Draft for Comment Version – March 1, 2014

Appendix A: Volatilization Screening Tool

Guidance Document

Office of Pesticide Programs

Office of Chemical Safety and Pollution Prevention

U.S. Environmental Protection Agency

Washington, DC

March 1, 2014
2

1 Table of Contents

2 1. Introduction ............................................................................................................................. 3

3 2. System Requirements.............................................................................................................. 3

4 3. Using the Tool ......................................................................................................................... 4

5 4. Uncertainty Characterization................................................................................................. 10

6 Attachment A. AERSCREEN Input Values................................................................................ 12

7 Attachment B. Development of Peak-to-Mean Scaling Ratios Using PERFUM......................... 15

8 Attachment C. Use of the Modified Woodrow Equation to Estimate Flux Rates........................ 20

9

10 Figures

11 Figure 1. Input Screen for HED Volatilization Screening Tool ..................................................... 5

12 Figure 2. Input Screen Depicting Dropdown List for Application Timing Selection .................... 6

13 Figure 3. Input Screen Depicting the Dropdown List for the Averaging Time for the 

14 Concentration of Concern............................................................................................................... 8

15 Figure 4. Output Results from HED Volatilization Screening Tool............................................... 9

16 Figure 5. Concentrations at Various Distances............................................................................. 10

17
3

18 1. Introduction

19

20 In 2012 a screening tool (i.e., the Volatilization Screening Tool) was developed for estimating

21 screening level air concentrations downwind of treated fields resulting from the volatilization of 

22 semi-volatile pesticides. Up until that time, the Probabilistic Exposure and Risk model for 

23 FUMigants (PERFUM) was used for such estimations. However, PERFUM is resource intensive

24 and requires the input of a time series of flux data (emission rates), which was often only 

25 available from field volatility studies that were not common for semi-volatile pesticides. 

Alternatively, the EPA Office of Air AERSCREEN1 26 model was used as the basis of this 

screening tool. AERSCREEN is a screening model based on AERMOD2 27 , which is an air quality 

28 model that quantifies air dispersion based on planetary boundary layer turbulence structure and 

29 scaling concepts, including treatment of both surface and elevated sources, and both simple and 

30 complex terrain. AERSCREEN produces estimates of "worst-case" 1-hour concentrations for a 

31 single source, without the need for hourly meteorological data. 

32

33 The Volatilization Screening Tool was developed using outputs from AERSCREEN based on a 

standard flux rate of 1x10-6 g/m2 34 -s, for various field sizes, crop scenarios, emissions/release 

35 scenarios, and meteorological conditions. These outputs were imported into an Excel workbook. 

36 Attachment A details the input parameters used in AERSCREEN model runs. ). Current 

37 AERSCREEN guidance for field source emissions does not support calculation of air 

38 concentrations beyond 1 hour periods to different exposure periods (e.g., 8-hour time weighted 

39 air concentration). However, a peak-to-mean scaling factor has been developed, based on runs 

40 done in PERFUM, to allow estimation of concentrations for averaging periods longer than one 

41 hour, but less than 24 hours in order to match the duration of the potential exposure period to a 

42 period similar to that used for defining the toxicological level of concern (e.g., 6 hour inhalation 

43 dose administration in a rodent study is typical) (see Attachment B).

44

45 In AERSCREEN, air concentrations are directly related to the emission rate from a field. For 

46 fumigants, it was assumed that the emission rate is directly proportional to the application rate

47 (e.g., the more pesticide that is applied, the more pesticide will volatilize). Using a similar

48 premise, the Volatilization Screening Tool scales the results from the AERSCREEN runs to 

49 estimate off-field air concentrations quickly, using only the application rate, different release 

50 rates (hourly flux rates), a combination of meteorological conditions, and crop information. The 

51 Volatilization Screening Tool provides estimates of the distance from the edge of the field where 

52 air concentrations fall below the concentration of concern for a chemical. In this way, the 

53 Volatilization Screening Tool can be used to easily eliminate the inhalation pathway for 

54 pesticides not posing a risk of concern to bystanders following applications. 

