F, Kettle WD, Sinn DE. 1982. The respons es of plankton communities in 982 experimental ponds to atrazine, the most heavily us ed pesticide in the United States. Ecol 983 63:1285-1293. 984

Detenbeck NE, Hermanutz R, Allen K, Swift MC. 1996. Fate and effects of the herbicide 985 atrazine in flow-through wetland mesocosms. Environ Toxicol Chem 15:937-946. 986

Dewey SL. 1986. Effects of the herbicide atrazine o n aquatic insect community structure and 987 emergence. Ecol 67:148-162. 988

Diana SG, Resetarits WJ, Schaeffer DJ, Beckmen KB, Beasley VR. 2000. Effects of atrzine on 989 amphibian growth and survival in artificial aquatic communities. Environ Toxicol Chem 990 19:2961-2967. 991

Downing HF, Delorenzo ME, Fulton MH, Scott GI, Madd en CJ, Kucklick JR. 2004. Effects of 992 the agricultural pesticides atrazine, chlorothaloni l, and endosulfan on South Florida microbial 993 assemblages. Ecotoxicology 13:245-260. 994

Erickson RJ. 2009. Critique of Syngenta Corporation 's Probabilistic Implementation of 995 CASM ATZ for Ecological Risk Assessment of Atrazine. Intern al Report, U.S. Environmental 996 Protection Agency, Mid-Continent Ecology Division, Duluth, MN, USA. 99735

Fairchild JF, Ruessler DS, Carlson AR. 1998. Compar ative sensitivity of five species of 998 macrophytes and six species of algae to atrazine, m etribuzin, alachlor, and metolachlor. Environ 999 Toxicol Chem 17:1830-1834. 1000

Fairchild J, Ruessler S, Nelson M, Haveland P. 1994 . An aquatic hazard assessment of four 1001 herbicides using six species of algae and five spec ies of aquatic macrophytes. Report, Midwest 1002 Science Center, National Biological Service, Columb ia, MO, USA. 1003

Fairchild JF, Ruessler DS, Haverland PS, Carlson AR . 1997. Comparative sensitivity of 1004 Selenastrum capricornutum and Lemna minor to sixteen herbicides. Arch Environ Contam 1005 Toxicol 32:353-357. 1006

Fairchild, JF, Ruessler SD, Lovely PA, Whites DA, H eine PR. 1995. An aquatic plant risk 1007 assessment of sixteen herbicides using toxicity tes ts with Selenastrum capricornutum and Lemna 1008 minor . Report, Midwest Science Center, National Biologi cal Service, Columbia, MO, USA. 1009

Faust M, Altenburger R, Boedeker W, Grimme LH. 1993 . Additive effects of herbicide 1010 combinations on aquatic non-target organisms. Sci T otal Environ 113/114:941-951. 1011

Field LJ, MacDonald DD, Norton SB, Severn CG, Inger soll CG. 1999. Evaluating sediment 1012 chemistry and toxicity data using logistic regressi on modeling. Environ Toxicol Chem 18:1311- 1013 1322. 1014

Field LJ, MacDonald DD, Norton, SB, Ingersoll CG, S evern CG, Smorong D, Lindskoof R. 1015 2002. Predicting amphipod toxicity from sediment ch emistry using logistic regression models. 1016 Environ Toxicol Chem 21:1993-2005. 1017

Forney DR. 1980. Effects of atrazine on Chesapeake aquatic plants. M.S. Thesis, Auburn 1018 University, Auburn, AL, USA. 1019

Hinman ML. 1989. Utility of rooted aquatic vascular plants for aquatic sediment hazard 1020 evaluation. Ph.D. Thesis, Memphis State University , Memphis, TN, USA. 1021

Forney DR, Davis DE. 1981. Effects of low concentra tions of herbicides on submersed aquatic 1022 plants. Weed Sci 29:677-685. 1023

Gala WR, Giesy JP. 1990. Flow cytometric techniques to assess toxicity to algae. In: Landis 1024 WG, van der Schalie WH (eds). Aquatic Toxicology an d Risk Assessment: 13th Volume. 1025 American Society for Testing and Materials, Philade lphia, PA, USA. 1026

Geyer H, Scheunert I, Korte F. 1985. The effects of organic environmental chemicals on the 1027 growth of the alga Scenedesmus subspicatus : a contribution to environmental biology. 1028 Chemosphere 14:1355-1369. 1029

Giddings, JM, Anderson TA, Hall Jr LW, Kendall RJ, Richards RP, Solomon KR, Williams 1030 WM. 2000. Aquatic Ecological Risk Assessment of Atr azine: A Tiered, Probabilistic Approach. 1031 Prepared by the Atrazine Ecological Risk Assessment Panel, ECORISK, Inc. Report 709-00, 1032 Novartis Crop Protection, Greensboro, NC, USA. 103336

Gramlich JV, Grams RE. 1964. Kinetics of Chlorella inhibition by herbicides. Weeds 12:184- 1034 189. 1035

Gruessner B, Watzin MC. 1996. Response of aquatic communities from a Vermont stream to 1036 environmentally realistic atrazine exposure in labo ratory microcosms. Environ Toxicol Chem 1037 15:410-419. 1038

Gustavson K, Wangberg SA. 1995. Tolerance induction and succession in microalgae 1039 communities exposed to copper and atrazine. Aquat T oxicol 32:283-302. 1040

Hamala JA, Kollig HP. 1985. The effects of atrazine on periphyton communities in controlled 1041 laboratory ecosystems. Chemosphere 14:1391-1408. 1042

Hamilton PB, Jackson GS, Kaushik NK, Solomon KR. 19 87. The impact of atrazine on lake 1043 periphyton communities, including carbon uptake dyn amics using track autoradiography. 1044 Environ Poll 46:83-103. 1045

Hamilton PB, Jackson GS, Kaushik NK, Solomon KR, St ephenson GL. 1988. The impact of two 1046 applications of atrazine on the plankton communitie s of in situ enclosures. Aquat Toxicol 1047 13:123-140. 1048

Hamilton PB, Lean DRS, Jackson GS, Kaushik NK, Solo mon KR. 1989. The effect of two 1049 applications of atrazine on the water quality of fr eshwater enclosures. Environ Pollut 60:291- 1050 304. 1051

Hersh CM, Crumpton WG. 1989. Atrazine tolerance of algae isolated from two agricultural 1052 streams. Environ Toxicol Chem 8:327-332. 1053

Hinman ML. 1989. Utility of rooted aquatic vascular plants for aquatic sediment hazard 1054 evaluation. Ph.D. Thesis. Memphis State University , Memphis, TN, USA. 1055

Hoberg JR. 1991a. Atrazine technical toxicity to th e freshwater green algae, Selenastrum 1056 capricornutum. SLI Report #91-1-3600. Springborn Laboratories, I nc., Wareham, MA, USA. 1057

Hoberg JR. 1991b. Atrazine technical toxicity to th e duckweed Lemna gibba . SLI Report #91-1- 1058 3613. Springborn Laboratories, Inc., Wareham, MA, U SA. 1059

Hoberg JR. 1993a. Atrazine technical - toxicity to the freshwater green algae, Selenastrum 1060 capricornutum . SLI Report # 93-4-4751. Springborn Laboratories, Inc., Wareham, MA, USA. 1061

Hoberg JR. 1993b. Atrazine technical - toxicity to duckweed ( Lemna gibba ). SLI Report #93- 1062 4-4755. Springborn Laboratories, Inc., Wareham, MA, USA. 1063

Hoberg JR. 1993c. Atrazine technical - toxicity to duckweed ( Lemna gibba ). SLI Report #93- 1064 11-5053. Springborn Laboratories, Inc., Wareham, MA , USA. 106537

Hoberg JR. 2007. The toxicity of atrazine to the fr eshwater macrophyte Elodea canadensis at 1066 three light intensities for 14 days. Report 1781.6 691. Springborn Smithers Laboratories, Inc., 1067 Wareham, MA, USA. 1068

Hughes JS, Alexander MM, Balu K. 1988. An evaluatio n of appropriate expressions of toxicity 1069 in aquatic plant bioassays as demonstrated by the e ffects of atrazine on algae and duckweed. In: 1070 Adams W, Chapman GA, Landis WG (eds). Aquatic Toxic ology and Hazard Assessment: 10th 1071 Volume. Philadelphia, PA: American Society for Test ing and Materials. 1072

Hughes JS. 1986. The toxicity of atrazine, lot no. FL-850612, to four species of aquatic plants. 1073 Report, Study 267-29-1100, Malcolm Pirnie, White Pl ans, NY, USA. 1074

Johnson BT. 1986. Potential impact of selected agri cultural chemical contaminants on a northern 1075 prairie wetland: A microcosm evaluation. Environ To xicol Chem 5:473-485. 1076

Jones TW, Kemp WM, Estes PS, Stevenson JC. 1986. At razine uptake, photosynthetic 1077 inhibition, and short-term recovery for the submers ed vascular plant, Potamogeton perfoliatus L. 1078 Arch Environ Contam Toxicol 15:277-283. 1079

Jurgensen TA,. Hoagland KD. 1990. Effects of short -term pulses of atrazine on attached algal 1080 communities in a small stream. Arch Environ Contam Toxicol 19:617-623. 1081

Juttner I, Peither A, Lay JP, Kettrup A, Ormerod SJ . 1995. An outdoor mesocosm study to assess 1082 ecotoxicological effects of atrazine on a natural p lankton community. Arch Environ Contam 1083 Toxicol 29:435-441. 1084

Kallqvist T, Romstad R. 1994. Effects of agricultur al pesticides on planktonic algae and 1085 cyanobacteria – examples of interspecies sensitivit y variations. Norw J Agric Sci 13:117-131. 1086

Kemp WM, Boynton WR, Cunningham JJ, Stevenson JC, J ones TW, Means JC. 1985. Effects of 1087 atrazine and linuron on photosynthesis and growth o f the macrophytes, Potamogeton perfoliatus 1088 and Myriophyllum spicatum in an estuarine environment. Mar Environ Res 16:2 55-280. 1089

Kettle WD, deNoyelles F, Heacock BD, Kadoum AM. 198 7. Diet and reproductive success of 1090 bluegill recovered from experimental ponds treated with atrazine. Bull Environ Contam Toxicol 1091 38:47-52. 1092

Kettle WD. 1982. Description and analysis of toxica nt-induced responses of aquatic 1093 communities in replicated experimental ponds. Ph.D. Thesis. University of Kansas, Lawrence, 1094 KS, USA. 1095

Kirby MF, Sheahan DA. 1994. Effects of atrazine, is oproturon, and mecoprop on the macrophyte 1096 Lemna minor

and the alga Scenedesmus subspicatus . Bull Environ Contam Toxicol 53:120-126. 1097

Knauert S, Dawo U, Hollender J, Hommen U, Knauer K. 2009. Effects of photosystem II 1098 inhibitors and their mixture on freshwater phytopla nkton succession in outdoor mesocosms. 1099 Environ Toxicol Chem 28:836-845. 110038

Knauert S, Escher B, Singer H, Hollender J, Knauer K. 2008. Mixture toxicity of three 1101 photosystem II inhibitors (atrazine, isoproturon, a nd diuron) toward photosynthesis of freshwater 1102 phytoplankton studied in outdoor mesocosms. Environ Sci Tech 42:6424-6430. 1103

Kosinski RJ. 1984. The effect of terrestrial herbic ides on the community structure of stream 1104 periphyton. Environ Pollut (Series A) 36:165-189. 1105

Kosinski RJ, Merkle MG. 1984. The effect of four te rrestrial herbicides on the productivity of 1106 artificial stream algal communities. J Environ Qual 13:75-82. 1107

Krieger KA, Baker DB, Kramer JW. 1988. Effects of h erbicides on stream aufwuchs 1108 productivity and nutrient uptake. Arch Environ Cont am Toxicol 17:299-306. 1109

Lampert W, Fleckner W, Pott E, Schober U, Storkel K U. 1989. Herbicide effects on planktonic 1110 systems of different complexity. Hydrobiologia 188/ 189:415-424. 1111

Larsen DP, DeNoyelles Jr. F, Stay F, Shiroyama T. 1 986. Comparisons of single-species, 1112 microcosm and experimental pond responses to atrazi ne exposure. Environ Toxicol Chem 5:179- 1113 190. 1114

Leboulanger C, Rimet F, de Lacotte MH, Berard A. 20 01. Effects of atrazine and nicosulfuron 1115 on freshwater microalgae. Environ Internat 26:131-1 35 1116

Lynch TR, Johnson HE, Adams WJ. 1985. Impact of atr azine and hexachlorobiphenyl on the 1117 structure and function of model stream ecosystems. Environ Toxicol Chem 4:399-413. 1118

Mayer P, Frickmann J, Christensen ER, Nyholm N. 199 8. Influence of growth conditions on the 1119 results obtained in algal toxicity tests. Environ T oxicol Chem 17:1091-1098. 1120

McGregor EB, Solomon KR, Hanson ML. 2008. Effects o f planting system design on the 1121 toxicological sensitivity of Myriophyllum spicatum and Elodea canadensis to atrazine. 1122 Chemosphere 73:249-260. 1123

Millie DF, Hersh CM. 1987. Statistical characteriza tions of the atrazine-induced photosynthetic 1124 inhibition of Cyclotella meneghiniana . Aquatic Toxicol 10:239-249. 1125

Miller WE, Greene, JC, T Shiroyama. 1978. The Selenastrum capricornutum (Printz) algal assay 1126 bottle test. EPA/600/9-78/018. U.S. Environmental Protection Agency, Corvallis, OR, USA. 1127

Moorhead DL, Kosinski RJ. 1986. Effect of atrazine on the productivity of artificial stream algal 1128 communities. Bull. Environ Contam Toxicol 37:330-33 6. 1129

Parrish R. 1978. Effects of atrazine on two freshwa ter and five marine algae. Ciba-Geigy 1130 Corporation, Greensboro, NC, USA. 1131

Pratt JR, Bowers NJ, Niederlehrer BR, Cairns Jr. J. 1988. Effects of atrazine on freshwater 1132 microbial communities. Arch Environ Contam Toxicol 17:449-457. 113339

Radetski CM, Ferard JF, Blaise C. 1995. A semistati c microplate-based phytotoxicity test. 1134 Environ Toxicol Chem 14:299-302. 1135

Relyea RA. 2009. A cocktail of contaminants: how mi xtures of pesticides at low concentrations 1136 affect aquatic communities. Oecologia 159:363-376. 1137

Roberts SP, Vasseur P, Dive D. 1990. Combined effec ts between atrazine, copper and pH on 1138 target and non-target species. Water Res 24:485-491

