-------
                            DPA
f
                                for

                                                                          LH

-------
                                                                35
40
                                                                                 —r
                                                                                 45
                 —T
                  50
                                              DPA
pieure "(c.  plot  of residue hazard  (conmon logarithm  of  T dACbF) as a function
            of  tine Mavs post-application,  ""A) for  Fthvl  ^arathlon as
            reported fron  1'ashi npton, ^ont>< Carolina  and Tpxas.
                                                                                          C7\

-------
o
*-
o
       -2-
                           —T
                           10
—T
 15
—T
 20
—r~
 25
 DPA
—r
 30
—T
 35
—T
 40
—T
 45
—T
 50
          ririire  °rf.  "lot of resl^no  hazard (connon  logarithm of T
                      of M*
-------
 1 -
 o-
-1 -
-2 -
                     10
                    15
20
25

DPA
 I
30
35
40
 i
45
 T
50
*>e . "lot  of  residue* ^azarrf fconiwon  logarithm
    of  tl"e  '''nvs nost-annl icat Ion,  ""A)  for
                                                                        as a function
                                                            Tthyl "arathlon as
                                                                                                   CO
-------
 2 i
 1 -
 0-
-2-
   T
   0
 T
 5
—r
 10
—r
 15
—r
 20
—r
 25
—r
 30
—r
 35
—r
 40
—r
 45
—r
 50
10. i»lot of  residue bar.art*  fconnon  logarithm of  *
    of tlr»e  (<*avn nost-ap»»Mcn t f on,  I'M)  for  ''alathion.
                                                                        ns a function
                                                                                                 •sO
-------
DPA
1?. "lot  of

    of  ti"ie
(connon 1o»»arithn of

                for
                         ns a function
                                              Lsl
                                              r>o
                                              o
-------
-2 -
                            0
—T
 30
—T
 35
—T
 40
—T
 45
—r
 50
                    —r
                    10
—r
 15
—r
 20
—i—
 25
 DPA
           lla.  "lot  of residue hazard  (cornon logarithm  of f dA^hr) as n function
                 of  tlr»p fdavB nos t-annl i cfi t i on,  T"»A) for  Methvl  "arathiop wftfi  an
                         Tnn  for  Its  oxon iM^th  that  of  Its parent.
-------
-2
45       50
                    10
                                                           of
                                                                       ns fl  Function
                                                                                             ro
-------
 1 -
 0-
-1 -
   i
   0
i
5
     i
    10
                          15
 i
20
25
 i
30
 i
35
 i
40
45
 T
50
Pt«»ure  1
"lot of
of ti»ie
                      h/irar^  fcoi-»p»on  toparfthn  of ^
                    nos t-apnl 1 ra t ton ,  "''A)  for   "evlnnhos
                                                                              a  function
                                                                                  i n>.
                                                                                                   04
-------
 2 1
 1 -
 0-
-1 -
-2-
    k*
            r
           5
10
15
20
25      30      35       40
 T

45
 T

50
     Ptpure 1'•. i*lot of roslHnp  bazar** fconnon lo»»arithn of ""  ^AChr) ns a Function
               of tfmp ^Havs pos f-n nnl f ca t ton, n"A) for  *'onocrotonho«s  f
                                                                                     LM
                                                                                     fV)
-------
 21
 H
-H
-21
   I
   0
5
 I
10
 I
15
20
25
30
35
40
45
50
          1 S. "lot of  rrsfHue hazard fcownon loirarlthn  of * d.Ar'hr) as a function
              of ttnr  f^avs nos t-annT i ca t f on, ^"M for   "xv^eneton (Metasvs
                                                                                            rv>
                                                                                            c_n
-------
o
its-
CD
         -2 H
1ft. "lot  of rosier h.izarf'  fconnon  loparltfin  of » HAChr)  as

    of  ttr»p Mavs nost-annl f cntion,  HT»A> for   "lienthoate.
                                                                                        function
                                                                                                                ON
-------
o
c
c.
        -2H
                                                        DPA
17. i>1ot of  rflsfrfue hazar^  (rr»inr»on logarithm  of f

    of tine  f^avjs oost-anol \ ca 1 1 on , ^^M  for   ^hosalone
                                                                               as a  function
                                                                                                      ro
-------
     LDACHE
          2H
          1 -
cn
         -1 •
         -2 H
                     T
                     5
—T
 10
—r
 15
—r
 20
—i—
 25

 DPA
—r
 30
—r
 35
—r
 40
45
50
                        "lot  of  rpsl^tie bazar''  (common  logarithm of  *  flAfhP) as a function
                        of  tine  f-*avs  nop t-annt lea t ion,  r»PA)  for  Pnosnhani rton f^
                                                                                                       CO
-------
o
C:
         2 1
         1 -
         o-
        -2-
                             10

—T
 15
—T
 20
                                             25
—T
 30
35
40
45
50
pl«Mire 1°.  W1ot  of
            of  tfr»p
                                        hnznrH  fronnon  logarithm of  *  ^^rhr) as a function
                                       DOB t-a nnl f ra t ton ,  nf»A)  for  T"iosnet fT
-------
 21
  H
 o-t
-H
-24
                                      20
25
35
40
45
30
50
                    of
-------
to
20-
15-
io:

5
0

: •
1 i
c c
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3 ^ 3 1 ° D
A B ? F3 CCB c
\ ?BA|B B C "•
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6 B •
i i 1 i
7 8 9 10
                                                                                               in
                                                AIA
       Figure 21.  Plot of initial residue deposition (ug/cm2) as a function
                  of application rate (Ib AIA) for all chemicals and all  crops.
-------
C\J
    RES
     20
     15
     10H
                     T
                      2
                             A
                                  B
                                  B
                                    3
                                    A
                                    A
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            B

            B
                                                     *
               8Bi
                                                                 e
                             A
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                                                                 n
                                                                 •X-
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 5

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                                                                                  ID
                                                                                  LO
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9
-------
  RES
   20 H
   15-
   10-
   5 -
LT1
    0-
         A
         A
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            B
                                                —I—
                                                  5
                                                 AIA
                                   T
                                   7
T
 9
T
 2
T
 3
T
 4
"T
 6
10
       Figure 2.1. Plot of  Initial  residue deposition (ug/cm2) as a function
                  of application rate  (Ib  AIA)  for  all  chemicals on  peaches,
-------
  15-
  10-
Ln
   5 -
   0-
              A A
                                        ~T
                                        4
—I—
  5

 AIA
 r
10
     Figure 24. ^lot  of  initial  residue deposition (ug/cm ) as a function
                of application rate  (Ib  AIA)  for  all  chemicals on grapes.
                                                                                            CM
-------
00
5 :
o-

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>
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^
.— .
A
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i I I
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i t i i i i i 	 r
3456789 10
                                               AIA


     Figure 25. Plot of Initial  residue  deposition  (ug/cmO as  a  function
                of application rate (Ib AIA) for all chemicals  on cotton.
                                                                                           O4
-------
  ioH
en
\O
   oi
         A
         A
              *
                      T
                       2
T
 3
T
4
 5
AIA
~T
 6
~T
7
T
 9
—T
 10
      FiRure 25. Plot of initial  residue  deposition  (uR/cm2)  as  a  function
                 of application rate (Ib AIA) for all chemicals  on cotton.
                                                                 Osl
                                                                 L/-J
                                                                 ON
-------
APPENDIX A: Standardized Data Sheet  for
            recording pertinent data from
            published report.
  LOCATION OF  STUDY	
  DATE OF STUDY
  TYPE OF CROP(s)	
  CHEMICAL(s)  APPLIED (Check one)
  Single Chemical  Applied	
  Multiple Chemicals Applied	
  Multiple Chemicals Applied Separately
  Multiple Applications  of Chemical(s)	
  CHEMICAL(s)  APPLIED        |
                              APPENDIX A


                    Space for
                    citation information.
                                                                               337
  FORMULATION  (Fill  in  the  blank)
   	 WP  	 EC  	 LF    ! 	 WP
       G   Other            j     G
	 EC
Other
LF
  APPLICATION RATE  (Active  Ingredient per 	 )
  1.	 Total/tree  J2.	 Total/tree
    	 Kg/Ha      j  	 Kg/Ha
    	 Ibs/acre   j  	 Ibs/acre
  FORMULATION ADDITIVES  OR  COMMENTS
  1.	 J2.	
                                                     3.
WP
G
	 EC
Other
LF
                3.
                Total/tree
                Kg/Ha
                Ibs/acre
                3.
  MIXTURE (gallons  per  acre)  	
  SAMPLING PROCEDURE  (check one)
    Dislodgeable  residue	   Other residue_
    Leaf Punch 	  Whole Leaf	   Other
    Leaf Area Calculated  as: (check)   One Side
  EXTRACTION SOLVENT  Aqueous	
    Organic (circle one)  Hex     Cloro     Tol
                         (1)      (2)      (3)
  WEATHER     Data  Present ( Amount, if given)
  Rainfall     	   	
  Temperature  	   	
  Humidity	   	
  Ozone         	   	
  Other         	   	
                     Units
            	   Two Sides  	

             MeCl     Benz     Other
             (A)       (5)      (6)
              Residue data presented
                   in Tables 	
                      Graph   	
                    Equation 	
              Leaf Dust Measured:
                   YES / NO
                Form  completed by
                   Date
                           APPENDIX A:  page  1 of  1
                                                                    60
-------
APPENDIX B: Bibliography for Residue Decay-Response Database.
            Not every one of the following citations either
            contained residue data or was included within the
            data base summarized in Appendix H.

 1. Awasthi,M.D., et al:
    Dissapation of metasystox and monocrotophos in or on
    soybean crop residues.
    Indian J Agric Sci. 48(4): 245-247, 1978.

 2. Berck,B., Iwata.Y., Gunther,F.A.:
    Worker environment research: rapid field method for
    estimation of organophosphorus insecticide residues on citrus
    foliage and in grove soil.
    J. Agric. Food. Chem. 29(2): 209-216, 1981.

 3. Bowman, A.U., Oiler,W.L., Kendall,D.C.,  Gosnell.A.B., Oliver,K.H.:
    Determination of foliar residues for safe reentry of agricultural
    workers in the field.
    Arch.Environ.Contain.Toxicol.  11(4):  447-455, 1982.

 4. Charmillot, P.J. and  Blaser, C.:
    Study of the persistance of acephate, phosmet and deltamethrin used
    for the control of the summer fruit tortrix Adoxpphyes orana F.v.R.
    CA/099/065875C
    Rev Suisse Vitic, Aboric. Hortic.  15(3): 195-201, 1983.

 5. Chen.Z. and Yue.R.:
    Degradative dynamics of pesticides in tea leaves  and the selective
    parameters of safe pesticide design.
    CA/099/138268U
    Zhonggo Nongye Kexue (Beijing). 1: 62-70, 1983.

 6. Davis,J.:
    Potential Exposure to Dislogable Residues of Two Formulations
    of Methyl Parathion to Apple Trees.
    Bull Environ Contam & Tox.  27: 95-100 (1981).

 7. Davis,J.E.,  Staiff.D.C.,  Butler,L.C.,  and Stevens,E.R.:
    Potential exposure to dislodgable residues after application of two
    formulations of methyl parathion to apple trees.
    NTIS/PB82-247537
    Govt Reports Announcements & Index (GRA&I), 23: 1982.

 8. Dikshit.A.K. and  Bhattacharjee.N.S.:
    Residues of carbofuran and malathion in  or on soybean crop.
    Indian J Agric Sci.  New Delhi,  Indian  Council of  Agricultural
    Research. 50(5): 441-443, 1980.
                                                                 APPENDIX B

                                                                     :     338
                       APPENDIX B: page 1 of 9          . 1
                                                        6 I
-------
 9.  Everhart.L.P. and  Holt.R.F.:
    Potential benlate exposure during mixer/loader operations, crop
    harvest, and home use.                                                    339
    J.Agric.Food.Chem.  30(2): 222-227, 1982.

10.  Fitzpatrick.G.E. and  Began,M.D.:
    Residue dynamics of acephate and methamidophos in urban dooryard
    citrus foliage. Pompano Beach Florida, August-September 1978.
    Pestic.Monit.J. 14(1): 3-6, 1980.

11.  Gegenava.N.G.,  Klisenco,M.A., and  Pis'mennaya.M.V.:
    Dynamics of carbophos and phosalone decomposition in grape leaves
    and grapes.
    CA/098/067078Y
    Khim. Sel'sk. Khoz. 12:45-47, 1982.

12.  Gehrich, John L., John A. Burkart, Dennis Y. Takade, Eugene R. Turner,
    Stanley D. Allen.  Final Report:  Assessment of Leaf Surface Residues
    for Selected Organophosphorus Insecticides.  University of Utah
    Research Institute, Salt Lake City, Utah (1976).

13.  Gehrich,J.L.,  Burkart,J.A., Takade,D.Y.,  Turner,E.R., and  Allen,S.D:
    Assessment of leaf surface residues for selected oganophosphorus
    insecticides.  NTIS/PB82-230046
    Govt Reports Announcements & Index (GRA&I), 21: 1982.

14.  Habeebullah.B. and Balasubramanian,M.:
    Dissapation and persistance of certain insecticides on/in cowpea pods
    (var. C. 152)
    CA/097/067761V
    Madras Agric. J. 68(8): 517-526, 1981.

15.  Hafner.M. and  Michel,H.G.:
    Investigations on the drift of sulfur and its residues in plants
    (red clover and apple leaves from the variety "golden delicious").
    Nachrichtenbl Dtsch Pflanzenschutzd. 27(2): 24-27- [Eng. Sum.] 1975.

16.  Iwata, Yutaka, Margarete Dusch, Glenn Carman, and Francis Gunther:
    Worker Environment Research*  Residues from Carbaryl,
    Chlorobensilate, Dimethoate, and Trichlorfon Applied to Citrus Trees.
    J. Agricultural and Food Chemistry, 27:1141-1145 (1979).

17.  Iwata, Yutaka, Glenn E. Carman, and Francis A. Gunther:
    Worker Environment  Research:  Methidathion Applied to Orange Trees.
    J.Agricultural Food Chemistry 27:119-129 (1979).

18.  Iwata, Yutaka:
    Minimizing Occupational Exposure to Pesticides:  Reentry Field
    Data- a Recapitulation.
    Residue Reviews 75: 127-147 (1980).

19.  Katiha.S.M. and Nath.A.:
    Persistance of oxydemeton methyl residues in cauliflower
    CA/099/135479C
    J Entom Res. 5(1): 47-9, 1981.


                       APPENDIX B: page 2 of 9

                                                             62
-------
20. Keil, Julian E., C. Boyd Loadholt, Bob L. Brown, Samuel H. Sandifer,
    and Wayne R. Sitterly:  Decay of Parathion and Endosulfan Residues on
    Field-Treated Tobacco, South Carolina-1971.
    Pesticides Monitoring Journal, 6:73-75 (1972).

                                                                              340
21. Keil, J.E., C.'B. Loadholt, S.H. Sandfer, W.R. Sitterly,
    and B.L. Brown: Decay of Parathion Residues on Field-Treated
    Tobacco South Carolina - 1972.
    Pesticides Monitoring Journal, 6:377 (1973).

22. Kido, H., J.B. Bailey, N.F. McCalley, W.E. Yates, and R.E. Cowden.
    The Effect of Overhead Sprinkler Irrigation on Methyl Parathion
    Residue on Grape Leaves.
    Bull. Environmental Contamination and Toxicology 14:209-213 (1975).

23. Kilgore, Wendell W., Ming-yu Li, Ronald L. Mull, Wray Winterlin,
    Nemat Borhani, Jess Draus, Peter Kurtz, Norman Akesson, and
    Wesley Yates:
    Human Physiological Effects of Organophosphorus Pesticides in a
    Normal Agricultural Field Labor Population, A Preliminary Report,
    II.  Scientific Aspects. Food Protection and Toxicology Center,
    University of California, Davis.   56 pages (1977).

24. Knaak, J.B., K.T. Maddy, M.A. Gallo, D.T. Lillie, E.M. Craine,
    and W.F. Serat:  Worker Reentry Study Involving Phosalone
    Application to Citrus Groves.
    J.Toxicology and Applied Pharmacology 46:363-374 (1978).

25. Knaak, J.B., S.A. Peoples, T.J. Jackson, A.S. Fredrickson, R. Enos,
    K.T. Maddy, J.B. Bailey, M.E. Dusch, F.A. Gunther, and W. L.
    Winterlin:
    Reentry Problems Involving the Use of Dialifor on Grapes in the San
    Joaquin Valley of California.
    Arch. Environmental Contamination and Toxicology 7:465-481 (1978).

26. Kraus,J.F. et al:
    Monitoring of grape harvesters for evidence of cholinesterase
    inhibition.
    J.Toxicol.Environ.Health. 7(1): 19-31, 1981.

27- Leffingwell, J. T., R.C. Spear, and David Jenkins.
    The Persistence of Ethion and Zolone Residues on Grape Foliage in
    the Central Valley of California.
    Arch. Environmental Contamination and Toxicology 3:49-53 (1975).

28. Lynch,V.P., Hudson,H.R., and Pianca.M.
    Identification and determination of mecarbam and its major
    degradation products in water and crops.
    PESTAB/81/1797
    Pestic Sci. 12(1): 65-73 1981.
                       APPENDIX B: page 3 of 9
-------
29. Maddy et al:
    CDFA Report //HS-181  (ACF 59-181)
    Worker Health and Safety Unit. A Study of Parathion and Paraoxon        7 *
    Residue in Citrus Groves Following Applicaton of Parathion at           ^^
    Various Dosage and Dilution Rates (Report //HS-181). California
    Department of Food and Agriculture, Sacramento, CA (ca 1975).

30. Maddy, Keith T., and Clifford Smith:
    A Study of the Decay Rates of Ethyl-Metyl Parathion and Endosulfan
    Applied as a Foliar Spray to a Tomato Field in the Sacramento
    Valley of California.  Report //HS-293.
    Worker Health and Safety Unit, California Department of Food and
    Agriculture, Sacramento, California.

31. Maddy, Keith T., G. Sprock, A. Scott Fredrickson, and T. Jackson.
    Parathion Residue on Orange Tree Foliage in Riverside County,
    California, May-June, 1975,  Report //318.
    Worker Health and Safety IfaiLt, California Department of Food and
    Agriculture, Sacramento, California (ca 1976).

32. Maddy, Keith T., Susan Edmiston, Charles Kahn, Lilia Rivera, and
    Terry Jackson.  A Study of Parathion on the Foliage of Peach Trees in
    Stanislaus County, California, June-July, 1977.  Report // HS-395.
    Worker Heath and Safety Unit, California Department of Food and
    Agriculture,  Sacramento, California (ca 1977).

33. Misra.S.S.,  Verma,S.,  Handa.S.K., and Lal.R.:
    BHC and parathion residues on crop of yellow-sarson (brassica).
    Indian J of Agric Sci. 41(3): 276-279, 1971.

34. Nigg, H.N., J.C. Allen, R.F. Brooks, G.J. Edwards, N.P. Thompson,
    Roy W. King, and A.H. Blagg.  Dislodgeable Residues of Ethion in
    Florida Citrus and Relationships to Weather Variables.
    Arch. Environ. Contam. and Toxicology     :257-267 (1977).

35. Nigg, H.N., J.C. Allen, R.W. King, N.P. Thompson, G.J. Edwards,
    and R.F. Brooks: Dislodgeable Residues of Parathion and
    Carbophenothion in Florida Citrus:  A Weather Model.
    Bull. Environmental Contamination and Toxicology 19:578-588 (1978).

36. Nigg, Herbert N:
    Comparison of Pesticide and Particulate Recoveries with the Vacuum
    and Dislodgeable Surface Pesticide Residue Techniques.
    Arch. Environmental Contamination Toxicology 8:369-381 (1979).

37. Nigg.H.N.,  Reinert,J.A.,  Stamper,J.H., and Fitzpatrick,G.E.:
    Dissapearance of acephate, methamidophos, and malathion from citrus
    foliage.     PESTAB/81/1555
    Bull Environ Contam Toxicol  26(1): 267-272, 1981.

38. Nigg, H.N. and J. H. Stamper:
    Comparative Disappearance of Dioxathion, Malathion,
    Oxydemetonmethyl and Dialifor from Florida Citrus leaf and Fruit
    Surfaces.
    Arch. Environmental Contamination Toxicology 10(4):497-504 (1981).


                       APPENDIX B: page 4 of 9
-------
39. Nigg, Herbert N., Leo G. Albrigo, Harold E. Nordby, and James H.
    Stamper:  A Method for Estimating Leaf Compartmentalization of
    Pesticides in Citrus.                                                 -7/10
    J. Agricultural and Food Chemistry 29:750-756 (1981).                 $ 4 
-------
50. Prasad, Rajendra-P. and  Ramasubbaiah.K.:
    Persistance of phosphamidon in bhendi crop Insecticide.
    Pesticides. Bombay: Colour Publications. 16(6): 25-26, 1982.

51. Pree.D.J.,  Butler,K.P.,  Kimball.E.R., and Stewart,D.K.R.:           343
    Persistance of foliar  residues of dimethoate and azinphosmethyl
    and their toxicity to the apple maggot rhagoletis pomonella.
    J Econ Entomol. 69(4): 473-478, 1976.

52. Rajput,G.B.,  Khaire.J.T., and Dethe.M.D.:
    Influence of dimethoate and endosulfan sprays on level of residues
    in/on brinjal fruits.
    CA/100/019264U
    J. Maharashtra Agric Univ.  8(3): 289-290, 1983.

53. Ramasubbaiah.K. and Lal.R.:
    Studies of residues of phophamidon in okra crop insecticides.
    Indian J Entomol. 36(4): 344-351, 1974 (Pub. Aug 1976).

54. Ramasubbaiah.K. and Lal.R.:
    Studies on residues of phosphamidon in cowpea crop
    drosophilamelanogaster, test insect.
    Indian J of Entomol.  37(2): 179-184, 1975.

55. Ramasubbaiah.K. and Lal.R.:
    Residues of phosphamidon insecticide in cabbage crop.
    Indian  J  Entomol. [New Delhi] 40(2): 182-186, 1978.

56. Ramasubbaiah.K. and  Lal.R.:
    Residues of phosphamidon insecticide in mustard crop.
    Indian  J  Entomol. [New Delhi] 40(3): 285-289, 1978.

57- Scheunert,!.  et al:
    Fate of 14C-allylalcohol herbicide in soils and crop residues.
    J.Environ.Sci.Health [B]. 16(6): 719-742, 1981.

58. Sharma.P.B. and Chopra,S.L.:
    Persistance of malathion residues on the cauliflower crop.
    Punjab Agr Univ J Res. 7(2): 216-220, 1970.

59. Shetgar.S.S., Pawar.V.M., Puri.S.N., Raodeo,A.K. and Patil,V.K.:
    Residues and residual toxicity of monocroptophos in/on okra leaves.
    CA/100/019262S
    J. Maharashtra Agric Univ. 8(3): 260-262, 1983.

60. Shinde,V.K.R. and Yadava.C.P.S.:
    Effect of crop stage on phosphamidon residues dissapation from some
    vegetables Eggplants, chilis, peas.
    Pesticides.  Bombay.  Colour Publications.  15(1): 20-21, 1981.

61. Shpirt.M.B. et al:
    Establishment of differential "wating times" in treating fruit and
    vegetable crops with organophosphate pecticides.
    Vopr-Pitan.  (1): 70-72, 1982.
                       APPENDIX B: page 6 of 9

                                               66
-------
62. Shpirt, M.B., Salpagarof,N.S., and Genis.V.I.:
    Predicting the waiting period for harvesting pesticide-treated
    agricultural crops.
    CA/099/018077J
    Zashch. Rast..(Moscow) 4: 19-20, 1983.                                344

63. Singh,M. and Yadav.P.R.:
    Persistance of dimethoate and methyl-demeton on and on pea pod
    shells and their movement into pea grains.
    KEEP/83/09586
    Indian J Entomol. 43(3): 306-311, 1981.