55 2. System Requirements

56

57 The spreadsheet tool was developed for Microsoft Excel 2007, so users must have this version or 

58 higher. The tool also uses Visual Basic functions, so users will have to enable the use of macros 

59 in order to use the tool.

 1 http://www.epa.gov/scram001/dispersion_screening.htm

2 http://www.epa.gov/ttn/scram/dispersion_prefrec.htm#aermod
4

60 3. Using the Tool

61

62 Before using the tool, users should collect the following information from labels, submitted flux 

63 studies (if available), and toxicity studies:

64 1. Maximum label rate, in lb ai/A, for the application of interest. This is only applicable if 

65 the pesticide is applied to soil.

66 2. Time of year the pesticide is applied

67 3. Inhalation and oral toxicity information 

68 4. Target Margin of exposure (MOE) or Level of Concern (LOC)

69 5. Flux profile, if available

70

71 In order to run the tool, the user should follow these steps:

72

73 1. Go to the Screening Calculator worksheet (Figure 1).

74

75 2. Enter the name of the active ingredient. This is for information purposes only and does not 

76 affect the results of the tool.

77

78 3. Enter the vapor pressure of the active ingredient in units of torr. This measurement will be 

79 used to estimate the flux rate for emissions from soil and plant canopy.

80

81 4. Enter the solubility of the active ingredient in units of mg/L. This parameter is only 

82 applicable to applications made to bare soil.

83

84 5. Enter the adsorption coefficient (Koc) in units of mL/g. If only a Kd value is available, divide

85 it by 0.01 (assume the soil has an organic carbon content of 1%). This parameter is only 

86 applicable to applications made to bare soil. For active ingredients with multiple Kds/Kocs, 

87 enter the mean value.

88

89 6. The maximum hourly flux estimates for bare soil and the various crop types are estimated 

90 using the Woodrow equations for soil (bare soil) and plants (e.g., cole crop, row crop, and 

91 orchard) (Attachment C). As the equation for soil requires the use of the application rate, 

92 users should enter the maximum label rate for pre-emergence applications. For foliar 

93 applications to crops, leave the application rate cell blank. If a field volatility study is 

94 available, users should identify the maximum hourly flux rate reported in the study and use it 

95 in the tool.

96
5

97

98 Figure 1. Input Screen for Volatilization Screening Tool

99

100 7. Select the application timing from the dropdown list (Figure 2). If the pesticide can be 

101 applied anytime during the year, the user should select an application time that coincides 

102 with the anticipated pest pressure. 
6

103

104 Figure 2. Input Screen Depicting Dropdown List for Application Timing Selection

105

106 8. Select the averaging time for the target concentration, in hours (Figure 3). This should 

107 correspond to the duration of the inhalation point of departure (POD) (see below). This will 

108 allow the model to scale the one-hour concentration generated by AERSCREEN to an 

109 averaging time comparable to the exposure duration for which the inhalation concentration of 

110 concern was derived. Users are limited to 1, 2, 4, 6, 8, 12, and 24-hour averaging periods.

111

112 9. Enter the inhalation POD. The inhalation POD refers to the chemical-specific human-health 

113 point of departure selected for the inhalation route of exposure in a chemical’s most recent 

114 human health exposure or risk assessment. For this input, there are three main possibilities:

115

116 • If a route-specific inhalation toxicological study was used in the latest risk assessment 

117 as the basis for the inhalation endpoint, a human equivalent concentration (HEC) was 

118 calculated (via use of the Agency’s reference concentration (RfC) methodology), the 

119 HEC can be utilized for the inhalation POD in the screening analysis.

120 • If a route-specific inhalation toxicological study was used in the latest risk assessment 

121 as the basis for the inhalation endpoint, but an HEC was not calculated, the no 

122 observed adverse effect level (NOAEL) can be utilized for the inhalation POD.