. 1139

Rohr JR, Crumrine PW. 1005. Effects of an herbicide and an insecticide on pond community 1140 structure and processes. Ecological Applications 15 :1135-1147. 1141

Rohr JR, Schotthoefer AM, Raffel TR, Carrick HG, Ha lstead N, Hoverman JT, Johnson CM, 1142 Johnson LB, Lieske C, Piwoni MD, Schoff P, Beasley VR.2008. Agrochemicals increase 1143 trematode infections in a declining amphibian speci es. Nature 455:1235-1239. 1144

Saenz ME, Alberi JL, DiMarzio WD, Accorinti J, Tort orelli MC. 1997. Paraquat toxicity to 1145 different green algae. Bull Environ Contam Toxicol 58:922-928. 1146

Schafer H, Hettler H, Fritsche U, Pitzen G, Roderer G, Wenzel A. 1994. Biotests using 1147 unicellular algae and ciliates for predicting long- term effects of toxicants. Ecotox Environ Saf 1148 27:64-81. 1149

Schafer H, Wenzel A, Fritsche U, Roderer G, Traunsp urger W. 1993. Long-term effects of 1150 selected xenobiotica on freshwater green algae: dev elopment of a flow-through test system. Sci 1151 Total Environ 113/114:735-740. 1152

Seguin F, Druart J-C, Le Cohu R. 2001. Effects of a trazine and nicosulfuron on periphytic 1153 diatom communities in freshwater outdoor lentic mes ocosms. Ann Limnol – Internat J Limnol 1154 37:3-8. 1155

Seguin F, Le Bihan F, Leboulanger C, Berard A. 2002 . A risk assessment of polution: induction 1156 of atrazine tolerance in phytoplankton communities in freshwater outdoor mesocosms, using 1157 chlorophyll fluorescence as an endpoint. Water Rese arch 36:3227-3236. 1158

Seguin F, Leboulanger C, Rimet F, Druart J-C, Berar d A. 2001. Effects of atrazine and 1159 nicosulfuron on phytoplankton in systems of increas ing complexity. Arch Environ Contam 1160 Toxicol 40:198-208. 1161

Solomon KR, Baker DB, Richards P, Dixon KR, Klaine SJ, LaPoint TW Kendall RJ, Giddings 1162 JM, Diesy JP, Hall Jr. LW, Weisskopf C, Williams M. 1996. Ecological risk assessment of 1163 atrazine in North American surface waters. Environ Toxicol Chem 15:31-76. 1164

Stay EF, Katko A, Rohm CM, Fix MA, Larsen DP. 1989. The effects of atrazine on microcosms 1165 developed from four natural plankton communities. A rch Environ Contam Toxicol 18:866-875. 1166

Stay FS, Larsen DP, Katko A, Rohm CM. 1985. Effects of atrazine on community level 1167 responses in Taub microcosms. In: Validation and pr edictability of laboratory methods for 116840

assessing the fate and effects of contaminants in a quatic ecosystems. Boyle, TP (ed.). ASTM 1169 STP 865. American Society for Testing and Materials , Philadelphia, PA, USA. 1170

Stratton, GW. 1981. The effects of selected pestici des and their degradation products on 1171 microorganisms and Daphnia magna . Ph.D. dissertation, University of Guelph, Guelph , Ontario, 1172 Canada. 1173

Stratton GW. 1984. Effects of the herbicide atrazin e and its degradation products, alone and in 1174 combination, on phototrophic microorganisms. Arch E nviron Contam Toxicol 13:35-42. 1175

Stratton GW, Giles J. 1990. Importance of bioassay volume in toxicity tests using algae and 1176 aquatic invertebrates. Bull Environ Contam Toxicol 44:420-427. 1177

Tang J-X, Hoagland KD, Siegfried BD. 1997. Differen tial toxicity of atrazine to selected 1178 freshwater algae. Bull Environ Contam Toxicol 59:6 31-637. 1179

Turbak SC, Olson SB, McFeters GS. 1986. Comparison of algal assay systems for detecting 1180 waterborne herbicides and metals. Water Res 20:91-9 6. 1181

University of Mississippi. 1991. Effects of atrazin e on Selenastrum capricornutum, Lemna minor 1182 and Elodea canadensis . Ciba-Geigy Corporation, Greensboro, NC, USA. 1183

U.S. Environmental Protection Agency. 1998. Guideli nes for Ecological Risk Assessment. 1184 EPA/630/R-95/002F, U.S.EPA Office of Research and D evelopment, Washington, DC, USA. 1185

U.S. Environmental Protection Agency. 2003. Interim Reregistration Eligibility Decision for 1186 Atrazine. Case No. 0062. U.S.EPA Office of Pestici de Programs, Washington, DC, USA. 1187

U.S. Environmental Protection Agency. 2009. The eco logical significance of atrazine effects on 1188 primary producers in surface water streams in the c orn and sorghum growing region of the 1189 United States (Part II). Briefing document to the FIFRA Scientific Advisory Panel, U.S. EPA 1190 Office of Pesticide Programs, Washington, DC, USA. 1191

U.S. Environmental Protection Agency. 2011. Bibliog raphy of Microcosm and Mesocosm 1192 Studies and Criteria Used to Screen Studies for Ana lysis of Atrazine Risks to Aquatic Plant 1193 Communities. Memorandum from Michael Lowit, Anita Pease, Dana Spatz, and Brian 1194 Anderson, Environmental Fate and Effects Division, to Melanie Biscoe, Anne Overstreet, 1195 Pesticide Reevaluation Division, Office of Pesticid e Programs, U.S. EPA. 1196

Van den Brink PJ, van Donk E, Gylstra R, Crum SJH, Brock TCM. 1995. Effects of chronic low 1197 concentrations of the pesticides chlorpyrifos and a trazine in indoor freshwater microcosms. 1198 Chemosphere 31:3181-3200. 1199

van der Heever JA, Grobbelaar JU. 1996. The use of Selenastrum capricornutum growth 1200 potential as a measure of toxicity of a few selecte d compounds. Water SA 22: 183-191. 120141

Versteeg DJ. 1990. Comparison of short- and long-te rm toxicity test results for the green alga, 1202 Selenastrum capricornutum . In: Wang W, Gorsuch JW, Lower WR (eds). Plants fo r Toxicity 1203 Assessment. Philadelphia, PA: American Society for Testing and Materials. 1204

Volz DC, Bartell SM, Nair SK, Hendley P. 2007. Mo deling the Potential for Atrazine-Induced 1205 Changes in Midwestern Stream Ecosystems Using the C omprehensive Aquatic System Model 1206 (CASM). Final Report, T001403-07, Syngenta Crop Pr otection, Greensboro, NC. 1207

Williams PJB, Robinson C, Sondergaard M, Jespersen A-M, Bentley TL, Lefevre D, Richardson 1208 K, Riemann B. 1996. Algal 14 C and total carbon metabolisms. 2. Experimental obs ervations 1209 with the diatom Skeletonema costatum . J Plank Res 18:1961-1975. 1210

Zagorc-Koncan J. 1996. Effects of atrazine and alac hlor on self-purification processes in 1211 receiving streams. Water Sci Technol 33:181-187. 1212

121342 121443 APPENDIX A 1215

SINGLE-SPECIES PLANT TOXICITY TEST REVIEW 1216

This appendix provides a summary for each report a nd journal article reviewed for 1217 developing the compilation of EC 50 s and steepness values for the relationship of plan t specific 1218 growth rate (SGR) to atrazine concentration. Bold numbers in the tables or text denote values 1219 from each study selected for inclusion in the compi lation. 1220

A.1 Protocol for Application of Toxicity Test Data 1221

A.1.1 Acceptability of measurement variables 1222

(1) The preferred measurement variable for assessing at razine effects was plant biomass (dry 1223 weight, or wet weight if procedures provided consis tent removal of adhering water), but 1224 measures that are approximately proportional to bio mass (algal cell count or cell volume, 1225 duckweed frond count) were also accepted. 1226

(2) If measures outlined in (1) were not available, O 2 evolution or 14 C fixation measurements 1227 were accepted provided that they were not significa ntly compromised by any lag in inducing 1228 effects and their relationship to SGR could be defi ned. 1229

(3) Data based just on chlorophyll content were not us ed because the chlorophyll content per cell 1230 can change markedly in response to atrazine, leadin g to markedly different EC 50 s for chlorophyll 1231 than for actual biomass (see discussion in Section 2.2.1 in main report text). Similarly, optical 1232 density was not accepted because it also is affecte d by chlorophyll content, often being measured 1233 near a chlorophyll absorbance maximum. 1234

A.1.2 Translating reported data into SGR EC 50 and steepness parameter values 1235

The nature of the data and the level of detail prov ided in the reviewed reports/papers varied 1236 widely, requiring several different procedures for translating the reported data into the elements 1237 of the data compilation: the SGR EC 50 , a steepness for the SGR vs. atrazine concentratio n 1238 relationship, and the SGR C
. 1239

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

The preferred data were reported initial and final biomasses (or acceptable surrogates) for all 1241 treatment concentrations, from which SGRs would the n be computed. A regression analysis of 1242 SGR vs. atrazine concentration (C ATZ ) was then conducted, resulting in characterizing b oth the 1243 EC 50 and the steepness for the relationship based on th e basic measurements in the study. The 1244 analyses were by least-square, nonlinear regression using Version 1.2 of the software package 1245 TRAP (Toxicity Relationship Analysis Program) (U.S. EPA Mid-Continent Ecology Division, 1246 Duluth, MN, http://www.epa.gov/medatwrk/Prods_Pubs/ trap.htm), using the “logistic equation” 1247 model option and the log-transform option for C ATZ . This model option uses the logistic 1248 equation to provide a sigmoidal regression function shape, but is a regression of a continuous 1249 variable, not binary logistic analysis: 125044 ( ) ( ) 

### ( ) 10 10 50 4 log log 

### 1 C

ATZ Steep C EC SGR SGR



### e ⋅ ⋅ − = 

### + 1251

The defining parameters for this function are the c ontrol SGR (SGR C ), the log 10 (EC 50 ) for the 1252 SGR, and a measure of relative steepness (“Steep”) defined as  d(SGR/SGR C )/d(log 10 (C ATZ ))  at 1253 the EC 50
. 1254

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

If the author reported SGRs (based on biomass or ac ceptable biomass surrogates) for all 1256 treatment concentrations, but not the actual initia l and final biomasses, these SGRs were used 1257 directly in regression analysis as described in (A1) above to obtain the SGR C , SGR EC 50 , and 1258 steepness parameter. If the reported SGRs were rel ative (fraction of the control), the regression 1259 was conducted to obtain an EC 50 and steepness to include in the compilation, but n ot an SGR C , 1260 although in some cases the latter was specified sep arately by the author(s). 1261

A.1.2.3 EC 50 for the SGR was reported with or without slope. 1262

If the author computed SGRs, but only reported an S GR-based EC 50 without SGRs for individual 1263 treatment concentrations, the author-calculated SGR EC 50 was included in the compilation. If 1264 the author also specified the type of relationship used in the EC 50 estimation and a slope for that 1265 relationship, this information was converted to the steepness parameter of the relationship used 1266 in EPA’s regressions; otherwise no steepness was co mpiled. If the author separately provided 1267 information on the SGR C , this also was included in the compilation. 1268

A.1.2.4 Multiple EC p s for growth reported; SGR C reported. 1269

(a) If multiple EC p s for growth over a specified duration (t) and the SGR C for that duration were 1270 reported, SGRs corresponding to these biomass-based EC p s were calculated using the equation: 1271 

# ( ) ( ) 

# ( )

100 1 ln 1 1 C p SGR t SGR e t ⋅ = − ⋅ + 1272

In other words, this is the value for the SGR at th e concentration causing a p% decrease in 1273 growth. The resultant SGRs (and their associated c oncentrations) were then subject to regression 1274 analysis to provide estimates for the SGR EC 50 and steepness. This provided a SGR EC 50 , a 1275 steepness, and a SGR C for the compilation. 1276

(b) If the author did not specify multiple EC p s for growth, but did provide the growth-based 1277 EC 50 , the type of relationship used in this EC 50 estimation, and the slope for that relationship, 1278 additional EC p s for growth (p ≤ 90%) were calculated for this author-reported curve and also 1279 converted to SGRs. These were then subject to regr ession analysis to provide estimates for the 1280 SGR EC 50 and steepness, although any confidence limits on t hese estimates would not be valid 1281 given that the data points were not independent. R ather, this was simply a mechanism to convert 1282 the the author-reported curve for biomass-based ECs to the equivalent curve for SGR-based ECs. 128345

(c) If the smallest SGR was more than 75% of the SGR C for either of the above options, the 1284 regression analysis was not conducted because this would involve too much extrapolation to 1285 estimate the SGR EC 50 . However, the possibility of extrapolating this S GR to the SGR EC50 per 1286 A.1.2.6 below was then considered. 1287

A.1.2.5 Multiple EC p s for growth reported; SGR C not reported. 1288

If multiple EC p s or an EC 50 /slope combination for algal growth were reported, but an SGR C was 1289 not reported, the process in A.1.2.4 above was still used, but using SGR C s reported for other 1290 studies on test species in the same taxonomic group . Because this involves using data from other 1291 experimental systems and test species, three separa te analyses were conducted using median 1292 (low-high) estimates for the SGR C of 1.35 (1.05-1.74) for green algae, 1.03 (0.80-1. 32) for 1293 diatoms, and 0.65 (0.50-0.83) for blue-green algae. The SGR EC 50 and steepness from the 1294 regression analysis using the median SGR C estimate were included in the compilation, provide d 1295 the SGR EC 50 s derived using the low and high SGR C estimates differed by no more than a factor 1296 of 2.0. 1297

[ The low/mid/high SGR C estimates were based on ANOVA of logSGR C s from algal studies in which SGR C 1298 was reported (see Table 1 in Section 2.2). Analyse s using Statistica (Version 8.0, StatSoft, Tulsa, O K) 1299 provided a log mean for each major algal taxonomic group (0.135 for green algae, 0.013 for diatoms, - 1300 0.189 for blue-green algae) and a pooled standard d eviation (0.122). The low/mid/high estimates for 1301
SGR C

were based on calculating the mean ± 1 std.dev. of these log values and then taking antilogarithms. 1302 Separate SGR C

values for species within a taxonomic group were n ot justified because of large within1303 species variability relative to between-species var iability, as evidenced in Table 1 and other sources (e.g., 1304 Saenz et al. 1997). ] 1305

A.1.2.6 EC 50 only for growth reported; SGR C reported. 1306

If the EC 50 s for growth over a specified duration (t) and the SGR C for that duration were 1307 reported, this biomass-based EC 50 was equated to an SGR EC p using the following equation to 1308 determine p: 1309 

