# Department of Pesticide Regulation

Paul E. Helliker, Director 830 K Street l Sacramento, California 95814-3510 l www.cdpr.ca.gov PtY+XliOll 

### MEMORANDUM TO: Randy Segawa, Senior Environmental Research Scientist Environmental Monitoring and Pest ManagementBranch FROM: Terri Barry, Senior Environmental Research

Environmental Monitoring and Pest ManagementBranch (916) 324-4140 DATE: December 2,1999 SUBJECT: METHYL BROMIDE EMISSION RATIO GROUPINGS This memorandumdescribes the procedures

usedto group the Methyl Bromide application types, Each application type is assigned the mean emission ratio from the appropriate group. The group mean emission ratio, togetherwith the size of the application, determinesthe buffer zone length for a particular permitted application. Materials and Methods Physical characteristics

of the application methods were initially used to assign each application method to a group. The initial groupingswere as follows: 1) broadcast/tarp/deep, 2) broadcast/tarp/shallow,

3) broadcast/non-tarp/deep, 4) broadcast/non-tarp/shallow, 5) bed/tarp/shallow, 6) hot-gas. This initial grouping resulted in several empty cells for all possible combinations of factors. For example,there are no bed/tarp/deep applications. Therefore,the initial analysisto investigate the effect of tarping and/or depth of injection on the emissionratio value was conductedonly on broadcastapplication types. All analysiswas conductedon both the unadjustedand the adjusted for 50% recoveryemission ratios. Further grouping was basedupon both physical characteristics of the application methods and the resultsfrom the statistical analysis of the broadcastapplication types. The application types represented

in the monitoring datasetwere grouped into four types: 1) broadcast/non-tarp, 2) broadcast/tarp,

3) bed/tarp, and 4) hot gas. Due to risk management concernsabout high air concentrations

associated

with hot gas,the emission ratio for this method remains at 1.O and this method was eliminated from further analysis. MINITAB statistical software, Release12, was usedfor all analysis and graphical presentations (Minitab Inc., 1997). One and two-way analysisof variance (ANOVA) were usedto perform the data analysis. The Anderson-Darling test for normality and Levene’s test for homogeneity of variancewere usedto screen for violations of the assumptions of ANOVA (Minitab Inc., 1997). When required, transformation techniques were employed. Mean separation was accomplished using Fisher’spairwise comparisonprocedure.

California Environmental Protection Agency

Q winled an RCCY&d PaprRandy Segawa

December2,1999 Page2

Results and Discussion Broadcast Application Types. Resultsof analysison the broadcast application types are shown in Tables 1 through 3 and Figures 1 and 2. Resultsfrom the statisticalteststo detect violations of the assumptions

of ANOVA are shown in Table 1. For both the unadjustedand the recovery adjustedemissionratios, the data met both the normality (A-squared=0.201, p=O.865,and A-squared=O.

192, p=O.885,respectively)and the homogeneity of varianceassumptions (Levene’sTest Statistic=1.017, p=O.381, and Levene’s Test Statistic=1.031, p=O.377, respectively). The two-way ANOVA for both the unadjusted emissionratios and the recovery adjustedemission ratios (Tables 2 and 3, respectively) show no interaction between the factors tarp and depth of injection (Ft,ts=0.30, p=O.590, and F r,ts=O.30,

p=O.590, respectively). Therefore, the effects of tarping and depth of injection may be interpretedseparately. For both unadjustedand recovery adjustedemissionratios, there is a significant effect due to tarp (Ft,rs=6.32,p=O.O22,

and Ft,ts=6.31, p=O.O22,

respectively) but no significant effect due to depth of injection (Ft,ts=2.55, p=O.128, and Ft,ts=2.57,p=O.126,respectively). Figures 1 and 2 clearly show the significant effect due to tarp and the non-significant effect due to depth of injection for both unadjustedand recovery adjustedemissionratios. For the unadjusted emission ratios, the mean for non-tarped applications is 0.19, while the mean for tarped applications is 0.12. The mean for shallow injections is 0.143, while the mean for deep injections is 0.145. For the recovery adjustedemission ratios, the meanfor non-tarped applicationsis 0.37, while the mean for tarped applications is 0.24. The meanfor shallow injections is 0.287, while the mean for deep injections is 0.290. Theseresults indicate that for both the unadjustedand the recovery adjustedemission ratios, for the broadcast applications, there are only two distinguishable groups: non-tarp and tarp. Broadcast and Bed Application Types. One-way ANOVA was conductedon the single factor of application type. The application types included were: 1)~ broadcast/non-tarp,