123 • If a route-specific inhalation toxicological study was not available and an oral 

124 toxicological study was used as the basis for the inhalation endpoint in the latest risk 

125 assessment, the NOAEL from the oral study can be converted to an inhalation 
7

126 equivalent concentration (IEC) as used as the inhalation POD. The IEC can be 

127 estimated using the following equation

128

129 Where:

130 IEC = inhalation equivalent concentration (μg/m3),

131 NOAEL = NOAEL from oral study (mg/kg/day),

132 CF1 = conversion factor to convert mg to μg,

133 A = absorption (unitless),

134 CF2 = conversion factor to account for species specific respiratory

135 volume and body weight (m3/hr/kg),

136 D = duration (hrs/day), and

137 AF = activity factor (unitless)

138

139 10. Enter the uncertainty factor (UF). This is equivalent to the target MOE or level of concern 

140 (LOC).

141

142 11. The concentration of concern (COC) is calculated in the spreadsheet, and is essentially the 

143 HEC divided by the UF. Any estimated air concentrations above the COC would represent a 

144 potential risk of concern. 

145
8

146

147 Figure 3. Input Screen Depicting the Dropdown List for the Averaging Time for the 

148 Concentration of Concern

149 Once the information above has been entered into the spreadsheet, the tool will automatically 

150 calculate the distance from the field at which the air concentration has decreased below the 

151 concentration of concern for the different crop types and field sizes (Figure 4). If the distance is 

152 greater than the maximum distance modeled in AERSCREEN, a greater than (>) symbol will 

153 appear in the results table. Because the receptor distances used in the AERSCREEN runs are 

154 fixed at 25 meter intervals, a simple linear interpolation routine is used to generate a more 

155 appropriate estimate.

156

157 Results are generated for four different crop types (bare soil, cole crops, row crops, and orchards) 

158 designed to cover the various release heights and canopy coverages most commonly experienced 

159 with semi-volatile applications. Results for cole crops should be used for low level crops, such 

160 as soybeans, strawberries, and vegetables. Results for field crops should be used for crops that 

161 have a somewhat higher canopy height, such as corn, alfalfa, and tomatoes. It should be noted 

162 that the 120-acre field is only applicable for row crops.

163
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164

165 Figure 4. Output Results from Volatilization Screening Tool

166 The distance to the concentration of concern is designed to give the user an idea of how far away 

167 a bystander would need to be from a treated field in order to not be exposed to air concentrations 

168 above the level of concern.

169

170 If users would like to view the concentrations at various distances away from the treated field for 

171 the model run, they should select the Concentration by Distance worksheet (Figure 5). This 

172 worksheet provides a summary of the results at various distances away from the treated field for 

173 all field sizes and all crop scenarios.

174

175 Once finished, the user should save the Excel file under a different name, so the results of the 

176 model run will be saved.

177
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178

179 Figure 5. Concentrations at Various Distances

180 4. Uncertainty Characterization

181

182 AERSCREEN generates a maximum one-hour air concentration assuming air flow remains 

183 constant. The values reported in the tool are worst case values (i.e., always single downwind 

184 direction) and should be characterized as such.

185

186 AERSCREEN models air flow for simple, uniform terrain. Because of the complex topography 

187 and air flow patterns in an orchard, use of the results generated for orchards should caveat the 

values as being highly uncertain.3 188 While the topography for cole or row crops is not completely 

189 uniform, it is believed that the differences are not significant enough to impact the air flow 

190 regime and concentrations outside the treated field. 

191

192 For simplicity, the fields in AERSCREEN were modeled as squares and the sizes were limited to 

193 five values. While this may not reflect real-world agricultural practices, the sizes and shape 

194 should be adequate for screening-level purposes. 

195

 3 Unlike AERSCREEN, the refined model AERMOD considers flow around or above complex mountainous terrain. 

However, neither AERSCREEN nor AERMOD considers complex flow patterns through other non-homogeneous 

surfaces such as orchard groves.
11

196 In most cases, the maximum AERSCREEN concentration at any distance corresponded to the 

197 minimum wind speed of 0.5 m/s. In the case of many, but not all, semivolatiles, the maximum 

198 flux rate occurs during the middle of the day, when temperatures are highest and the air 

199 turbulence and wind speed have increased. As a result, for semivolatiles, matching the 

200 maximum flux rate with the minimum wind speed will overestimate air concentrations around a 

201 treated field. Users may want to look at the impact of lower flux rates and characterize the 

202 results accordingly.