# ( ) 

# ( ) ( ) 1 ln 0.5 1

1 C

C SGR t

SGR SGR SGR e t p ⋅ = ⋅ +

= − 1310

When only the SGR C and this single SGR are available, no regression a nalysis is possible. 1311 Rather, this SGR EC p was extrapolated to an SGR EC 50 using the equation 2 50 10 p S

P EC EC ⋅ = ⋅ , 1312 where S is based on regression curve steepnesses fr om other studies. Because this involves using 1313 data from other experimental systems and test speci es, three estimates of the SGR EC 50 were 1314 made using low, middle, and high estimates for the steepness of 0.68, 0.95, and 1.31. The 1315 estimate for the SGR EC 50 from the middle steepness estimate was included in the compilation, 1316 but only if the estimates based on low and high ste epness differed by less than a factor of 2. This 1317 factor of 2 requirement was met if p>16 for the est imated SGR EC p . 131846

[ An ANOVA of all the log steepness determined in all studies indicated no significant differences 1319 among species or broader taxonomic groups, so the o verall mean and standard deviation of the 1320 log steepness were used to set low/mid/high estimat es. ] 1321

A.1.2.7 EC 50 only for growth reported; SGR C not reported. 1322

When only an EC 50 for growth was reported and a study-specific SGR C was not reported, the 1323 biomass-based EC 50 was equated to SGR-based EC p s per section A.1.2.5 using low, middle, and 1324 high estimates for SGR C . Then, each of these SGR-based EC p estimates was extrapolated to 1325 SGR EC 50 estimates per section A.1.2.6 using low, middle, and high steepness estimates. The 1326 SGR EC 50 estimate based on the middle SGR C and steepness estimates was included in the 1327 compilation, provided the extremes of the estimates varied by less than a factor of 2. This factor 1328 of 2 requirement resulted in this procedure being a pplicable for green algae tests of up to 2 d 1329 long, but tests could be up to 4-d long for blue-gr een algae and up to 3-d long for diatoms. 1330 Extrapolating EC 50 s for net growth to SGR EC 50 s were just too uncertain for tests longer than 1331 this. 1332

A.1.2.8 Oxygen evolution or 14 C fixation reported 1333

(a) If the exposure and measurement periods were short enough so that biomass did not change 1334 appreciably during these periods, and if initial bi omasses were either measured or could be 1335 treated as approximately the same among treatments, oxygen evolution and radiocarbon fixation 1336 rates were treated as proportional to SGR and EC p s for these rates were treated as comparable to 1337 SGR-based EC p s. However, this also required consideration of wh ether these periods were so 1338 short that any lag in the induction of toxicity wou ld significantly perturb the measurement. 1339 Hersh and Crumpton (1989) and Millie and Hersh (198 7) reported effects on oxygen evolution 1340 that were >50% within several minutes of exposure t o atrazine concentrations that caused similar 1341 effects on biomass-based SGRs. Thus, data were acc epted provided an induction lag of 5 min 1342 would not significantly confound results. 1343

(b) When the exposure and measurement periods were the same and biomass changed enough 1344 over the period to substantially affect estimated E C p s, oxygen evolution and radiocarbon fixation 1345 were treated as being proportional to net growth ( e SGR

⋅ t ), and ECs were converted to an SGR 1346 basis analogously to procedures described above for biomass-based ECs. 1347

(c) If a substantial exposure period of duration “t” p receded a short measurement period, so that 1348 the treatments would start with significantly diffe rent initial biomasses for the oxygen 1349 evolution/radiocarbon fixation measurement period, these measures were treated as being 1350 proportional to SGR

⋅ e SGR

⋅ t ; i.e., the biomass accretion in the exposure perio d prior to the start of 1351 measurement is e SGR

⋅ t and the oxygen evolution/radiocarbon fixation rate is proportional to the 1352 SGR times that biomass accretion. This required co nverting ECs to an SGR-basis using 1353 approaches analogous to that described above for bi omass. 1354

A.1.3 Issues regarding biomass surrogates and varia bility. 1355

One uncertainty issue occurred when the biomass sur rogate was cell counts made manually using 1356 a hemocytometer or similar device. In some cases, cell density estimates were based on <100 135747

cells counted in total for the control treatment an d just several cells for atrazine treatments with 1358 large effects. Even 100 cells represents about +/- 20% uncertainty in the cell density. Therefore, 1359 it was desired to have >200 cells counted in the co ntrol treatment in order to have reasonable 1360 discrimination between the control and treatments w ith 25-50% reduced growth. Another area of 1361 concern was frond counts for duckweed, and how clos ely such counts mirror biomass when 1362 growth is limited and thus might have a greater per centage of newer, small fronds. Where 1363 possible, it was desired to have at least a 4-fold increase in the number of control fronds so that 1364 the counts were not excessively dominated by new, s mall fronds. A final area of concern was 1365 macrophyte shoot tests at times when controls had n ot increased by at least 50%, especially if 1366 this was measured by shoot length, which can change disproportionately to shoot weight when 1367 photosynthesis is inhibited. No firm rules were im posed with regard to any of these concerns, 1368 because any uncertainty depends on the number of re plicates in a test, the specific times, the 1369 variability among replicates, etc. How these conce rns are addressed in the summaries for each 1370 study in Appendix A. 1371

A.1.4 Treatment of data at multiple times 1372

When biomasses or biomass surrogates were reported at multiple times within a test's duration, 1373 analyses were conducted for each time; however, the compilation selected results from only one 1374 of these times. The time was selected to be long e nough to avoid problems with uncertain 1375 measurements of biomass early in some tests (e.g., the hemocytometer count issue discussed 1376 above), but short enough to avoid potential biases associated with declining SGR C discussed 1377 earlier. Again, no firm rules could be adopted for this because of various study-specific factors 1378 and because it involved balancing uncertainties at early times with those at later times. The 1379 decision process regarding this is provided in the summaries for each study below. 1380

A.2 Review Summaries 1381 1382 A.2.1 Algae 1383 1384 (1) Gala and Giesy 1990 1385 1386 The authors conducted a 96-h flask test of Selenastrum capricornutum growth at multiple 1387 atrazine concentrations, enumerating cell density b ased on hemocytometer cell counts. 1388 Concentrations were measured. Illumination was con tinuous at 40 μ E/m 2 /s, temperature was 24 1389 C. They reported average SGRs over 96 h at each tr eatment concentration, which were directly 1390 used in EPA regression analyses. Data for earlier times were not reported, but authors noted the 1391 use of extra nutrients to maintain exponential grow th. Due to the duration and growth rates, cell 1392 densities would have been high enough to avoid conc erns about low numbers of individuals 1393 manually counted. 1394 Measured (Target) Concentration ( μ g/L) Author Measured SGR (1/d)
Control 1.007 64 (60) 0.773 121 (120) 0.508 261 (250) 0.24448 499 (500) 0.013
EC 50 ( μ g/L) 125

(80-194) Steepness 1.07

(0.46-1.77) 1395 (2) van der Heever and Grobbelaar 1996 1396 1397 The authors conducted a 72-h flask test of Selenastrum capricornutum growth at multiple 1398 atrazine concentrations, determining biomass (dry w eight), cell density (electronic particle 1399 counter), and chlorophyll (by both spectrometry and fluorometry) at 0, 24, 48, and 72 h. 1400 Concentrations were nominal. Illumination was cont inuous at 300 μ E/m 2 /s and temperature was 1401 23 C. The authors graphically reported relative (to control) SGRs based on all these measures. 1402 Author-reported ECs based on chlorophyll were subst antially (almost 3X) higher than for cell 1403 density and biomass, and were not used in accordanc e with the review guidelines. Relative 1404 SGRs for cell density and biomass were estimated fr om the figures, reported in the table below, 1405 and used in EPA regression analyses to determine EC 50 and steepness. The results based on dry 1406 weight were selected for use because EC 50 s were modestly higher for cell density (average LC 50 1407 = 406 by cell density, 311 by weight) indicative of decreases in mass per cell at higher atrazine 1408 concentrations, so that using cell density would sl ightly reduce the apparent sensitivity of 1409 biomass to atrazine. The results at 1 d were selec ted for use because it was unknown whether 1410 control growth rates declined with time, given that only relative SGRs were reported, and 1411 because use of an electronic particle counter shoul d have avoided the problems with low manual 1412 cell counts at early times. 1413 1414

Nominal

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

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

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

1415 (3) van der Heever and Grobbelaar 1997 1416 1417 The authors conducted a 30-min oxygen evolution ass ay for Selenastrum capricornutum 1418 exposed to multiple atrazine concentrations. Conce ntrations were nominal. Illumination was 1419 continuous at 300 μ E/m 2 /s and temperature was 23 C. Oxygen evolution rate s relative to the 142049

control were reported graphically and the values in the table below were estimated from the 1421 figure. Because of negative responses at high conc entrations, the regression in this review 1422 included a non-zero asymptote at high concentration s, but the EC 50 is still defined relative to zero 1423 oxygen evolution, not this negative asymptote, so t hat this would best reflect net production. 1424 Although there was no prior exposure before oxygen evolution measurements were made, the 1425 measurement period was long enough relative to the 5-min induction standard that these results 1426 were accepted. It should be noted that the results are consistent with those for a flask test by the 1427 same authors discussed above. 1428 1429

Nominal Conc ( μ g/L) Author Relative

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

(144-346) Steepness 0.61

(0.42-0.80) 1430 (4) Kallqvist and Romstad 1994 1431 1432 The authors conducted a 72-h flask test of Selenastrum capricornutum growth at multiple 1433 atrazine concentrations, enumerating cell density u sing an electronic particle counter. 1434 Concentrations were nominal. Illumination was cont inuous at 70 μ E/m 2 /s and temperature was 1435 not reported but followed OECD standards of 23±2 C. The authors conducted a regression 1436 analysis of probit-transformed relative SGRs, reporting an SGR EC 50 of 110 μ g/L (95% cl = 99- 1437 121) and an EC 10 of 27 μ g/L. Individual SGRs were not reported, but these two ECs allow 1438 estimating a steepness of 0.90 for the sigmoidal function used in this review. 1439 1440 The authors also conducted 3- to 6-d microplate exp osures of several algal species to atrazine. 1441 The duration of the test varied with species in ord er to be within the period of exponential 1442 growth. Illumination was continuous at 70 μ E/m 2 /s for green algae and 30 μ E/m 2 /s for others. 1443 For these exposures, relative SGRs for each treatme nt were reported graphically. Values 1444 estimated from the figures are provided in the foll owing table, along with EC 50 s and steepnesses 1445 estimated from regression analysis of this data. T he EC 50 for Selenastrum was higher for the 1446 microplate exposures than for the flask tests (alth ough by less than 2-fold), suggesting that the 1447 microplate exposure methodology might involve facto rs that lead to decreased apparent 1448 sensitivity (e.g., nutrient or atrazine reductions, although the former would not be expected if 1449 exponential growth was maintained). These micropla te-based numbers were still compiled for 1450 use in subsequent analyses because the Selenastrum EC 50 s was well within the reported range of 1451 results for this species from other studies; howeve r, this possible source of uncertainty was 1452 recognized in applications of these data. 145350 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
EC 50 201

(177-227) 378

(313-456) 462

(383-556) 494

(415-587) 603

(443-820) 136

(116-159) Steepness 0.79

(0.68-0.90) 0.65

(0.53-0.77) 1.22

(0.80-1.64) 1.15

(0.85-1.45) 0.77

(0.43-1.11) 0.59

(0.52-0.66) 1455 (5) Hoberg 1991a 1456 1457 The author conducted a 96-h flask test of Selenastrum capricornutum growth at multiple atrazine 1458 concentrations, enumerating cell density based on h emocytometer cell counts. The author 1459 provided a data table of cell counts at 1, 2, 3, 4 d at multiple concentrations; initial cell counts 1460 were reported to be 1  10 4 . Concentrations were measured and were stable for 4 d 1461 (concentrations were 2X higher than target due to d iluting error). Light was continuous at 450- 1462 500 ft-c and temperature was 24-25 C. SGRs were ca lculated by EPA for each duration and 1463 concentration and used in regression analyses to es timate EC 50 and steepness. Substantial and 1464 continuing declines in control SGRs were observed, so that the growth rate over 2 d was 24% 1465 less than that over the first day. However, cell c ounts over the first day were lower than desired 1466 for good quantification and the drop in SGR could b e partly due to uncertainty in both the initial 1467 and day 1 cell counts. Therefore, day 2 values wer e selected for the data compilation. 1468 1469 Conc ( μ g/L) Author Cell Counts (/10 4 ) Calculated SGR (1/d)

Target Measured 1d 2d 3d` 4d 1d 2d 3d 4d
0 - 10.0 33.0 71.7 105.0 2.30 1.75 1.42 1.16
32 76 5.0 9.3 49.7 101.7 1.61 1.12 1.30 1.16
63 130 2.3 5.0 31.7 27.7 0.83 0.80 1.15 0.83
120 250 1.7 4.0 1.7 2.0 0.53 0.69 0.18 0.1751

240 510 0.7 2.3 2.0 1.0 <0.00 0.42 0.23 0.00
490 970 0 0 0 0 - - - -
EC 50 ( μ g/L) 109 131 (59-290) 180 161