64. Singh,M. and Gupta,D.S.:
    Dissapation of monocroptophos in/on chickpea crop.
    CA/097/176960E
    Indian J Entomol. 43(4): 373-377, 1981

65. Smith,C.N.  et al:
    The persistance and dissapearance by washoff and dryfall of
    metoxychlor from soybean foliage.--a prelimanary study.
    J.Environ.Sci.Health [B].  16(6): 777-794, 1981.

66. Spear, R. C., T. H. Milby, J. T. Leffingwell and W. J. Popendorf.
    Some Findings on the Exposure and Response of Pickers in Parathion-
    Treated Orange Groves.  Delivered at the Annual Meeting of The
    American Chemical Society, Los Angeles.  April 3, 1974.

67- Spear, Robert C., William J. Popendorf, John T. Leffingwell, and
    David Jenkins.  Parathion Residues on Citrus Foliage.  Decay and
    Composition as Related to Worker Hazard.  Journal of Agricultural
    and Food Chemistry 23:808-810 (1975).

68. Spear,R.C., Lee.Y.S., Leffingwell,J.T., and Jenkins,D.:
    Conversion of organophosphate insecticide parathion to paraoxon in
    toxic foliar residues: effect of dust level and ozone
    concentration.
    J. Agric Food Chem. 26(2): 434-436, 1978.

69. Spencer,W.F., Cliath.M.M., and Davis,K.R.:
    Persistance of parathion and its oxidation to paraoxon on the soil
    surface as related to worker reentry into treated crops.
    Bull Environ Contam Toxicol. 14(3): 265-272, 1975.

70. Staiff, D.C., S. W. Comer, and R. J. Foster.
    Residues of Parathion and Conversion Product on Apple and Peach
    Foliage Resulting from Repeated Spray Applications.
    Bull. Environmental Contamination and Toxicology 14:135-9 (1975).

71. Strankowski.K.J. and  Stanley,C.W.:
    Determination of residues of Mesurol and its sulfoxide and sulfone
    in plant, animal and soil samples.
    HEEP/82/04059
    J Agric Food Chem.  29(5): 1034-1037. 1981.
                       APPENDIX B:  page 7 of 9

                                                 67
-------
72. Thompson, Neal P., Herbert N. Nigg, and Robert F. Brooks.
    Dislodgeable Residue of Supracide on Citrus Leaves.
    J. Agricultural and Food Chemistry 27:589-592 (1979).
                                                                        345
73. Verma.S. and Dikshit.A.K.:
    Persistance of different insecticides against pea leaf-miner,
    Phytomyza atricornis Meige
    Indian J. Agric Sci. 52(1): 4-6, 1982.

74. Ware, G.W., Betty Estesen, and W. P. Cahill:
    Organophosphate Residues on Cotton in Arizona.
    Bull. Environmental Contamination and Toxicology 8:361-2 (1972).

75. Ware, G.W., Betty Estesen, and W. P. Cahill:
    Establishment of Reentry Intervals for Organqphosphate-Treated
    Cotton   Fields Based on Human  Data:  I. Ethyl and Methyl
    Parathion.
    Arch. Environmental Contamination and Toxicology 1:  48-59 (1973).

76. Ware, G. W., Betty Estesen, and W. P. Cahill:
    Dislodgeable Leaf Residues of Insecticides on Cotton.
    Bull. Environmental Contamination and Toxicology 2:434-7 (1974).

77. Ware et al:
    Dislodgeable Leaf Residues of Insecticides on Cotton.
    Bull Env Contam & Tox.  11(5):       1974.

78. Ware, G.W., Betty Estesen, and W. P- Cahill.
    Establishment of Reentry Intervals for Organophosphate-Treated
    Cotton   Fields Based on Human Data:  II. Azodrin, Ethyl and Methyl
    Parathion.
    Arch. Environmental Contamination and Toxicology 2:117-129 (1974).

79. Ware, G.W., D.P- Morgan, Betty Estesen, and W. P. Cahill.
    Establishment of Reentry Intervals for Organophosphate-Treated
    Cotton   Fields Based on Human Data:  III. 12 to 72 Hours Post-
    Treatment   Exposure to Monocrotophos, Ethyl- and Metyl Parathion.
    Arch. Environmental Contamination and Toxicology 3:  289-306 (1975).

80. Westlake, W.E., F.A. Gunther, and G.E. Carman:
    Worker Environment Research: Dioxathion (Delnav) Residues On and In
    Orange Fruits and Leaves, in Dislodgeable Particulate Matter, and
    in   the Soil Beneath Sprayed Trees.
    Arch. Environmental Contamination and Toxicology  1(1): 60-83 (1973).

81. Westlake,W.E., Ittig.M., Ott.D.E., and Gunther,F.A.:
    Persistance of residues of the insecticide phosphamidon on and in
    oranges, lemons, and grapefruit, and on and in orange leaves and in
    dried citrus pulp cattle feed.
    J Agric Food Chen. 21(5): 846-850, 1973.

82. Winterlin, Wray, J. Blair Bailey, L. Langbehn, and C. Mourer:
    Degradation of Parathion Applied to Peach Leaves.
    Pesticides Monitoring Journal 8:263-269 (1975).
                       APPENDIX B: page 8 of 9
                                                         68
-------
83. Winterlin, W., W. Kilgore, C. Mourer, R. Mull, G. Walker, J. Knaak
    and K. Maddy:  Dilodgeable Residues of Dialifor and Phosalone and
    Their Oxygen Analogs Following a Reported Worker-Injury Incident in
    the San Joaquin Valley, California.                                    346
    Bull. Environmental Contamination and Toxicology 20:255-260 (1978).

84. Winterlin, Wray, Gregory Hall, Charles Mourer, and Glen Walker:
    Dissipation of Parathion and Paraoxon on Citrus Foliage Dust and
    Dry Soil Surfaces in a Treated Orchard.
    Arch. Environmental Contamination & Toxicology.  11(1):11-121 (1982).

85. Winkler.V.W.  et al:
    Determination of norflurazon residues in mixed crop matrices.
    J.Assoc.Off.Anal.Chem. 64(6): 1309-1311, 1981.

86. Woodham,D.W., Reeves,R.G., Williams,C.B., Richardson,H.,and Bond.C.:
    Residues of dimethoate and its oxygen analog on and in citrus leaves
    following a helicopter treatment of the trees with dimethoate ultra-
    low volume concentrate and high volume spray.
    J Agric Food Chem. 22(4): 731-733, 1974.

87. Woodrow et al:
    Airborne and Surface Residues of Ethyl Parathion and its Conversion
    Products in a Treated Plum Orchard Environment.
    Arch Env Contam & Tox. 6:175-191 (1977).
                       APPENDIX B: page 9 of 9
                                                             69
-------
                                                              APPENDIX C
Appendix Q: Computer database format for reported residues as
            corresponds to INPUT FILE SPECIFICATION (as used by
            program "REEN1").

                                                         format  column
 LINE "1" (precedes each set of residues)

     chem  = name of chemical (abbreviated)                 A8      1-8
             (see "chemco" file for abbreviation code)
     nart  « article number (within reference system)       12    10-11
     ndset = dataset number (within article)                12    13-14
     nox   = number of oxons or other metabolites           II       16
             reported (one line nameing each oxon
             required after optional comments); nox
             cannot be larger than nor can names differ
             in sequence from those specified in the
             library file "chemco".
     loc   = location by state [and within state]           A3    18-20
               AK = Arkanas
               AZ = Arizona
               CA « California
                  CAi = Imperial Valley. CA
                  CAs - Southern CA
                  CAv « Central Valley, CA
               FL • Florida
               GA = Georgia
               IND- India
               SC = South Carolina
               TX = Texas
               WA = Washington
     crop  = crop code (see "cropco" library file);         A6    22-27
             selective searching by crop is an option
             in some programs, e.g. reen2.
     igph  « blank (0) if data are from a table;            II       29
             1 if data points are taken from a graph.
     form  = formulation (eg. SEC, 25WP, etc.)              A4    31-34
               CP = encapsulated, n * % active by weight
               EC = emulsifiable concentrate, n = pounds
                    active per gallon
               WP = wettable powder, n = % active by
                    weight
     aia   = mixture active ingredient per acre             F5.2  36-40
             (Ibs./acre) [1 Liter/hectare =
             0.892 gal/acrej
     gpa   = gallons water per acre                         F4.0  42-45
     ex    = extraction solvent used:                        II      47
               blank/default = aqueous with surfactant
               1 * hexane      4 » Methylene chloride
               2 s chloroform  5 = benzene
               3 « toluene     6 = other (see comments)
                Appendix C, page  1 of 3

                                                                 70
-------
   unit  = units reported                                  II      49
             blank/default = ug/cm2                                      348
             1 = ng/cm2      3 = ppm
             2 = mg/m2       4 = other
   nside = number of sides of leaf used to calculate       II      51
             residue
             1=1 side      2=2 sides
   dapp  = day of application                              12   53-54
   mapp  = month of application                            A3   56-58
   yapp  = year of application                             12   60-61
   ncomm = number of comment lines [optional up to 6]      II      63
   npun  = number of punches total [if unusual]            13   65-67
   isd   = blank if standard deviation not given;           II      69
           1 if given.

LINE  "2" (comments as specified by "ncomm")
   comm  = [optional] 1-6 comment line(s)                A79    1-79
           usually used to cite source reference
           or observations on application, data, etc.

LINE "3"
   lox   = metabolite name(s), one per line;               A30    1-30
           the number of names is not optional but
           must match "nox" as listed in LINE 1.

LINE "4"
   nsam  = number of residue intervals measured;           12     1-2
           1 line [manditory] corresponding to the
           number of residue lines which follow.

LINE "5" (Residue data)
   dpa   = days post application                          F4.0    1-4
   res   = residue value, in units as given in            F6.0   6-11
           original data set, unless transformed
           to standard units (ng/cm2) by REEN1 or
           similar calculation.
   sd    = standard deviation, if reported;               F6.0  13-18
           negative values are % relative deviation
           (coefficient of variation).
           An example is provided on the following page:
              Appendix C, page  2 of 3
71
-------
          example from file "pholon":
11
column

c see
h che-
e mco
m

1
0
n |n
a |d
r |s
t |e
It


n
0
X


2
0
1
o
c


3 4
0 0
c see |ijf |A Jg
r cro-|g|o |I jp
o pco|p|r |A |a
P Xm I 1
it i i
ii i i
i
(
e|u
x|n
s|i
o!t
1|
> 6
) 0
n|d |m Jy
s|a |a |a
i!p IP IP
d|p IP IP
e| 1 I


njn
c!p
o|u
m|
11

C
i
s
d


                                                                                      349
          ethion  :34.01:1:FL :orgval:  :  4EC: 3.00:1200: :l:2:29:may:75:4:160:1.
          Nigg, et al: Dislodgeable Residues of Ethion in Florida Citrus and
            Relationships to Weather Variables.
          Arch Env Contam & Tox.   6:  257-267,  1977.
          wet season
          ethion monoxon
           6
0: 99.4: 5.5:
1: 61.2: 26.3:
3: 7.4: 5.4:
5: 3.0: .9:
7: 2.8: 2.0:
14: 2.0: .9:
ethion :34.02:1:FL
dry season
ethion monoxon
9
0: 141.6: 25.8:
1: 32.8: 8.7:
3: 23.3: 1.8:
5: 16.6: 2.3:
7: 17.1: .9:
14: 16.7: 4.3:
21: 5.1: 1.1:
28: 3.4: 1.7:
35: 4.1: .7:
ethion :34.03:1:FL
wet season
ethion monoxon
6
0: 284.6: 142.9:
1: 124.6: 52.4:
etc. . .
4.1:
6.0:
3.9:
2.7:
1.7:
1.3:
:orgval:



12.0:
2.5:
3.2:
4.0:
5.1:
4.8:
3.9:
2.2:
2.2:
:orgval:



42.2:
21.7:

2.3:
3.6:
1.4:
.8:
1.1:
.5:
: 4EC: 4.50:1200:



1.9:
.2:
.2:
1.1:
.1:
.5:
.5:
1.5:
.3:
: 4EC: 4.50:1200:



3.2: : :
AD.
*t . O . . .

                                                         :l:2:10:nov:75:1:160:1.
                                                         :l:2:29:may:75:l:160:l.
                          Appendix C,  page   3 Of 3
                                                                          72
-------
                                                             APPENDIX D
Appendix D: Computer library of Unified  Field Model Chemcial Coefficients.

            "CHEMCO" is a tabulation of  chemical codes, their
            dermal LD50 values, the number  of metabolites
            possible for each chemical,  common or trade names,
            and whether the reentry interval is regulated by EPA
            (E) or California (C)  or both.  Data for dial,
            ethion, and mep are replicated  in a second set of
            parameters with the suffix "2"  in which 1D50 values
            for the metabolites were assumed to be different from
            the parent.
                                                                            350
columns allocated to each parameter are as follows:
1-8 10-13 15 17-41(=25) 42-66(=25)

azinme

carbaryl
car bop



chlort



dcroto
demet
dial

dia!2

diaz
dimeth

dioxat
endrin
epn
ethion


ethio2


etp

malat
mep

mep 2

i i
220 1
7
4000
40 3
40
40
40
58 3



42
11
124 1

124 1
12
640
610 1

120
15
127
153 2


153 2
15
7
14 1
1
4444
67 1

67 1
7
i
azinphosmethyl
azinphosmethyl oxon
carbaryl
carbophenothion
carbophenothion oxon
carbopheno. sulfoxide
carbophenothion sulfone
chlorthiophos
chlorthiophos sulfoxide
chlorthiophos sulfone
chlor. oxon sulfoxide
dicrotophos
demeton or demeton-s
dialifor
dialifor oxon
dialifor
dialifor oxon
diazinon
dimethoate
dimethoxon
dioxathion
endrin
epn
ethion
ethion monooxon
ethion dioxon
ethion
ethion monooxon
ethion dioxon
ethyl parathion
ethyl paraoxon
malathion
methyl parathion
methyl paraoxon
methyl parathion
methyl paraoxon

[Guthion]

[Sevin] 63-252 FC59500
[Trithion]



21923-239 TF15900



[Bidrin]
[Systox]
[Torak]

[Torak]

[Spectracide]
[Cygon],[De-Fend]

[Delnav]
-chlorinated HC-
[EPN-300]
[Ethion], [Nialate]


[Ethion], [Nialate]


[Phostox] , [Thiophos]

[Carbophos]
[Metron]

[Metron]

69 71
i i _ i _ i
i i i i
E C
n ii

E C
ii n
n ii
n ii




E C
E C
C
n
C
II
C
C
It
C
E C
E C
E C
n n
n n
E C
n n
ft ti
E C
n n
C
E C
n n
E C
n n
                Appendix D,  page  1  of 2
73
-------
methom
met ion

mevin
monoos
naled
oxyme
phenth
pholon

phorat
phosam
phosmet
tepp
trie

73 1
20
4.5 1
119
800
165
700 1
1530 1
380
4
124
1550
2.4
2000
endosulf
                methomyl
                methidathion
                 methidathion  oxon
                mevinphos
                 beta isomer
                monocrotophos
                naled
                oxydemeton-methyl
                phenthoate
                 phenthoate oxon
                phosalone
                 phosalone oxon
                phorate
                phosphamidon
                phosmet
                tetraethyl pyrophosate
                trichlorfon
  carbosulfan
  chlorpyrifos
  disulfoton
2 endosulfan
   alpha isomer
   beta isomer
  propargite
  oxymyl
  sulphur
[Lannate],[NudrinJ
[SupracideJ

[PhosdrinJ

[Azodrin]                  E
[Dibrom]
[Metasystox-R]             E
Fenthoate 2597-037 AI78750

[Zolone]

[Thimet]
[Dimecron]
[Imidan]
[TEPP],[Vapotone]
[Dylox] 52-686  TA07000

[Advantage]

[Di-syston]
[Thiodan]
                                                       C
                                                       C
                                                             351
                                                       c
                                                       II
                                                       c
                                                       c
                                                       c
                                                       c
c
II
                                         [Omite]
                                         [Vydate]
                chemical requested not on  file
                Appendix D, page  2 Of 2
                                              74
-------
                                                                APPENDIX E
Appendix E: Computer library of Unified Field Model Crop Coefficients.      352

            "CROPCO" is a tabulation of crop codes, their dosing
            coefficient (K,), the full name of the crop, and the
            leaf density (estimated (as noted by ?) when
            otherwise unknown) to be used to convert residue
            reported as ppm to ng/cm2.  For purposes of
            calculation, when the K. was unknown,  a conservative
            (high) default value corresponding to  5.0 (5000
            cm2/hr) was assumed.  Some replicated  crop codes were
            included to match on crops coded within the data base
            which were sometimes right-justified.

            columns allocated to each parameter are as follows:
            1-6        12-15      23-42(=20)           45-48 49
1 1
apple
apple
artchk
cabbag
chili
chili
citrus
cotton
cowpea
grape
grape
graper
grapet
grapew
grapfr
lemon
lemon
lettuc
mustar
okra
okra
orange
orgnav
orgval
peach
peach
plum
plum
sobean
tobaco
tomato
wheat
kd
i i i i i
3.5
3.5




5.1


3.5
3.5
3.5
3.5
3.5
5.1
5.1
5.1
5.1



5.1
5.1
5.1
1.9
1.9
1.9
1.9




=
i i i i i i i
apple
apple
artichoke
cabbage
chili
chili
citrus
cotton
cowpea
grape-unclassified
grape-unclassified
grape-raisin
grape-table
grape-wine
grapefruit
lemon
lemon
lettuce-unclassified
mustard
okra
okra
orange-unclassified
orange-navel
orange- Valencia
peach
peach
plum
plum
soybean
tobacco
tomato-unclassified
wheat
crop not on file

20. ?
20. ?




27.5
25. ?

21.
21.
21.
21.
21.
27.5
27.5
27.5




27.5
27.5
27.5
20. ?
20. ?
20. ?
20. ?

20. ?
17. ?

=
                                                              ug/cm2
                Appendix E,  page  1  of 1
                                                            75
-------
APPENDIX F: A Generalized Pesticide Decay Model Computer Program.        _    353

     In order to interpolate, extrapolate, and tabulate two-component (parent
and one metabolite) residue data from any single study to a preselected
criterion for re-entry days of specific interest to this report, a generalized
four compartment submodel was investigated.  This submodel is depicted
diagrammatically in Figure Fl.  Conceptually, this submodel assumes that
the initial parent residue is partitioned on the leaf into fast and slow
decay compartments.  Each of these compartments can decay by two routes; it
can either become a metabolite (e.g. oxon) or become nonmeasureable (e.g.
by vaporization, hydrolysis, leaf absorption, etc).  One additional route
attributable to material in the fast decay compartment is a transition by
various mechanisms from the relatively unstable (fast) compartment into the
environmentally protected (slow) decay compartment.  The metabolite is also
partitioned into two parallel fast and slow compartments both initially and
as a result of decay from the parent, and both metabolite compartments are
susceptable to transitional pathways identical to their corresponding
parent compartments.
     In nature, the partitioning is dictated by natural forces.  In a model,
the partitioning is accomplished by fitting coefficients of the equations
characterizing each compartment and pathway to the experimental data .
Pesticide decay is most commonly characterized by first-order differential
equations, i.e. that the rate of decay is proportional to the amount material
present (e.g. Popendorf and Leffingwell, 1978).  The subscript numbers
assigned in Figure Fl were chosen pragmatically to permit the modelling of
single component pesticides (those without measureable metabolites) by only
the first 5 coefficients, versus 12 for two component (four compartmet)
residues.  Thus, the amount of material present in each compartment is
designated as follows:
                   fast compartment      slow compartment
     parent              a1                    a^
     metabolite          a..                   a..

The corresponding kinetic coefficients were designated as follows:
                   fast compartment      slow compartment
     parent to           a.                    a,
       nonmeasureable
     parent fast         a..
       to slow
     parent to           a&                    a._
       metabolite
     metabolite  fast    a7
       to slow
     metabolite  to      a.                    a.
       nonmeasureable

     A generalized pesticide decay FORTRAN computer program was developed
during this study to fit the coefficients to the reported data.  A number of
difficulties were encountered with this approach, among which were that it was
time consuming (requiring considerable operator interaction), the data was
often sparse or did not include initial residues, and the improvements in
smoothing the variability among reported residues within any one study was
outweighed by the much larger variability between studies for any given
pesticide.  Nonetheless, the following program can estimate the coefficients of
the model described above.

                                                            76
                     APPENDIX F: page  1 of 22
-------
                                              354
APPENDIX F: page  2 of  22        -      ' '
-------
                                                              355
  "fast"
  Parent
  "fast"
Metabolite

    An
                               Y
                   nonmeasureable
  "slow"
  Parent
   "slow"
Metabolite

    A12
   Figure Fl.   Schematic diagram of generalized pesticide
   decay model with zero or one metabolite.  Coefficients
   defined as  used in accompaning algorithm.
                                                  78
-------
      program REPAID
APPENDIX  F
       356
C	 EPA version of Rose MAIN LINE PROGRAM
C	 originally adapted from ROSENBROCK HILLCLIMB program in
C                'OPTIMIZATION TECHNIQUES WITH FORTRAN'
C                   J.L. KUESTER & J. MIZE PG 386.
C	 THIS SPECIAL HILLCLIMB PACKAGE WAS WRITTEN FOR RESIDUE DECAY ANALYSIS
C     with a model of 10 coefficients.
C	 MODIFICATIONS FOR IBM 1130 BY W. POPENDORF (1974)
C	 further modifications in lower case for FORTRAN 77 by W. Popendorf (1984)
C     USE IN GOOD HEALTH.


C	 THE FOLLOWING VARIABLES can be read from a special RPRAM file
C             [defaults are provided for in all cases following line 36]:
C     LOOPY = MAXIMUM NUMBER OF STAGES TO BE CALCULATED  (10 to 20 IS GOOD)
C     NCOEF = NUMBER OF COEFFICIENTS (UNKNOWNS) WITH FIRST GUESSES
C             [usually set to 0 to use defaults]
C     NOASK = an index setting logical LASK to bypass user options after
C             selecting filenames, thus operating in an unattended batch mode
C     NSTEP - STEP SIZE CONTROLER: 0 FOR ORIGINAL STEP SIZE AFTER ROTATION
C                                  1 FOR RETENTION OF SIZE PRIOR TO ROTATION
C     PR      NUMBER OF STAGES BETWEEN PRINTED OUTPUTS  [3 is default];
C             A stage is when all E's have reversed direction twice.
C             Because 2 is an even number, this routine has a tendancy to
C             overestimate all coefficients (which for pesticide decay tends
C             to underestimate especially oxons); so it is better to
C             underestimate the value of each first guess.
C     delF  = THE ACCEPTABLE LIMITING DIFFERENCE IN THE OBJECTIVE FUNCTION
C             BETWEEN PRESENT AND PRIOR STAGE  [l.OE-6]
C     EV      Sets VECTOR OF INITIAL STEP SIZES [ 0.01 ];
C             original ROSE permitted setting individual E's.
C	 the following are PROGRAMMED VARIABLES
C     DA    = A GENERAL STORAGE VECTOR (MAX LENGTH LIMITED BY DIMENSION)
C     DELE  = THE MINIMUM STEP SIZE BEFORE PLOTTING RESULTS FOR USER
C     LA      MAXIMUM NUMBER OF NCOEF PERMITTED BY ARRAY LIMITS AS WRITTEN
C     MM    = +1 FOR MAXIMIZATION	 -1 FOR MINIMIZATION
C     NCC   = NUMBER OF VARIABLES PLUS IMPLICIT CONSTRAINTS (SPECIFIED BY CX)
C     N     = NCC (see line 40-2)
C     NinDA = NUMBER OF DATA POINTS TO BE READ AND STORED IN DA
C     NIV   = NUMBER OF INPUT VARIABLES (KNOWNS) WITHIN STORED DATA SET
C     NPAR  = MAXIMUM NUMBER OF DA PERMITTED BY ARRAY LIMITS AS WRITTEN
C     X       MULTIPLICATION or Sealer FACTORS UPON FIRST GUESSES;
C             these are the variables actual optimized.
C
C	 INPUT FILE SPECIFICATION (same file as for "REEN1)
C     chem  = name of chemical (abbreviated)                  A8
C             (see LDvals file for abbreviation code)
C     nart  = article number                                  12
C     ndset - dataset number                                  12
C     nox   = number of oxons or other metabolites reported   II
C     loc   = location by state (except for CA: location by   A3
C               region within state, eg.