2) broadcast/ tarp, and 3) bed/tarp. Before the one-wayANOVA was conducted, the data was examined for violations of the assumptions of ANOVA. The results of that analysisare shown in Table 4. The Anderson-Darling Test indicatesthat, for both the unadjustedand the recovery adjusted emission ratios, the raw data violate the assumption of normality (A-squared=l.304, p=O.O02, and A-squared=1

.164, p=O.O04,

respectively). However, for both the unadjustedand the recovery adjustedemission ratios, Levene’sTest indicatesthat the data do not violate the assumptionof homogeneity of variancebetweengroups (Levene’s Test Statistic=l.553, p=O.229, and Levene’sTest Statistic=0.648, p=O.53 1, respectively). The results in Table 5 show that for both the unadjustedand the recovery adjustedemission ratios, a logto transformation corrected the violation of the normality assumption (A-squared=0.381,p=O.379, and A-squared=0.485, p=O.211, respectively) and maintained homogeneous variancesbetweengroups (Levene’s Test Statistic=O.OllO,

p=O.933, and Levene’s

Test Statistic=0.105,p=O.900, respectively).Randy Segawa

December2, 1999 Page3
The one-way ANOVA was conductedon both the raw emission ratios and the log,, transformed data. For both the unadjustedand the recovery adjusted emissionratios, the results obtained from both analysiswere consistent. For simplicity, only resultsfrom analysisof the raw emission ratios are presented. Results from the one-way ANOVA on the raw emission ratios are shown in Tables 6 and 7. For both the unadjusted and the recovery adjustedemission ratios there is a significant difference betweenapplication types (F?,zs=21.81, p=O.OOO, and Fs,*s=21.26, p=O.OOO, respectively). Further, the Fisher’spairwise comparison(p=O.O5) indicates that bed/tarp application type is significantly different from both the broadcast/non-tarp and the broadcast/tarp. Figures 3 and 4 show the main effects means for application type. Thesefigures clearly show the difference betweenthe bed/tarp and the two broadcast application types. For the unadjustedemission ratios, the meanfor broadcast/non-tarp is 0.19, the mean for broadcast/tarpis 0.12, while the meanfor bed/tarp is 0.43. ‘For the recovery adjusted emission ratios, the meanfor broadcast/non-tarp is 0.24, the mean for broadcast/tarp is 0.37, while the mean for bed/tarp is 0.8 1. Conclusions Based upon the analysisdiscussed above, and the risk management decisionto separate

the hot gas method, the application types separate into four distinguishable groups. These groups are: 1) broadcast/non-tarp,

2) broadcastitarp,

3) bed/tarp, and 4) hot gas. The mean unadjusted emission ratios and recovery adjustedemissionratios and their respective standarddeviations and coefficients of variation (CV), for the four groups are shown in Table 8. Box-plots of the final groupings are shown in Figures 5 and 6.Randy Segawa

December2,1999 Page4

References Minitab Inc. 1997. User’s Guide 2: Data analysisand quality tools. Release12. Minitab statistical softwareRandy Segawa

December2, 1999 Page5
Table 1. Results of testsfor the violation of assumptions of ANOVA, broadcast

application types only. Normality Homogeneity of Variance Anderson-Darling Test Levene’s Test
unadjusted emission ratio Test Statistic

0.201 p-value

0.865 Test Statistic

1.017 p-value

0.381 adjusted emission ratio 0.192 0.885 1.031 0.377

Table 2. ANOVA results for unadjustedemissionratios broadcast application types only.
Source tarp depth tarp*depth error total DF