Steepness 1.13 0.62 (0.18-1.10) 2.61 2.42

1470 (6) Hoberg 1993a 1471 1472 The author conducted a 96-h flask test of Selenastrum capricornutum growth at multiple atrazine 1473 concentrations, enumerating cell density based on h emocytometer cell counts. The author 1474 provided a data table of cell counts at 1, 2, 3, 4 d at multiple concentrations; initial cell counts 1475 were reported to be 0.3  10 4 . Concentrations were measured and were stable for 4 d. Light was 1476 continuous at 300-450 ft-c and temperature was 24 C . SGRs were calculated by EPA for each 1477 duration and concentration from these counts. The control SGR during the first day was 1478 exceptionally high (3.32/d) and dropped to more typ ical levels during subsequent days. In 1479 addition, SGRs were high during the first day even at the highest atrazine concentration (2.30/d 1480 at 450 μ g/L), and also dropped to more typical values durin g subsequent days (<0.1/d). These 1481 atypical results might represent an error in the in itial cell density, the reported value of which 1482 was atypically low and could not be verified. Thes e data were therefore not used. 1483 1484 (7) Caux et al. 1996 1485 1486 The authors conducted a 4-d microplate test of Selenastrum capricornutum growth at multiple 1487 atrazine concentrations, enumerating cell density u sing an electronic particle counter. Light was 1488 continuous at 60 μ E/m 2 /s and temperature was 24 C. The authors only prov ided a 4-d EC 50 for 1489 cell density (26 μ g/L), with no data on actual cell counts at test te rmination for atrazine 1490 treatments. No information was provided on actual treatment concentrations. However, they did 1491 report an initial cell density of 1  10 4 and a final control cell density of 1-2  10 6 , corresponding to 1492 an SGR C of 1.15-1.32/d, a relatively narrow range. Based on the midrange of the reported final 1493 control cell counts, an SGR C of 1.25/d was used for adjusting the cell density- based EC 50 to the 1494 SGR (1.08/d) that would result in half the final co ntrol density. The authors also reported a 1495 probit slope of 4.95 for the cell density vs. log 10 C relationship, which allowed calculation of 1496 other EC p s for cell density (e.g., EC 16 and EC 84 corresponding to ±1 standard deviation in probit 1497 equation) and their corresponding SGRs. Per item A.1.2.4(b) in the protocol, these estimated 1498 SGRs were subject to regression analysis to estimat e the SGR EC 50 and steepness. Confidence 1499 limits are not reported because this regression was not based on independent data points, but on a 1500 conversion of the reported relationship for the cel l density ECs. 1501 1502

p

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

(10 4 cell/ml) Estimated SGR (1/d)
0 1.50 1.25
16 16.4 1.26 1.21
50 26 0.75 1.08
84 41 0.24 0.79552 EC 50 ( μ g/L) 50 Steepness 1.66 1503 (8) Versteeg 1990 1504 1505 The author compared three assays of atrazine effect s on Selenastrum capricornutum growth: a 4- 1506 d flask test enumerating cell density based on hemo cytometer cell counts, 5-min 14 C fixation 1507 after 30-min exposure, and 30-min oxygen evolution. Light was continuous at 86 μ E/m 2 /s for 1508 the flask test, 350 μ E/m 2 /s for the 14 C fixation, and 250 μ E/m 2 /s for the oxygen evolution; 1509 temperature was 24 C. Reported EC 50 s were 50 μ g/L for 4-d cell density, 100 μ g/L for 14 C 1510 fixation, and 380 μ g/L for oxygen evolution. Data for individual treatmen ts were not reported 1511 for atrazine, but were for simazine, another triazi ne herbicide. Measurement variables (cell 1512 densities, 14 C fixation rate, oxygen evolution rate) relative to the control are provided in the 1513 following table for simazine. Simazine showed diff erences among the EC 50 s based on cell 1514 densities, 14 C fixation rate, and oxygen evolution similar to at razine. SGRs based on cell density 1515 effects were also estimated per item A.1.2.5 of the protocol, resulting in an SGR-based EC 50 1516 similar to that for 14 C fixation. This simazine analysis also resulted i n a slope for SGR-based 1517 ECs that was included in the compilation. 1518 1519 Analysis of Versteeg 1990 Results for Simazine

Concentration ( μ g/L) Cell Density

(% of Control) SGR

(% of Control) 14 C Fixation Rate

(% of Control) Oxygen Evolution

(% of Control)
0 100 100 100 100
25 104
50 78 95 103
100 47 86
150 23 73 93
175 59
200 10 58
225 80
300 38 70
500 43
EC 50 ( μ g/L) 95 180 215 437 Steepness 1.58 1.50 1.19 1.26 1520 Based on the experimental procedures and the result s for both atrazine and simazine, this study 1521 was applied as follows regarding EC 50 s: 1522 (a) Because the oxygen evolution assay involved pur ging oxygen, with uncertain effects on 1523 photosynthesis rates and sensitivity to atrazine, t hese data were not used. 1524 (b) Because the 14 C fixation assay included prior exposure, the resul ts will be used. Because of 1525 the short exposure and measurement periods, the EC 50 ( 

### 100 μ g/L ) for 14 C fixation will be 1526 treated as being equivalent to those for SGRs. 1527 (c) The smaller EC 50 for the flask test cell density is likely due to i t being for cumulative growth 1528 over 4 d. Per item A.1.2.7 in the protocol, this had too long a duration to e xtrapolate the cell 1529 density-based EC 50 to an SGR-based EC 50 given the range of estimates for the unknown SGR C 153053

and steepness. However, if the steepness for simaz ine was used, the procedure would result in 1531 the estimates for the SGR EC 50 from 95-115 μ g/L, consistent with that for 14 C fixation. 1532 1533 (9) Larsen et al. 1986 1534 1535 The authors reported EC 50 s for 14 C fixation rates of several algal species, measured over 2 h after 1536 24 h prior exposure to atrazine. Light was continu ous at 400 ft-c and temperature was 24 C. 1537 Because the 24-h prior exposure would result in sub stantially different biomasses among 1538 treatments, this measure is not proportional to the SGR, and because fixation was not cumulative 1539 over the entire period (26 h), it is also not propo rtional to net growth. Assuming that SGR is 1540 approximately constant within each treatment, the b iomass at 24 h would be e SGR and the carbon 1541 fixation over the 2-h measurement period would be p roportional to SGR  e SGR , ignoring the small 1542 amount of growth over that 2 h and assuming that th e measured fixation over the 2 h is 1543 approximately proportional to the SGR. Given this relationship, per item A.1.2.7 of the 1544 protocol, an EC 50 for the SGR can still be calculated from this info rmation, if an SGR C and 1545 steepness can be estimated for use in the following calculations: 1546 1547 (a) Solve for SGR p ( p = percent reduction in SGR relative to control) 1548 corresponding to the EC 50 for 14 C fixation using the equation 1549 0.5 C P SGR SGR P C SGR e SGR e

= ⋅ (i.e., this equation describes what the SGR would 1550 have to be so that the function SGR

⋅ e SGR is at half of its control value). 1551 1552 (b) Calculate p as 100

⋅ (1-SGR p /SGR C ). 1553 1554 (c) Use the estimated steepness for the toxicity re lationship to extrapolate the 1555 known SGR EC p (=EC 50 for 14 C fixation) to the SGR EC 50
. 1556 1557 For Selenastrum capricornutum , the authors reported EC 50 s for 14 C fixation of 34-53 μ g/L (three 1558 tests, average 43). Using this average EC 50 , the procedure described above was conducted 1559 multiple times using the low, middle, and high esti mates for SGR C and steepness identified in the 1560 protocol for this review. The range of the resulta nt SGR EC 50 s was 66-114 μ g/L, narrow enough 1561 to include the median SGR EC 50 (78 μ g/L) in the data compilation. For the other specie s, the 1562 following table summarizes comparable calculations. For green algae, the same ratio (1.88) 1563 between the carbon fixation and SGR EC50s was used as for Selenastrum . For blue-green algae, 1564 the ratio used was 1.43 based on the estimates for SGR C for blue-green algae specified in the 1565 review guidelines. 1566 1567 Test Species 14 C EC 50 ( μ g/L) SGR EC 50

( μ g/L) Selenastrum capricornutum 43 78 Ankistrodesmus sp. 66 119 Chlamydomonas reinhardi 37 67 Scenedesmus obliquus 48 87 Chlorella vulgaris 308 557 Stigeoclonium tenue 175 31754 Ulothrix subconstricta 88 159 Anabaena cylindrica 204 286 1568 (10) Mayer et al. 1998 1569 1570 The authors provided an EC 10 , EC 50 , and EC 90 for SGRs from a standard ISO 8692 toxicity flask 1571 test (3 d) with Selenastrum capricornutum . The actual temperature and light intensity was n ot 1572 reported, but the cited test protocol specified 60- 120 μ E/m2/s and 23±2 C. The author-reported 1573 SGR EC 50 of 

### 164 μ g/L will be used, but the multiple ECs can also be use d to estimate the 1574 steepness parameter for the sigmoidal relationship used in this review. The author also reported 1575 information on effects of light, temperature, pH, a nd nitrogen source on both control growth and 1576 toxic effects. This information indicated the SGR C for this study under standard conditions was 1577 about 

### 1.8/d , but insufficient information was available to use other toxicity information for the 1578 present analysis. This study did document a 10-fol d increase in chlorophyll content per cell due 1579 to atrazine exposure (200 μ g/L), which provides some of the basis for not acce pting this as a 1580 surrogate for biomass. 1581 1582

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

0 1.0
10 17.2 0.90
50 164 0.50
90 688 0.10
EC 50 ( μ g/L) 164 Steepness 0.79 1583 (11) Roberts et al. 1990 1584 1585 The authors conducted a 7-d flask test of Selenastrum capricornutum growth at multiple atrazine 1586 concentrations, enumerating cell density based on h emocytometer cell counts. Concentrations 1587 were nominal. Light was continuous at 2300 ft-c an d temperature was 24 C. The authors 1588 reported the number for the doublings (cell count b asis) over 3 d. This number of doublings was 1589 converted to a factor increase, which was converted to an SGR and subject to regression 1590 analysis. 1591 1592

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

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

50 6.64 100 1.53
100 5.08 33.8 1.17
150 4.10 17.2 0.95
EC 50 ( μ g/L) 163 Steepness 1.22 159355

(12) Parrish, 1978 1594 1595 The author conducted 5-d flask tests of Selenastrum capricornutum and Microcystis aeruginosa 1596 growth at multiple atrazine concentrations, enumera ting cell density based on hemocytometer 1597 cell counts. Concentrations were nominal. Light w as continuous at 400 ft-c and temperature 1598 was 24 C. The author provided a data table of cell counts at 3 and 5 d at multiple concentrations; 1599 initial cell counts were 2  10 4 for Selenastrum and 5  10 4 for Microcystis . SGRs were calculated 1600 from the counts for each duration and concentration . Results for Selenastrum are in the 1601 following table. Because there was not a substanti al decline in the SGR C and results agreed 1602 between the two durations, the 5-d results were sel ected for use. 1603 1604 Conc ( μ g/L) (nominal) Author Cell Counts (/10 4 ) Calculated SGR (1/d)
3d 5d 3d 5d
0 55.8 249.6 1.110 0.965
32 50.6 207.3 1.077 0.928
54 34.5 130.3 0.949 0.835
90 14.6 28.2 0.663 0.529
150 8.9 8.9 0.498 0.300
250 0.7 0.7 <0 <0
EC 50 115 101 (79-130) Steepness 1.47 1.61 (0.67-2.55) 1605 Results for Microcystis are in the following table. Control growth actual ly increased later in the 1606 test and EC 50 s were similar for both durations, so the 5-d resul ts were selected for use. 1607 1608 Conc ( μ g/L) (nominal) Author Cell Counts (/10 4 ) Calculated SGR (1/d)
3d 5d 3d 5d
0 14.3 77.1 0.350 0.547
65 13.2 71.6 0.324 0.532
108 12.9 26.1 0.316 0.330
180 6.5 21.5 0.087 0.292
300 5.1 9.6 0.007 0.130
500 4.7 4.0 0.000 0.000
EC 50 154 164 (95-285) Steepness 4.2 1.25 (0.24-2.46) 1609 (13) Turbak et al. 1986 1610 161156

The authors reported an EC 50 of 

### 70 μ g/L based on a 30-min oxygen evolution assay with 1612 Selenastrum capricornutum , with no additional information to determine the s teepness of the 1613 relationship. The actual temperature and light int ensity was not reported, but the test protocol 1614 specified 400 ft-c and 24 C. The methods descriptio n did indicate that there was some exposure 1615 prior to oxygen measurements, and 30 min is long en ough not to be greatly perturbed by 1616 induction lags of several minutes. Therefore, this EC 50 based on rate of oxygen evolution was 1617 accepted as informative of an SGR EC 50 . They also reported a 59 μ g/L SGR EC 50 based on a 2- 1618 3 week bottle test. Because of the length of this test and the lack of specifics regarding it, this 1619 EC 50 was not used, but this result does not contradict the EC 50 based on oxygen evolution. 1620 1621 (14) Radetski et al. 1995 1622 1623 The authors reported a 72-h EC 50 of 118 μ g/L for Selenastrum capricornutum based on cell 1624 counts (Coulter counter) in a semistatic microplate well test. The actual temperature and light 1625 intensity was not reported, but the cited test prot ocol specified 60-120 μ E/m2/s and 23±2 C. 1626 They also reported an initial cell count of 2  10 4 and a final control cell count of 6.6  10 6

, 1627 corresponding to an SGR C of 

### 1.93/d . At the reported EC 50 , the final cell count would thus have 1628 been 3.3  10 6 , equivalent to an SGR of 1.70, corresponding to a 12% reduction from the control 1629 value (i.e., the growth EC 50 is an SGR EC 12 ). Per protocol item A.1.2.6 , this is too long of an 1630 extrapolation to estimate an SGR EC 50 given the uncertainty in the steepness of the rela tionship, 1631 so an SGR EC 50 was not computed. However, the SGR C was used in the compilation. 1632 1633 (15) Abou-Waly et al. 1991 1634 1635 The authors conducted 7-d flask tests of Selenastrum capricornutum and Anabaena flos-aquae 1636 aeruginosa growth at multiple atrazine concentrations, measur ing weights and chlorophyll 1637 concentrations. Concentrations were nominal. The authors reported SGRs for multiple durations 1638 and concentrations, but only for chlorophyll measur ements. Therefore, these data were not used 1639 in accordance with item (A3) of the protocol. Reported chlorophyll-based growt h rates and 1640 EC 50 s had complex relationships to time and exposure co ncentration, thereby substantiating 1641 concerns about using chlorophyll measurements. For Anabaena , transferring organisms to 1642 control media after the end of the exposure test sh owed rapid recovery of growth rates. 1643 1644 (16) Hughes et al. 1988, Hughes 1986 1645 1646 The authors conducted 5-d flask tests of the growth of two algal species, Anabaena flos-aquae 1647 and Navicula pelliculosa , at multiple atrazine concentrations, enumerating cell density by 1648 electronic particle counting. Concentrations were not measured. Light was continuous, and light 1649 intensity/temperatures were 200 ft-c/24 C for Anaba ena and 400 ft-c/20 C for Navicula. The 1650 author provided data tables of algal cell densities at 3 and 5 d. SGRs were calculated for each 1651 duration and concentration from these counts, based on the reported initial algal cell densities of 1652 2  10 4 cells/ml. 1653 1654 The following table provides results for Anabaena flos-aquae . Because no significant effects of 1655 duration are evident on either control growth rates or the EC 50 , the 5-d results were selected for 1656 further use. 165757 1658 Conc ( μ g/L) (nominal) Author Cell Counts (/10 4 ) Calculated SGR (1/d)
3d 5d 3d 5d
0 23.4 88.0 0.82 0.76

100 16.9 68.4 0.71 0.71
200 16.1 47.5 0.69 0.63
400 8.4 24.7 0.48 0.50
800 6.7 10.2 0.40 0.33
1600 3.9 5.6 0.22 0.21
3200 4.5 5.5 0.27 0.20
EC 50 736 706 (440-1131) Steepness 0.48 0.59 (0.35-0.83) 1659 The following table provides results for Navicula pellculosa . Because control growth was 1660 maintained or even increased through 5 d, the 5-d r esults were selected for further use. 1661 1662 Conc ( μ g/L) (nominal) Author Cell Counts (/10 4 ) Calculated SGR (1/d)
3d 5d 3d 5d
0 26.2 347 0.86 1.03