                     APPENDIX F:  page  3 of 22               79
-------
C               AZ = Arizona
C               CA « California                                         357
C                  CAi • Imperial Valley, CA
C                  CAs « Southern CA
C                  CAv = Central Valley, CA
C               FL = Florida
C               SC = South Carolina
C               TX = Texas
C               WA « Washington
C     crop  = crop code (see cropco file)                     A6
C     igph  * blank (0) if data are from a table,             II
C              1 if data points are taken from a graph
C     form  » formulation (eg. SEC, 25WP: etc.)               A4
C               CP = encapsulated
C               EC * emulsifiable concentrate
C               WP = wettable powder
C     aia   * active ingredient per acre (Ibs./acre)          F5.2
C     gpa   = gallons water per acre                          F4.0
C     ex    = extraction solvent used:                        II
C               blank/default * aqueous
C               1s hexane      4 « Me chloride
C               2     - chloroform  5 » benzene
C               3     - toluene     6 = other (see comments)
C     unit  = units reported                            *     II
C               blank/default « ug/cm2
C               1     * ng/cm2      3 * ppm
C               2     « mg/m2       4 « other
C     nside = number of sides of leaf used to calculate       II
C               residue
C               1=1 side      2 « 2 sides
C     dapp  = day of application                              12
C     mapp  = month of application                            A3
C     yapp  = year of application                             12
C     ncomc «= number of comment cards, up to 6                II
C     npun  = number of punches total                         13
C     isd   = blank if standard deviation not given,          II
C             1 if given
C     comm  = comment line                                    A80
C     lox   = metabolite name                                 A30
C     nsam  = number of residue intervals measured            12
C     dpa   = days post application                           F4.0
C     res   = residue value, in units as given in             F6.0
C             original data set
C     sd    * standard deviation, if given                    F6.0
C

      INTEGER      PR.NE(IO)
      REAL         LC,DA(120)
      real         AL(10),B(10t10).BX(10),D(10),E(10),EINT(10).H(10)(
     +             PH(10),V(10,10),W(10,10),X(10),Z(10)
      integer      ex, unit, dapp, yapp
      real         dpa(15), res(15,3), sd(15,3), adif(3),pdif(3)
      character*!  file
      character*3  loc, mapp
      character*4  form
                     APPENDIX F: page  4 of 22
80
-------
       character*6   crop
       character^S   chem
       character'" 12  input, output, newin
       character*30  lox(2)
       characterise  spec, comro(6), data(15)
       logical      lfile,lask,init,term

 C	  ni = unit specifier for input filename
 C	  no =  "      "       "  output   "  various fit results
 C....  nu =  "      "       "  user (screen and console)
 C	nx =  "      "       "  new input file with coefficients
 C	npram =      "       "  rpram  "    "  (rose parameters)
       NU    =0
       NI    =2
       NO    =3
       NX    = 4
       NPRAM = 5
       maxc  = 8
       open (npram,file='rpramlO')

     2  write  (nu,1003)
  1003  format (' Type pesticide INPUT filename, up to 12 characters:1)
 C....  (for code, see LDvals library file)...
       read   (nu.lOOA) input
  1004  format (a!2)
       if     ((input .eq. 'quit').or.(input .eq. 'exit').or.
     +       (input .eq. 'stop').or.(input .eq. 'end')) go to 30000
  1006  format (al)
       open   (ni, file=input)
       rewind ni
       xv    =0.0

       write  (nu,1007)
  1007  format (/,' A duplicate inputfile SHALL be created herein with new
     + model coefficients',/,' Type NEW filename, up to 12 characters:')
       read   (nu,1004) newin
       open   (nx, file=newin, status='new')

       write  (nu.1008)
  1008  format(/,' Do you want model results also written to disk? Y/N ')
       read   (nu,1006) file
       call yes(file.lfile)
       if      (.not. Ifile) go to 20

       write  (nu,1009)
  1009  format  (' Type general OUTPUT filename,  up to 12 characters:1)
       read    (nu,1004) output
       open    (no, file=output, status='new*)
       rewind  no

   20 continue
C.... reading data specification line
      read   (ni,2001,end=24) chem,nart,ndset,nox,loc,crop,igph,form,
     +                      aia,gpa,ex,unit,ns ide,dapp,mapp,yapp,ncomc,
     +                      npun.isd
        358
                     APPENDIX F: page  5 of 22
81
-------
      write  (nu,2001      ) chem,nart,ndset,nox,loc,crop,igph,form,
     +                      aia.gpa,ex,unit,nside.dapp.mapp,yapp,ncomc,
     +                      npun.isd
 2001 format (a8,Ix,2(12,Ix),il,lx,a3,Ix.a6,Ix,il,lx,a4,lx,f5.2,Ix.f4.0
     +        ,lx,3-(il, Ix),i2,lx,a3,Ix,i2,lx.il,Ix,i3,lx.il)
      backspace ni
      read   (ni,2002) spec
      nres  = nox+1
      NCOEF « 4+(2*nox)
      if     (nsam .eq. 2 .and. nox .eq.  0) NCOEF=2

C.... reading up to 6 comment lines
      read   (ni,2002) (comm(i),i=l,ncomc)
      write  (nu,2002) (comm(i),i=l,ncomc)
 2002 format (a80)
C.... reading the name of the oxons or other metabolites
C.... and model coefficients 9 and 10
      read   (ni,2003) lox,da(9),da(10)
      write  (nu,2003) lox,da(9),da(10)
 2003 format (2a30,2el0.4)
C.... reading the number of samples in the following set of data,
C.... also the number of previous runs and goodness of  fit  indices
      read   (ni,2004) nsara,nruns,(pdif(kj.k*!,nres),(adif(k),k=l,nres)
      write  (nu,2004) nsam,nruns,(pdif(k),k=l,nres),(adif(k),k=l,nres)
 2004 format (I2,i3,6el0.4)
C.... reading residue data ...
      do 21 j = l.nsam
      read   (ni,2005) dpa(j),(res(j,k),sd(j,k),k=l,nres)
      write  (nu,2005) dpa(j),(res(j,k),sd(j,k),k=l,nres)
      backspace ni
      read   (ni,2002)   data(j)
   21 continue
 2005 format (f4.0,lx,6(f6.0,lx))
C.... reading model coefficients 1-8
C.... do-loop sets da s previous A (or Z)
C.... unless this is the first run of program with new  data  (pesticide)  file
C.... then loop sets da = 0 in preparation for subroutine "TX".
      DO 22 i = l.maxc
      if     (xv .ne. 1.0) z(i)=0.0
      da(i) = z(i)
   22 continue
      if     (nruns .eq. 0) go to 23
      READ   (NI.2006) (DA(i),i=l,maxc)
      write  (nu,2006) (DA(i),i=l,maxc)
 2006 FORMAT (8E10.4)

   23 if     ((nsam*nres) .ge. ncoef) go  to 35
      write  (no,1013) nart.ndset
      write  (nu,1013) nart.ndset
 1013 format ('  The data for article1,i3,' dataset',i2,
     +       '  are insufficient to model!1)
      go to 20
   24 write  (nu,*) 'Reading end of old input datafile.'
      go to 2
                     APPENDIX F:  page  6 of 22              Q2
-------
 C....  subroutine  to convert data to ug/cm2 projected area
 C....  units  may be nonstandard and must be individualy converted.
    35  call units(res,sd,nsam.nres,unit,nside,crop)
       if      (unit  .It. 4) go to 36
       write   (no,1014) nart, ndset
       write   (nu,1014) nart, ndset
  1014  format (' Nonstandard units in article1,i3,' dataset',i3,/,
     +        ' - further calculations are discontinued!1)
       go  to  20
    36  continue

       CALL tx(DA,dpa,res,nsam.nres,ncoef)

       lask  = .true.
       rewind npram
       read   (npram,4001) loopy,nco,noask,nstep,pr.delF.ev
  4001  format (5i2,2fl0.9)
if
if
if
if
if
if
if
LA
dele
MM
NCC
next
niv
NinDA
(loopy .eq.
(nco .ne.
(noask .eq.
(nstep .ne.
(pr .eq.
(delF.eq. 0
(ev .eq. 0
= 10
= l.e-8
= -1
= ncoef
= 1
= 2+nox
0)
0)
0)
0)
0)
.0)
.0)






loopy =
ncoef =
lask •
nstep =
pr
delF -
ev =






20
nco
.false.
1
3
l.e-6
0.01






= NCOEF+(nsara*niv)

                                                                        *
                                                                        *
   37 CONTINUE                                                          *
      XV    = 1.0                                                       *
      DO 200 K = l.maxc                                                 *
      E(K)  = EV                                                        *
      X(K)  = XV                                                        *
      Z(K)  =0.0                                                       ft
  200 CONTINUE                                                          *
  300 CONTINUE                                                          *
      WRITE  (nu,3013) nart, ndset,ncoef,nsam,chem,crop                 *
 3013 FORMAT (///,' Article Number1,13,', data set #',I3,/,f Results of1*
     +,i3, ' coefficient decay model for ',i2,lx,a8,' sample points on '*
     +,a6, '.')                                                         *
      if     (.not. Ifile) go to 39                                     *
      write  (no,3013) nart, ndset,ncoef,nsam,chem,crop                 *

C	 pseudoORIGINAL ROSE BEGINS HERE
   39 INIT   - .true.
      TERM  . = .false.
      LAP    =0
      LOOP   « 0
      KOUNT  = 0
      Fl     = 0.0

                                                              :       83
                    APPENDIX F: page  7 of 22
-------
      DO 40 K= l.NCC
   40 AL(K)  = (CH(X,DA,NCC,NinDA,K)-CG(X.DA,NCC,NinDA,K))*.001
      DO 60 J= l.NCOEF                                                   361
      DO 60 1= l.NCOEF
      V(I,J) = 0.0
      IF       (I-J) 60,61,60
   61 V(I,J) = 1.0
   60 CONTINUE
      DO 62 K= l.NCOEF
      EINT(K)= E(K)
   62 CONTINUE
C...
   64 continue
      write  (nu,1020)                                                  *
 1020 format (' Stage/Limit Stage/Print_Limit  #_F._Evals.   Avg._F_Error*
     +     Avg. Step Size1)            ~        ~                       *
   65 DO 70 K  = l.NCOEF
C.... line 65 starts new stage
C	 Nstep=0 means return to original step size (EINT) after rotation
      IF     (NSTEP-0) 67,66,67
   66 E(K)     - EINT(K)
   67 NE(K)    = 0
   70 D(K)     « 0.0
      FBEST    - Fl
C.... an implicit loop on I from 1 to ncoef starts here, ends line 441.  .  .
   80 I        =1
      IF     (INIT)  go to 120
   90 DO 95  K = l.NCOEF
   95 X(K)     = X(K) + E(I)*V(I,K)
  100 DO 110 j " 1,ncoef
      XC       • CX(X,DA,NCC,NinDA,J)
      LC       = CG(X,DA,NCC,NinDA,J)
      UC       = CH(X,DA,NCC,NinDA,J)
      if (xc .le. Ic) go to 420
      if (xc .ge. uc) go to 420
  110 continue

  120 Fl       = MM * F(X,DA,NCOEF,NinDA.NIV.dpa,res,nres.nsam)
      KOUNT    • KOUNT + 1
      IF (INIT) FO=MM*l.E+38
      IF (ABS(FBEST-Fl) .le. delF) go to 125
  122 TERM     - .true.
      GO TO 450
  125 CONTINUE

C.... an implicit loop on J from 1 to ncoef starts here, ends line 211.  .  .
      J        - 1
  130 h(j)     - fO
*     the following section preceded by * was bypassed from the original prog.
*     BW    '   • AL(J)
*     XC       • CX(X,DA,NCC,NinDA,J)
*     LC       - CG(X,DA,NCC,NinDA,J)
*     UC       = CH(X,DA,NCC,NinDA,J)


                     APPENDIX F: page  8 of 22
                                                                   84
-------
C ---- remember, F1=MM*F. FO=lastFl, and MM=-1 for minimum error fit;
C ---- thus, it is desired that Fl be .gt. FO; else, change direction.
      IF (Fl .It. FO) go to 420
*     IF (XC .It. (LC+ALU))) go to 160
*     IF (XC .gt. (UC-AL(J))) go to 170
*     GO TO 210
* 160 PW    = (LC+BW-XO/BW
*     GO TO 180
* 170 PW    = (XC-UC+BVO/BW
* 180 PH(J) = 1.0-3.0*PW+4.0*PW**2-2.0+PW**3
* 190 Fl    = H(J)+(F1-H(J))*PH(J)

* 210 CONTINUE
*     IF (J-NCC)        211,220,211
* 211 J     -  J+l
*     GO TO 130
C.... end of implicit J loop ..................
  220 INIT  = .false.
            = 3.0*E(I)
      FO    = Fl
      IF (NE(I) .EQ. 0) NE(I) » 1

  230 DO 240  K - l.NCOEF
      IF (NE(K) .LT. 2) GO TO 440
C.... note a "stage" is when ALL X advances have changed direction twice.
  240 CONTINUE
C	 AXES ROTATION  . . to line 420 ...
C	 BMAG » progress
C	 BBMAG = lateral progress
      DO 250 Kl = l.NCOEF
      DO 250 K2 = l.NCOEF
  250 W(K1,K2) = 0.0
      DO 260 Kl = l.NCOEF
      DO 260 K2 - l.NCOEF
      DO 265 K3 = Kl.NCOEF
  265 WU1.K2) = D(K3)*V(K3,K2)+W(K1,K2)
  260 B(K1,K2)  = W(K1,K2)
      BMAG      = 0.0
      DO 280 K '= l.NCOEF
      BMAG      = BMAG+B(1,K)**2
  280 CONTINUE
      BMAG      = SQRT(BMAG)
      BX(1)     = BMAG
      DO 310 K  = l.NCOEF
  310 V(1,K)    = B(1,K)/BMAG

      DO 340 Kl = 2.NCOEF
      DO 340 K2 = l.NCOEF
      SUMVM     » 0.0
      DO 330 K3 = 1.K1-1
      SUMAV     - 0.0
      DO 320 K4 « l.NCOEF
  320 SUMAV     = SUMAV+W(K1,K4)*V(K3,K4)
                     APPENDIX F: page  9 of 22       '       05
-------
  330 SUMVM     = SUMAV*V(K3,K2)+SUMVM
  340 BCK1.K2)  = W(K1,K2) -SUMVM

      DO 360 Kl = 2.NCOEF                                                 363
      BBMAG     = 0.0
      DO 350 K2 = l.NCOEF
  350 BBMAG     = BBMAG+B(K1,K2)**2
      BBMAG     - SQRT( BBMAG)
      DO 360 K2 = l.NCOEF
  360 V(K1,K2)  - B(K1,K2)/BBMAG

      EA        = 0.0                                                   *
      DO 399  k = l.ncoef                                               *
      EA        - EA + abs(E(k))                                        *
  399 continue                                                          *
      EA        » EA / ncoef                                            *
      Ferr      = FO / (nsam*nres)                                      *
      LOOP      * LOOP+1
      LAP       = LAP+1
      write  (nu,1021) loop, loopy, lap, pr.kount, Ferr, EA                  *
 1021 format (3x,i2,' / ',i2,5x,i3,' /' ,i2,10x,i5,8x, Ipel2.5,5x,lpel2.5)*
      IF (LAP-PR)               65,450,65
C. . . . line 420 etc either (1) lists coefficients and indicates starting point
C. . . . has violated constraints if INIT is true, or
C. . . . (2) undoes array step advance (E) and reverses E(I) 2/3 of a step.
  420 IF (INIT) go to 450
  421 DO 430 K = l.NCOEF
  430 X(K)     « X(K) - E(I)*V(I,K)
      IF (abs(E(I» .It. DELE)   go to 122
      IF (NE(I) .ge. 1)          NE(I) » 2
      GO TO 230

  440 CONTINUE
      IF (I-NCOEF)        441,80,441
  441 I        = 1+1
      GO TO 90
C.... end of implicit I loop ..............
  450 continue
      DO 455 I * l.NCOEF                                                *
  455 Z(I)     « X(I)*DA(I)                                             *
C ____ PRINT CURRENT VALUES OF X AND Z (COEFFICIENTS)
      WRITE  (nu,3005)
 3005 FORMAT (/,'  Sealers to 1st Guesses,  Model Coefficients,     and S*
     +tep Sizes')
      WRITE  (nu,3006) (I.X(I).I.Z(D.I.E(I), 1=1, NCOEF)
 3006 FORMAT (: ,4X,2HX( ,I1,4H) - ,OpF10.6,5X,2HA( ,I1,4H) = ,  1PE11.4,
     +                                    3X,2HS(,I2,4H) - .  1PE12.4)

      LAP      • 0
  461 IF (INIT)        go to 470
  462 IF (TERM)        go to 480
  463 IF (LOOP-LOOPY)  64,480,480
                     APPENDIX F: page  10 of 22
-------
  470 WRITE  (nu,007)
    7 FORMAT (///, 2X,
     + 'THE STARTING POINT APPEARS TO HAVE VIOLATED THE CONSTRAINTS')
               -o.o                                                        354
      go to 37

  480 CONTINUE
      if (.not. Ifile) go to 601
      WRITE  (NO,3003)
 3003 FORMAT (' At exit1, /,2X,5HSTAGE,4X,8HFUNCTION,5X,8HPROGRESS,5X,
     +         16HLATERAL PROGRESS,3X,'# of F EVALS.1  )
      WRITE  (NO,3004) LOOP,FO,BMAG,BBMAG,KOUNT
 3004 FORMAT (1H .I4.3E15.5,10x,I5)
      WRITE  (NO,3005)
      WRITE  (NO,3006) (I,X(I),I,Z(I).I,E(I),  I=1,NCOEF)                 *

C	This section TO LIST AND PLOT THE MEASURED AND PREDICTED VALUES.
C	THE TELL SUBROUTINE WAS DEVELOPED DURING THE INITIAL PLANNING (1974),
C	BUT RPI WAS DEVELOPED FROM 'DKRP1 DURING 1976.   SEE PROGRAM DK.

  601 continue
  605 CALL RPI  (   z.ncoef,dpa,res,nsam,nres,nart,ndset,next,lfile)
      CALL TELL (da,z,ncoef,dpa,res,nsam,nres.nart.ndset,next,Ifile,
     +           adif.pdif)

C.... ask if coefficients are OK,  then
C.... recalulate if necessary or go on to next set of  data
      if (lask) go to 611
  607 write  (nu,3021)
 3021 format (' Type ''//'' of any coefficient you wish to reset,',
     +      /,'    a ''II11 if you wish to reoptimize  from here,  or1,
     +      /,'    a "0" if this is OK and to continue:1  )
      read   (nu,*) next
      next = next +1
      go to  (611,612,612,612,612,612,612,612,612,612,612,619),  next

C—. the following write statements  recreate the specification line,
C.... comment line(s), metabolite-name line,  sample-number line,
C.... residue lines, and coefficient  lines,  respectively.
  611 nruns = nruns+1
      write  (nx,2002)  spec
      write .(nx,2002) (comm(i),i=l,ncomc)
      write  (nx,2003) lox,z(9),z(10)
      write  (nx,2004) nsam,nruns,(pdif(k),k=l,nres),(adif(k),k=l,nres)
      write  (nx,2002) (data(i),i=l,nsam)
      write  (nx.2006) (z(j),j=l,maxc)
      next  = 0
      CALL RPI  (   z.ncoef,dpa,res,nsam,nres.nart.ndset,next,Ifile)
      CALL TELL (da,z.ncoef,dpa,res,nsam,nres.nart.ndset,next,Ifile,
     +           adif.pdif)
      go to 20

 612 next  = next - 1
      write  (nu,3023) next.z(next)
 3023 format ('  The current value  of  coef(',i2,') = MpelO.4,/,


                     APPENDIX F: page  11  of 22
-------
     +        '  Type the desired new value; minus value to end1)
      read   (nu,*) z(next)
C.... a "backdoor" exit with filesave is provided here for user to manually     _
C.... set any coefficient to a MINUS value.  Use it in good health.             365
      if     (z(next))   615,613,614
  613 z(next) = da(next)*x(next)
      go to 607
  614 da(next)= z(next)
      x(next) =0.0
      write  (nu,3024)
 3024 format ('  Type "//" of another coefficient you wish to reset,1,
     +      /,'     a "11" to see fit with reset coefs, or1,
     4-      /,'     a "0" to reoptimize from here,1  )
      read   (nu,*) next
      next = next +1
      go to (619,612,612,612,612,612,612,612,612,612,612,605), next

  615 z(next) * da(next)*x(next)
      nruns   = nruns+1
      write  (nx,2002)  spec
      write  (nx,2002) (comm(i),i=l,ncomc)
      write  (nx,2003) lox,z(9),z(10)
      write  (nx,2004) nsam,nruns,(pdif(k),k=l,nres),(adif(k),k=l,nres)
      write  (nx,2002) (data(i),i=l,nsam),
      write  (nx,2006) (z(j),j=l,maxc)
      next * 0
      CALL RP1  (   z,ncoef,dpa,res,nsam,nres,nart,ndset,next,Ifile)
      CALL TELL (da,z,ncoef,dpa,res,nsam,nres.nart.ndset,next,Ifile,
     +           adif.pdif)
      go to 2

  619 next = 1
      DO 620 j = l.ncoef
  620 if (x(j) .ne. 0.0) da(j)-da(j)*x(j)
      go to   37

C	 A few notes on the logic of "next"
C     initially set next * 1 at line 36+7.
C     in RP1  line 130+2: if next=0 (and other conditions) the plot is copied
C                      onto file NO for later examination.
C     in TELL line 1+2: unless next=0, results will not be written to file NO;
C             line 96: if next=0, model error is not typed to the screen (in
C                      otherwords, next should = 0 only on last pass thru TELL)
C     in REPA line 607+1: read next from screen, il, therefore -Knext<10;
C                  607+2: next=next+l and used in computed goto;
C        if next=0, next reset=0,
C                   call TELL (for final output),
C                   goto 20 and on to 36+7 as initially run;
C        if 0
-------
      subroutine units (res,sd.nsam.nres,unit,sides,crop)
C	 to convert residue units to ug/cm2 and 1-side (projected area)         "^ 6 6
C       unit   = units reported
C                  blank/default = ug/cm2
C                  1       ng/cm2      3 = ppm
C                  2     = mg/m2       4 = other
      integer     sides, unit
      real        res(15,3), sd(15,3)
      character*6 crop

      if (unit .eq. 0) go to 17
       do 16 k=l,nres
          do 15 j=l,nsam
             if (unit.gt.l) go to 12
             res(j,k) = res(j,k)/1000.
             go to 15
   12        if (unit.gt.2) go to 13
             res(j.k) = res(j,k)/10.
             go to 15
   13        if (unit.gt.3) go to 20
             call conppm (res,sd,crop,unit,nsam.nres)
   15        continue
   16     continue
   17  if (sides.ne.2) go to 20
       do 19 k=l,nres
          do 18 j=l,nsam
             res(j.k) = res(j,k)*2.
   18        continue
   19     continue
   20  continue
       return
       end
C	
      Subroutine conppm (res,sd,crop,unit,nsam.nres)
C.... to convert ppm to ng/cm2
      integer     unit
      dimension   res(15,3), sd(15,3)
      character*6 crop
C.... for now, hand calc...
      write (nu,*) ' residue data must be converted from ppm1
      unit * 9
      return
      end
C	
      subroutine tx (da,dpa,res,nsam,nres,ncoef)
C.... to provide preset guesses into DA as a first starting point
C     if the coefficients in input datafile were zero (previously
C     estimated coefficients will be used after the first dset), and
C	 to copy dpa and residue data into remaining DA array expected by ROSE;
C     in the process, it is assumed that any dpa-0 (exactly) can in reality
C     be no less than 0.1 (or two hours post application).

      real da(120),dpa(15),res(15,3),wag(10)
      data wag/1.,.3,.3,.3,.05,.1,.3,.05,.05,.1/

                                                               89
                     APPENDIX F: page  13 of 22
-------
      if (da(l) .ne.  0.0) go to 20
      do 10  k = l.ncoef
      da(k)    = wag(k)
   10 continue                                                          '      7 £ 7
   20 do 30  j = l.nsam                                                       ^ ° '
      k        = ncoef+l+((l+nres)*(j-l))
      da(k)    = dpa(j)
      do 30  i = l.nres
      da(k+i)  = res(j.i)
   30 continue
      if (dpa(l) .eq. 0.0) da(ncoef+l)=0.1
      return
      end
C	
      FUNCTION  F(X,DA,N,NinDA,NIV,dpa,res,nres,nsam)
      DIMENSION X(10),DA(120),A(10),dpa(15),res(15,3)
      DO 2 I - 1,N
      A(I)   = DA(I)*X(I)
    2 CONTINUE
      FX     = 0.
O»»USER WORKING AREA	««««««««««««««««««
   27 CONTINUE
      A23    = a(2)+a(3)
      A234   = A23 +a(4)
      A56    = a(5)+a(6)
      A78    = a(7Ha(8)
      Alx3   = a(l)*a(3)
      A13d6  = Alx3/(A56-A234)
      AlxA   = a(l)*a(4)
      A14d8  - AlxA/(A78-A23A)
      if (nres .gt. 3) STOP 'NRES error in FUNCTION F1
      DO 70 i= l.nsam
      di     = dpa(i)
      if (dpa(i) .eq. 0.0) di=0.1
      go to (55,53,51),nres
   51 Stop 'No model is provided for 2 metabolites!1
   53 F2     =                (A14d8*(exp(-A234*di)-exp(-A78 *di)))      XI
     + +(((a(7)*A14d8)+(a(6)*A13d6))*(exp(-A234*di)-exp(-a(9)*di))/
     +                                                     (a(9)-A23A))  X2
     + +(((a(7)*AUd8/(a(9)-A78)))  *(exp(-A78 *di)-exp(-a(9)*di)))      X2
     + +((a(6)*(a(10)-A13d6))       *(exp(-A56 *di)-exp(-a(9)*di))/
     +                                                     (a(9)-A56))   X2
      IF (res(i,2) .gt. 0.0 .and. F2 .gt. 0.0)
     +    FX = FX + exp(ABS(alog(res(i,2)/F2))) -1.