203

204 Users need appropriate justification for selecting field sizes, crop scenarios, and percent 

205 volatilized used in the modeling runs. Any differences between what was modeled and what is 

206 expected to occur in the real-world should be characterized.

207

208
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209 Attachment A. AERSCREEN Input Values

210

211 The Volatilization Screening Tool uses the results of several AERSCREEN runs as its

212 underlying basis. AERSCREEN model runs were conducted for a series of crop types (bare 

213 ground, cole crop, row crop, and orchard), field sizes (10, 20, 40, 60, 80, and 120 (row crop 

214 only) acres), and seasonal conditions (spring, summer, fall, and winter) to simulate pesticide 

215 applications at various times during the year. For simplicity, the fields were all squares. The 

216 release heights were based on the maximum crop heights of 3, 6 and 18 feet for cole crops, row 

217 crops, and orchards, respectively. Cole and row crops were expected to be at half of their 

218 maximum height in the spring and reach maturity in the summer. By fall, these crops would have 

219 similar heights to summer before harvesting. In the winter, the height of release in row and cole 

220 crop fields were equivalent to bare ground, as these crops would have been harvested. Receptor 

221 distances were automatically generated by AERSCREEN by providing a minimum distance 

222 value of 1 meter and a maximum distance value of 5,000 meters. A receptor height of 1.5 meters 

223 was used to represent the breathing height of a human. 

A flux rate of 1 x 10-6 g/m2-s, or 1 μg/m2 224 -s, was used for each field. In AERSCREEN, the air 

225 concentrations are directly related to emission rate from a field. For fumigants, OPP has 

226 assumed that the emission rate is directly related to the application rate. Using these 

relationships, the application rate can be converted to an emission rate in μg/m2 227 -s, assuming the 

228 amount applied is released over a given period. The air concentration values can be estimated 

229 from the AERSCREEN runs simply by multiplying the results by the converted application rate.

230 Additionally, the following meteorological and land surface input parameters were used:

Spring Summer Fall Winter

Minimum temp (K) 273 283 273 273

Maximum temp (K) 305 311 305 300

Minimum wind speed (m/s) 0.5 0.5 0.5 0.5

Anemometer height (m) 10 10 10 10

Land use type 0 0 0 0

Spring1

Bare soil Cole crop Row crop Orchard

Albedo 0.14 0.12 0.13 0.13

Bowen ratio 3 0.7 2 2

Surface roughness length (m) 0.001 0.015 0.03 1

Release height (m) 0 0.46 0.91 5.5

Summer1

Bare soil Cole crop Row crop Orchard

Albedo 0.2 0.12 0.13 0.13

Bowen ratio 3 0.3 1 1

Surface roughness length (m) 0.001 0.1 0.2 1.3

Release height (m) 0 0.91 1.83 5.5

Fall1

Bare soil Cole crop Row crop Orchard
13

Albedo 0.18 0.12 0.13 0.13

Bowen ratio 3 1 2 2

Surface roughness length (m) 0.001 0.1 0.2 0.8

Release height (m) 0 0.91 1.83 5.5

Winter1

Bare soil Cole crop Row crop Orchard

Albedo 0.6 0.6 0.6 0.35

Bowen ratio 3 3 3 1.5

Surface roughness length (m) 0.001 0.001 0.001 0.5

Release height (m) 0 0 0 5.5

231 1. See discussion below for more details on how values were derived.

232 Minimum temperatures for fall, spring, and winter were kept at 32°F, as pest pressures are not 

233 expected below this temperature. Other temperatures were selected to represent the range that 

234 might be reasonably anticipated. The minimum wind speed and anemometer height were default 

235 values used in the AERSCREEN model. The land use type of 0 allowed for the use of specific 

236 albedo, Bowen ratio, and surface roughness values.