100 9.4 132 0.53 0.84
200 6.0 29.3 0.37 0.54
400 3.6 7.7 0.20 0.27
800 2.3 2.8 0.05 0.07
1600 1.9 1.9 0.00 0.00
3200 2.1 1.8
EC 50 153 217 (189-248) Steepness 0.80 1.08 (0.87-1.29) 1663 (17) Fairchild et al. 1994, 1998 1664 1665 The authors assessed the effects of four herbicides on plant growth using 4-d tests with six algal 1666 species. Concentrations were not measured in expos ure chambers, but the stock concentrations 1667 were verified. Because chlorophyll was used to qua ntify algal biomass, these data were not used 1668 here per item (A3) of the protocol. 1669 1670 (18) Fairchild et al. 1995, 1997 1671 1672 The authors conducted 4-d tests of Selenastrum capricornutum at multiple atrazine 1673 concentrations (as well as 15 other herbicides). C oncentrations were not measured. Because 167458

chlorophyll was used to quantify Selenastrum biomass, these data were not used here per item 1675 (A3) of the protocol. 1676 1677 (19) Burrell et al. 1985 1678 1679 The authors conducted an 11-d flask tests of the gr owth of Chlorella vulgaris and 1680 Ankistrodesmus braunii at multiple atrazine concentrations, enumerating ce ll density based on 1681 optical density and hemocytometer cell counts. Con centrations were not measured. Illumination 1682 was continuous at 30 μ E/m 2 /s and temperature was 24 C. Initial cell densitie s were 1  10 5 and 1683 exponential cell growth was reported to be maintain ed for the test duration, culminating in a final 1684 cell density of 1.7  10 6 (SGR C =0.26/d) in the Chlorella test and 3.8  10 6 (SGR C =0.33/d) in the 1685 Ankistrodesmus test. The authors graphically reported the percen t reduction in the final cell 1686 density at each atrazine concentration, which were estimated from the figure and reported in the 1687 table below. Based on the final cell densities in the control and the test durations, these percent 1688 reductions in cell density were converted to SGRs a t each atrazine concentration and subject to 1689 regression analyses to determine the SGR EC 50 and steepness. Although this test was longer 1690 than would typically be used for this compilation, the SGR C were low enough (at least in part 1691 due to low light intensities) that total cell densi ties were not so high as to confound results or to 1692 doubt the authors’ statement that exponential growt h was maintained. However, because these 1693 SGR C s were so low they were not used for estimating SGR C s for other studies. 1694 1695 Ankistrodesmus Chlorella

Nominal
Atrazine Conc ( μ g/L) % Reduction

in Growth SGR

(1/d) Nominal

Atrazine Conc ( μ g/L) % Reduction

in Growth SGR

(1/d)

Control 0 .331 Control 0 .258
40 19 .312 10 27 .229
60 49 .269 30 55 .185
70 66 .232 50 67 .157
100 81 .180 70 72 .142
100 75 .131
EC 50 ( μ g/L) 104 (83-131) EC 50 ( μ g/L) 91 (70-118) Steepness 1.41 (0.56-2.36) Steepness 0.47 (0.32-0.63) 1696 (20) Kirby and Sheahan 1994 1697 1698 The authors conducted a 4-d flask test of the growt h of Scenedesmus subspicatus at multiple 1699 atrazine concentrations; concentrations were measur ed. Illumination was continuous at 3500 lux 1700 and temperature was 25 C. The authors only reporte d EC 50 s based on final biomass, without any 1701 information on specific treatments, growth rates, e tc. Initial cell density was 1

⋅ 10 4 cell/ml and 1702 growth was quantified by spectrophotometric absorba nce calibrated to cell density. The EC 50 1703 based on final cell density was 21 μ g/L. Because only an EC 50 was reported and an SGR C was 1704 not reported, estimation of the SGR EC 50 would be per item A.1.2.7 of the protocol, but this was 170559

not done because the extrapolation would be too gre at (the extrapolated value would be 80 μ g/L 1706 with a range of 50 to 150 μ g/L). In addition, this study used optical density near the chlorophyll 1707 a maximum, and so would not be used per the review guidelines. 1708 1709 (21) Millie and Hersh 1987 1710 1711 The authors determined oxygen evolution rates in an electrode chamber for three geographical 1712 races of Cyclotella meneghiana exposed to different atrazine concentrations (unme asured). 1713 Illumination was at 300 μ E/m 2 /s and temperature was 25 C. The authors graphical ly reported the 1714 percent inhibition of oxygen evolution rate relativ e to controls at each concentration, and these 1715 percentages were determined from the graph and subj ect to regression analysis to determine 1716 oxygen evolution EC 50 and steepness. Because these were based on a shor t-term (1 min) oxygen 1717 evolution and because there was prior exposure to e ach atrazine concentration of several minutes 1718 before oxygen evolution was measured, ECs from thes e oxygen evolution rates were accepted as 1719 being comparable to SGR ECs. 1720 1721

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

Minnesota Race Arizona Race Iowa Race

1 94 92
6 95 85
31 80 77
64 89 58 62
95 78 51 54
143 71 39 40
213 53 31 34
277 40 25 21
338 32 15 22
EC 50 ( μ g/L) 225

(202-251) 100

(86-116) 114

(93-141) Steepness 1.00

(0.79-1.20) 0.67

(0.56-0.79 0.65

(0.49-0.81) 1722 (22) Hersh and Crumpton 1989 1723 1724 The authors determined oxygen evolution rates in an electrode chamber of a commercial strain of 1725 Chlamydomonas reinhardii and of three isolates of Chlorella sp. obtained from an 1726 uncontaminated natural system exposed to different atrazine concentrations (unmeasured). 1727 Illumination was at 300 μ E/m 2 /s and temperature was 25 C. Only the EC 50 for the reduction in 1728 oxygen evolution rates relative to control were rep orted (no data on actual oxygen evolution vs. 1729 concentration), but because these were based on a s hort-term (1 min) oxygen evolution and 1730 because there was prior exposure to each atrazine c oncentration of several minutes before 1731 oxygen evolution was measured, these oxygen evoluti on EC 50 s were accepted as being 1732 comparable to SGR EC 50 s. For Chlamydomonas , the EC50 was 

### 45 μ g/L and for Chlorella it 1733 averaged 

### 37 μ g/L across the three isolates (range=36-41). 173460 1735 (23) Stratton 1981, 1984 1736 1737 The author measured 14 C fixation over 3 h and cell growth rate (by optica l density) over 12-14 d 1738 for five algal species exposed to various atrazine and atrazine metabolite concentrations. 1739 Concentrations were unmeasured. For the 14 C fixation tests, light intensity was 7000 lux and 1740 temperature was 20 C; these were not specified for the growth test, but presumably were the 1741 same because these were also the culture conditions . For the growth tests, data other than EC 50 s 1742 at the end of the test were not provided, except fo r A. inaequalis , and this showed non1743 exponential growth throughout the last 10 d of the test and indicated the EC50 was lower at 4-5 d 1744 than later in the text, although the plotted data w ere insufficient to quantify this. In addition, 1745 optical density was measured at wavelengths with su bstantial chlorophyll absorption for at least 1746 three of the species. For these reasons, the ECs f rom the long growth test were not used, and 1747 only the 14 C fixation EC50s were compiled: 1748 1749

Anabaena inaequalis Anabaena

cylindrica Anabaena

variabilis Chlorella

pyrenoidosa Scenedesmus

quadricauda

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

1750 (24) Schafer et al. 1994 1751 1752 The authors conducted a 10-d test of the growth of Chlamydomonas reinhardi in a flow-through 1753 apparatus that maintained exponential cell growth, and reported EC 50 s and EC 10 s for growth at 4, 1754 7, and 10 d. Concentrations were measured. The li ght intensity was 7000 lux with a 14/10 1755 photoperiod and the temperature was 24 C. Informat ion was also provided to allow estimation of 1756 the SGR C to be 1.06/d, but no additional information on act ual or relative cell counts at different 1757 concentrations and times, etc. was given. These EC s were reported to be for growth (not growth 1758 rate) and to be derived per OECD method 201, so pre sumably were based on “area under the 1759 curve” (AUC). They thus do not represent the diffe rence between the biomass at the stated time 1760 and the biomass at test start, but rather the sum o f these differences across the whole time 1761 interval (and thus a measure of the average increas e). Because this system maintained an 1762 exponential growth and because the SGR C is known, the EC 50 s can be used to estimate SGRs for 1763 those concentrations, as summarized in the followin g table. The magnitudes of these estimated 1764 effects on the SGR are insufficient to support a re gression analysis to estimate the SGR EC 50 and 1765 steepness (due to the large extrapolation from 16% effect to 50% effect). However, per item 1766 A.1.2.6

in the protocol, this SGR EC 16 of 51 μ g/L can be extrapolated to an estimate of 

### 141 1767 

### μ g/L for the SGR EC50. 1768 1769

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

concentration is AUC EC 50 SGR (1/d)

Control N/A 1.060
10.2 10 0.99
21 7 0.96
51 4 0.8961 1770 The authors also conducted 3-d flask tests of the g rowth of Chlamydomonas reinhardii and 1771 Scenedesumus subspicatus at different atrazine concentrations, measuring ce ll densities at 1, 2, 1772 and 3 d with an electronic particle counter. Illum ination was continuous at 8000 lux and the 1773 temperature was 20 C. The authors reported 3-d EC 50 s and EC 10 s from these tests, but without 1774 any other effect information (e.g., actual or relat ive cell counts at different concentrations and 1775 times, growth rates). Because of high initial cell densities (2

⋅ 10 5 cell/ml) that would have led to 1776 growth-inhibiting densities based on the SGR C from the flow-through test, the growth EC 50 for 1777 Chlamydomonas (350 μ g/L) cannot be converted to information on an SGR E C. For 1778 Scenedesmus , initial cell densities were low enough (5

⋅ 10 4 cell/ml) to make converting the 1779 growth EC 50 (72 μ g/L) reasonable; however, this would follow item A.1.2.7 of the protocol, and 1780 the duration of the test is too long for this extra polation given uncertainties in both SGR C and 1781 steepness. 1782 1783 (25) Faust et al. 1993 1784 1785 The authors conducted 1-d tests of Chlorella fusca growth at multiple atrazine concentrations. 1786 This was a synchronized culture of 1 generation per day, in which a cell grows during the light 1787 period (14 h) and releases a set of daughter cells in the subsequent dark period (10 h); cell counts 1788 were by Coulter counter. The SGR C for cell number would be ln(# of daughter cells) f or the 1789 control treatment, but this number was not reported . This number can be as low as 4 1790 (SGR C =1.4/d), but in a related paper by Altenburger et a l. (1990), a value of 12 was indicated 1791 (SGR C =2.5/d). The authors reported a probit equation fo r cell reproduction over 24 h. The 1792 points on this probit equation corresponding to -2, -1, 0, 1, and 2 probit units from the median 1793 were calculated to provide EC p s for cell “reproduction” (table below). Then, two sets of SGR 1794 estimates corresponding to these EC p s were calculated based on the two alternatives for the 1795 SGR C , and regression analyses were conducted on each of these sets of SGRs. The resultant 1796 SGR EC50 estimates did not differ markedly (table b elow), so the average of these were 1797 included in the data compilation. 1798 1799

Concentration ( μ g/L) Percent of Control

Reproduction SGR (1/d)

Control 100 1.4 2.4
2.45 97.5 1.381 2.377
6.1 84 1.272 2.243
15.1 50 0.927 1.794
37.2 16 0.398 0.957
92 2.5 0.074 0.224
EC 50 ( μ g/L) 22 29 Steepness 1.08 1.06 1800 (26) Geyer et al. 1985 1801 1802 The authors conducted 4-d flask tests of Scenedesmus subspicatus growth at multiple atrazine 1803 concentrations. The AUC EC 50 was reported to be 110 μ g/L, but other information (effects at 180462

higher concentrations, control SGR) were not report ed. This test does not meet the protocols 1805 stated earlier for extrapolating such an EC 50 to one for the SGR. 1806 1807 (27) Zagorc-Koncan 1996 1808 1809 The author determined the net production of oxygen over 24 h (by liberated gas via Warburg1810 type apparatus) and increased biomass as measured b y chlorophyll over 72 h of Scenedesmus 1811 subspicatus exposed to multiple atrazine concentrations. Ligh t was continuous at 800 lux and 1812 temperature was 20 C. As noted in the protocol, ch lorophyll is not an acceptable surrogate for 1813 biomass. Regarding oxygen evolution, the authors r eported an EC50 of 25 μ g/L, but because of 1814 the lengthy incubation this should be proportional to net biomass gain and not directly related to 1815 effects on SGR. To convert to an SGR-basis require s estimating SGRs based on the oxygen 1816 production and assumptions regarding SGR C . Such estimates based on the range of SGR C for 1817 green algae observed in other studies are included in the table below and subject to regression 1818 analysis. Variation in the assumed SGR C did not cause great variation in the estimated SGR 1819 EC50; because of the low temperature and light inte nsity, the compilation used the value from 1820 the lowest SGR C value. 1821 1822

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

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

Control 1.050 1.350 1.740
0.1 1.038 1.336 1.724
1.0 1.004 1.297 1.681
5.0 0.926 1.208 1.580
10 0.896 1.173 1.54
50 0.431 0.604 0.86

EC 50 ( μ g/L) 39

(27-56) 44 51

Steepness 0.73

(0.45-1.01) 0.72 0.70

1823 (28) Tang et al. 1997 1824 1825 The authors conducted 28 d tests with several algal species. Growth was measured based on 1826 chlorophyll measurements and optical density near t he chlorophyll a maximum. Due to both the 1827 length and the type of measurement, these data were not used. 1828 1829 (29) Gramlich and Frans 1964 1830 1831 The authors conducted a 5-d flask test with Chlorella pyrenoidosa at several atrazine 1832 concentrations. Because biomass was measured by op tical density and because initial values for 1833 biomass were not given, useful results for the comp ilation could not be obtained from this study. 1834 183563