   55 Fl     = (a(l) *exp(-A234*di))
     + +   (A13d6*exp(-A234*di)) + ((a(10)-A13d6)*exp(-A56*di))
      IF (res(i.l) .gt. 0.0 .and. Fl .gt. 0.0)
     +    FX - FX + exp(ABS(alog(res(i,l)/Fl))) -1.
   70 continue
O»»USER WORKING AREA	«««««««««««««««««««
    1 CONTINUE
      F  « FX
      RETURN
      END


                     APPENDIX F: page  14 of 22

                                                                    90
-------
      FUNCTION CX(X,DA,N,NinDA,K)                                          368
      DIMENSION X(10). DA(120)
      CX = X(K)
      RETURN
      END
C	
      FUNCTION CG(X,DA,N,NinDA,K)
      DIMENSION X(10),DA(120)
      CG = 0.0
      RETURN
      END
C	
      FUNCTION CH(X,DA,N,NinDA,K)
C—. to limit the upper bounds of X
      DIMENSION X(10),DA(120)
      GO TO (10,10,50,50,100,50,100,10,50,10),K
   10 CH = 10.
      RETURN
   50 CH = 50.
      RETURN
  100 CH = 100.
      RETURN
      END
C	
      SUBROUTINE RP1 (a.ncoef,x,yor,nsam,nres,nart,ndset,next,lfile)
      character*! MARK(8). LINE(62,16)
      real x(15),y(15,3),yor(15,3),a(lO),vx(7)
      logical Ifile
C	            12345678
      DATA MARK/' + Vo1,'//',' ', '*'.'.'.'-', 'x'/
      TLOG(X)=ALOG(X)12.30258

      NU    =0
      NO    =3
C.... on the last pass thru RP next will = 0,
C	 resulting in plot printed onto 'output'  instead of user screen.
      if (( next .eq. 0) .and. Ifile ) nu - no
C.... line marks for Blank, Asterisk,  Dot, hyphen, etc.
      LB    = A
      LA    =5
      LD    =6
      LH    =7
      LT    =8
C.... limits of vertical Y axis and horizontal X axis are set and arrays + 1
      Ix    = 50
      ly    = 15
      Ipx   = lx+1
      Ipy   = ly+1

      WRITE  (nu,205) nart, ndset
  205 FORMAT (//,'  PLOT:   article1,i3,',  set ',i2,
     +':   Parent  Ist-Metab.  2nd-Metab.      Other1,/,llx,
     ^'SYMBOLS Measured:     +         o           f/       * = Overlay',/
     + ,19x,  'Modelled:     -         .           x       x = initial +')


                     APPENDIX F: page   15 of 22

                                                               91
-------
C.... the following finds max and min (greater than zero) residues reported
      YMAX    = -1.0E30
      YMIN    =  1.0E30
      DO A7 L = l.nsam
      DO A 7 M • l.nres
      y(l,m)  = 0.0
      if (yor(l.m) .le. 0.0) go to A7
      y(l,ra)  = tlog(yord.m))
   A3 IF (y(l,ra) .gt. YMAX) YMAX - y(l,m)
   A5 IF (y(l,m) .It. YMIN) YMIN «= y(l,m)
   A 7 CONTINUE
      yy      = tlog(a(l)+a(7))
      if (yy .gt. YMAX)     YMAX • yy

      xll     • 0.
      xul     = fnext2(x(nsam) )
      YUL     - FLOAT(IFIX(YMAX+1.))
      YLL     = FLOAT(IFIX(YMIN))
      if (YMIN .It. 0.0)     YLL = YLL-1.
C ---- we want yul-yll to be either I,3,or5 orders-of-magnitude (logs of Y)
C.... to be spaced conveniently on the screen.
   50 logy    = ifix(yul-yll+.0001)
   51 if (logy - 1)                    5A,59,52
   52 if (logy - 3)                    5A.60.53
   53 if (logy - 5)                    5A.60.59
   5A IF (ABS(YUL-YMAX)-ABS(YMIN-YLL)) 55,57,57
   55 YUL     = YUL+1.
   56 GO TO 50
   57 YLL     - YLL-1.
   58 GO TO 50
   59 logy    = 5
   60 continue
      kl      - ly/logy

      YI      = (YUL-YLL)/ly
      XI      = (XUL-XLD/lx
   63 continue
w • * • • •   •   •   •   •   •   •   »   •   •    •   •    •    *    •    •    •
C     The following bigins plotting array "line"
C     with i subscript for row
C          i subscript for column
C          k index  for X and Y labels
C          L subscript for samples
C          M subscript for residues within samples
C     characters start blank
C     borders are added (dots and pluses) in lines 6A-68
C     reported data are scanned and added in lines 68-91
C     the model is run and dots are added when in plot in lines 91-110
C         MARK   1   2   3   A   5   6   7   8
C               l + 'lnlly/ltl
w • * • •            T f  w y If f

      DO   6A I  « l.lpx
      DO   6A J  - l.lpy
      LINE(I.J)  •  MARK(LB)
                     APPENDIX F: page  16 of 22                    Q2
-------
   64 CONTINUE
      DO   65 I  = l.lpx
      LINE(I.l)  -  MARK(LD)                                              37Q
      LINE(I.lpy)-  MARK(LD)
   65 continue
      DO   66 I  *'l,lpx,5
      LINE(I.l)  =  MARK(l)
      LINE(I.lpy)»  MARK(l)
   66 continue
      DO   67 J  = l.lpy
      LINE(l.J)  *  MARK(LD)
      LINE(lpx.J)-  MARK(LD)
   67 continue
      DO   68 J  * l.lpy.kl
      LINE(l.J)  =  MARK(l)
      LINE(lpx,J)=  MARK(l)
   68 continue
C.... mark the model initial parent with an x
      yy         * (yy-YLL)/YI
      J          » lpy-ifix(round(yy,0))
      LINE(l.J)  =  MARK(LT)

      DO  91 L   = l.nsam
      xx         * (x(L)-XLL)/XI
      I          = 1 + ifix(round(xx,0))
      if ((I .It. 1) .or. (I .gt.  Ipx)) go to 91
      DO  90 M   ,= l.nres
      if (yor(L.M) .le. 0.0) go to 90
      LM         = M
      vy         = (y(L,M)-YLL)/YI
      J          = Ipy-ifix(round(yy,0))
      if ((J .It. 1) .or. (J .gt.  Ipy)) go to 90
      IF (LINE(I.J) .ne. MARK(LB)) LM=LA
      LINE(I.J)  = MARK(LM)
   90 continue
   91 continue

C.... find values of model
C....   DI = x interval (i.e. day post-application)
C	  PYn = plotted value of model at DI
   27 CONTINUE
      A23  = a(2Ha(3)
      A234 • A23 +a(4)
      A56  * a(5)+a(6)
      A78  = a(7)H-a(8)
      Alx3 = a(l)*a(3)
      A13d6* Alx3/(A56-A23A)
             Alx4/(A78-A234)
      DO 110 I = l.lpx
      DI   = XI*FLOAT(I-1)
      go to (155,153,151),nres
  151 Stop 'No model is provided for 2 metabolites!'
  153 F2   =                 (A14d8*(exp(-A234*di)-exp(-A78 *di)))      XI
     + -l-(((a(7)*AlAd8)+(a(6)>*A13d6))*(exP(-A234*di)-exp(-a(9)*di))/


                     APPENDIX F: page  17 of 22
-------
     +                                                     (a(9)-A234))  X2
     + +(((a(7)*A14d8/(a(9)-A78)))  *(exp(-A78 *di)-exp(-a(9)*di)))      X2
     -t- +((a(6)*(a(10)-A13d6))       *(exp(-A56 *di)-exp(-a(9)*di))/
     +                                                     (a(9)-A56))   X2     37]

  155 Fl  = (a(l) *exp(-A234*di))
     + +    (A13d6*exp(-A234*di)) + ((a(10)-A13d6)*exp(-A56*di))
      if (Fl .le. 0.0) go to 100
      PY1 - TLOG(Fl)
      yy  = (PY1-YLD/YI
      J   - lpy-ifix(round(yy,0))
      if ((J .It. 1) .or. (J .gt. Ipy))  go to 100
      IF (LINE(I.J) .eq. MARK(LB)) lined,J)  - mark(LD)
  100 if (DI .le. 0.0 .or. F2 .le. 0.0)  go to 110
      PY2 - TLOG(F2)
      yy  « (PY2-YLD/YI
      J   « Ipy-ifix(round(yy,0))
      if ((J .It. 1) .or. (J .gt. Ipy))  go to 110
      IF (LINEd.J) .eq. MARK(LB)) lined,J)  - mark(LH)
  110 CONTINUE
  111 CONTINUE

      k   = kl-1
      nkl - 0                                                           !
C	 DUMMY K STARTS«kl-l AND PUTS unit  labels
C	 ON EVERY  3RD or 5TH Y, AND 10TH X
      DO 125  J = l.lpy
      k   - k+1
      if (k-kl)           121,123,121
  121 if (J .le. ncoef+nkl) go to 122                                   !
      WRITE  (nu,206) (LINE(I,J),I=l,lpx)
      go to 125
  122 nc  - j-nkl
      WRITE  (nu,209) (LINE(I,J),I=l,lpx),nc,a(nc)
      go to 125
C	 VY IS MANTISSA AND iPY IS CHARACTERISTIC ON BASE  10  LOG  (TLOG)
  123 k   - k-kl
      nkl = nkl + 1
      YY  = YLL + (float(lpy-j)*yi)
      iPY = IFIX(ROUND(YY,0))
      VY  • 10.**(YY-iPY)
      VY  • ROUND(VY,2)
      WRITE  (nu.207) VY.iPY,(lined.J),I-l,lpx)
  125 CONTINUE
  206 FORMAT (1H ,12X,61A1)
  209 FORMAT (1H ,12X,51A1,  2x,'A',11,'•'.elO.A)
  207 FORMAT (1H .1X.F5.2,1  E1,13,lx,61al)
      DO 130 I - 1,7
      VX(I)    « XLL+(XI*10.*float(I-l))
      VX(I)    = ROUND(VX(I),1)
  130 CONTINUE
      WRITE  (nu,208)  (VX(I),I»1,6)
  208 FORMAT (1H ,  4X,7(5x,F5.D)
  150 RETURN
      END
                     APPENDIX F:  page  18  of  22
94
-------
c	  372
      function fnext2 (x)
C	 to find the next smallest multiple of x among 2,4,6,8,orlOx
      xd     = (10.**ifix(aloglO(x)))
      do 1 n = 1,5
      fnext2 = 2.*float(n)*xd
      if (fnextZ .ge. x) go to 2
     1 continue
     2 return
      end
C	
      function round(r,i)
C	 where r=number to be rounded and
C....       i=intergers remaining after decimal
      round = 0.0
      n     = 1
      rO    = 10.**i
      if (i .eq. 0)          rO = 1.
      if (r)                 11,9,1
   11 n     = -1
     i rl    = r*rO*float(n)
     2 il    = ifix(rl)
     3 r2    = rl-float(il)
     4 if (r2-0.5)             8,5,7
     5 r3    = float(il)/2.
     6 if (r3-ifix(r3)-.25)    7,7,8
     7 il    = il+1
     8 round = float(il*n)/rO
     9 return
      end
C	
      SUBROUTINE TELL (da.a.ncoef,dpa,res,nsam,nres,nart,ndset,next,
     +                 lfile.adif.pdif)

C	 ROUTINE WILL LIST PREDICTED POINTS AND COMPARE THEM TO INPUTS
C	 IT IS IDEALLY SUITED TO TIME SERIES DATA IN WHICH TIME IS FIRST
C	 USER MUST SUPPLY DEFINITIONS OF Fl, F2, F3, IN USER'S WORKING AREA

      DIMENSION X(10), DA(120), A(10), dpa(15), res(l5,3)
      integer nnzr(3)
      real pred(3),dif(3),pdif(3),chi2(3),adif(3)
      logical Ifile.lhold,match

      nu      =0
      NO      =3
      do 1 i  = l.nres
      PDIF(i) - 0.
      CHI2U) = 0.
      ADIF(i) = 0.
      nnzr(i) = 0
     1 CONTINUE

C	 a Holding status is introduced into file to hold printing of model results
C	 until the last pass through TELL.
      Ihold = Ifile
                     APPENDIX F: page  19 of 22              >-      Q _
-------
      if (next .ne. 0) Ifile - .false.
      if (.not. Ifile) go to 23
      WRITE  (NO, 100)
  100 FORMAT (/,' MODEL RESULTS',7('.'),'parent',12('.').lx,12('.'),
     +          'metabolite(s)',10('.' ),'>',
     +  /,'  TIME   ',2(   9HPREDICTED,2X,8HMEASURED,3X,9HPCT.DIFF. ),
     +  /,'  days',   2(5x.'ug/cm2',4x,'ug/cm2I.8x,'%',lx))
C.... format 100 will currently only accept 2 residues; 3 OK on wide paper.

   23 CONTINUE
      list   = 61
C	 PROGRAM REQUIRES INPUT DATA TO BE CHRONOLOGICALLY ORDERED
C	 lastd SET AT 1.5 TIMES THE LAST INPUT TIME
C.... but list of days shall not exceed 61
      lastd  - IFIX(dpa(nsam)*1.5)
      NT     = 1
   15 IF ((lastd/NT)-list)  17,17,16
   16 NT     = 2*NT
      GO TO 15
   17 CONTINUE

      i      =0
      A23    - a(2)+a(3)
      A234   = A23 +a(4)
      A56    = a(5)+a(6)
      A78    - a(7)+a(8)
      Alx3   = a(l)*a(3)
      A13d6  » Alx3/(A56-A234)
      AlxA   = a(l)*a(4)
      AlAdS  « Alx4/(A78-A234)
C.... This is a BIG 'double' (i and j) do loop down to line 90
      DO 90 j-NT.lastd,NT
   18 match  = .false.
      DI     = FLOAT(j-NT)
      if (i+1 .gt. nsam) go to 20
      if (dpa(i+l) .gt. float(j)-(.5*float(nt))) go to 20
      i      = i+1
      DI     = dpa(i)
      if (dpa(i) .eq. 0.0) di=0.1
      match  = .true.
   20 CONTINUE
O»»THIS IS THE USER'S WORKING AREA...««««««««««««««««
O»»WITHIN ITS BOUNDS, DEFINE pred(l)=OP. pred(2)=metabolite, etc.««
   27 CONTINUE
      go to (55,53,51),nres
   51 Stop 'No model is provided for 2 metabolites!'
   53 F2      -               (A14d8*(exp(-A234*di)-exp(-A78 *di)))      XI
     + +(((a(7)*A14d8)+(a(6)*A13d6))*(exp(-A234*di)-exp(-a(9)*di))/
     +                                                     (a(9)-A234))  X2
     + +(((a(7)*A14d8/(a(9)-A78)))  *(exp(-A78 *di)-exp(-a(9)*di)))      X2
     + -l-((a(6)*(a(10)-A13d6))       *(exp(-A56 *di)-exp(-a(9)*di))/
     •••                                                     (a(9)-A56))   X2
      ored(2)  = F2

   55 Fl      = (a(l) *exp(-A23A*di))


                     APPENDIX F:  page   20 of 22
-------
     + +   (A13d6*exp(-A234*di)) + ((a(10)-A13d6)*exp(-A56*di))
      pred(l) = Fl
                                                                           374
C	 THIS model WAS DEVELOPED TO ESTIMATE 7 COEFFICIENTS
C     PARENT     P. =     PI +    PI
C         d(P)/d(t) = -(a2+a3+a4)*Pl + (a3*Pl) - (a5+a6)*P2
C     METABOLITE X  =     XI +    X2
C         d(X)/d(t) =  a4*Pl -I- a6*P2 + (a3*Xl) - a2*Xl - a5*X2
C
C     AT  t = 0.
C     Pl(0) = Al
C     P2(0) = A7
C     Xl(0) = X2(0) = 0.
C     TO model parent only, SET DA(4)=0.0
C                               DA(6)=0.0
O»»THIS IS THE END OF THE USER'S WORKING AREA. ..<««««««««««
   80 CONTINUE

      if (.not. match) go to 86
C.... the following composite values are determined and reported
C.... pdif - percent error in observed over expected
C.... chi2 = sum of squares of dif (Chi-squared)
C.... adif = percent absolute error in observed over expected
      do 82 k = l.nres
      if     (res(i.k) .eq. 0.0) go to 82
      if     (pred(k)  .le. 0.0) go to 82
      dif(k)  = (pred(k)- res(i,k))/pred(k)
      chi2(k) = chi2(k) -t- (dif(k)**2)
      dif(k)  = dif(k) * 100.
      pdif(k) = pdif(k) + dif(k)
      adif(k) = adif(k) + abs(dif(k))
      nnzr(k) = nnzr(k) + 1
   82 CONTINUE

      if     (.not. Ifile) go to 18
      WRITE  (NO,102) di.(pred(k),res(i,k),dif(k),k=l,nres)
  102 FORMAT ( F7.2,2x,3(:,lpe9.3,2x,lpE9.3.1X,Opf7.2,3x))
      go to 18
   86 if     (.not. Ifile) go to 90
      WRITE  (NO,101) DI,(pred(k),k=l,nres)
  101 FORMAT (F7.2,2x,3(:, lpE9.3,:,22X))
   90 CONTINUE

      do 92 k = l.nres
      if     (nnzr(k) .le. 0) go to 92
      pdif(k) = pdif(k)/FLOAT(nnzr(k))
      adif(k) = adif(k)/FLOAT(nnzr(k))
   92 CONTINUE

      if     (.not. Ifile) go to 96
      WRITE  (NO,103) (chi2(k),k=l,nres)
      WRITE  (NO,104) (pdif(k),k=l,nres)
      WRITE  (NO,105) (adif(k),k=l,nres)
  103 FORMAT (/,14X,         'CHI SQUARE =  ' ,2x,3(IpElO.3,:,20X))
  104 FORMAT (   8X,   'MEAN   %   ERROR =',     3(2pE12.4,:,18x))


                     APPENDIX F: page  21 of 22                *
-------
  105 FORMAT (   8X,    'MEAN Z ABS ERROR « ',     3(2pE12.4,:,18X))

   96 if     (next .eq.  0) go to 98
      write  (nu,106)  (k.pdif(k),k«l,nres)
  106 format ('  MEAN   %  ERROR: Res(',il,')»',f6.1,
     +                    2(:,' ResC.il,')='.f6.1))
      write  (nu,107)  (k.adif(k),k«l,nres)
  107 format ('  MEAN  |%J  ERROR: Res(',il,')=',f6.1,
     +                    2(:,' ResC.il.')-'.f6.D)

   98 Ifile « Ihold
      RETURN
      END
C
      subroutine yes  (letter,choice)
      character*! letter
      logical choice
      choice ** .false.
      if (letter .eq.  'y') choice = .true.
      if (letter .eq.  'Y') choice = .true.
      return
      end
                     APPENDIX F:  page  22 of 22
                                                               98
-------
                                                                 APPENDIX G


Appendix G: Computer Program to Standardize Residues and Tabulate Responses    376
            Using the Unified Field Model.
            A short description of each symbol is given at the end of this •
            appendix (see also Appendix C).

      program REEN

c.... version 3 (to create a one- line/sample file containing
c         chem,nart,ndset,loc,crop,aia and form [as sped],
c         dpa , ( res ( i ) , i=l , 4 ) , den , dAChE , kdcom , Idass

c**************ft****************^
c                                                                             c
c     Purpose: To estimate AChE inhibition [optionally for a                  c
c              given crop and pesticide] for a given level(s) of              c
c              residue.   Estimations are determined using the                 c
c              'unified field model':                                         c
c                                                                             c
c              A compilation of dislodgeable foliar residue                   c
c              studies serves as the data base, along with                    c
c              various other coefficient and toxicity files.                  c
c                                                                             c
c     Dose:  D = kd*t*R/m                                                     c
c                where D    = dose, mg/kg                                     c
c                      kd   = crop specific dosing constant                   c
c                      t    = duration of exposure (hrs)                      c
c                      R    = residue, ug/cm2                                  c
c                      m    = body mass, = 70  kg (50 %ile man)                c
c                           .                                                  c
c     Response: dAChE = 1 - exp(-ke*D/LD50)                                   c
c                where dAChE = a fraction (Z/100) of RBC cholinesterase       c
c                              inhibited                                      c
c                       LD50 = acute toxicity, mg/kg                          c
c                       ke   = enzyme constant, =6.0                         c
c                                                                             c
c                                                                             C
      integer      ex, unit,  dapp,  yapp,
     +             fileO, filel,  fileZ,  file3,  file4,  fileS,  file6
      real         dpa(30) ,res(30,4),sd(30,4) ,LD50(A) ,dAChE(30) .kd.leafd
      character*!  file, den, kdcom
      character*3  loc, locrq,  mapp
      character*4  form
      character*6  crop.croprq
      character*8  chem,chemrq
      character*12 input, output, spec!
      character*20 cropname
      character*25 chemname(4),comname
      character*30 lox(3)
      character*79 comm(6)
      logical      crmat.chmat


                                                                  99
                     Appendix G,  page   1   of 9                     ' '
-------
c.... file 1 = user screen and console...format 1000 series reserved I/O.
c              (PC assumes this device to be "0")                             "^77
c	 file 2 = pesticide residue data base (filename =  'INPUT')              J ' '
c              (filename specified by user)
c	file 3 = the output file             (  "      =  'OUTPUT')
c              (filename specified by user)
c	 file 4 = crop coefficient library     (   "      =  'cropco.lib1)
c.... file 5 = chemical coefficient library (   "      =  'chemco.lib1)

       fileO=0
       file2=2
       file3=3
       file5=5
       file6=6

c     limits set for max number of requested effects (array),  coefficients,
c     number of samples and residues within each sample.
      maxr   «= 4
      maxs   = 30
      crmat  = .false.
    1 continue