237 Values for albedo, Bowen ratios, and surface roughness were initially developed using Tables 4-

1, 4-2b, and 4-3 in the AERMET4 238 (the meteorological processor for the AERMOD model)

manual (USEPA, 20045 239 ). Daytime Bowen ratio, daytime albedo, and surface roughness length 

240 values were used to determine planetary boundary layer values used by AERSCREEN for 

241 convective conditions. The tables in the AERMET manual have different land use categories 

242 than those considered in this modeling effort, so justification for the values chosen are described 

243 below. 

244 Albedo

245 The daytime albedo is the fraction of total incident solar radiation reflected by the surface back 

246 to space without absorption. Surface color highly influences albedo values (lighter colors tend to 

247 reflect more effectively than darker colors). Albedo values for bare soil were equivalent to 

248 values for cultivated fields from Table 4-1 in the AERMET manual. Albedo values for cole 

249 crops were equivalent to values for a deciduous forest as provided in Table 4-1, since both 

250 surfaces possessed similar colored surfaces. The only exception was that the albedo for cole 

251 crops was assumed to be equivalent to bare soil in the winter, given that these fields would be 

252 barren in the winter. For both row crops and orchards, albedo values were selected in between 

253 those for the bare soil and cole crops, given the staggered distribution of plants and bare areas on 

254 these fields. The only exception was during the winter when barren row crop fields have the 

255 same albedo as bare fields. Wintertime albedos for orchards would be lower than bare soil, 

256 given the coverage of dark colored woody material.

 4 http://www.epa.gov/ttn/scram/metobsdata_procaccprogs.htm#aermet

5 http://www.epa.gov/scram001/7thconf/aermod/aermetugb.pdf
14

257 Bowen Ratio

258 The daytime Bowen ratio, an indicator of surface moisture, is the ratio of the sensible heat flux to 

259 the latent heat flux. Higher Bowen ratios indicate dry surfaces where total evaporation flux is 

260 low, and low Bowen ratios indicate moister surfaces where total evaporation flux is higher. 

261 Average atmospheric moisture conditions were assumed to determine Bowen ratios. Estimates 

262 for the Bowen ratio for bare soil were assumed to be 3.0 for all seasons, which is typical for an 

263 area that does not have much evaporation and evapo-transpiration contributing to atmospheric 

264 moisture levels. Bowen ratios for cole crops were assumed to be equivalent to those for a 

265 deciduous forest (Table 4.2b from the AERMET manual). Even though this is not an equivalent 

266 land use category, the evaporation characteristics were expected to be the same assuming similar 

267 foliage characteristics and evapo-transpiration rates. Bowen ratios for orchards and row crops 

268 were selected between those for the bare soil and cole crops, given the staggered distribution of 

269 plants and bare areas on these fields. One exception was that a slightly lower Bowen ratio was 

270 assumed for orchards in the winter, given that there would be more evapo-transpiration from the 

271 trunks and limbs of orchard trees than bare row crop fields. In winter, both row crops and cole 

272 crops were assumed to have the same Bowen ratios as bare soil.

273 Surface Roughness Length

274 The surface roughness length is related to the height of obstacles to the wind flow and is, in 

275 principle, the maximum height at which the mean horizontal wind speed is no longer zero due to 

276 frictional drag. Below this height exists a thin laminar surface boundary layer characterized by 

277 no wind. The values for bare soil were closest to the grassland value for winter from Table 4-3 

278 in the AERMET manual, assuming the surface was void of significant vegetation. Surface 

279 roughness length values for orchards were derived from the deciduous forest estimates provided 

280 in Table 4-3 in the AERMET manual. Roughness lengths for cole and row crops were assumed 

281 to be less than for orchards and greater than bare fields. Row crops were assumed to have twice 

282 the roughness length of cole crops since row crops were generally taller than cole crops. In 

283 winter, both cole crops and row crops were assumed to have the same surface roughness as bare 

284 soil.