1 1 1 18 21 Adj. MS F-ratio p-value 0.0331 0.0133 0.0016 0.0052 6.32 0.022

2.55 0.128 0.30 0.590

Table 3. ANOVA resultsfor recovery adjustedemissionratios, broadcast application types only. Seq. SS 0.0909 0.0473 0.0063 0.3762 0.5208 p-value 0.022 Adj. SS Adj. MS F-ratio 0.1318 0.1318 

# I- 6.31 0.0537 0.0537 2.57 0.126 0.0063 0.0063 0.30 0.590 0.3762 0.0209Randy Segawa

December2, 1999 Page6
Table 4. Results of testsfor the violation of assumptions of ANOVA, broadcast

and bed application types only. Normality Homogeneity of Variance Anderson-Darling Test Levene’s Test Test Statistic p-value Test Statistic p-value unadjusted emission ratio 1.304 0.002 1.553 0.229 adjusted emission ratio 1.164 0.004 0.648 0.531

Table 5. Results of testsfor the violation of assumptions of ANOVA on logrc transformed data, broadcast and bed application types only.

Normality Homogeneity of Variance Anderson-Darling Test Levene’s Test
unadjusted emission ratio Test Statistic p-value Test Statistic p-value

0.381 0.379 0.070 0.933 adjusted emission ratio 0.485 0.211 0.105 0.900Randy Segawa

December2, 1999 Page7
Table 6. ANOVA results for unadjusted emission ratios, broadcast

and bed application types only.

0.5509 0.3536 0.9045 MS

0.2755 0.0126 F-ratio p-value

21.81 0.000

Table 7. ANOVA resultsfor recovery adjusted emissionratios, broadcast and bed application types only.
Source
aw. We

r error total DF ss

2 1.8102 28 1.1920 30 3.0022 MS 0.9051 0.0426 F-ratio

21.26 p-value

0.000

i

Table 8. Summary table of unadjusted emission ratios and recovery adjusted emission ratios for broadcast and bed application types only.

Unadjusted emission ratio recovery adjusted emission ratio
n em. ratio st. dev. cv em. ratio st. dev. cv broadcast/ 8 0.19 0.087 46% 0.37 0.174 47% non-tarp broadcast/ 14 0.12 0.065 54% 0.24 0.129 52% tarp bed/tarp 9 0.43 0.175 41% 0.81 0.309 38%Randy Segawa

December 2, 1999 Page8 Figure 1. Main effects plots for the unadjustedemission ratios. Data shown are treatment means. For tarp code, non-tarp = 0, tarp = 1. For depth code, shallow = 0, deep = 1.

Main Effects Plot - Data Means for unadj em ratio/

Randy Segawa

December 2, 1999 Page9 Figure 2. Main effects plots for the recovery adjusted emissionratios. Data shown are treatment means. For tarp code, non-tarp = 0, tarp = 1. For depth code,shallow = 0, deep = 1.

Main Effects Plot - Data Means for adj em ratio

tarp code depth codeRandy Segawa

December2, 1999 Page 10 Figure 3. Main effects plots for the unadjusted emission ratios. Data shown are treatment means. For app. code, broadcast/non-tarp = 0, broadcast/tarp

= 1, bed/tarp= 2.

Main Effects Plot - Data Means for unadj em ratio

, 0 , 1 wp. We 2Randy Segawa

December2, 1999 Page 11 Figure 4. Main effects plots for the adjustedemission ratios. Data shown are treatment means, For app. code, broadcast/non-tarp = 0, broadcast/tarp

= 1, bed/tarp = 2.

Main Effects Plot - Data Means for adj em ratioRandy Segawa

December2, 1999 Page 12 Figure 5. Box-plot of the final groupings of application types for unadjusted emission ratios. For app. code, broadcast/non-tarp = 0, broadcast/tarp

= 1, bed/tarp = 2, hot gas = 3.

Boxplots of unadj em by app. type (meansam indicatedby solid circles). . .

Randy Segawa

December2, 1999 Page 13 Figure 6. Box-plot of the final groupings of application types for recovery adjustedemission ratios. For app. code, broadcast/non-tarp = 0, broadcast/tarp

= 1, bed/tarp = 2, hot gas= 3.

Boxplots of adj em ratio by app. type (means are indicated by solid circles)