      DO 3 i = l.maxs
         DO  2  j = l.maxr
            res(i.j) =0.0
            sd(i,j)  « 0.0
            Id50(j)  = 0.0
    2       continue
    3    continue

      chmat  = .false.
      write  (fileO,1003)
 1003 format (' Type residue INPUT filename, up to 12 characters:')
      read   (fileO.1004) input
 1004 format (a!2)
      if     ((input .eq. 'quit*).or.(input .eq. 'exit').or.
     +        (input .eq. 'stop1).or.(input .eq. 'end'))  goto  30000

      open   (file2, file = input)
      open   (file4, file='cropco.lib')
      open   (fileS, file=lchemco.lib1)
      rewind file2
      rewind file4
      rewind fileS

    5 continue
      write  (fileO.1005)
 1005 format (' A specific chemical (coded) can be requested;',/,
     +        ' type chemical (a8) or ''all11  for all crops:')
      read   (fileO,1006) cherarq
 1006 format (a6)
      write  (fileO,1007)
 1007 format (' A specific location (coded) can be requested;',/,


                     Appendix G, page  2  of 9

                                                           .     100
-------
     +        '  type location (a3) or ''all11  for  all  locations:')
      read   (fileO,1008) locrq
 1008 format (a3)
      write  (fileO,1009)
 1009 format ('  A specific crop (coded)  can be requested;1,/,
     +        '  type crop (a6) or "all"  for  all  crops:')
      read   (fileO.1010) croprq
 1010 format (a6)

      write  (fileO.1011)
 1011 format ('  Type data OUTPUT filename,  up  to 12  characters:1)
      read   (fileO,1004) output
      write  (fileO,1012)
 1012 format ('  Type 0 to show input data  on screen',/,
     +        '    or 1 to exclude this option:1)
      read   (fileO.1013) filel
 1013 format (il)
      if     (filel .eq.  0) goto 7
      open   (filel)
      rewind  filel
    7 open   (file3, file = output,  status='new')
      rewind  file3

c	 READ DATA SPECIFICATION FILE 	
   51 read  (file2,2001,end=85) chem,nart,ndset,nox,loc,crop,igraph,
     +form,aia,gpa,ex,unit,nsides,dapp.mapp,yapp,ncomm,npunpr.isd
 2001 format(a8,lx,2(I2,lx),il,lx,a3,lx,a6,lx,il,lx,a4,lx,f5.2,lx,f4.0,
     +      Ix,3(il,lx),i2,lx,a3,lx,i2,lx,il,lx,i3,lx.il)
      write (filel,2001      ) chem,nart,ndset,nox,loc,crop,igraph,
     +form,aia,gpa,ex,unit,nsides,dapp.mapp,yapp,ncomm,npunpr.isd
      backspace file2
      read  (file2,2011)  spec!
 2011 format(34x,a!2)
      call chemck (chem.noxa,Id50,chemname,comname,chmat,file5)
c.... the above subroutine searches  to match chemical  with  data  in  chemco file
      nres = nox + 1
      Idass = 0

c.... reading up to 6 comment lines
   52 if    (ncomm .le. 0) goto 53
      read  (file2,2002,end=9002) (comm(i),i=l,ncomm)
      write (filel,2002)           (comm(i),i=l,ncomm)
 2002 format(a79)

c.... reading the name of the oxons  or other metabolites
   53 if    (nox .le. 0)  goto 54
      read  (file2,2003,end=9003) (lox(i),  i=l,nox)
      write (filel.2003)           (lox(i),  i=l,nox)
 2003 format(a30)

c	 reading the number  of samples  in the following set of data
   54 read  (file2,2004,end=9004) nsam
      write (filel,2004)           nsam
 2004 format(i2)
                     Appendix G,  page  3  of 9
101
-------
c.... reading residue data ...
      if       (nsam .le. 0) goto 56
      DO 55 j=l,nsam
         read  (f Ue2,2005,end=9005) dpa( j ),(res( j ,k),sd( j ,k),k=l,nres)
         write (filel.2005)          dpa(j),(res(j,k),sd(j,k),k=l,nres)
   55    continue
 2005 format(f4.0,lx,8(f6.0,lx))

C.... QC-tolerance checks follow (line 56 to line 71):
   56 continue
      if    (chemrq .ne. chem .and. chemrq .ne.  'all     ')  goto 51
      if    (croprq .ne. crop .and. croprq .ne.  'all   '   )  goto 51
      if    (locrq  .ne. loc  .and. locrq  .ne.  'all1      )  goto 51
      if    ( nox   .gt. noxa)  goto 9015
      if    ( nres  .gt. maxr)  goto 9016
      if    ( nsam  .gt. maxs)  goto 9017
      if    (.not. chmat)       gotti 9018
      if    (Id50(l) .le. 0.01  goto 9019
      if    (nox .le. 0) goto 64
      do 58 k=2,nres
         if (Id50(k) .ne. 0.0) goto 58
         write  (fileO.1021) chemname(k),nart,ndset
 1021    format (' The LD50 for ',a25,' in article ',12,'  data set ',i2,
     +         /,' is unknown but asssamed * parent.1)
         Idass » Idass + 1
         Id50(k)=ld50(l)
   58    continue

   64 kdcom • ' '
      call cropck (crop,kd.cropname,leafd,den,crmat,file4)
c.... the above subroutine searches to match crop with data  in cropco file
      if    (.not. crmat) goto 9028
   65 if    (kd .ne. 0.0) goto 66
      write (fileO,1026) crop,nart,ndset
 1026 format(' Dosing coefficient (kd) for ',a6,'  in article ',i2,
     +' dataset ',i2,/, '  not listed in cropco library!  Default value e
     + 5000 cm2/hr.')
      kd = 5.0
      kdcom = '?'
   66 continue

   70 call units (res,sd,nsam,nres,unit,nsides,crop,leafd)
c.... above subroutine to convert to ug/cm2 projected area
c     units may be nonstandard and must be individualy converted.
      if    (unit  .ge.   4) goto 9014
      if    (leafd .le. 0.0) goto 9029

   71 call dosres  (dpa,res,LD50,dAChE,nres,nsam,kd)
c.... above subroutine to convert from residue to dose and AChE...
                     Appendix G, page  4  of 9

                                                                   102
-------
C.... the following write statements create the modified (one-line)
C     "output" file
      DO 75 j=l,nsam
C                                 spec! equivalent to :aia:gpa:
         write  (file3,3005) chem,nart,ndset,loc,crop,specl,
     +                       dpa(j),(res(j,k),k=l,maxr),den,dAChE(j),
     +                       kdcom.ldass
c	            V       V       V  <-- potentially removable delimiters
 3005 format(a8,':',2i2,':',a3,a6,a!2,fA.I.':',
     +       4(f6.3,':l),al,f7.3,al,il)
         do 74 k=l,maxr
C           arrays reset to avoid carry-over of "oxon" values between data sets
            res(j.k) * 0.0
            sd(j,k)  = 0.0
   74       continue
   75    continue
      goto 51

   85 write (fileO,*) 'Reading end of requested chem.-input datafile.'
      goto 1

C.... The following lines are various error and data-set reject  modes:
 9002 write (*,*)  '  Unexpected End-of-file  while reading comment lines!1
      goto 1
 9003 write (*.*)  '  Unexpected End-of-file  while reading "oxon"  lines!1
      goto 1
 9004 write (*,*)  '  End-of-file while reading number-of-samples  lines!1
      goto 1
 9005 write (*,*)  '  End-of-file while reading sample-data lines!1
      goto 1
 9014 write (fileO,1014) nart, ndset
 1014 format('  Nonstandard units in article ',i2,' dataset ',i2,/,
     +       '  - no further calculations are made on this data.')
      goto 51
 9015 write (fileO,1015) nart,ndset
 1015 formatC  More metabolites in article  ',12,' dataset ',i2,
     +       '  than listed in chemco.1)
      goto 1
 9016 write (fileO,1016) nart,ndset,maxr
 1016 format('Metabolites in article ',i2,' dataset ',i2,' exceed array
     •KLimit of ',il)
      goto 1
 9017 write (fileO.1017) nart,ndset,maxs
 1017 format('Samples in article ',i2,' dataset ',i2,' exceed array limi
     +t of «.i2)
      nsam = maxs
      goto 64
 9018 write (fileO,1018) chem,input,input
 1018 formatdx.aS,1  as listed in ',al2,
     +' was not found within chemco library.',/,
     +' Please check ',al2,'  for proper code or update cheraco.lib.')
      goto 51
 9019 write (fileO.1019) chem
 1019 format(Ix,a8,'  found within chemco library but without LD50!1)
                     Appendix G,  page  5  of 9
103
-------
 9028 write (filed,1028) crop
 1028 format(Ix,a6,'  not found within cropco library.  Please  check  listi
     +ng for proper code.1)
      goto 51
 9029 write (fileO,1029) crop,nart,ndset
 1029 format('  Unable to convert ppm for ',a6,'  in article  ',12,
     +       '  dataset ',12,' toug/cm2')                                      381
      goto 51

30000 STOP 'Have a good day1
      END
C	
      subroutine units (res,sd,nsam,nres,unit,nsides,crop,leafd)
c ... to convert residue units to ug/cm2 and 1-side (projected area) values
C       unit   = units reported
C                0/blank = ug/cm2 [default]
C                1       « ng/cm2      3 * ppm
C                2       = mg/m2       4 * other
      integer     unit
      real        res(30,4),  sd(30,4),leafd
      character*6 crop

       if (unit .eq.  0) goto 17
       do 16 k=l,nres
          do 15 j^l.nsam
             if (unit .gt. 1) goto 12
c....        to convert from ng/cm2
             res(j.k) • res(j,k)/1000.
             goto 15
   12        if (unit .gt. 2) goto 13
c....        to convert from mg/m2
             res(j.k) - res(j,k)/10.
             goto 15
   13        if (unit .gt. 3) goto 20
c....        to convert from ppm
             if (leafd .eq. 0.0) goto 20
             res(j.k) « res(j,k)*leafd/1000.
   15        continue
   16     continue
       unit = 0
   17  if (nsides .ne. 2) goto 20
c....  to convert from residues per 2 sides of a leaf
       do 19 k=l,nres
          do 18 j=l,nsam
             res(j.k) • res(j,k)*2.
   18        continue
   19     continue
       nsides « 1
   20  continue
       return
       end
                     Appendix G,  page  6  of 9
                                                                  104
-------
      Subroutine cropck (crop,kd,cropname,leafd,den,crmat,file4)
c—.  to find crop match and transfer pertinent parameters
      character*!  den
      character*6  .crop,code,stdcode
      character*20 cropname
      integer      fileA
      logical      crmat
      real         kd.leafd

      crmat - .false.
      rewind fileA
    1 read  (file4,4001,end=10) code,kd,cropname,leafd,den,stdcode
 4001 format(a6,5x,f4.1,7x,a20,2x,f4.1,al,2x,a6)
      if (crop .ne. code) goto 1
      crmat = .true.
      crop  = stdcode
   10 return
      end
c
      Subroutine chemck (chem,noxa,ld50,chemname,comname,chmat,fileS)
c....  to find chemical match and transfer pertinent parameters
      character*8  chem.code
      character*25 chemname(A),comname
      integer      fileS
      logical      chmat
      real         Id50(4)

      chmat = .false.
      rewind fileS
    1 read  (file5,5001,end=10) code,ld50(l),noxa,chemname(l),comname
 5001 format(a8,Ix,f4.0,Ix,il,Ix,a25,a25,2x,al,lx,al)
      if (chem .ne. code) goto 1
      do 3 i - l.noxa
         read  (file5,5002,end=10)   Id50(i+l),   chemname(i+l)
 5002 format(   9x,f4.0,      3x,a25                )
    3    continue
    9 chmat = .true.
   10 return
      end
c
      Subroutine dosres (dpa,res,LD50,dAChE,nres,nsam,kd)
c....  to convert residue to dose (mg/kg) and dAChE (%) ...
      real dpa(30),res(30,4),LD50(4),dAChE(30),kd,mass
c....  kd   = crop specific dose rate, from cropco.lib file
c	  LD50 = dermal toxicity(s),      from chemco.lib file
c....  mass = 70 kg for 50Zile man
      mass = 70.
c....  workday = 8 hours
      work =8.0
c....  ke   = enzyme constant =6.0
      ke   = 6.0"

      do 35 i=l,nsam
         sum =0.0
                     Appendix G, page  7  of 9                        105
-------
         do 30 j«=l,nres
            if (mass .eq. 0.0 .or.
     +           mass, j, Id50(j)
            D=kd*work*res(i,j)/mass
            sum - sum + (D/ld50(j))
   30       continue
         dAChE(i)=100.*(1.-exp(-ke*sum))
   35    continue
       return
       end
                            Id50(j) .eq. 0.0) write (*,*)
                                                                   383
Name   Type
AIA
CHEM
CHEMNA
CHEMRQ
CHMAT
COMM
COMNAM
CRMAT
CROP
CROPNA
CROPRQ
DACHE
DAPP
DEN
DPA
EX
FILED
FILE1
FILE2
FILE3
FILE4
FILES
FILE6
FORM
GPA
I
IGRAPH
INPUT
ISO
J
K
KD
KDCOM
LD50
LDASS
LEAFD
LOG
LOCRQ
LOX
MAPP
MAXR
REAL
CHAR
CHAR
CHAR
LOGI
CHAR
CHAR
LOGI
CHAR
CHAR
CHAR
REAL
INTE
CHAR
REAL
INTE
INTE
INTE
INTE
INTE
INTE
INTE
INTE
CHAR
REAL
INTE
INTE
CHAR
INTE
INTE
INTE
REAL
CHAR
REAL
INTE
REAL
CHAR
CHAR
CHAR
CHAR
INTE
Variables use in main program REEN1

Active Ingredient per Acre
coded CHEMical name
full CHEMical NAme
CHEMical ReQuested by user
CHemical MATch
COMMents (up to 6 lines)
COMmon chemical NAMe
CRop MATch
coded CROP name
full CROP NAme
CROP ReQuested by user
delta AChE (%)
Date of Application
symbol for surety of leaf DENsity
Days Post-Application
coded foliar residue Extraction solvent
input/output device
input file on disk
output file on disk
input file on disk
input file on disk
input file on disk
input file on disk
FORMulation of parent chemical
Gallons mixture Per Acre applied
subscript
code for Input data originally from a GRAPH or equation
INPUT file name
code designating statistical parameters on residues
subscript
subscript
Dosing coefficient
symbol for surety of Dosing coefficient
dermal Lethal Dose 50%
number of metabolite LDSOs assumed equal to parent
LEAF Density (mg/cm2)
coded LOCation
coded LOCation ReQuested by user
name of metabolites [Label of OXon]
Month of Application
MAXimum number of Residues permitted by array definition
                     Appendix G, page  8  of 9
                                                                      106
-------
MAXS   INTE      MAXimum number of Samples permitted by array definition
NART   INTE      citation Number of ARTicle                                _ o  .
NCOMM  INTE      Number of COMMent lines                                   384
NDSET  INTE      Number of DataSET within cited article
NOX    INTE      Number of metabolites (OXons)  in study
NOXA   INTE      maximum Number of "OXons" Already in chemco.lib
NPUNPR INTE      Number of PUNches within sample (if not nominal  48-60)
NRES   INTE      Number of RESidues reported in study [= NOX+1]
NSAM   INTE      Number of SAMples in study
NSIDES INTE      Number of SIDES used to calculate ug/cm2
OUTPUT CHAR      name of OUTPUT file
RES    REAL      RESidue values
SD     REAL      Standard or relative Deviation of residues
SPEC1  CHAR      dummy array to avoid write errors
UNIT   INTE      UNITs of residue reported
YAPP   INTE      Year of Application
                     Appendix G,  page  9   of  9

                                                              107
-------
                                                               APPENDIX H
Appendix H: Compilation of Reported Residue and  Calculated Response Data.

            Tabulation format includes the chemical name  (coded),
            the article (citation)  number, the study number
            within the article (sequential),  the location
            (coded), crop (coded),  application rate (Ib AIA),
            mixture (gallons/acre), the day post-application, 4
            residues (ug/cm ), an optional "?" when the residues
            were converted from ppm with only estimated leaf
            density, the calculated 8-hour cholinesterase
            response (% dAChE), a second optional "?"  for crops
            for which the default dosing coefficient was  assumed
            (default = 5000 cm /hr), and a single digit
            indicating the number of metabolites whose dermal
            toxicity was assumed equal to the parent.
385
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
:18
:18
:18
:18
:18
:18
:18
:18
:18
:18
:18
:18
:18
:18
:18
:18
:18
:18
:18
:18
:18
:18
:18
:18
: 2
: 2
: 2
: 2
: 2
: 2
: 2
: 2
: 2
: 2
: 2
: 2
1 : CAsOrange
1 : CAsOrange
1 : CAsOrange
1 : CAsOrange
1 : CAsOrange
1 : CAsOrange
2: CAsOrange
2 : CAsOrange
2 : CAsOrange
2 : CAsOrange
2 : CAsOrange
2: CAsOrange
3 : CAsOrange
3 : CAsOrange
3 : CAsOrange
3 : CAsOrange
3 : CAsOrange
3 : CAsOrange
4 : CAsOrange
4 : CAsOrange
4 : CAsOrange
4 : CAsOrange
4 : CAsOrange
4 : CAsOrange
1 : CAvPeach
1 : CAvPeach
1 : CAvPeach
1 : CAvPeach
1 : CAvPeach
1 : CAvPeach
1 : CAvPeach
1 : CAvPeach
2 : CAvPeach
2 : CAvPeach
2 : CAvPeach
2 : CAvPeach
:06.00
:06.00
:06.00
:06.00
:06.00
:06.00
:06.00
:06.00
:06.00
:06.00
:06.00
:06.00
:02.00
:02.00
:02.00
:02.00
:02.00
:02.00
:01.00
:01.00
:01.00
:01.00
:01.00
:01.00
:03.00
:03.00
:03.00
:03.00
:03.00
:03.00
:03.00
:03.00
:03.00
:03.00
:03.00
:03.00
:0100:
:0100:
:0100:
:0100:
:0100:
:0100:
:1200:
:1200:
:1200:
:1200:
:1200:
:1200:
:0500:
:0500:
:0500:
:0500:
:0500:
:0500:
:0500:
:0500:
:0500:
:0500:
:0500:
:0500:
:0105:
:0105:
:0105:
:0105:
:0105:
:0105:
:0105:
:0105:
:0100:
:0100:
:0100:
:0100:
3.0:20.200:
10.0:11.400:
17.0:10.600:
31.0:10.400:
44.0:
59.0:
3.0:
10.0:
17.0:
31.0:
44.0:
59.0:
3.0:
10.0:
17.0:
31.0:
44.0:
59.0:
3.0:
10.0:
17.0:
31.0:
44.0:
59.0:
.0:
4.0:
6.0:
10.0:
13.0:
14.0:
16.0:
20.0:
.0:
1.0:
3.0:
4.0:
9.400:
7.600:
9.200:
3.200:
2.600:
1.860:
2.000:
1.600:
4.000:
1.400:
1.240:
.940:
.760:
.600:
1.560:
.600:
.560:
.400:
.280:
.240:
5.800:
3.600:
2.000:
1.360:
.960:
4.600:
4.000:
3.000:
5.000:
5.200:
5.600:
5.800:
.078:
.066:
.106:
.090:
.076:
.012:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:?
.000:?
.000:?
.000:?
.000:?
.000:?
.000:?
.000:?
.000:?
.000:?
.000:?
.000:?
30.
19.
19.
18.
17.
11.
13.
4.
4.
2.
3.
2.
6.
2.
1.
1.
1.
•
2.
•
•
.
•
•
3.
2.
1.
•
.
2.
2.
1.
2.
3.
3.
3.
237 0
280 0
865 0
964 0
088 0
909 0
605 0
960 0
049 0
913 0
129 0
511 0
161 0
201 0
952 0
483 0
201 0
949 0
449 0
949 0
886 0
634 0
444 0
381 0
376 0
109 0
177 0
802 0
567 0
687 0
341 0
761 0
918 0
033 0
262 0
376 0
                     Appendix H,  page  1
                                                             108
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azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
azinme
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: 2 2:CAvPeach
: 2 2:CAvPeach
: 2 2:CAvPeach
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: 2 2:CAvPeach
: 2 2:CAvPeach
:77 1:AZ Cotton
:77 1:AZ Cotton
:77 1:AZ Cotton
:77 1:AZ Cotton
:77 1:AZ Cotton
:51 l:CANApple
:51 l:CANApple
:51 l:CANApple
:51 l:CANApple
:51 l:CANApple
:51 l:CANApple
:51 2:CANApple
:51 2:CANApple
:51 2:CANApple
:51 2:CANApple
:51 2:CANApple
:51 2:CANApple
:51 3:CANApple
:51 3:CANApple
:51 3:CANApple
:51 3:CANApple
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:51 5:CANApple
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:51 6:CANApple
:51 6:CANApple
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0100: 6.0: 5.400: .000: .000: .000:? 3.147 0
0100: 9.0: 5.000: .000: .000: .000:? 2.918 0
0100:11.0: 4.400: .000: .000: .000:? 2.572 0
0100:14.0: 4.200: .000: .000: .000:? 2.457 0
0100:17.0: 3.600: .000: .000: .000:7 2.109 0
0100:20.0: 4.200: .000: .000: .000:? 2.457 0
0009: .0: .843: .000: .000: .000:? 1.30570
0009: 1.0: .568: .000: .000: .000:7 .88170
0009: 2.0: .289: .000: .000: .000:7 .44970
0009: 3.0: .229: .000: .000: .000:7 .35670
0009: 4.0: .149: .000: .000: .000:7 .23270
: .0: 1.920: .000: .000: .000:7 2.073 0
: 3.0: .968: .000: .000: .000:? 1.050 0
: 7.0: .392: .000: .000: .000:? .427 0
:10.0: .380: .000: .000: .000:7 .414 0
:14.0: .224: .000: .000: .000:? .244 0
:21.0: .144: .000: .000: .000:7 .157 0
: .0: 3.720: .000: .000: .000:7 3.977 0
: 3.0: 1.364: .000: .000: .000:7 1.477 0
: 7.0: 1.228: .000: .000: .000:7 1.331 0
:10.0: .560: .000: .000: .000:7 .609 0
:14.0: .464: .000: .000: .000:? .505 0
:21.0: .148: .000: .000: .000:7 .161 0
: .0: 1.440: .000: .000: .000:7 1.559 0
: 3.0: .712: .000: .000: .000:7 .774 0
: 7.0: .532: .000: .000: .000:7 .579 0
:10.0: .272: .000: .000: .000:7 .296 0
:14.0: .424: .000: .000: .000:7 .461 0
:21.0: .148: .000: .000: .000:7 .161 0
:28.0: .068: .000: .000: .000:7 .074 0
: .0: 2.940: .000: .000: .000:? 3.156 0
: 3.0: 1.932: .000: .000: .000:? 2.086 0
: 7.0: 1.228: .000: .000: .000:7 1.331 0
:10.0: 1.176: .000: .000: .000:7 1.275 0
:14.0: .652: .000: .000: .000:7 .709 0
:21.0: .348: .000: .000: .000:? .379 0
:28.0: .196: .000: .000: .000:7 .214 0
: .0:17.000: .000: .000: .000:7 16.927 0
: 3.0:13.200: .000: .000: .000:7 13.411 0
: 7.0: 7.200: .000: .000: .000:7 7.554 0
:10.0: 4.600: .000: .000: .000:7 4.894 0
:14.0: 3.600: .000: .000: .000:7 3.851 0
:21.0: 2.400: .000: .000: .000:7 2.584 0
:28.0: 1.800: .000: .000: .000:? 1.944 0
:35.0: 1.400: .000: .000: .000:? 1.516 0
:42.0: 1.200: .000: .000: .000:7 1.301 0
: .0:11.000: .000: .000: .000:? 11.308 0
: 3.0: 9.000: .000: .000: .000:7 9.352 0
: 7.0: 5.400: .000: .000: .000:? 5.721 0
:10.0: 3.000: .000: .000: .000:? 3.220 0
:14.0: 2.000: .000: .000: .000:? 2.158 0
:21.0: 1.400: .000: .000: .000:? 1.516 0
:28.0: .800: .000: .000: .000:7 .869 0
:35.0: .600: .000: .000: .000:7 .652 0
Appendix H, page  2
                               109
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azinme  : 3
azinme  : 3
azinme  : 3
azinme  : 3
az inme  : 3
azinme  : 3
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carbaryl: 3
carbaryl: 3
carbaryl: 3
carbaryl: 3
carbaryl: 3
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carbaryl: 3
carbaryl: 3
carbaryl: 3
carbaryl: 3
carbop  :12
carbop  :12
carbop  :12
carbop  :12
carbop  :12
carbop  :12
carbop  :12
carbop  :12
carbop  :12
carbop  :12
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carbop  :35
carbop  :35
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2:AZ Orange:
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2:FL Orange:
2:FL Orange:
2:FL Orange:
2:FL Orange:
2:FL Orange:
3:FL Orange:
3:FL Orange:
3:FL Orange:
3:FL Orange:
3:FL Orange:
3:FL Orange:
4:FL Orange:
4:FL Orange:
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                     Appendix H, page  3
                                                             110
-------
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.400:
.800:
.360:
.140:
.150:
.044:
.000:
.800:
.200:
.140:
.540:
.860:
.260:
.740:
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.540:
.660:
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.340:
.660:
.400:
.214:
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.121:
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.922:
.650:
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.384:
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.076:
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.044:
.042:
.028:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.340:
.260:
.164:
.140:
.116:
.086:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.032:
.044:
.050:
.056:
.064:
.042:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
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.000:
.000:
.000:?
.000:?
.000:?
.000:?
.000:?
.914
.709
.556
.255
.224
.100
.093
19.279
11.061
5.931
6.169
5.453
3.040
.805
7.091
6.006
5.896
4.797
3.536
2.409
1.422
7.807
4.797
5.018
4.280
2.560
1.269
.771
.497
.486
.321
3.328
2.567
1.816
1.400
1.077
.926
.562
.377
.225
.152
.101
.959
.402
.116
.071
.959
.231
.047
.016
.437
.181
.118
.041
.035
0
0
°388
0
0
0
0
3
3
3
3
3
3
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
0
0
0
0
0
Appendix H, page  4
                                      111
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dimeth
dimeth
dimeth
dimeth
dimeth
dimeth
dimeth
dimeth
dimeth
dimeth
dimeth
dimeth
dimeth
dimeth
dimeth
dimeth
dimeth
dimeth
'dimeth
dimeth
dimeth
dimeth
dimeth
dimeth
dimeth
dimeth
dimeth
dimeth
dimeth
dimeth
dimeth
dimeth
dimeth
dimeth
dimeth
dimeth
dimeth
dimeth
dimeth
dimeth
dimeth
dimeth
dimeth
dimeth
dimeth
dimeth
dimeth
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
:51
:51
:51
:51
:51
:51
:51
:51
:51
:51
:51
:51
:51
:51
:51
:51
:51
:51
:51
:51
:51
:51
:51
:51
:51
:51
:51
:51
:51
:51
:51
:51
:51
:51
:51
:51
:51
:51
:51
:86
:86
:86
:86
:86
:86
:86
:86
:80
:80
:80
:80
:80
:80
:80
:80
l:CANApple
2:CANApple
2:CANApple
2:CANApple
2:CANApple
2:CANApple
2:CANApple
3:CANApple
3:CANApple
3:CANApple
3:CANApple
3:CANApple
3:CANApple
3:CANApple
4:CANApple
4:CANApple
4:CANApple
4:CANApple
4:CANApple
4:CANApple
4:CANApple
5:CANApple
5:CANApple
5:CANApple
5:CANApple
5:CANApple
5:CANApple
5:CANApple
5:CANApple
5:CANApple
6:CANApple
6:CANApple
6:CANApple
6:CANApple
6:CANApple
6:CANApple
6:CANApple
6:CANApple
6:CANApple
1:TX Orange
1:TX Orange
1:TX Orange
1:TX Orange
2:TX Orange
2:TX Orange
2:TX Orange
2:TX Orange
1 : CAsOrange
1 : CAsOrange
1 : CAsOrange
1: CAsOrange
1: CAsOrange
1 : CAsOrange
2: CAsOrange
2: CAsOrange
*
.
.
.
.
*
.
.
*