(30) Stratton and Giles 1990 1836 1837 The authors examined the effect of volume and initi al cell density on the toxicity of atrazine to 1838 Chlorella pyrenoidosa , measured by radiocarbon uptake over 24 h. Althou gh these experiments 1839 demonstrated inhibition relative to the control and did include some treatments with 1840 approximately 50% inhibition, only one concentratio n was tested, absolute fixation rates were 1841 not tested, and a variety of processes might be aff ecting the observed inhibition. This precluded 1842 applying these data to the data compilation of inte rest here. 1843 1844 1845 (31) Boger and Schlue 1976 1846 1847 The authors evaluated photosynthesis based on oxyge n evolution rate after several days of 1848 exposure to atrazine and the recovery of photosynth esis upon transfer of exposed algae to clean 1849 medium and control algae to contaminated medium. H owever, only one concentration was 1850 tested and results could not be related to the effe ct concentrations desired in this review. 1851 1852 (32) University of Mississippi 1991 1853 1854 The authors evaluated growth of Selenastrum capricornutum (4 d) at multiple atrazine 1855 concentrations. This test involved methodological and performance problems that precluded its 1856 use, especially for determining SGR-based ECs. Chl orophyll measurements were made, but 1857 were erratic in addition to being not accepted in t he protocol used here. Both cell densities and 1858 weights were also measured, but no initial cell den sity was specified, final densities were based 1859 on inadequate numbers of cells, and many of the mea surements of final weight were negative. 1860 Atrazine effects were evident at 100 μ g/L, but the next lower and higher concentration wa s 10- 1861 fold different (10 and 1000 μ g/L) , precluding any good characterization of dose -response. 1862 1863 A.2.2 Vascular plants 1864 1865 (1) Hughes et al. 1988, Hughes 1986 1866 The authors conducted a 5-d test with the duckweed, Lemna gibba , at multiple atrazine 1867 concentrations, assessing growth by frond count. C oncentrations were not measured. Light was 1868 at 500 ft-c and temperature was 25 C. The authors p rovided data tables of duckweed frond 1869 counts at 3 and 5 d. SGRs were calculated for each duration and concentration from these 1870 counts, based on an initial frond count of 16. The following table summarizes observations and 1871 the estimated SGRs. Because control growth was les s than a factor of two at 3 d, the 5-d results 1872 were selected for further use. 1873 1874 Conc ( μ g/L) (nominal) Average Frond
Counts SGR (1/d)

3d 5d 3d 5d
0 29.0 49.3 0.198 0.225
100 27.0 40.0 0.174 0.183
200 19.7 29.7 0.069 0.12464 400 16.3 21.7 0.006 0.061
800 16.0 16.3 0.000 0.004
1600 1.9 1.9
3200 2.1 1.8
EC 50 169 224 (151-332) Steepness 2.17 1.14 (0.43-1.85) 1875 1876 (2) Hoberg 2007 1877 1878 The author conducted growth tests with isolated sho ots of Elodea canadensis at multiple atrazine 1879 concentrations and at zero, dim (500 lux), and opti mal (6000 lux) light levels (only the higher 1880 light level is appropriate for this review). Conce ntrations were measured and temperature was 1881 20-25C. Data tables were provided for individual s hoot lengths at 0 and 14 d and individual 1882 shoot dry weights at 14 d for multiple concentratio ns. Only dry weight is considered here (shoot 1883 lengths were a poor surrogate for growth because su bstantial shoot elongation was observed in 1884 low light and at high atrazine concentrations were no growth in weight was observed). This 1885 requires having an estimate of the initial dry weig ht, which the author reported for a separate 1886 initial sample of shoots as being 0.1346 g/shoot. It was assumed that this weight applied to the 1887 average initial shoot length (8.3 cm/shoot) so that the initial weight per cm 0.0162 g/cm. This 1888 factor was used to estimate the initial weights for each replicate tanks based on the initial shoot 1889 lengths within that tank, allowing SGRs to be compu ted for each tank. The following table lists 1890 the reported final weights, the estimated initial w eights, and the resultant shoot weight SGRs, 1891 along with the EC50 and steepness parameter estimat ed by regression analysis. This regression 1892 analysis is relatively uncertain because the lowest treatment concentration corresponds to an 1893 EC68, leaving an absence of data at low to moderate effect. However, the estimated steepness is 1894 similar to others reported for this species (Table XX) so the EC50 estimate was still deemed 1895 acceptable for us. 1896 1897
Measured Concentration

( μ g/L) Estimated Initial Average

Shoot Weight (g dwt) Reported Final Average Shoot Weight (g dwt) Shoot Weight SGR (1/d) 0 0.133,0.120,0.129,0.121 0.420,0.415,0.420,0.471 0 .082,0.089,0.084,0.097 464 0.126,0.131,0.141,0.129 0.166,0.218,0.225,0.178 0.020,0.036,0.034,0.023 853 0.137,0.139,0.136,0.153 0.213,0.179,0.184,0.185 0.031,0.018,0.022,0.009 1761 0.131,0.133,0.149,0.136 0.128,0.166,0.214,0.12 6 -0.001,0.016,0.026,-0.005 Regression EC 50 ( μ g/L) 204 (59-600) Regression Steepness 0.52 (0.15-0.98) 1898 (3) Hoberg 1991b 1899 190065

The author conducted a 7-d test of Lemna gibba growth at multiple atrazine concentrations; 1901 concentrations were measured. Light was continuous and temperature was 24 C. The author 1902 provided a data table of frond counts at 3, 6, and 7 d at multiple concentrations; initial frond 1903 counts were 15. SGRs were calculated for each dura tion and concentration from the counts and 1904 regression analyses were conducted on these SGRs. Because of the absence of growth on day 7, 1905 the 6-d values were compiled. 1906 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
EC 50 230 202 (174-234) 189

Steepness 1.14 1.24 (0.85-1.62) 1.24

1908 (4) Hoberg 1993b 1909 1910 The author conducted a 14-d test of Lemna gibba growth at multiple atrazine concentrations. 1911 Concentrations were measured. Light was at 400 ft- c and temperature at 24 C. The author 1912 provided a data table of frond counts at 3, 6, 9, 1 2, and 14 d and dry weight at 14 d. Initial frond 1913 counts were 15. Initial dry weight was unreported but it was assumed for this analysis that the 1914 initial dry weight per frond was equal to that in t he control at the end (=110 mg/529=0.208 1915 mg/frond), so that the initial dry weight would be 3.12 mg. SGRs were calculated for each 1916 duration and concentration from the counts and dry weights and regression analyses were 1917 conducted on these SGRs. Based on frond count, som e reduction in control growth rate occurred 1918 after 9 d, but did not appreciably affect estimated SGR EC 50 s. For the 14-d data, dry weights 1919 resulted in an EC 50 29% lower than that based on frond count. This is likely attributable to the 1920 lower dry weight/frond at higher atrazine concentra tions (i.e., smaller fronds due to atrazine 1921 effects), but also could be contributed to by overe stimation of the initial dry weight if control 1922 fronds at the end were on average larger than those at the beginning. This illustrates a possible 1923 weakness in the use of frond counts for duckweed te sts, but also a weakness in most tests 1924 regarding measuring initial weights. Due to it bei ng a direct measure of biomass rather than an 1925 indicator, the dry weight-based results were compil ed. 1926 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 .25 4 .254
3.4 35.3 91.0 244 426 440 96 .285 .300 .310 .279 .2 41 .24566

7.2 36.0 89.0 253 475 470 117 .292 .297 .313 .288 . 246 .259 17 36.3 76.0 202 334 364 77 ,295 .270 .289 .259 .22 8 .229
47 32.3 71.7 163 303 310 17 .256 .261 .265 .250 .21 6 .222
92 26.7 45.0 79 117 117 16 .192 .183 .185 .171 .147 .116
240 20.7 25.7 35 36 43 5 .107 .090 .094 .073 .075 . 036
EC 50 156 133 130 129 134 93 (72-120) Steepness 0.87 0.85 0.85 1.09 0.90 1.33 (.58-2.07) 1928 1929 (5) Hoberg 1993c 1930 1931 The author conducted a 14-d test of Lemna gibba growth at multiple atrazine concentrations. 1932 Concentrations were measured. Light was continuous at 450-500 ft-c and temperature was 25 C. 1933 The author provided a data table of frond counts at 3, 6, 9, 12, and 14 d and dry weight at 14 d. 1934 Initial frond counts were 15. Initial dry weight w as unreported but it was assumed for this 1935 analysis that the initial dry weight per frond was equal to that in the control at the end, resulting 1936 in a estimated initial dry weight of 3.7 mg. SGRs were calculated for each duration and 1937 concentration from the counts and dry weights and r egression analyses were conducted on these 1938 SGRs. As for Hoberg 1993b, dry weight-based SGRs s howed a lower EC 50 and higher steepness 1939 than frond count-basis, and were selected for the c ompilation. 1940 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
EC 50 61 63 67 82 81 49 (42-58) Steepness 0.78 0.95 0.91 0.099 0.96 1.71 (.82-2.60) 1942 (6) Desjardin et al., 2003 1943 1944 The authors conducted tests on Lemna gibba growth at multiple atrazine concentrations and for 1945 multiple durations (1-14 d) followed by examination of recovery. Concentrations were 1946 measured. Temperature was 24-25 C and light intens ity 4250-5750 lux. Rapid recovery was 1947 demonstrated, but the analyses here are concerned w ith effects during the exposure period. 194867

Furthermore, this analysis will be restricted to a 7-d test, because both the longer tests (9-14 d) 1949 produced less than a 20% reduction in the SGR and t he 1-3 d tests provided uncertain results due 1950 to the short duration and limited concentration ran ge. The authors provided data at day 2, 4, and 1951 7 d and dry weight at 7 d at multiple concentration s. Initial frond counts were 15 at day -1 and 1952 were 20-21 at the start of exposure (this 1 d perio d of growth was done to identify/discard 1953 chambers that showed little or no growth; despite t his precaution, one control replicate had poor 1954 enough growth to be excluded as an outlier). The i nitial dry weight was estimated to be 2.8 mg 1955 based on the average dry weight/frond in the no-eff ect concentrations at the end of the exposure. 1956 SGRs were calculated for each duration and concentr ation from the counts and dry weights. 1957 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
EC 50 159 165 116 90 (75-108) Steepness 1.09 1.05 1.06 1.18 (.75-1.62) 1959 (7) Fairchild et al. 1994, 1998 1960 1961 The authors assessed the effects of four herbicides on plant growth using 4-d tests with Lemna 1962 minor

and 14-d tests with Ceratophyllum dermersum

, Elodea canadensis

, Myriophyllum 1963 heterophyllum , and Najas sp. Temperature was 25 C and light was 60 μ E/m 2 /sConcentrations 1964 were not measured in exposure chambers, but the sto ck concentrations were verified. The 1994 1965 report provided detailed biomass measurements absen t in the 1998 journal article. 1966 1967 Lemna Initial frond counts were 12 in each replicate and final frond counts are listed in the 1968 following table. The limited duration resulted in limited growth (barely 2-fold in the control) 1969 that makes these results rather uncertain, particul arly based on frond counts. 1970 1971

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

in replicates SGRs

(1/d)

0 34,26,23 0.260,0.193,0.163 37.5 25,25,19 0.184,0.115,0.163 75 19,20,15 0.128,0.056,0.101 150 15,17,20 0.087,0.128,0.092 300 16,18,22 0.101,0.152,0.11068 600 12,14,14 0.000,0.038,0.026
EC 50 ( μ g/L) 114 (34-390) Steepness 0.42 (0.06-0.79) 1972 Najas: Replicates were created by placing natural pond sed iments from Najas beds in beakers, 1973 from which plants germinated. Plants were grown fo r approximately 2 weeks to approximately 3 1974 cm in height, at which time the 14-d chemical expos ure began. After the exposure, plants were 1975 sieved and wet weights were determined. Initial we t weights were not determined, but based on 1976 the similarity in the average weights in the highes t three treatments (following table) it was 1977 assumed that these treatments had zero net growth a nd SGRs were estimated based on an initial 1978 wet weight of 69.5 mg, the overall average final we ight of these treatments. Given the number 1979 of replicates with lower final weights, the initial weights obviously varied considerably across 1980 replicates, but by basing SGR on the mean weight ac ross replicates, this variability is reduced 1981 enough to produce a clear dose-response. To the ex tent that the highest three treatments did not 1982 have zero net growth the estimated EC50 will be bia sed, but substantial bias would be unlikely 1983 because (a) if substantial positive growth was occu rring a concentration effect should be evident 1984 and (b) if substantial negative growth was occurrin g this would imply a high initial weight 1985 incompatible with the information on control growth (i.e. a disproportionate amount of control 1986 growth in the two weeks prior to exposure compared to the 2 weeks of exposure). 1987 1988

Nominal
Atrazine Conc ( μ g/L) Final wwt

for replicates (mg) Final mean wwt

for treatment (mg) SGRs

(1/d)

Control 306,111,122 180 0.068 Solvent Control 285,168,57 170 0.064 8.4 66,170,185 140 0.050 18.8 164,68,57 96 0.023 37.5 57,91,55 68 -0.001 75 65,7,137 70 +0.001 150 49,75,90 71 +0.002
EC 50 ( μ g/L) 14.5 (12.3-17.2) Steepness 1.67 (1.00-2.33) 1989 Ceratophyllum: The authors provided wet weights for each replica te at 0, 7, and 14 d, allowing 1990 calculation of SGRs and regression analysis of thes e SGRs to determine the EC 50 and steepness 1991 of the SGR vs concentration relationship. There wa s nearly a doubling of weight in the controls 1992 over the 14-d, allowing sufficient growth so that e ffects were apparent and could be quantified. 1993 1994 1995 1996 199769 Nominal
Atrazine Conc ( μ g/L) Initial wwt

for replicates (mg) Final (14 d) wwt

for replicates (mg) SGR

for replicates (1/d) Control 1578,1202,1730 2292,2409,2735 0.027,0.050,0 .033 Solvent Control 1310,1746,1622 2010,2965,2477 0.031 ,0.038,0.030 18.8 1209,937,1232 1476,1262,1798 0.014,0.021,0.027 37.5 1960,1777,1089 2281,2076,1378 0.011,0.011,0.01 7 75 2649,1062,2420 2410,1078,2434 -0.007,0.001,0.000 150 1362,1322,1482 1454,1446,1415 0.005,0.006,-0.00 3 300 1166,1516,878 1102,1563,1023 -0.004,0.002,0.010
EC 50 ( μ g/L) 24 (14-42) Steepness 0.81 (0.12-1.50) 1998 Myriophyllum: The authors provided wet weights for each replica te at 0, 7, and 14 d, allowing 1999 calculation of the SGR for each replicate. However , the growth in controls and in NOECs was 2000 too small and variable for good quantification of e ffects on SGR. At day 14 (table below), the 2001 weight gain of individual replicates varied from -4 -16% (average 8%) in the control, 1-31% 2002 (13%) in the solvent control, from 11-16% (15%) at 37.5 μ g/L, and 2-26% (15%) at 75 μ g/L. In 2003 addition, at day 7, the weight gains were 12-17% (1 5%) in the controls, 25-33% (28%) in the 2004 solvent controls, 13-16% (13%) at 37.5 μ g/L, and 6-21% (11%) at 75 μ g/L. These data illustrate 2005 not just a small amount of growth and great variabi lity relative to the average net growth, but 2006 also no or negative growth in most replicates durin g the second week, which the authors also 2007 noted in other experiments. In addition, there is an inconsistency between the 7- and 14-d data in 2008 that the 14-d data show no difference among the con trols and the two lowest concentrations, 2009 whereas the 7-d data indicate better growth in the solvent controls relative to the control without 2010 solvent and the two lowest concentrations. Althoug h there are clear effects at 150 μ g/L and 2011 above, there is not a good reference against which to quantify effects on the SGR. This 2012 underscores the requirement in the protocol that co ntrol growth be large and consistent enough to 2013 quantify ECs with reasonable precision. The most that can be inferred from this test is that 37.5 2014 and 75 μ g/L are apparently NOECs and the SGR EC 50 is probably ≈ <150 μ g/L. 2015 2016