285 References

286

287 USEPA. 2004. User’s guide for the AERMOD Meteorological Preprocessor (AERMET). 

288 USEPA, Office of Air Quality Planning and Standards, Emissions, Monitoring, and Analysis 

289 Division, Research Triangle Park, North Carolina 27711. EPA-454/B-03-002, November 2004 

290 (http://www.epa.gov/ttn/scram/metobsdata_procaccprogs.htm#aermet)

291
15

292 Attachment B. Development of Peak-to-Mean Scaling Ratios Using PERFUM

293 Introduction

294 The Volatilization Screening Tool uses results generated from AERSCREEN to estimate 

295 screening-level concentrations to bystanders near a treated field. AERSCREEN provides a 1-

296 hour maximum average air concentration, while averaging periods of concern can vary from 1 to 

297 24 hours. As such, while the AERSCREEN estimates will be conservative because of the 

298 mismatched exposure durations which makes this screening tool inappropriate for use in 

299 quantitative risk assessment without further adjustment of the 1 hour concentrations. EPA’s 

300 Office of Air has developed 3, 8, and 24-hour peak to mean scaling factors for AERSCREEN 

301 estimates, but recommends, per EPA’s screening guidance (USEPA, 1992), that these values 

302 should not be used for area sources, as 

303 “....concentrations close to an area source will not vary as much as those for point 

304 sources in response to varying wind directions, and the meteorological conditions which 

305 are likely to give maximum 1-hour concentrations and can persist for several hours. 

306 Therefore it is recommended that the maximum 1-hour concentration be conservatively 

307 assumed to apply for averaging periods out to 24 hours.”

308 In an effort to find a way to use the one-hour AERSCREEN estimates and scale them to the 

309 appropriate exposure averaging period for HED, the PERFUM model was used to evaluate air 

310 concentrations around treated fields. Using various field sizes and meteorological regions, OPP 

311 developed peak to mean scaling factors for area sources to be used in the interim HED 

312 Volatilization Screening Tool.

313 Methodology

314 Five field sizes (10, 20, 40, 60, and 80 acres) and 2 meteorological areas (Ventura, CA and 

315 Bradenton, FL) were examined to estimate peak to mean scaling factors for 2, 4, 6, 8, 12, and 24 

316 hour periods. Ventura and Bradenton were selected as, historically, these meteorological 

317 conditions have routinely produced the highest near-field concentrations. Fields were modeled 

318 as squares. 

319 For each meteorological region, OPP conducted initial runs using a 10-acre field and a constant 

320 flux rate to determine the application time that would generate the maximum period 

321 concentrations for both meteorological regions. These analyses were only done for 2, 4, 6, 8, 

322 and 12-hour averaging periods, as 1 and 24-hour averaging periods are not affected by an 

323 application’s start time. The application time that generated the highest period concentrations 

324 was then held constant during the remainder of the model runs for other field sizes. Table B-1

325 depicts the start times for the various field sizes and meteorological regions used in the 

326 modeling.
16

327 Table B-1. Start Time of Application for PERFUM Runs, Based on Meteorological Region

Averaging period (hours)

Start hour of application

California meteorological 

region

Florida meteorological region

2 2 2

4 3 2

6 4 6

8 6 7

12 9 6

328

329 OPP then conducted 1-hour averaging period runs with constant flux rates over a 24-hour period. 

A threshold concentration of 500 μg/m3 330 in PERFUM was specified. The flux rates were selected 

so that the 99.9th 331 percentile concentration did not exceed 10 times the rounded threshold 

concentration of 500 μg/m3 332 . This would allow a more accurate comparison of the maximum 

333 concentrations generated by PERFUM, as resulting concentrations on the highest end of the 

range would not be capped6 334 . Once acceptable flux rates were determined, these flux rates were 

335 used with 2, 4, 6, 8, 12, and 24-hour averaging periods.

336 The values at each ring for the 2, 4, 6, 8, 12, and 24-hour averaging periods were compared to 

337 the corresponding values for the 1-hour averaging period to estimate a peak to mean scaling 

338 ratio. In order to be conservative, the maximum ratio was selected for each averaging period. 