*
.
.
*
.
.
.
.
.
.
.
.
*
.
.
.
.
.
.
.
.
*
.
.
.
.
.
.
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
5.00
5.00
5.00
5.00
5.00
5.00
5.00
5.00
:21.0:
: .0:
: 3.0:
: 7.0:
:10.0:
:14.0:
:21.0:
: .0:
: 3.0:
: 7.0:
:10.0:
:14.0:
:21.0:
:28.0:
: .0:
: 3.0:
: 7.0:
:10.0:
:14.0:
:21.0:
:28.0:
: .0:
: 3.0:
: 7.0:
:10.0:
:14.0:
:21.0:
:28.0:
:35.0:
:42.0:
: .0:
: 3.0:
: 7.0:
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:14.0:
:21.0:
:28.0:
:35.0:
:42.0:
5: 1.0:
5: 2.0:
5: 7.0:
5:14.0:
0: 1.0:
0: 2.0:
0: 7.0:
0:14.0:
100: 4.0:
100:10.0:
100:18.0:
100:31.0:
100:46.0:
100:59.0:
100: 4.0:
100:10.0:
.012:
2.456:
.936:
.476:
.264:
.324:
.040:
1.028:
.600:
.332:
.136:
.100:
.052:
.068:
2.256:
1.616:
.564:
.436:
.316:
.084:
.024:
.192:
.184:
.132:
.056:
.044:
.024:
.008:
.008:
.004:
.196:
.136:
.096:
.044:
.028:
.008:
.004:
.004:
.004:
.432:
.248:
.011:
.001:
1.922:
1.274:
.042:
.004:
2.600:
2.200:
1.400:
1.600:
1.600:
1.200:
2.600:
2.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
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.000:
.000:
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.000:
.000:
.000:
.000:
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.000:
.000:
.000:
.000:
.000:
.000:
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.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.012:
.010:
.002:
.000:
.018:
.039:
.011:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
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.000:
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.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
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.000:
.000:
.000:
.000:
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.000:
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.000:
.000:
.000:
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.000:
.000:
.000:
.000:
.000:
.000:
.000:
.005 0
.962 0
.368 0
.187 0
.104 0
.127 0
.016 0
.404 0
.236 0
.131 0
.053 0
.039 0
.020 0
.027 0
.884 0
.634 0
.222 0
.171 0
.124 0
.033 0
.009 0
.076 0
.072 0
.052 0
.022 0
.017 0
.009 0
.003 0
.003 0
.002 0
.077 0
.053 0
.038 0
.017 0
.011 0
.003 0
.002 0
.002 0
.002 0
.254 1
.148 1
.007 1
.000 1
1.106 1
.750 1
.030 1
.002 1
7.297 0
6.210 0
3.998 0
4.556 0
4.556 0
3.437 0
7.297 0
5.662 0
                                             385
Appendix H, page  5
                              112
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dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
:80 2:
:80 2:
:80 2:
:80 2:
:80 3:
:80 3:
:80 3:
:80 3:
:80 3:
:80 3:
:80 A:
:80 A:
:80 A:
:80 A:
:80 A:
:80 A:
:80 5:
:80 5:
:80 5:
:80 5:
:80 5:
:80 5:
:80 6:
:80 6:
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:80 9:
:80 9:
:8010:
:8010:
:8010:
:8010:
:8011:
:8011:
:8011:
:8011:
:38 1:
CAsOrange :
CAsOrange :
CAsOrange :
CAsOrange:
CAsOrange:
CAsOrange :
CAsOrange:
CAsOrange :
CAsOrange :
CAsOrange :
CAsOrange:
CAsOrange:
CAsOrange :
CAsOrange:
CAsOrange :
CAsOrange :
CAsOrange :
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CAsOrange :
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CAsOrange :
CAsOrange :
CAsOrange:
CAsOrange :
CAsOrange:
CAsOrange:
CAsOrange :
CAsOrange :
CAsOrange:
CAsOrange:
FL Orange:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
5.00:
A. 00:
100:
100:
100:
100:
100:
100:
100:
100:
100:
100:
250:
250:
250:
250:
250:
250:
250:
250:
250:
250:
250:
250:
250:
250:
250:
250:
250:
250:
1250:
1250:
1250:
1250:
1250:
1250:
1250:
1250:
1250:
1250:
1250:
1250:
1250:
1250:
1250:
1250:
1250:
1250:
100:
100:
100:
100:
100:
100:
100:
100:
750:
18.0:
31.0:
A6.0:
59.0:
A.O:
10.0:
18.0:
31.0:
A6.0:
59.0:
A.O:
10.0:
18.0:
31.0:
A6.0:
59.0:
A.O:
10.0:
18.0:
31.0:
A6.0:
59.0:
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10.0:
18.0:
31.0:
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59.0:
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10.0:
18.0:
31.0:
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59.0:
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10.0:
18.0:
31.0:
A6.0:
59.0:
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10.0:
18.0:
31.0:
A6.0:
59.0:
7.0:
13.0:
27.0:
35.0:
A.O:
10.0:
38.0:
A6.0:
1.0:
1.200:
l.AOO:
1.000:
1.600:
3.200:
2.600:
1.600:
l.AOO:
1.200:
l.AOO:
1.800:
2.000:
1.800:
1.600:
1.800:
1.000:
2.200:
2.000:
1.600:
l.AOO:
l.AOO:
1.000:
1.800:
1.600:
2.000:
l.AOO:
1.200:
.600:
1.000:
.800:
.600:
.800:
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1.200:
1.000:
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.600:
.600:
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1.000:
.800:
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2.600:
2.200:
1.800:
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2.800:
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.000:
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.000:
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.000:
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.000:
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.000:
.000:
.000:
.000:
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.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
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.000:
.000:
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.000:
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.000:
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.000:
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.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
3.A37
3.998
2.872
A. 556
8.90A
7.297
A. 556
3.998
3.A37
3.998
5.111
5.662
5.111
A. 556
5.111
2.872
6.210
5.662
A. 556
3.998
3.998
2.872
5.111
A. 556
5.662
3.998
3.A37
1.733
2.872
2.30A
1.733
2.30A
1.159
1.159
3.A37
2.872
1.159
1.733
1.733
1.159
2.872
2.30A
1.159
1.159
1.159
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7.297
6.210
5.111
3.998
7.836
A. 556
5.662
3.998
1.733
0^
0390
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Appendix H, page  6
                                    113
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dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
dioxat
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
:38
:38
:38
:38
:38
:38
:38
:38
:38
:38
:38
:27
:27
:27
:27
:27
:27
:27
:27
:27
:27
:27
:27
:27
:27
:47
:47
:47
:47
:47
:47
:47
:47
:47
:47
:47
:47
:47
:47
:47
:47
:47
:47
:12
:12
: 12
:12
:12
:12
:12
:12
:12
:12
:12
:12
1:FL Orange:
1:FL Orange:
1:FL Orange:
1:FL Orange:
1:FL Orange:
2:FL Orange:
2:FL Orange:
2:FL Orange:
2:FL Orange:
2:FL Orange:
2:FL Orange:
1 : CAvGrapes :
1 : CAvGrapes :
1 : CAvGrapes :
1 : CAvGrapes :
1 : CAvGrapes :
1 : CAvGrapes :
1 : CAvGrapes :
2 : CAvGrapes :
2 : CAvGrapes :
2 : CAvGrapes :
2: CAvGrapes:
2 : CAvGrapes :
2 : CAvGrapes :
2 : CAvGrapes :
1 : CAvOrange :
1 : CAvOrange :
1 : CAvOrange :
1 : CAvOrange :
1: CAvOrange:
1 : CAvOrange:
2 : CAvOrange :
2 : CAvOrange :
2 : CAvOrange :
2: CAvOrange:
2 : CAvOrange :
2: CAvOrange:
3 : CAvOrange :
3 : CAvOrange :
3: CAvOrange:
3: CAvOrange:
3 : CAvOrange :
3 : CAvOrange :
1:AZ Orange:
1:AZ Orange:
1:AZ Orange:
1:AZ Orange:
1:AZ Orange:
1:AZ Orange:
1:AZ Orange:
1:AZ Orange:
1:AZ Orange:
2:FL Orange:
2:FL Orange:
2:FL Orange:
4.00:
4.00:
4.00:
4.00:
4.00:
4.00:
4.00:
4.00:
4.00:
4.00:
4.00:
1.0:
1.0:
1.0:
1.0:
1.0:
1.0:
1.0:
1.25:
1.25:
1.25:
1.25:
1.25:
1.25:
1.25:
3.0:
3.0:
3.0:
3.0:
3.0:
3.0:
4.0:
4.0:
4.0:
4.0:
4.0:
4.0:
4.0:
4.0:
4.0:
4.0:
4.0:
4.0:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
4.00:
4.00:
4.00:
750: 2
750: 5
750:10
750:20
750:30
750: 1
750: 2
750: 5
750:10
750:20
750:30
30:
30: 1
30: 3
30: 7
30:14
30:21
30:28
200:
200: 1
200: 3
200: 7
200:14
200:21
200:28
500: 2
500: 5
500: 9
500:16
500:23
500:33
500: 2
500: 5
500: 9
500:16
500:23
500:33
500: 2
500: 5
500: 9
500:16
500:23
500:33
350:
350: 1
350: 3
350: 5
350: 7
350:14
350:21
350:28
350:42
350:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
350: 1.0:
350: 3
.0:
.460:
.326:
.250:
.192:
.164:
.080:
.056:
.036:
.024:
.018:
.014:
.800:
.600:
.200:
.065:
.021:
.019:
.015:
.900:
.700:
.350:
.070:
.032:
.020:
.012:
1.102:
.475:
.236:
.149:
.078:
.037:
.516:
.754:
.342:
.548:
.298:
.230:
1.771:
.834:
.669:
.268:
.176:
.127:
18.136:
17.612:
15.848:
10.392:
12.040:
9.420:
7.398:
4.442:
2.356:
10.252:
9.072:
7.856:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.070:
.072:
.078:
.055:
.025:
.011:
.007:
.080:
.092:
.105:
.058:
.030:
.019:
.010:
.100:
.087:
.064:
.048:
.024:
.011:
.028:
.054:
.034:
.090:
.042:
.034:
.110:
.076:
.112:
.063:
.044:
.034:
.172:
.548:
.930:
.944:
1.220:
1.386:
1.412:
1.238:
1.006:
.122:
.130:
.142:
.uuu:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.005:
.020:
.048:
.060:
.050:
.035:
.004:
.004:
.011:
.039:
.045:
.039:
.032:
.000:
.029:
.041:
.032:
.028:
.029:
.008:
.012:
.011:
.047:
.039:
.044:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.022:
.070:
.066:
.106:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
. uuu .
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
^ . _/...-
.946
.726
.558
.477
.233
.163
.105
.070
.052
.041
1.356
1.057
.466
.263
.166
.125
.089
1.532
1.241
.728
.262
.168
.122
.085
2.710
1.340
.777
.523
.297
.175
1.255
1.858
.881
1.554
.862
.702
4.207
2.058
1.769
.754
.501
.369
34.195
34.005
31.962
22.942
26.325
21.885
18.239
12.175
7.397
21.111
18.969
16.708
0
°391
0
0
0
0
0
0
0
0
0
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
Appendix H, page  7
                                     114
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ethion
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ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
ethion
U2
:12
:12
:12
:12
:12
:12
:12
:12
-.12
: 12
:12
: 12
: 12
:12
: 12
:12
:12
:12
: 12
:34
:34
:34
:34
:34
:34
:34
:34
:34
:34
:34
:34
:34
:34
:34
:34
:34
:34
:34
:34
:34
:34
:34
:34
:34
:34
:34
:34
:34
:34
:34
2:FL
2:FL
2:FL
2:FL
2:FL
2:FL
3:TX
3:TX
3:TX
3:TX
3:TX
3:TX
3:TX
3:TX
3:TX
4:CA
4:CA
4:CA
4:CA
4:CA
4:CA
4:CA
4:CA
4:CA
1:FL
1:FL
1:FL
1:FL
1:FL
1:FL
2:FL
2:FL
2:FL
2:FL
2:FL
2:FL
2:FL
2:FL
2:FL
3:FL
3:FL
3:FL
3:FL
3:FL
3:FL
4:FL
4:FL
4:FL
4:FL
4:FL
4:FL
4:FL
4:FL
4:FL
5:FL
Orange:
Orange:
Orange:
Orange:
Orange :
Orange:
Orange :
Orange :
Orange:
Orange:
Orange:
Orange :
Orange:
Orange :
Orange:
Orange:
Orange :
Orange :
Orange:
Orange :
Orange:
Orange:
Orange :
Orange :
Orange:
Orange:
Orange :
Orange:
Orange :
Orange :
Orange :
Orange :
Orange:
Orange:
Orange:
Orange :
Orange:
Orange :
Orange :
Orange:
Orange:
Orange:
Orange :
Orange :
Orange :
Orange :
Orange :
Orange:
Orange:
Orange:
Orange:
Orange:
Orange :
Orange:
Orange:
4.00:
4.00:
4.00:
4.00:
4.00:
4.00:
4.00:
4.00:
4.00:
4.00:
4.00:
4.00:
4.00:
4.00:
4.00:
4.00:
4.00:
4.00:
4.00:
4.00:
4.00:
4.00:
4.00:
4.00:
3.00:
3.00:
3.00:
3.00:
3.00:
3.00:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
350: 5
350: 7
350:14
350:21
350:28
350:42
350:
350: 1
350: 3
350: 5
350: 7
350:14
350:21
350:28
350:42
350:
350: 1
350: 3
350: 5
350: 7
350:14
350:21
350:28
350:42
1200:
1200: 1
1200: 3
1200: 5
1200: 7
1200:14
1200:
1200: 1
1200: 3
1200: 5
1200: 7
1200:14
1200:21
1200:28
1200:35
1200:
1200: 1
1200: 3
1200: 5
1200: 7
1200:14
1200:
1200: 1
1200: 3
1200: 5
1200: 7
1200:14
1200:21
1200:28
1200:35
1200:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
5.498:
3.024:
1.954:
.844:
.726:
.368:
6.776:
4.534:
3.846:
2.952:
1.968:
.420:
.226:
.126:
.030:
4.336:
3.900:
3.504:
2.894:
2.274:
.988:
.476:
.290:
.072:
.199:
.122:
.015:
.006:
.006:
.004:
.283:
.066:
.047:
.033:
.034:
.033:
.010:
.007:
.008:
.569:
.249:
.050:
.009:
.003:
.005:
.265:
.116:
.079:
.061:
.059:
.053:
.020:
.015:
.008:
.505:
.124:
.092:
.072:
.076:
.062:
.044:
.102:
.090:
.092:
.092:
.084:
.062:
.048:
.042:
.012:
..104:
.110:
.106:
.126:
.116:
.072:
.082:
.064:
.026:
.008:
.012:
.008:
.005:
.003:
.003:
.024:
.005:
.006:
.008:
.010:
.010:
.008:
.004:
.004:
.084:
.043:
.010:
.009:
.005:
.004:
.033:
.005:
.008:
.010:
.013:
.012:
.011:
.010:
.006:
.024:
.000:
.001:
.001:
.008:
.007:
.006:
.000:
.000:
.000:
.000:
.003:
.007:
.013:
.014:
.007:
.000:
.000:
.000:
.005:
.006:
.005:
.015:
.032:
.021:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
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.000:
.000:
.000:
.000:
.000:
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.000:
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.000:
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.000:
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.000:
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.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
12.059 2
6.876 2
4.527 2
2.100 2
1.800 2
.952 2
14.548 2
10.030 2
8.608 2
6.722 2
4.589 2
1.112 2
.653 2
.416 2
.111 2
9.651 2
8.759 2
7.921 2
6.681 2
5.329 2
2.405 2
1.300 2
.878 2
.273 2
.472 1
.307 1
.052 1
.026 1
.021 1
.015 1
.700 1
.161 1
.121 1
.094 1
.101 1
.098 1
.041 1
.026 1
.029 1
1.483 1
.667 1
.138 1
.041 1
.018 1
.020 1
.678 1
.277 1
.199 1
.163 1
.165 1
.148 1
.071 1
.057 1
.031 1
1.203 1
                                            392
Appendix H, page  8
                              115
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ethion
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etp
etp
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:34 5:FL Orange:
:34 5
:34 5
:34 5
:34 5
:34 6
:34 6
:34 6
:34 6
:34 6
:34 6
:34 6
:34 6
:34 6
;34 7
:34 7
:34 7
:34 7
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:34 7
:34 8
:34 8
:34 8
:34 8
:34 8
:34 8
:34 8
:34 8
:34 8
:34 9
:34 9
:34 9
:34 9
:34 9
:34 9
:3410
:3410
:3410
:3410
:3410
:3410
:3410
:3410
:3410
:18 1
:18 1
:18 1
:18 1
:18 1
:18 1
:18 1
:18 1
:18 1
:18 1
:18 1
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
:FL Orange:
.•CAsOrange:
: CAsOrange :
: CAsOrange:
: CAsOrange :
: CAsOrange:
: CAsOrange :
: CAsOrange :
: CAsOrange:
: CAsOrange:
: CAsOrange:
: CAsOrange:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
4.50:
9.00:
9.00:
9.00:
9.00:
9.00:
9.00:
9.00:
9.00:
9.00:
9.00:
9.00:
9.00:
9.00:
9.00:
9.00:
12.50:
12.50:
12.50:
12.50:
12.50:
12.50:
12.50:
12.50:
12.50:
12.50:
12.50:
1200: 1.
1200: 3.
1200: 5.
1200: 7.
1200:14.
1200: .
1200: 1.
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1200: 5.
1200: 7.
1200:14.
1200:21.
1200:28.
1200:35.
1200: .
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1200: 7.
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1200: 7.
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1200:28.
1200:35.
2000: .
2000: .
2000: 1.
2000: 1.
2000: 2.
2000: 3.
2000: 4.
2000: 5.
2000: 6.
2000: 7.
2000: 8.
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
5:
8:
0:
5:
0:
0:
0:
0:
0:
0:
0:
.126:
.034:
.027:
.022:
.009:
.468:
.203:
.103:
.058:
.021:
.044:
.010:
.009:
.007:
.416:
.095:
.048:
.024:
.015:
.011:
.405:
.190:
.222:
.108:
.041:
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.014:
.016:
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.635:
.275:
.116:
.018:
.016:
.009:
.752:
.316:
.237:
.262:
.164:
.042:
.024:
.024:
.016:
4.200:
3.600:
2.400:
2.800:
2.600:
1.940:
1.420:
1.400:
1.620:
1.540:
1.160:
.014:
.006:
.003:
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.049:
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.032:
.000:
.056:
.086:
.066:
.068:
.090:
.086:
.070:
.000:
.000:
.000:
.000:
.000:
.000:
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1