Nominal

Atrazine Conc ( μ g/L) Initial wwt

for replicates (mg) Final (14 d) wwt

for replicates (mg) SGR

for replicates (1/d) SGR

for treatment (1/d) Control 3330,4547,3200 3696,4379,3712 0.007,-0.003, 0.011 0.005 Solvent Control 3137,3767,3817 3184,3981,5017 0.001 ,0.004,0.020 0.008 37.5 2600,3077,3084 3021,3402,3603 0.011,0.007,0.01 1 0.010 75 3046,2872,4122 3895,3382,4197 0.018,0.012,0.001 0.010 150 3262,3854,4414 3782,3726,4454 0.011,-0.002,0.00 1 0.003 300 3559,3039,2756 3359,2074,2829 -0.004,-0.027,0.0 02 -0.010 600 2812,3748,3341 1877,3363,2992 -0.029,-0.008,-0. 008 -0.015
EC 50 ( μ g/L) < ≈ 15070 Steepness 2017 Elodea: The authors provided both wet weights for each re plicate at 0, 7, and 14 d, allowing 2018 calculation of the SGR for each replicate. However , as for Myriophyllum , the control growth 2019 was very small, averaging only about 15% over the t wo weeks. Although, this growth was not as 2020 variable as for Myriophyllum , it still is a questionable reference against whic h to quantify effects 2021 on SGRs. In addition, the lowest treatment concent ration produced no growth on average, and 2022 negative growth became progressively greater at hig her concentrations, so that ECs for SGR 2023 could not be quantified even if the controls were g ood references for quantifying the SGR. The 2024 most that can be inferred from this test is that th e SGR EC 50 is <38 μ g/L, although even this 2025 might be confounded by the low control growth. 2026 2027 2028

Nominal

Atrazine Conc ( μ g/L) Initial wwt

for replicates (mg) Final (14 d) wwt

for replicates (mg) SGR

for replicates (1/d) SGR

for treatment

Control 4820,5564,6866 5949,6345,7802 0.015,0.009,0 .009 0.014 Solvent Control 5554,5672,6624 6336,6140,7016 0.009 ,0.006,0.004 0.008 37.5 7146,3370,5500 7258,3232,5556 0.001,-0.003,0.0 01 0.001 75 6028,5477,6477 5435,5178,6478 -0.007,-0.004,0.00 0 -0.002 150 4941,4929,4992 4778,4851,5554 -0.002,-0.001,0.0 07 -0.002 300 6080,5937,5398 5575,5543,5087 -0.006,-0.005,-0. 004 -0.004 600 6902,7160,6200 3960,6302,5605 -0.040,-0.009,-0. 007 -0.018
EC 50 ( μ g/L) <37.5 Steepness 2029 (8) Fairchild et al. 1995, 1997 2030 2031 The authors conducted 4-d tests of Lemna minor growth at multiple atrazine concentrations (as 2032 well as 15 other herbicides). Concentrations were not measured. For Lemna , the reported EC 50 2033 of 153 μ g/L was based on growth (frond count basis), and in sufficient information was provided 2034 to convert this to a growth rate basis. Based on a control growth rate of 0.21/d for identical 2035 methodology used above by Fairchild et al. (1994, 1 998) this EC 50 would correspond to an EC 82
. 2036 Because this extrapolation was greater than allowed in the protocol, this data just indicate that 2037 the SGR EC 50 is <153 μ g/L, which does not contradict the results of Fairc hild et al. (1994, 1998). 2038 2039 (9) Kirby and Sheahan 1994 2040 2041 The authors conducted a 10-d test of the growth of Lemna minor at multiple atrazine 2042 concentrations; concentrations were measured. Temp erature was 25 C and light intensity was 2043 3500 lux. The authors only reported EC50s based on final biomass, without any information on 2044 specific treatments, growth rates, etc. The initia l biomass was 10 fronds and growth was 2045 quantified by chlorophyll, frond count, and fresh w eight, with the respective EC 50 s being 56, 60, 2046 and 62 μ g/L. Using the average SGR C from other studies with Lemna (0.27/d, range 0.21- 2047 0.38/d), the EC 50 for frond count would correspond to an EC 25 for SGR. Using the average 204871

steepness for SGR vs. concentration from other stud ies with Lemna (1.0 for frond count increase, 2049 1.4 for weight increase), the SGR EC 50 would then be 

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

### 95 2050 

### μ g/L based on weight. 2051 2052 (10) University of Mississippi 1991 2053 2054 The authors evaluated growth of Lemna gibba (14 d), and Elodea canadensis (10 d) at multiple 2055 atrazine concentrations. These assays entailed met hodological and performance problems that 2056 precluded their use, especially for determining SGR -based ECs. Chlorophyll measurements were 2057 erratic in addition to being not accepted in the pr otocol used here. For Lemna , both frond counts 2058 and weights were measured, but frond counts indicat ed poor control growth (an SGR of 0.1/d, 2059 compared to 0.2-0.4/d in other studies), no initial weights were given, and final weights had poor 2060 precision. For Elodea , final dry weights did show a substantial effect o f atrazine, but initial 2061 weights were not given, so that growth could not be assessed either as a rate or an absolute 2062 amount. For both species, atrazine effects were ev ident at 100 μ g/L, but the next lower and 2063 higher concentration was 10-fold different (10 and 1000 μ g/L) , precluding any good 2064 characterization of dose-response. 2065 2066 (11) Forney and Davis 1981; Davis 1980, Forney 1980 2067 2068 The authors evaluated growth of Elodea canadensis

, Myriophyllum spicatum

, Potamogeton 2069 perfoliatus , and Vallisneria americana in exposures of 3-9 weeks to multiple atrazine 2070 concentrations. Depending on the experiment and te st species, light varied from 3 to 170 2071 μ E/m 2 /s (14/10 h photoperiod) and temperature was 20-30 C. Unfortunately, most of the 2072 evaluations were of shoot length increase, which as discussed above is a questionable surrogate 2073 for growth. In three instances, useful information regarding the SGR EC 50 could be obtained: 2074 2075 For Potamogeton , in one experiment, dry weight was measured in add ition to shoot length. 2076 However, the nature of the weight measurements was unclear (gross weight vs. growth, how 2077 much of plant included) and the authors noted that food reserves in the tuber used to sprout 2078 Potamogeton would partially mask herbicide effects, so that th ese weight measurements would 2079 overestimate ECs. This experiment also showed atra zine-dependent mortality at concentrations 2080 of 32 μ g/L and above. The following table shows the avera ge dry weight of plants (at death or 2081 end of test for survivors), the percent survival, a nd the product of dry weight and survival as an 2082 estimate of live biomass at the end of the study. For issues regarding weight effects already 2083 noted, this product might still underestimate bioma ss production, but was considered adequately 2084 informative of atrazine effects on the SGR of a pop ulation of this plant. A regression analysis 2085 was thus conducted on this product and used for the compilation. 2086 2087

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

Dry Weight % Survival % of Control
Biomass

0 100 100 100
10 86 100 86
32 86 73 6372 100 74 62 46
320 55 0 0
EC 50 ( μ g/L) 63 Steepness 0.69 2088 For Vallisneria , leaf length was measured and was used as a surrog ate for growth because it 2089 would be less susceptible than shoot length to elon gation with little or no weight increase. Even 2090 with this acceptance, most data could not be used b ecause the authors noted that effects of 2091 atrazine were not evident early in the experiments, likely due to food reserves in the tubers, and 2092 that some experiments had light intensities high en ough to inhibit leaf growth in favor of tuber 2093 and lateral shoot development. Thus, analysis here was restricted to the latter part of one test 2094 that the authors reported as being most informative about atrazine effects. The following table 2095 provides the percentage increase in leaf length dur ing the last week of this experiment, which 2096 should be approximately proportional to the SGR. I n another experiment with insufficient data 2097 for analysis here, there was information on the rat io of plant weight to leaf length as a function of 2098 atrazine, which did indicate some thinning of the l eaves due to atrazine. The following table 2099 includes those ratios, which provided a basis for e stimating weight based on leaf length (only 2100 three measured values – so interpolated value used for 32 μ g/L and possible extrapolated values 2101 for 1000 μ g/L). This resulted in a decrease in the SGR EC50 of about 28%. 2102 2103

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

Leaf Length
in Week 6 Dry Weight/

Leaf Length (fraction of control) Estimated
% Increase in Weight
0 14.3 1.00 14.3
32 9.8 0.97 9.5
100 10.2 0.94 9.6
320 5.9 0.82 4.8
1000 3.6 0.7-0.8 2.5-2.9
EC 50 ( μ g/L) 195 140-141 Steepness 0.36 0.39-0.41 2104 For Elodea , in one experiment dry weight increase was measure d. The following table provides 2105 these data. Because initial and final dry weights weren’t provided, SGRs cannot be calculated, 2106 but the slow growth rates of these plants should ma ke the net increase proportional to SGR. 2107 Because of the widely space concentrations, the est imated parameters are uncertain, but clearly 2108 indicate the SGR EC50 to be less than 100 μ g/L. 2109 2110

Nominal
Atrazine Conc ( μ g/L) Average Increase

in Plant Dry Wt. (mg)
0 37
10 28
100 17
1000 1173 EC 50 ( μ g/L) 65 Steepness 0.28 2111 (10) Hinman 1989 2112 2113 The author tested the effects of atrazine on both r oot and shoot growth of Hydrilla verticillata in 2114 both water and sediment exposures (14 d). Concentr ations were nominal, light was 40-50 2115 μ E/m 2 /s, and temperature was 25 C. Both shoot and root growth was monitored by increase in 2116 length. Increases in shoot length are subject to q uestions about elongation without increasing 2117 weight, but this is not true for root growth, which should still be an indicator of atrazine effects 2118 on primary production. The following table compare s the data on root and shoot growth for the 2119 water-based exposures. Shoot lengths do indicate a higher threshold for effects, but then a 2120 steeper decline, with the EC50 being about 80% high er than for root length. 2121 2122 2123