339 Depending on the averaging period, the number of concentration distributions generated in each 

340 output file from PERFUM was equivalent to 24 divided by the averaging period of interest. For 

341 instance, 24 concentration distributions were generated for a 1-hour averaging period, while 12 

342 concentrations were generated for a 2-hour averaging period. The output files from each 

PERFUM run were analyzed to determine which averaging period resulted in the highest 99.9th 343

percentile concentrations. The highest 99.9th 344 percentile values were obtained for each receptor 

345 ring and exported into a spreadsheet. 

346 This process was repeated for the remaining field sizes and meteorological regions. Elevated area 

347 sources to mimic cole and row crops, to see if the elevated source would generate different peak 

348 to mean scaling factors were also included. It was assumed that seasonally dependent variables, 

349 such as albedo, surface roughness length, and Bowen Ratio, were the same between model runs, 

350 as PERFUM does not distinguish between varying surface characteristics.

351 Results and Conclusions

352 The overall results of the analysis are presented in Table B-2. Results from individual runs are 

353 presented in Tables B-3 and B-4. The results from the modeling runs present similar peak to 

354 mean scaling factors for the different averaging periods, with standard deviations of less than 

 6 The current version of PERFUM automatically caps estimated concentrations at 10 times above the threshold 

concentration to conserve processing time.
17

355 3%. Therefore, the maximum peak to mean scaling factor found for each averaging period is 

356 being proposed to be applied, with the exception of the 2-hour averaging period, where the peak 

357 to mean scaling factor is being set to 100%.

358 Table B-2. Overall Scaling Results from PERFUM Runs

Avg 

Time 

(hr)

Overall Proposed 

scaling 

Max Avg Stdev factor

2 101%1 96% 3% 100%

4 93% 91% 1% 93%

6 86% 83% 1% 86%

8 83% 81% 1% 83%

12 75% 73% 2% 75%

24 47% 45% 1% 47%

359 1. The maximum peak to mean ratio for the 2-hour period is greater than 100 percent. This is 

360 believed to be an artifact of the binning process used in PERFUM to generate concentration 

361 distributions.

362 Uncertainties

363 PERFUM provides concentrations as distributions for a 5-year period. The highest percentile is 

99.9th 364 percentile. While not the maximum concentration, for purposes of air modeling pesticide 

365 applications, it is assumed to representative of the maximum concentration at the prescribed 

366 distance from the edge of the field. 

Since peak to mean scaling factors are computed based on the ratio of the 99.9th 367 percentile one368 hour concentration and concentration of other averaging period at the same ring distance, there is 

369 the potential that the actual ratio for an individual receptor could be higher. For instance, 

370 suppose there are two receptors 5 meters from the field (Figure B-1). For the 1-hour 

concentration distribution, the point east of the field is the 99.9th 371 percentile 1-hour concentration 

372 on 1/10/95. The point to the southwest is comparable, but not quite as high, and occurred on 

373 7/10/97. However, the 2-hour concentration estimation at the point to the east results in a lower 

374 2-hour concentration than the point to the southwest, where the meteorological conditions were 

375 more stagnant in the second hour. PERFUM does not provide results at every point along the 

376 ring, just at the percentile specified for a certain ring. As a result, the scaling factor that is 

377 arrived at in this analysis would be 95% (190/200), instead of 97% (190/195). Given the number 

378 of PERFUM runs used to generate the scaling factors, OPP does not believe this situation 

379 significantly impacts the scaling factor results (e.g., the scaling factor for a particular averaging 

380 period would change by only 1-2%). 

381 Lastly, PERFUM post-processes the results from Industrial Source Complex Short Term 

382 (ISCST3) model. AERSCREEN is a screening-level version of the AERMOD model. While 

383 both ISCST3 and AERMOD are Gaussian-plume models, they differ on how they process and 
18

384 use meteorological parameters. However, OPP does not the differences between to two models 

385 is significant enough to prevent the use of peak to mean scaling factors, generated using ISCST3, 

386 on AERMOD results. On the contrary, OPP believes similar, if not identical, scaling factors 

387 would have been generated if PERFUM used AERMOD instead of ISCST3.