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64
59
50
50
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54
44
44
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41
.320 1
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.976 0
.313 0
.905 0
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.404 0
.318 0
.430 0
.296 0
.613 0
.407 0
                                                 39
Appendix H, page  9
                                 116
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CA Orange:
CA Orange:
CA Orange:
CA Orange:
CA Orange:
CA Orange:
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                    Appendix H, page  10
                                                               117
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1:SC tobaco:0.375: : 3.0:
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2:SC tobaco: 0.375: : 1.0:
2:SC tobaco: 0.375: : 3.0:
2:SC tobaco:0.375: : 5.0:
3:SC tobaco: 0.375: : 1.0:
3:SC tobaco: 0.375: : 3.0:
3:SC tobaco:0.375: : 5.0:
4:SC tobaco:0.375: : 1.0:
4:SC tobaco:0.375: : 3.0:
1:FL Orange: 0.75: 100: 1.0:
1:FL Orange: 0.75: 100: 6.0:
1:FL Orange: 0.75: 100: 8.0:
2:FL Orange: 0.75: 100: 1.0:
2:FL Orange: 0.75: 100: 2.0:
2:FL Orange: 0.75: 100: 3.0:
2:FL Orange: 0.75: 100: 7.0:
2:FL Orange: 0.75: 100: 9.0:
3:FL Orange: 2.00: 100: 1.0:
3:FL Orange: 2.00: 100: 3.0:
3:FL Orange: 2.00: 100: 6.0:
3:FL Orange: 2.00: 100; 8.0;
4:FL Orange: .75: 100: 1.0:
4:FL Orange: .75: 100: 3.0:
4:FL Orange: .75: 100: 6.0:
4:FL Orange: .75: 100: 8.0:
l:CAsPeach : 2.00: 400: .0:
l:CAsPeach : 2.00: 400: 3.0:
l:CAsPeach : 2.00: 400: 7.0:
l:CAsPeach : 2.00: 400:14.0:
l:CAsPeach : 2.00: 400:21.0:
l:CAsPeach : 2.00: 400:28.0:
l:CAsPeach : 2.00: 400:70.0:
2:CAsPeach : 1.00:0400: .0:
2:CAsPeach : 1.00:0400: 3.0:
2:CAsPeach : 1.00:0400: 7.0:
2:CAsPeach : 1.00:0400:14.0:
2:CAsPeach : 1.00:0400:21.0:
2:CAsPeach : 1.00:0400:28.0:
2:CAsPeach : 1.00:0400:70.0:
3:CAsPeach : 1.00:0400: .0:
3:CAsPeach : 1.00:0400: 3.0:
3:CAsPeach : 1.00:0400: 7.0:
3:CAsPeach : 1.00:0400:14.0:
3:CAsPeach : 1.00:0400:21.0:
3:CAsPeach : 1.00:0400:28.0:
3:CAsPeach : 1.00:0400:70.0:
1:AZ Cotton: 1.00: 9: .0:
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                                                  39j
Appendix H, page  11
                                     118
-------
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                   396
        Appendix H,  page   12
119
-------
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5: CAvCitrus: 10. 00: 1600: 4.0:
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6: CAvCitrus: 10. 00: 1600: 4.0:
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l:CAvPeach : 2.00: 50: 9.0:
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2:CAvPeach : 1.30: 17: .5:
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Appendix H, page  13
                              120
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35.0:
1.0:
3.0:
5.0:
7.0:
14.0:
.0:
1.0:
3.0:
5.0:
7.0:
14.0:
21.0:
28.0:
42.0:
.0:
1.0:
3.0:
7.0:
14.0:
28.0:
42.0:
.0:
1.0:
3.0:
7.0:
14.0:
28.0:
42.0:
.0:
1.0:
3.0:
5.0:
7.0:
14.0:
21.0:
28.0:
42.0:
.440:
.040:
.025:
.021:
.011:
.008:
.008:
.003:
.002:
2.719:
.551:
.174:
.102:
.041:
.023:
.019:
.008:
.002:
14.086:
3.604:
2.102:
.836:
.286:
11.246:
8.590:
6.126:
4.130:
3.832:
1.832:
.674:
.186:
.086:
3.642:
2.036:
.890:
.272:
.060:
.010:
.004:
3.212:
1.924:
.974:
.218:
.064:
.012:
.006:
3.280:
2.552:
.786:
.540:
.286:
.112:
.034:
.026:
.004:
.022:
.015:
.013:
.011:
.005:
.002:
.004:
.000:
.000:
.085:
.034:
.030:
.016:
.006:
.006:
.003:
.000:
.000:
.262:
.170:
.134:
.094:
.092:
.038:
.134:
.222:
.228:
.302:
.152:
.122:
.090:
.090:
.034:
.052:
.114:
.090:
.050:
.014:
.006:
.034:
.058:
.064:
.036:
.022:
.008:
.006:
.018:
.048:
.024:
.028:
.026:
.008:
.006:
.012:
.004:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
17.097
5.988
4.906
4.272
2.135
.821
1.517
.085
.060
62.386
22.728
13.798
7.880
3.147
2.569
1.640
.195
.055
98.814
77.570
62.979
41.583
32.509
94.724
92.679
90.040
83.943
86.646
62.812
44.845
30.316
28.554
64.251
49.864
46.259
31.797
17.291
5.016
2.174
60.198
49.512
37.319
16.502
8.874
3.050
2.223
58.616
55.306
24.442
20.770
14.987
5.442
2.905
4.730
1.488
0
0
0
0
o 39C
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
                     Appendix H, page  14
121
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etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
etp
12 6:CA
12 6:CA
12 6:CA
12 6:CA
12 6:CA
12 6:CA
   6:CA
:12
:12  6
:12  6
:12
:12
:12
:12
:12
:12
:12
:12
:12
     :CA
     :CA
     lAZ
     :AZ
     ;AZ
     :AZ
     :AZ
     :AZ
     :AZ
     :AZ
     :AZ
   7
   7
   7
   7
   7
   7
   7
   7
   7
12 8:CA
12 8:CA
12 8:CA
12 8:CA
12 8:CA
12 8:CA
12 8:CA
12 8:CA
12 9:CA
12 9:CA
12 9:CA
12 9:CA
12 9:CA
12 9:CA
12 9:CA
1210:FL
1210:FL
1210:FL
1210:FL
1210:FL
1210:FL
1210:FL
1210:FL
1210:FL
    :TX
    :TX
    :TX
    :TX
    :TX
    :TX
    :TX
    :TX
    :TX
    :AZ
    :AZ
    :AZ
:75
:75
:75
:75 1:AZ
Orange:
Orange:
Orange:
Orange:
Orange:
Orange:
Orange:
Orange:
Orange:
Orange:
Orange:
Orange:
Orange:
Orange:
Orange:
Orange:
Orange:
Orange:
Grapes:
Grapes:
Grapes:
Grapes:
Grapes:
Grapes:
Grapes:
Grapes:
Apple  :
Apple  :
Apple  :
Apple  :
Apple  :
Apple  :
Apple  :
Orange:
Orange:
Orange:
Orange:
Orange:
Orange:
Orange:
Orange:
Orange:
Orange:
Orange:
Orange:
Orange:
Orange:
Orange:
Orange:
Orange:
Orange:
Cotton:
Cotton:
Cotton:
Cotton:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
  .50:
  .50:
  .50:
  .50:
                       350:
  5:
  5:
  5:
  5:
350
350
350
350
350
350:14.0:
350:21.0:
350:28.0:
350:42.0:
350
350
350
350
                            7.0
350: 7.0:
350:14.0:
350:21.0:
350:28.0:
350:42.0:
350
350
350
350
350
350:14.0
350:28.0
350:42.0
350
350
350
350
350
350:14.0
350:42.0
350
350
350
350
                             .0:
                            1.0:
                            3.0:
                            5.0:
                            7.0:
                       350:14.0:
                       350:21.0;
                       350:28.0:
                       350:42.0:
                       350
                       350
                       350
                       350
                       350
                       350:14.0
                       350:21.0
                       350:28
                       350:42
                            7.0
  0;
  0:
 .0:
 .5
1.0:
     7.440:
     5.396:
     2.102:
     1.346:
      .792:
      .202:
      .068:
      .024:
      .004:
    11.200:
     6.646:
       826:
       642:
       918:
      .578:
      .270:
      .156:
      .040:
    15.378:
    14.086:
 3.
 2.
 1.
                                 3,
                                 2.
   604:
   102:
  .836:
  .286:
  .052:
  .008:
18.690:
11.970:
 3.892:
 1.636:
 1.286:
  .056:
  .010:
10.276:
   508:
   342:
   624:
   964:
  .560:
  .226:
  .128:
  .048:
11.530:
   426:
   490;
   668:
   166:
   098:
   024:
   010:
   004:
   020:
   750:
   580:
     5.
     3.
     2,
     1.
          7.
          2.
          2.
          1,
                                 4.
                                 2.
                                 2.
                            2.0:  1.960
.030:
.118:
.050:
.060:
.038:
.010:
.006:
.008:
.004:
.126:
.216:
.212:
.274:
.274:
.134:
.110:
.080:
.034:
.188:
.262:
.170:
.134:
.094:
.092:
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.004:
.162:
.292:
.382:
.230:
.176:
.040:
.012:
.056:
.034:
.058:
.072:
.078:
.042:
.038:
.026:
.014:
.070:
.104:
.074:
.104:
.060:
.016:
.008:
.006:
.002:
.020:
.040:
.040:
.050:
.000:
.000:
.000:
.000:
.000:
.000:
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.000:
.000:
.000:
.000:
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.000:
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.000:
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.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
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.000:
.000:
.000:
.000:
.000:
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.000:
.000:
.000:
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.000:
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.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
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.000:?
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.000:?
.000:?
.000:?
.000:?
85. yo/ 0
82.805 0
50.338 0
42.077 0
28.160 0
8.188 0
3.726 0
3.340 0
1.488 0
96.077 0
91.068 0
81.679 0
80.174 0
76.244 0
45.828 0
36.373 0
27.294 0
12.093 0
95.438 0
95.233 0
64.150 0
49.437 0
30.852 0
23.649 0
7.773 0
1.091 0
97.248 0
93.625 0
79.485 0
56.502 0
47.421 0
10.022 0
3.005 0
93.688 0
77.570 0
64.571 0
59.637 0
53.391 0
24.931 0
17.250 0
11.565 0
5.913 0
95.606 0
89.125 0
58.554 0
64.305 0
39.413 0
7.728 0
3.340 0
2.321 0
.796 0
65.11370
55.54170
53.65170
47.87070
             Appendix  H,  page   15
                                                        122
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etp
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etp
etp
etp
etp
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etp
etp
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etp
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etp
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:75
:75
:75
:75
:75
:75
:76
:76
:79
:79
:79
:84
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:84
1:AZ Cotton:
2:AZ Cotton:
2:AZ Cotton:
2:AZ Cotton:
2:AZ Cotton:
2:AZ Cotton:
1:AZ Cotton:
1:AZ Cotton:
1:AZ Cotton:
1:AZ Cotton:
1:AZ Cotton:
l:CAvCitrus:
l:CAvCitrus:
l:CAvCitrus:
l:CAvCitrus:
l:CAvCitrus:
l:CAvCitrus:
l:CAvCitrus:
l:CAvCitrus:
l:CAvCitrus:
2:CAvCitrus:
2:CAvCitrus:
2:CAvCitrus:
2:CAvCitrus:
2:CAvCitrus:
2:CAvCitrus:
2:CAvCitrus:
2:CAvCitrus:
2:CAvCitrus:
2:CAvCitrus:
2:CAvCitrus:
2:CAvCitrus:
3:CAvCitrus:
3:CAvCitrus:
3:CAvCitrus:
3:CAvCitrus:
3:CAvCitrus:
3:CAvCitrus:
3:CAvCitrus:
3:CAvCitrus:
3:CAvCitrus:
3:CAvCitrus:
3:CAvCitrus:
3:CAvCitrus:
4:CAvCitrus:
4:CAvCitrus:
4:CAvCitrus:
4:CAvCitrus:
4:CAvCitrus:
4:CAvCitrus:
4:CAvCitrus:
4:CAvCitrus:
5:CAvCitrus:
5:CAvCitrus:
5:CAvCitrus:
.50:
.50:
.50:
.50:
.50:
.50:
1.00:
1.00:
1.00:
1.00:
1.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
5: 3.
5: .
5: .
5: 1.
5: 2.
5: 3.
9: 1.
9: 1.
9: .
9: 1.
9: 2.
100: .
100: 1.
100: 2.
100: 3.
100: 7.
100:10.
100:14.
100:21.
100:28.
100: .
100: 1.
100: 2.
100: 3.
100: 5.
100: 7.
100:10.
100:14.
100:21.
100:28.
100:57.
100:63.
100: .
100: 1.
100: 2.
100: 3.
100: 5.
100: 7.
100:10.
100:14.
100:21.
100:28.
100:35.
100:63.
100: .
100: 1.
100: 3.
100: 7.
100:10.
100:14.
100:21.
100:28.
100: .
100: 1.
100: 3.
0:
0:
5:
0:
0:
0:
0:
5:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
1.240:
.920:
.760:
.590:
.440:
.380:
.350:
.370:
2.330:
.270:
.150:
9.000:
.000:
3.200:
1.400:
.000:
.360:
.120:
.060:
.036:
8.000:
.000:
4.000:
3.000:
1.700:
.900:
.520:
.300:
.066:
.000:
.020:
.000:
7.000:
.000:
5.000:
1.800:
1.040:
.600:
.240:
.140:
.044:
.000:
.034:
.014:
.780:
3.200:
1.600:
.340:
.180:
.060:
.030:
.018:
8.000:
4.400:
1.400:
.040:
.000:
.010:
.010:
.010:
.010:
.004:
.002:
.000:
.017:
.012:
.110:
.380:
.000:
.300:
.340:
.320:
.200:
.140:
.104:
.044:
.200:
.000:
.540:
.540:
.400:
.440:
.360:
.160:
.110:
.000:
.080:
.050:
.120:
.000:
.280:
.180:
.220:
.240:
.220:
.044:
.032:
.000:
.012:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
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.000:
.000:
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.000:
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.000:
.000:
.000:
35
20
19
16
13
11
9
9
43
11
7
92
73
55
75
69
70
51
39
31
88
50
63
92
90
80
81
73
43
31

24
85
34
71
76
58
60
59
55
15
10

4
17
55
32
8
4
1


86
66
29
.649?0
.173?0
.78170
.37170
.24170
.95770
.46570
.22570
.48270
.69870
.49270
.813 0
.524 0
.038 0
.312 0
.548 0
.151 0
.780 0
.625 0
.113 0
.378 0
.313 0
.182 0
.848 C
.105 0
.283 0
.150 0
.655 0
.787 0
.934 0
.498 0
.404 0
.389 0
.273 0
.320 0
.041 0
.905 0
.118 0
.313 0
.262 0
.199 0
.587 0
.846 0
.444 0
.703 0
.038 0
.946 0
.142 0
.397 0
.488 0
.747 0
.449 0
.444 0
.683 0
.511 0
                                            400
Appendix H, page  16
                              123
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etp
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:84 5:CAvCitrus:
:8A 5:CAvCitrus:
:8A 5:CAvCitrus:
:8A 5: CAvCitrus:
:8A 5
:8A 5
:8A 6
:8A 6
:8A 6
:8A 6
:8A 6
:8A 6
:84 6
:8A 6
:8A 6
:67 1
:67 1
:67 1
:67 2
:67 2
:67 2
:67 2
:67 3
:67 3
:67 3
:67 3
:67 4
:67 A
:67 A
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:67 5
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:67 6
:67 6
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:67 7
:67 7
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:67 8
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:CAvCitrus:
:CAvCitrus:
:CAvCitrus:
: CAvCitrus:
:CAvCitrus:
:CAvCitrus:
:CAvCitrus:
: CAvCitrus:
: CAvCitrus :
:CAvCitrus:
: CAvCitrus:
:CAvOrange:
: CAvOrange :
: CAvOrange :
: CAvOrange :
: CAvOrange :
: CAvOrange :
: CAvOrange :
: CAvOrange :
: CAvOrange :
: CAvOrange :
: CAvOrange :
: CAvOrange :
: CAvOrange :
: CAvOrange :
: CAvOrange :
: CAvOrange:
: CAvOrange :
: CAvOrange :
: CAvOrange :
: CAvOrange :
: CAvOrange :
: CAvOrange :
: CAvOrange:
: CAvOrange :
: CAvOrange :
: CAvOrange :
: CAvOrange:
: CAvOrange :
: CAvOrange :
: CAvOrange :
: CAvOrange :
: CAvOrange :
: CAvOrange:
: CAvOrange :
: CAvOrange :
: CAvOrange :
: CAvOrange:
: CAvOrange:
: CAvOrange :
: CAvOrange:
8.00
8.00
8.00
8.00
8.00
8.00
8.00
8.00
8.00
8.00
8.00
8.00
8.00
8.00
8.00
6.0
6.0
6.0
5.0
5.0
5.0
5.0
5.0
5.0
5.0
5.0
2.25
2.25
2.25
2.25
2.0
2.0
2.0
2.0
2.0
2.0
2.5
2.5
2.5
1.5
1.5
1.5
1.5
1.5
1.5
1.3
1.3
1.3
2.0
2.0
2.0
2.0
3.1
3.1
3.1
: 100
: 100
: 100
: 100
: 100
: 100
: 100
: 100
: 100
: 100
: 100
: 100
: 100
: 100
: 100
:1200
:1200
:1200
:1000
:1000
:1000
:1000
:1000
:1000
:1000
:1000
: 900
: 900
: 900
: 900
: 300
: 300
: 300
: 300
: 300
: 300
:1000
:1000
:1000
: 750
: 750
: 750
: 600
: 600
: 600
: 500
: 500
: 500
: 200
: 200
: 200
: 200
:1250
:1250
:1250
: 5.0:
: 7.0:
:10.0:
:1A.O:
:21.0:
:28.0:
: .0:
: 1.0:
: 3.0:
: 5.0:
: 7.0:
: 9.0:
:13.0:
:20.0:
:3A.O:
: 2.0:
: 9.0:
:16.0:
: 2.0:
: 9.0:
:16.0:
:23.0:
: 2.0:
: 9.0:
:16.0:
:23.0:
: 2.0:
: 9.0:
:16.0:
:23.0:
: 2.0:
: 9.0:
:16.0:
: 2.0:
: 9.0:
:16.0:
: 2.0:
: 9.0:
:16.0:
: 2.0:
: 9.0:
:16.0:
: 2.0:
: 9.0:
:16.0:
: 2.0:
: 9.0:
:16.0:
: 2.0:
: 9.0:
:16.0:
:22.0:
: 2.0:
: 9.0:
:16.0:
.500:
.300:
.090:
.056:
.020:
.020:
5.000:
A. 800:
1.200:
.800:
.A20:
.130:
.120:
.OAO:
.036:
.26A:
.030:
.010:
.398:
.055:
.025:
.008:
.A13:
.095:
.050:
.011:
1.A3A:
.311:
.11A:
.OA2:
.181:
.023:
.007:
.171:
.017:
.009:
.150:
.006:
.006:
.191:
.013:
.003:
.050:
.007:
.001:
.252:
.027:
.010:
.187:
.021:
.007:
.003:
.10A:
.023:
.008:
.000;
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.019:
.013:
.007:
.032:
.033:
.009:
.006:
.030:
.012:
.010:
.006:
.109:
.065:
.050:
.020:
.090:
.025:
.007:
.071:
.020:
.008:
.109:
.026:
.010:
.011:
.003:
.002:
.006:
.001:
.001:
.03A:
.020:
.010:
.053:
.017:
.007:
.005:
.021:
.009:
.007:
.C!OG;
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
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• v>l/"w •
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
7.220
2.223
1.389
.A98
.A98
71.320
69.851
25.900
18.114
9.960
3.195
2.953
.994
.895
12.289
5.027
2.635
19.079
12.122
3.684
2.316
18.695
6.262
4.669
2.250
52.293
26.250
18.395
7.650
30.128
9.040
2.698
25.362
7.295
2.938
34.179
8.816
3.572
8.102
1.293
.638
3.378
.622
.255
16.552
7.348
3.703
20.654
6.253
2.425
1.830
9.582
3.745
2.717
0*401
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Appendix H, page  17
-------
etp     :6713:CAvOrange:  1.9: 750: 2.0:  .175:  .035:  .000:  .000:  15.337 0
etp     :6713:CAvOrange:  1.9: 750: 9.0:  .028:  .014:  .000:  .000:   5.449
etp     :6713:CAvOrange:  1.9: 750:16.0:  .011:  .009:  .000:  .000:   3.491
etp     :6714:CAvOrange:  2.5: 300: 2.0:  .295:  .034:  .000:  .000:  17.431 0
etp     :6714:CAvOrange:  2.5: 300: 9.0:  .033:  .012:  .000:  .000:   5.034 0
etp     :6714:CAvOrange:  2.5: 300:16.0:  .006:  .007:  .000:  .000:   2.467 0
etp     :6715:CAvOrange:  3.0:1200: 2.0:  .089:  .023:  .000:  .000:   9.779 0
etp     :6715:CAvOrange:  3.0:1200: 9.0:  .020:  .006:  .000:  .000:   2.624 0
etp     :6715:CAvOrange:  3.0:1200:16.0:  .013:  .002:  .000:  .000:    .977 0
etp     :6716:CAvOrange:  2.0: 500: 2.0:  .241:  .021:  .000:  .000:  12.471 0
etp     :6716:CAvOrange:  2.0: 500: 9.0:  .044:  .018:  .000:  .000:   7.161 0
etp     :6716:CAvOrange:  2.0: 500:16.0:  .008:  .007:  .000:  .000:   2.691 0
etp     :6717:CAvOrange:  1.9: 750: 2.0:  .688:  .025:  .000:  .000:  22.974 0
etp     :6717:CAvOrange:  1.9: 750: 9.0:  .048:  .011:  .000:  .000:   4.972 0
etp     :6717:CAvOrange:  1.9: 750:16.0:  .011:  .007:  .000:  .000:   2.731 0
etp     :6718:CAvOrange:  1.9: 750: 2.0:  .229:  .052:  .000:  .000:  21.168 0
etp     :6718:CAvOrange:  1.9: 750: 9.0:  .058:  .025:  .000:  .000:   9.681 0
etp     :6718:CAvOrange:  1.9: 750:16.0:  .012:  .012:  .000:  .000:   4.232 0
etp     :6719:CAvOrange:  2.0: 300: 2.0:  .483:  .167:  .000:  .000:  50.491 0
etp     :6719:CAvOrange:  2.0: 300: 9.0:  .012:  .054:  .000:  .000:  17.388 0
etp     :6719:CAvOrange:  2.0: 300:16.0:  .005:  .021:  .000:  .000:   7.338 0
etp     :6720:CAvOrange:  7.5:1800: 2.0: 1.297:  .227:  .000:  .000:  67.336 0
etp     :6720:CAvOrange:  7.5:1800: 9.0:  .120:  .135:  .000:  .000:  39.492 0
etp     :6720:CAvOrange:  7.5:1800:16.0:  .039:  .021:  .000:  .000:   8.011 0
etp     :6720:CAvOrange:  7.5:1800:21.0:  .009:  .017:  .000:  .000:   6.095 0
etp     :6721:CAvOrange:  2.0: 300: 2.0:  .173:  .328:  .000:  .000:  69.551 0
etp     :6721:CAvOrange:  2.0: 300: 9.0:  .021:  .066:  .000:  .000:  20.967 0
etp     :6721:CAvOrange:  2.0: 300:16.0:  .033:  .021:  .000:  .000:   7.698 0
etp     :6722:CAvOrange:  2.0: 300: 2.0:  .439:  .396:  .000:  .000:  77.570 0
etp     :6722:CAvOrange:  2.0: 300: 9.0:  .043:  .072:  .000:  .000:  23.060 0
etp     :6722:CAvOrange:  2.0: 300:16.0:  .002:  .018:  .000:  .000:   6.146 0
etp     :6723:CAvOrange:  1.9: 750: 3.0:  .170:  .001:  .000:  .000:   4.582 0
etp     :6723:CAvOrange:  1.9: 750:10.0:  .015:  .004:  .000:  .000:   1.660 0
etp     :6723:CAvOrange:  1.9: 750:17.0:  .005:  .003:  .000:  .000:   1.139 0
etp     :6724:CAvOrange:  1.9: 750: 2.0:  .153:  .016:  .000:  .000:   8.875 0
etp     :6724:CAvOrange:  1.9: 750: 9.0:  .008:  .005:  .000:  .000:   1.768 0
etp     :6724:CAvOrange:  1.9: 750:16.0:  .004:  .002:  .000:  .000:    .965 0
etp     :6725:CAvOrange:  2.5:1000: 2.0:  .417:  .025:  .000:  .000:  17.439 0
etp     :6725:CAvOrange:  2.5:1000: 9.0:  .038:  .016:  .000:  .000:   6.474 0
etp     :6725:CAvOrange:  2.5:1000:16.0:  .004:  .005:  .000:  .000:   1.806 0
etp     :6726:CAvOrange:  2.0: 250: 2.0:  .771:  .366:  .000:  .000:  77.026 0
etp     :6726:CAvOrange:  2.0: 250: 9.0:  .069:  .051:  .000:  .000:  17.821 0
etp     :6726:CAvOrange:  2.0: 250:16.0:  .021:  .025:  .000:  .000:   8.994 0
etp     :6727:CAvOrange:  4.6:1850: 2.0:  .387:  .011:  .000:  .000:  12.768 0
etp     :6727:CAvOrange:  4.6:1850: 9.0:  .043:  .016:  .000:  .000:   6.289 0
etp     :6727:CAvOrange:  4.6:1850:16.0:  .012:  .005:  .000:  .000:   1.928 0
etp     :6728:CAvOrange:  7.5:1500: 2.0:  .618:  .321:  .000:  .000:  72.081 0
etp     :6728:CAvOrange:  7.5:1500: 9.0:  .067:  .115:  .000:  .000:  34.296 0
etp     :6728:CAvOrange:  7.5:1500:16.0:  .024:  .058:  .000:  .000:  18.848 0
etp     :6728:CAvOrange:  7.5:1500:22.0:  .010:  .018:  .000:  .000:   6.373 0
etp     :6729:CAvOrange:  2.3: 900: 2.0:  .363:  .032:  .000:  .000:  18.360 0
etp     :6729:CAvOrange:  2.3: 900: 9.0:  .020:  .013:  .000:  .000:   4.981 0
etp     :6729:CAvOrange:  2.3: 900:16.0:  .004:  .007:  .000:  .000:   2.535 0
etp     :6730:CAvOrange:  1.9: 750: 2.0:  .200:  .145:  .000:  .000:  42.706 0
etp     :6730:CAvOrange:  1.9: 750: 9.0:  .024:  .022:  .000:  .000:   8.088 0
                     Appendix H, page  18
125
-------
etp
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etp
etp
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:45 0:CAvOrange:
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:45 0:CAvOrange:
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:45 0:CAvOrange:
:45 0:CAvOrange:
:45 OrCAvOrange:
:45 OrCAvOranger
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:46 IrCAvOranger
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:46 l:CAvOranger
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:46 IrCAvOranger
:46 2:CAvOranger
:46 2:CAvOrange:
:46 2:CAvOrange:
:46 2rCAvOrange:
:46 2rCAvOrange:
:46 2rCAvOrange:
:46 3:CAvOrange:
:46 SrCAvOrange:
:46 SrCAvOrange:
r46 3:CAvOrange:
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:46 4:CAvOrange:
:46 4:CAvOrange:
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:46 6rCAvOrange:
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:46 6:CAvOrange:
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.326:
.235:
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.090:
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71.624
56.547
20.727
27.288
11.117
11.910
8.864
12.306
11.716
11.604
5.983
13.609
9.210
8.104
37.081
21.662
10.432
7.751
2.752
69.611
39.167
29.272
6.850
1.028
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23.934
7.244
2.614
2.406
1.162
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13.093
3.660
1.607
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52.966
28.611
11.289
7.320
3.359
1.332
71.673
61.165
46.971
41.692
15.768
12.083
4.291
81.947
72.534
63.215
46.284
25.580
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0
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0
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0
0
0
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0
0
0
0
0
0
0
Appendix H, page  19                      1 O^
                                          I CO
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:4613:CAvOrange:
:4614:CAvOrange:
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:4614:CAvOrange:
:4615:CAvOrange:
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:4615:CAvOrange:
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6.3:
6.3:
7.5:
7.5:
7.5:
7.5:
7.5:
2.0:
2.0:
2.0:
2.0:
2.0:
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4.0:
4.0:
4.0:
4.0:
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                     Appendix H, page  20
                                                      127
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10.788
50.881
61.257
50.064
37.178
15.832
8.349
7.301
46.366
43.237
13.742
18.928
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0
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0
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Appendix H, page  21
                                  128
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8:CA Grapes: .75: 100: 3.0:
8:CA Grapes: .75: 100: 7.0:
9:CA Grapes: .75: 25: .0:
9:CA Grapes: .75: 25: 1.0:
9:CA Grapes: .75: 25: 3.0:
9:CA Grapes: .75: 25: 7.0:
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1:AZ Cotton: 1.00: 9: .0:
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1:AZ Cotton: 1.00: 9: 2.0:
1:AZ Cotton: 1.00: 9: 3.0:
1:AZ Cotton: 1.00: 9: 4.0:
l:CAvtomato: .019: 10: .0:
l:CAvtomato: .019: 10: 1.0:
l:CAvtomato: .019: 10: 2.0:
l:CAvtomato: .019: 10: 3.0:
1:AZ Orange: 8.00: 350: .0:
1:AZ Orange: 8.00: 350: 1.0:
1:AZ Orange: 8.00: 350: 3.0:
1:AZ Orange: 8.00: 350: 5.0:
1:AZ Orange: 8.00: 350: 7.0:
1:AZ Orange: 8.00: 350:14.0:
1:AZ Orange: 8.00: 350:21.0:
1:AZ Orange: 8.00: 350:28.0:
1:AZ Orange: 8.00: 350:42.0:
1:AZ Cotton: .50: 5: .0:
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2:AZ Cotton: .50: 5: .0:
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                                            406
Appendix H, page  22
129
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B*i 0
951 0
869 0
280 0
384 0
137 0
260 0
728 0
316 0
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369 0
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643 0
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124 0
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678 0
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850 0
379 0
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283 0
968 0
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611 0
796 0
105 0
115 0
496 0
291 0
132 0
743 0
                                               407
Appendix H, page  23
                                  130
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3.75
7.00
7.00
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3.75
3.75
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5.63
5.63
5.63
5.63
5.63
5.63
11.30
11.30
11.30
11.30
11.30
11.30
5.63
5.63
5.63
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5.63
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11.30
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5.63
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5.63
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:1500
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: 100
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: 3.0:
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.078:
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1.980:
1.080:
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2.600:
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10
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                                             408
Appendix H, page  24
                               131
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: 1720: CAvOrange: 11. 30: 2250: .0
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: 1725: CAvOrange: 3.63:1450: .0
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Appendix H, page  25
                              132
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.478 0
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.030 0
.056 0
.020 0
1.332 0
.764 0
.535 0
.325 0
.115 0
.030 0
.034 0
2.647 0
1.804 0
1.143 0
.830 0
.134 0
.059 0
.041 0
1.427 0
.354 0
.096 0
.077 0
.066 0
.040 0
.057 0
1.332 0
.316 0
.079 0
.067 0
.057 0
.057 0
.045 0
2.890 0
.512 0
.152 0
.176 0
.093 0
.115 0
.205 0
.239 0
                                            410
Appendix H, page  26
                              133
-------
metion
metion
metion
metion
metion
metion
metion
metion
metion
metion
metion
metion
metion
metion
metion
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metion
metion
metion
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metion
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monoos
monoos
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monoos