Nominal
Atrazine Conc ( μ g/L) Shoot Length

Increase (% of Control) Root Length

Increase (% of Control)
0 100 100
16 97 98
80 127 71
160 83 25
800 5 25
1600 5 8
EC 50 ( μ g/L) 222 118 Steepness 2.26 0.6 2124 212574 212675APPENDIX B.EXPERIMENTAL ECOSYSTEM DATA213076Table B1. Summary of experimental ecosystem studies used in development of PATILOC. ID# identifies treatment and crossreferences exposure time-series provided in Table B2. Effect is binary (yes/no) regarding whether substantial impact on plantcommunity occurred.ID #Duration(d)Initial Conc.(μg/L Atrazine)SignificantEffect?Reference1 365 500 Y Carney 1983; Kettle et al. 1987; deNoyelles et al. 1989; deNoyelles et al. 19942 365 20 YCarney 1983; Kettle et al. 1987; deNoyelles et al.1989; deNoyelles et al. 1994,deNoyelles & Kettle 1980, Dewey 19863 63 500 Y deNoyelles et al. 1982; deNoyelles et al. 19894 365 100 Y deNoyelles et al. 1989 Carney 19835 340 200 Y deNoyelles et al. 1989 Carney 19837 56 80 Y Hamilton et al. 19878 56 140 Y Hamilton et al. 19879 96 100 Y Hamilton et al. 198810 96 100 Y Herman et al. 1986; Hamilton et al. 198913 53 430 Y Stay et al. 198514 53 820 Y Stay et al. 198515 53 3980 Y Stay et al. 198517 7 100 Y Brockway et al. 198418 12 500 Y Brockway et al. 198419 12 5000 Y Brockway et al. 198422 15 15 Y Detenback et al. 199623 43 25 Y Detenback et al. 199624 32 50 Y Detenback et al. 199625 17 79 Y Detenback et al. 199626 14 100 Y Hamala and Kollig 198527 30 1000 Y Johnson 198628 21 10 Y Kosinski 1984; Kosinski and Merkle 198429 21 1000 Y Kosinski 1984; Kosinski and Merkle 198430 21 10000 Y Kosinski 1984; Kosinski and Merkle 198431 12 24 Y Krieger et al. 198832 12 134 Y Krieger et al. 198833 7 10000 Y Moorhead and Kosinski 198677Table B1 (continued).ID # Duration(d)Initial Conc.(μg/L Atrazine)SignificantEffect?Reference34 21 337 Y Pratt et al. 198835 42 204 Y Stay et al. 198936 42 500 Y Stay et al. 198937 42 1000 Y Stay et al. 198938 42 5000 Y Stay et al. 198939 55 50 Y Brockway et al. 198440 15 100 Y Brockway et al. 198441 360 100 Y deNoyelles et al. 198942 360 200 Y deNoyelles et al. 198944 21 100 Y Kosinski 1984; Kosinski and Merkle 198445 7 100 Y Moorhead and Kosinski 198646 7 1000 Y Moorhead and Kosinski 198647 53 53 Y Stay et al. 198548 53 84 Y Stay et al. 198549 53 170 Y Stay et al. 198550 42 100 Y Stay et al. 198951 12 50 Y Brockway et al. 198452 63 20 Y deNoyelles et al. 1982; deNoyelles et al. 198953 30 10 N Johnson 198654 30 100 N Johnson 198658 18 1 Y Lampert et al 198958b 42 0.1 Y Lampert et al 198959 21 32 Y Pratt et al. 198860 21 110 Y Pratt et al. 198861 42 20 N Stay et al. 198962 35 5 N van den Brink et al. 199563 7 0.5 N Brockway et al. 198464 7 5 N Brockway et al. 198465 29 0.5 N Brockway et al. 198466 70 5 N Brockway et al. 198478Table B1 (continued).ID # Duration(d)Initial Conc.(μg/L Atrazine)SignificantEffect?Reference67 14 5 N Gruessner and Watzin 199668 20 1 N Gustavson and Wängberg 199569 20 20 N Gustavson and Wängberg 199570 20 10 N Gustavson and Wängberg 199571 28 2 N Jurgensen and Hoagland 199072 28 30 N Jurgensen and Hoagland 199073 28 100 N Jurgensen and Hoagland 199075 30 25 N Lynch et al. 198576 21 3.2 N Pratt et al. 198877 21 10 N Pratt et al. 198878 30 25 Y Rohr and Crumrine, 200579 28 117 Y Rohr et al., 200880 36 6.4 N Relyea, 200981 173 84 Y Knauert et al., 2008; Knauert et al., 200982 23 10 YBerard et al. 1999a, Berard et al. 1999b, Berard and Benninghoff 2001, Sequinet al. 2001b, Leboulanger et al. 200183 40 30 N Seguin et al. 2001a84 40 2 N Seguin et al. 2001a85 40 30 Y Seguin et al. 2001b86 40 2 Y Seguin et al. 2001b87 25 30 Y Seguin et al. 200288 7 148 Y Downing et al. 200489 7 24.3 Y Downing et al. 200490 25 207 N Boone and James 200395 51 20 N Diana et al. 200096 51 196 Y Diana et al. 200097 51 2036 Y Diana et al. 200098 42 25 N McGregor et al. 200899 42 50 N McGregor et al. 2008100 42 100 Y McGregor et al. 2008101 42 250 Y McGregor et al. 200879Table B2. Atrazine exposure time-series for experimental ecosystem treatments, with ID# as specifiedin Table B1.ID #1 ID#2 ID#3 ID#4 ID#5 ID#7 ID#8 ID#9Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)0 500 0 20.0 0 500 0 100 0 200 1 80 1 140 1 10010 525 10 16.0 2 490 10 90 20 190 3 79 56 110 5 11720 490 20 16.0 25 465 20 85 40 120 5 78 14 10840 350 40 16.0 30 453 40 90 60 160 7 78 20 10770 490 70 15.0 55 390 70 80 70 140 9 77 24 87100 400 100 12.0 63 360 100 75 80 150 11 76 34 105130 400 130 14.0 130 70 105 120 13 76 37 142180 375 180 15.0 180 70 130 120 15 75 42 148285 250 285 7.0 285 35 160 110 17 75 54 132330 200 330 5.0 330 30 190 140 19 74 68 115365 160 365 4.0 365 25 220 120 21 73 96 53250 100 23 73290 90 25 72340 50 27 7129 7131 7033 7035 6937 6939 6841 6743 6745 6647 6649 6551 6553 6455 64802135Table B2, Page 2.ID #10 ID#13 ID#14 ID#15 ID#17 ID#18 ID#19 ID#22Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)1 100 0 430 0 820 0 3980 0 100 0 500 0 5000 1 15.05 117 21 264 21 505 21 1890 1 100 1 498 1 4979 2 13.614 108 46 223 46 443 46 1390 2 99 2 496 2 4958 3 12.920 107 53 198 53 417 53 1540 3 99 3 494 3 4937 4 12.324 87 4 98 4 492 4 4917 5 11.734 105 5 98 5 490 5 4896 6 11.137 142 6 98 6 488 6 4876 7 10.642 148 7 97 7 486 7 4855 8 10.154 132 8 484 8 4835 9 9.668 115 9 481 9 4815 10 9.196 53 10 479 10 4794 11 8.711 477 11 4774 12 8.312 475 12 4754 13 7.914 7.515 7.181Table B2, Page 3.ID #23 ID#24 ID#25 ID#26 ID#27 ID#28 ID#29 ID#30Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)1 25.1 1 50 1 79 0 100 0 1000 1 10.0 1 1000 1 100003 21.6 3 43 2 72 14 100 2 992 21 10.0 2 648 2 64845 19.6 5 39 3 68 4 983 3 522 3 52217 17.7 7 35 4 65 6 975 4 420 4 42059 16.1 9 32 5 62 8 967 5 339 5 338611 14.6 11 29 6 59 10 959 6 273 6 272613 13.2 13 26 7 56 12 951 7 220 7 219515 11.9 15 24 8 53 14 943 8 177 8 176817 10.8 17 21 9 51 16 935 9 142 9 142419 9.8 19 19 10 48 18 927 10 115 10 114621 8.9 21 18 11 46 20 919 11 92 11 92323 8.0 23 16 12 44 22 912 12 74 12 74325 7.3 25 14 13 42 24 904 13 60 13 59927 6.6 27 13 14 40 26 897 14 48 14 48229 6.0 29 12 15 38 28 889 15 39 15 38831 5.4 31 11 16 36 30 882 16 31 16 31333 4.9 17 34 17 25 17 25235 4.4 18 20 18 20337 4.0 19 16 19 16339 3.6 20 13 20 13141 3.3 21 11 21 10643 3.082Table B2, Page 4.ID #31 ID#32 ID#33 ID#34 ID#35 ID#36 ID#37 ID#38Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)0 24.0 0 134 0 10000 0 337 1 204 1 492 1 961 1 492912 24.0 12 134 1 9958 21 337 3 199 3 474 3 931 3 48062 9916 5 196 5 463 5 918 5 47583 9875 7 193 7 452 7 907 7 47104 9833 9 190 9 441 9 895 9 46625 9792 11 187 11 430 11 883 11 46156 9751 13 184 13 420 13 872 13 45697 9710 15 181 15 410 15 860 15 452317 178 17 400 17 849 17 447719 175 19 390 19 838 19 443221 172 21 381 21 827 21 438823 169 23 372 23 816 23 434425 167 25 363 25 806 25 430027 164 27 354 27 795 27 425729 161 29 346 29 785 29 421431 159 31 337 31 775 31 417133 156 33 329 33 765 33 412935 154 35 321 35 755 35 408837 151 37 314 37 745 37 404739 149 39 306 39 735 39 400641 146 41 299 41 726 41 396683Table B2, Page 5.ID #39 ID#40 ID#41 ID#42 ID#44 ID#45 ID#46 ID#47Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)0 50 0 100 0 100 0 200 1 100 1 100 1 1000 0 5255 50 15 100 180 70 180 140 2 65 2 99 2 992 21 48360 25 360 50 3 52 3 99 3 988 46 414 42 4 98 4 983 53 345 34 5 98 5 9796 27 6 98 6 9757 22 7 97 7 9718 189 1410 1211 912 713 614 515 416 317 318 219 220 184Table B2, Page 6.ID #48 ID#49 ID#50 ID#51 ID#52 ID#53 ID#54 ID#58Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)0 84 0 169 1 100 0 50 1 20.0 0 10.0 0 100 0 1.021 63 21 114 3 97 1 50 2 19.5 2 9.9 2 99 1 1.046 60 46 95 5 96 2 50 25 18.0 4 9.8 4 98 2 1.053 51 53 98 7 94 3 49 30 17.0 6 9.8 6 98 3 1.09 92 4 49 55 15.0 8 9.7 8 97 4 1.011 91 5 49 63 14.5 10 9.6 10 96 5 1.013 89 6 49 12 9.5 12 95 6 1.015 88 7 49 14 9.4 14 94 7 1.017 86 8 48 16 9.3 16 94 8 1.019 85 9 48 18 9.3 18 93 9 1.021 83 10 48 20 9.2 20 92 10 1.023 82 11 48 22 9.1 22 91 11 1.025 80 12 48 24 9.0 24 90 12 1.027 79 26 9.0 26 90 13 0.929 78 28 8.9 28 89 14 0.931 76 30 8.8 30 88 15 0.933 75 16 0.935 74 17 0.937 72 18 0.939 7141 70852140Table B2, Page 7.ID #58b ID#59 ID#60 ID#61 ID#62 ID#63 ID#64 ID#65Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)0 0.1 0 32 0 110 1 17.7 0 5.0 1 0.5 1 5.0 0 0.542 0.1 10 32 10 110 3 17.4 35 5.0 2 0.5 2 5.0 29 0.521 32 21 110 5 17.1 3 0.5 3 4.97 16.9 4 0.5 4 4.99 16.7 5 0.5 5 4.911 16.5 6 0.5 6 4.913 16.3 7 0.5 7 4.915 16.117 15.919 15.721 15.523 15.325 15.127 14.929 14.731 14.533 14.335 14.137 14.039 13.841 13.686Table B2, Page 8.ID #66 ID#67 ID#68 ID#69 ID#70 ID#71 ID#72 ID#73Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)0 5.0 1 4.7 1 1.0 1 20.0 1 10.0 1 2.0 1 30 1 10070 5.0 5 3.6 2 1.0 2 19.8 2 9.9 2 1.6 2 23 2 7810 1.2 3 1.0 3 19.7 3 9.9 3 0.0 3 0 3 014 1.2 4 1.0 4 19.7 4 9.8 4 0.0 4 0 4 05 1.0 5 19.6 5 9.8 5 0.0 5 0 5 06 1.0 6 19.5 6 9.8 6 0.0 6 0 6 07 1.0 7 19.4 7 9.7 7 0.0 7 0 7 08 1.0 8 19.3 8 9.7 8 0.0 8 0 8 09 1.0 9 19.3 9 9.6 9 0.0 9 0 9 010 1.0 10 19.2 10 9.6 10 0.0 10 0 10 011 0.9 11 19.1 11 9.5 11 0.0 11 0 11 012 0.9 12 19.0 12 9.5 12 0.0 12 0 12 013 0.9 13 18.9 13 9.5 13 0.0 13 0 13 014 0.9 14 18.9 14 9.4 14 2.0 14 30 14 10015 0.9 15 18.8 15 9.4 15 1.6 15 23 15 7816 0.9 16 18.7 16 9.3 16 0.0 16 0 16 017 0.9 17 18.6 17 9.3 17 0.0 17 0 17 018 0.9 18 18.5 18 9.3 18 0.0 18 0 18 019 0.9 19 18.5 19 9.2 19 0.0 19 0 19 020 0.9 20 18.4 20 9.2 20 0.0 20 0 20 021 0.0 21 0 21 022 0.0 22 0 22 023 0.0 23 0 23 024 0.0 24 0 24 025 0.0 25 0 25 026 0.0 26 0 26 027 0.0 27 0 27 028 0.0 28 0 28 087Table B2, Page 9.ID #75 ID#76 ID#77 ID#78 ID#79 ID#80 ID#81 ID#82Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)0 25.0 0 3.2 0 10.0 1 25.0 1 117 1 6.4 1 84 1 10.030 25.0 21 3.2 21 10.0 2 24.8 2 116 3 6.3 7 80 2 9.93 24.7 3 116 5 6.3 13 77 3 9.94 24.6 4 115 7 6.2 19 74 4 9.85 24.5 5 115 9 6.2 25 78 5 9.86 24.4 6 114 11 6.1 31 75 6 9.87 24.3 7 114 13 6.1 37 72 7 9.78 24.2 8 113 15 6.0 43 69 8 9.79 24.1 9 113 17 6.0 49 66 9 9.610 24.0 10 112 19 5.9 55 64 10 9.611 23.9 11 112 21 5.9 61 61 11 9.512 23.8 12 111 23 5.8 67 59 12 9.513 23.7 13 111 25 5.8 73 57 13 9.514 23.6 14 110 27 5.7 79 55 14 9.415 48.5 15 110 29 5.7 85 53 15 9.416 48.3 16 109 31 5.6 91 51 16 9.317 48.1 17 109 33 5.6 97 49 17 9.318 47.9 18 109 35 5.5 103 47 18 9.319 47.7 19 108 109 45 19 9.220 47.5 20 108 115 43 20 9.221 47.3 21 107 121 42 21 9.222 47.1 22 107 127 40 22 9.223 46.9 23 106 133 39 23 9.224 46.7 24 106 139 3725 46.5 25 105 145 3626 46.3 26 105 151 3427 46.1 27 105 157 3328 45.9 28 104 163 3229 45.7 169 3130 45.588Table B2, Page 10.ID #83 ID#84 ID#85 ID#86 ID#87 ID#87 ID#89 ID#90Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)1 30 1 2.0 1 30 1 2.0 1 30 1 148 1 24.3 1 2073 30 3 2.0 3 30 3 2.0 2 30 2 127 2 18.3 3 1705 29 5 2.0 5 29 5 2.0 3 30 3 120 3 20.7 5 1487 29 7 1.9 7 29 7 1.9 4 30 4 112 4 19.6 7 1309 29 9 1.9 9 29 9 1.9 5 29 5 105 5 18.6 9 11411 29 11 1.9 11 29 11 1.9 6 29 6 98 6 17.6 11 9913 28 13 1.9 13 28 13 1.9 7 29 7 88 7 15.4 13 8715 28 15 1.9 15 28 15 1.9 8 29 15 7617 28 17 1.9 17 28 17 1.9 9 29 17 6719 28 19 1.8 19 28 19 1.8 10 29 19 5821 28 21 1.8 21 28 21 1.8 11 29 21 5123 27 23 1.8 23 27 23 1.8 12 29 23 4525 27 25 1.8 25 27 25 1.8 13 28 25 3927 27 27 1.8 27 27 27 1.8 14 28 27 3429 27 29 1.8 29 27 29 1.8 15 28 29 3031 26 31 1.8 31 26 31 1.8 16 28 31 2633 26 33 1.7 33 26 33 1.7 17 28 33 2335 26 35 1.7 35 26 35 1.7 18 28 35 2037 26 37 1.7 37 26 37 1.7 19 28 37 1839 26 39 1.7 39 26 39 1.7 20 28 39 1521 28 41 1422 27 43 1223 27 45 1024 27 47 925 27 49 851 753 655 589Table B2, Page 11.ID #95 ID#96 ID#97 ID#98 ID#99 ID#100 ID#101Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)Time(d)Conc(μg/L)1 20.1 1 196 1 2036 1 24.5 1 50 1 104 1 2483 19.5 3 193 3 1986 42 24.5 42 50 42 104 42 2485 19.0 5 191 5 19547 18.6 7 189 7 19229 18.2 9 188 9 189011 17.8 11 186 11 185913 17.4 13 184 13 182915 17.0 15 183 15 179917 16.6 17 181 17 176919 16.3 19 180 19 174021 15.9 21 178 21 171223 15.6 23 176 23 168425 15.2 25 175 25 165627 14.9 27 173 27 162929 14.6 29 172 29 160331 14.2 31 170 31 157633 13.9 33 169 33 155135 13.6 35 167 35 152537 13.3 37 166 37 150039 13.0 39 164 39 147641 12.7 41 163 41 145243 12.4 43 161 43 142845 12.2 45 160 45 140447 11.9 47 158 47 138149 11.6 49 157 49 135951 11.4 51 155 51 1337902145