388 Figure B-6. PERFUM Results Illustration

200 μg/m3, 1/10/95 (99.9th)

195 μg/m3, 7/10/97 (99.8th)

1-hour concentration

185 μg/m3, 1/10/95 (99.8th)

190 μg/m3, 7/10/97 (99.9th)

2-hour concentration

Note: PERFUM does not provide results for the 99.8th percentile value. These results are provided for illustrative purposes only.
19

389 Table B-3. Individual Results, Bare Soil PERFUM Runs

Avg 

Time 

(hrs)

CA - Max Ratio FL - Max Ratio

Bare soil Bare soil

10 A 20 A 40 A 60 A 80 A 10 A 20 A 40 A 60 A 80 A

2 92% 95% 93% 93% 93% 97% 98% 99% 98% 98%

4 88% 90% 90% 90% 91% 90% 91% 92% 92% 93%

6 80% 82% 82% 84% 84% 83% 83% 84% 85% 85%

8 78% 80% 80% 81% 83% 80% 81% 82% 82% 83%

12 71% 72% 72% 74% 75% 72% 73% 74% 74% 75%

24 46% 47% 46% 47% 47% 44% 44% 44% 44% 45%

390

391 Table B-4. Individual Results, Elevated Source PERFUM Runs

Avg 

Time 

(hrs)

CA- Max Ratio FL- Max Ratio

Cole crop Row crop Cole crop Row crop

10 A 40 A 80 A 10 A 40 A 80 A 10 A 40 A 80 A 10 A 40 A 80 A

2 94% 93% 97% 101% 93% 94% 96% 98% 100% 96% 98% 98%

4 88% 90% 92% 89% 90% 91% 92% 92% 92% 91% 92% 92%

6 80% 84% 85% 82% 83% 84% 84% 84% 86% 84% 84% 85%

8 78% 81% 83% 80% 81% 82% 81% 82% 83% 81% 82% 83%

12 71% 73% 75% 72% 73% 74% 73% 70% 75% 73% 69% 74%

24 46% 46% 47% 46% 46% 46% 44% 44% 44% 44% 43% 43%
20

392 Attachment C. Use of the Modified Woodrow Equation to Estimate Flux Rates

393

394 In Woodrow et al (1997) and Woodrow et al (2001), study authors developed a linear 

395 relationship between the natural logarithm of a chemical’s physical and chemical properties to 

396 the natural logarithm of the amount of chemical emitted from soil or a plant surface. However, 

397 like all linear relationships, half of the emissions were underestimated when compared to the 

398 measured values used to derive the relationship. In order to be conservative and health 

399 protective, EFED used the data provided in the Woodrow et al (1997) to construct an alternative 

400 linear model using the 90% lower confidence limit of the slope and 90% upper confidence limit 

401 of the intercept as the model parameters. The results of the regressions are provided in Figures 

402 C-1 and C-2, for the soil- and foliar-applied pesticides, respectively.

403

404 For soil applied pesticides, the following equation can be used to derive a flux rate

405

flux = e(0.8688R+21.535)/3600

406

R = ln � VP x AR

Sol x KOC

�

407

408 where

409

flux = flux rate (μg/m2 410 -s)

411 VP = vapor pressure (Pa)

412 AR = application rate (kg/ha)

413 Sol = solubility (mg/L)

414 Koc = organic carbon sorption coefficient (mL/g)

415

416 For foliar applied pesticides, the following equation can be used to derive a flux rate

417

flux = e(0.8268ln (VP)+12.081)/3600

418

419 where

420

flux = flux rate (μg/m2 421 -s)

422 VP = vapor pressure (Pa)

423

424 Reference

425 Woodrow, J., Seiber, J., Baker, L. 1997. Correlation Techniques for Estimating Pesticide 

426 Volatilization Flux and Downwind Concentrations. Environ. Sci. Technol., 31:523-529

427 Woodrow, J., Seiber, J., Dary, C. 2001. Predicting Pesticide Emissions and Downwind 

428 concentrations Using Correlations with Estimated Vapor Pressures. J. Agric. Food Chem. 

429 49:3841-3846
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430

431
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432

433

434