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74
74
74
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78
77
77
77
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7:FL Orange: 2.30:1200: 1.0:
7:FL Orange: 2.30:1200: 3.0:
7:FL Orange: 2.30:1200: 5.0:
7:FL Orange: 2.30:1200: 7.0:
7:FL Orange: 2.30:1200:14.0:
7:FL Orange: 2.30:1200:21.0:
8:FL Orange: 4.60:1200: .0:
8:FL Orange: 4.60:1200: 1.0:
8:FL Orange: 4.60:1200: 3.0:
8:FL Orange: 4.60:1200: 5.0:
8:FL Orange: 4.60:1200: 7.0:
8:FL Orange: 4.60:1200:14.0:
8:FL Orange: 4.60:1200:21.0:
9:FL Orange: 6.90:1200: .0:
9:FL Orange: 6.90:1200: 1.0:
9:FL Orange: 6.90:1200: 3.0:
9:FL Orange: 6.90:1200: 5.0:
9:FL Orange: 6.90:1200: 7.0:
9:FL Orange: 6.90:1200:14.0:
9:FL Orange: 6.90:1200:21.0:
1:CA Orange: 5.60: 100: .0:
1:CA Orange: 5.60: 100: 3.0:
1:CA Orange: 5.60: 100:10.0:
1:CA Orange: 5.60: 100:17.0:
1:CA Orange: 5.60: 100:30.0:
1:CA Orange: 5.60: 100:45.0:
1:CA Orange: 5.60: 100:59.0:
2:CA Orange: 5.60:2250: .0:
2:CA Orange: 5.60:2250: 3.0:
2:CA Orange: 5.60:2250:10.0:
2:CA Orange: 5.60:2250:17.0:
2:CA Orange: 5.60:2250:30.0:
2:CA Orange: 5.60:2250:45.0:
3:CA Orange: 7.50:1500: .0:
3:CA Orange: 7.50:1500: 3.0:
3:CA Orange: 7.50:1500:10.0:
3:CA Orange: 7.50:1500:17.0:
3:CA Orange: 7.50:1500:30.0:
1:CA Citrus:003. 5:0100: 7.0:
1:CA Citrus:003. 5:0100:14.0:
2:CA Citrus:003. 5:1000:11.0:
2:CA Citrus:003. 5:1000:14.0:
1:AZ Cotton: 1.00: 9: .0:
1:AZ Cotton: 1.00: 9: .5:
1:AZ Cotton: 1.00: 9: 1.0:
1:AZ Cotton: 1.00: 9: 2.0:
1:AZ Cotton: 1.00: 9: 3.0:
1:AZ Cotton: 1.00: 9: 4.0:
1:AZ Cotton: 1.28: 9: 1.0:
1:AZ Cotton: 1.28: 9: 1.5:
1:AZ Cotton: 1.00: 9: .0:
1:AZ Cotton: 1.00: 9: 1.0:
1:AZ Cotton: 1.00: 9: 2.0:
1:AZ Cotton: 1.00: 9: 3.0:
1:AZ Cotton: 1.00: 9: 4.0:
Appendix H, page 2




















9
6
1




1
1




1








4
3
3
3
2
1


1
1
1


7
.010:
.015:
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.024:
.038:
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.032:
.022:
.024:
.192:
.054:
.074:
.056:
.044:
.050:
.044:
.200:
.800:
.540:
.660:
.220:
.066:
.020:
.960:
.120:
.260:
.110:
.052:
.010:
.760:
.920:
.200:
.096:
.030:
.240:
.080:
.320:
.100:
.900:
.950:
.750:
.125:
.750:
.792:
.460:
.560:
.420:
.480:
.100:
.770:
.700:

.000:
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.098:
.144:
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.000:
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.070:
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.094:
.012:
.000:
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.052:
.030:
.000:
.000:
.000:
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.360:
.140:
.060:
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.006:
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.000:
.000:
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.000:

.000:
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34










































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7
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35.
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344 0
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105 0
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029 0
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4V
-------
monoos
monoos
monoos
monoos
oxyme
oxyme
oxyme
oxyme
oxyme
oxyme
oxyme
oxyme
oxyme
oxyme
phenth
phenth
phenth
phenth
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phenth
phenth
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phenth
phenth
phenth
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pholon
pholon
pholon
pholon
pholon
pholon
pholon
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pholon
pholon
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pholon
pholon
pholon
pholon
pholon
pholon
pholon
pholon
:79
:79
:79
:79
:38
:38
:38
:38
:38
:38
:38
:38
:38
:38
:18
:18
:18
:18
:18
:18
:18
:18
:18
:18
:18
:18
:18
:18
:18
:18
:18
:18
:18
:18
:18
:24
:24
:24
:24
:24
:24
:24
:24
:24
:24
:24
:24
:48
:48
:48
:48
:48
:48
:48
:48
1:AZ Cotton:
1:AZ Cotton:
1:AZ Cotton:
1:AZ Cotton:
1:FL Orange:
1:FL Orange:
1:FL Orange:
1:FL Orange:
1:FL Orange:
1:FL Orange:
2:FL Orange:
2:FL Orange:
2:FL Orange:
2:FL Orange:
1 : CAsOrange :
l:CAsOrange:
1: CAsOrange:
1 : CAsOrange :
1 : CAsOrange :
1: CAsOrange:
1 : CAsOrange :
1 : CAsOrange :
2 : CAsOrange :
2 : CAsOrange :
2: CAsOrange:
2: CAsOrange:
2: CAsOrange:
2: CAsOrange:
2 : CAsOrange :
3 : CAsOrange :
3: CAsOrange:
3 : CAsOrange :
3: CAsOrange:
3: CAsOrange:
3 : CAsOrange :
1 : CAvOrange :
1 : CAvOrange :
1 : CAvOrange :
1 : CAvOrange :
1 : CAvOrange :
1 : CAvOrange :
2 : CAvOrange :
2 : CAvOrange :
2 : CAvOrange :
2 : CAvOrange :
2 : CAvOrange :
2 : CAvOrange :
l:CAvPeach :
l:CAvPeach :
l:CAvPeach :
IrCAvPeach :
l:CAv Peach :
l:CAvPeach :
l:CAvPeach :
2:CAvPeach :
1.00:
1.00:
1.00:
1.00:
4.00:
4.00:
4.00:
4.00:
4.00:
4.00:
4.00:
4.00:
4.00:
4.00:
7.50:
7.50:
7.50:
7.50:
7.50:
7.50:
7.50:
7.50:
7.50:
7.50:
7.50:
7.50:
7.50:
7.50:
7.50:
7.50:
7.50:
7.50:
7.50:
7.50:
7.50:
6.00:
6.00:
6.00:
6.00:
6.00:
6.00:
6.00:
6.00:
6.00:
6.00:
6.00:
6.00:
4. :
4. :
4. :
4. :
4. :
4. :
4. :
4. :
9
9
9
9
750
750
750
750
750
750
750
750
750
750
100
100
100
100
100
100
100
100
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
600
600
600
600
600
600
600
600
600
600
600
600
30
30
30
30
30
30
30
375
: .0:
: 1.0:
: 2.0:
: 3.0:
: 1.0:
: 2.0:
: 5.0:
:10.0:
:20.0:
:30.0:
: 1.0:
: 2.0:
: 5.0:
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: 2.0:
: 9.0:
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: 2.0:
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: .0:
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: 1.0:
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:19.0:
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:23.0:
: 1.0:
: 8.0:
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:23.0:
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n*
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1.280:
1.060:
.630:
.430:
.400:
.274:
.164:
.112:
.076:
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.003:
.001:
.000:
4.000:
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.400:
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.140:
.048:
.000:
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1.800:
.660:
.140:
.046:
.010:
.000:
7.000:
4.300:
5.100:
3.800:
3.800:
2.800:
6.500:
4.800:
4.100:
2.600:
3.200:
2.400:
6.484:
5.358:
3.094:
2.024:
4.026:
4.326:
5.518:
10.906:
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3.621?0
3.00870
1.79970
1.23170
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2.029
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1.587
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1.159
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1.475
1.091
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0
0
0
0
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1
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0
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0
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0
0
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                                               41;
Appendix H, page  28
                               135
-------
pholon
pholon
pholon
pholon
pholon
pholon
pholon
pholon
pholon
pholon
pholon
pholon
pholon
pholon
pholon
pholon
pholon
pholon
pholon
pholon
pholon
pholon
pholon
pholon
pholon
pholon
pholon
pholon
pholon
pholon
pholon
pholon
pholon
pholon
pholon
pholon
pholon
phosam
phosam
phosam
phosam
phosam
phosam
phosmet
phosmet
phosmet
phosmet
phosmet
phosmet
phosmet
phosmet
phosmet
phosmet
phosmet
phosmet
:48 2:CAvPeach
:48 2:CAvPeach
:48 2:CAvPeach
:48 2:CAvPeach
:48 2:CAvPeach
:48 2:CAvPeach
:48 2:CAvPeach
:48 2:CAvPeach
:48 3:CAvPeach
:48 3:CAvPeach
:48 4:CAvPeach
:48 4:CAvPeach
:48 4:CAvPeach
:48 4:CAvPeach
:48 4:CAvPeach
:48 5:CAvPeach
:48 5:CAvPeach
:48 5:CAvPeach
:48 5:CAvPeach
:48 6:CAvPeach
:48 6:CAvPeach
:48 6:CAvPeach
:48 6:CAvPeach
:48 6:CAvPeach
:48 6:CAvPeach
:48 6:CAvPeach
:48 6:CAvPeach
:48 6:CAvPeach
:48 6:CAvPeach
:48 7:CAvPeach
:48 7:CAvPeach
:48 7:CAvPeach
:48 7:CAvPeach
:48 7:CAvPeach
:48 7:CAvPeach
:48 7:CAvPeach
:48 7:CAvPeach
:81 l:CAsOrange
:81 l:CAsOrange
:81 l:CAsOrange
:81 l:CAsOrange
:81 l:CAsOrange
:81 l:CAsOrange
: 3 1:AR Peach
: 3 1:AR Peach
: 3 2:AR Peach
: 3 2:AR Peach
: 3 2:AR Peach
: 3 2:AR Peach
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: 3 3:AR Peach
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4. :
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4. :
4. :
4. :
4. :
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4. :
4. :
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01.00:
01.00:
01.00:
01.00:
01.00:
01.00:
0.716:
0.716:
0.716:
0.716:
0.716:
0.716:
0.716:
0.716:
0.716:
0.716:
0.716:
0.716:
375:
375:
375:
375:
375:
375:
375:
375:
375:
375:
375:
375:
375:
375:
375:
375:
375:
375:
375:
200:
200:
200:
200:
200:
200:
200:
200:
200:
200:
250:
250:
250:
250:
250:
250:
250:
250:
0200:
0200:
0200:
0200:
0200:
0200:
0150:
0150:
0150:
0150:
0150:
0150:
0150:
0150:
0150:
0150:
0150:
0150:
.0:
5.0:
13.0:
13.0:
14.0:
14.0:
15.0:
15.0:
7.0:
14.0:
.0:
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3.0:
3.0:
5.0:
.0:
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7.0:
11.0:
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Appendix H, page  29
                                     136
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         Appendix H, page  30
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3.998 0
3.012 0
2.114 0
1.774 0
2.207 0
3.519 0
2.102 0
5.540 0
3.544 0
3.455 0
6.399 0
3.611 0
4.056 0
2.060 0
1.418 0
1.077 0
36.532 0
41.802 0
45.884 0
38.774 0
45.306 0
41.635 0
39.243 0
32.305 0
25.053 0
23.108 0
21.154 0
19.158 0
14.32Z 0
8.705 0
6.007 0
4.018 0
3.396 0
Z.143 0
16.361 0
11.717 0
10.359 0
8.508 0
6.393 0
2.718 0
Z.259 0
1.947 0
.696 0
11.605 0
10.845 0
9.950 0
9.337 0
7.875 0
4.100 0
3.680 0
3.684 0
1.803 0
.639 0
.557 0
.221 0
Appendix H, page  31
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ethioZ
ethioZ
ethioZ
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1:FL
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.130 0
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3.206 0
1.559 0
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1.366 0
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1.699 0
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2.188 0
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1.633 0
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Appendix H, page  32
                                   139
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                                           417
ethio2
ethio2
ethioZ
ethioZ
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ethio2
ethio2
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ethio2
ethio2
ethio2
ethio2
mep2
mep2
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mep2
mep2
mep2
mep2
mep2
mep2
mep2
mep2
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mep2
mep2
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mep2
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.200 0
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1.899 0
1.181 0
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3.723 0
.905 0
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1.036 0
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1.218 0
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1.694 0
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1.278 0
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1.479 0
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Appendix H.  page 33
14Q
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mep2
mep2
mep2
mep2
mep2
mep2
mep2
mep2
mep2
mep2
mep2
mep2
mep2
mep2
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mep2
mep2
mep2
mep2
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mep2
mep2
mep2
mep2
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mep2
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mep2
mep2
mep2
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mep2
map 2
mep2
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2:AZ
2:AZ
2:AZ
1:AZ
1:AZ
1:AZ
1:AZ
1:WA
1:WA
1:WA
1:WA
1:WA
2:WA
2:WA
2:WA
2:WA
2:WA
Orange:
Orange:
Orange:
Orange:
Orange:
Orange:
Orange:
Orange:
Orange:
Cotton:
Cotton:
Cotton:
Cotton:
Cotton:
Cotton:
Cotton:
Cotton:
Cotton:
Cotton:
Cotton:
Cotton:
Cotton:
Cotton:
Apple :
Apple :
Apple :
Apple :
Apple :
Apple :
Apple :
Apple :
Apple :
Apple :
.75:
.75:
.75:
.75:
1.00:
1.00:
1.00:
1.00:
1.00:
1.00:
.019:
.019:
.019:
.019:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
8.00:
.50:
.50:
.50:
.50:
.50:
.50:
.50:
.50:
.50:
.50:
1.00:
1.00:
1.00:
1.00:
01.50:
01.50:
01.50:
01.50:
01.50:
01.50:
01.50:
01.50:
01.50:
01.50:
100:
100: 1
100: 3
100: 7
9:
9:
9: 1
9: 2
9: 3
9: 4
10:
10: 1
10: 2
10: 3
350:
350: 1
350: 3
350: 5
350: 7
350:14
350:21
350:28
350:42
5:
5:
5: 1
5: 2
5: 3
5:
5:
5: 1
5: 2
5: 3
9: 1
9: 1
9:
9:
0400:
0400: 1
0400: 2
0400: 3
0400: 7
0400:
0400: 1
0400: 2
0400: 3
0400: 7
.0:
.0:
.0:
.0:
.0:
.5:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.5:
.0:
.0:
.0:
.0:
.5:
.0:
.0:
.0:
.0:
.5:
.0:
.5:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.0:
.155:
.055:
.015:
.004:
2.650:
.498:
.250:
.153:
.127:
.098:
.242:
.151:
.064:
.010:
18.162:
14.550:
9.208:
6.158:
4.392:
.846:
.204:
.070:
.014:
5.610:
3.310:
2.840:
1.890:
1.090:
1.370:
1.090:
.810:
.560:
.490:
.150:
.060:
2.370:
1.630:
4.740:
1.704:
1.042:
.710:
.284:
5.600:
4.396:
3.136:
3. 080:
1.400:
.000:
.000:
.000:
.000:
.014:
.015:
.007:
.002:
.002:
.000:
.000:
.004:
.000:
.000:
.072:
.186:
.244:
.238:
.226:
.192:
.128:
. 096 :
.014:
.050:
.120:
.070:
.060:
.050:
.020:
.020:
.010:
.010:
.010:
.004:
.002:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:?
.000:?
.000:?
.000:?
.000:?
.000:?
.000:?
.000:?
.000:?
.000:?
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:
.000:?
.000:?
.000:?
.000:?
.000:?
.000:?
.000:?
.000:?
.000:?
.000:?
.000:?
.000:?
.000:?
.000:?
.000:?
.000:?
.000:?
.000:?
.000:?
.000:?
.000:?
.000:?
.000:?
.000:?
.. 418
.554 0
.197 0
.054 0
.014 0
13.278?0
3.23070
1.60970
.87770
.74570
.50070
1.23170
.96470
.32770
.05170
62.617 0
57.360 0
45.257 0
35.617 0
28.976 0
13.071 0
7.188 0
5.031 0
.770 0
26.77070
20.40070
16.44170
11.84870
7.71370
7.67970
6.34770
4.52970
3.30070
2.95370
.95970
.40470
11.42170
8.00370
15.616 0
5.921 0
3.664 0
2.511 0
1.012 0
18.176 0
14.570 0
10.625 0
10.446 C
4.891 0
Appendix H, page  34
                              141
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                                                         419
      The Effects of Organophosphate Pesticide
     Residue Variability Upon Reentry Intervals

             Preliminary Phase II  Report
                      July 1986

                          by
               William Popendorf,  Ph.D.
University of Iowa,  Institute of Agricultural Medicine
-------
Table of Contents;
                                                                          420
Abstract
Introduction
Background
Methods
Results and Discussion
                                                                     2
                                                                     2
                                                                     4
                                                                    10
Conclusion ............................   13
References ...........................  '•   14
Table I
Table II
Table III
Table IV
Table V
Table VI

Figure  1
Figure  2
Figure  3
Figure  4
Figure  5
Figure  6

Appendix A:
Appendix B:
Appendix C:
Appendix D:
            parameter notation and subscripts  	   16
            parameter mean and variability	17
            cross-sectional variability of foliar residues  ....   18
            simulated chronic response 	  ....   19
            simulated acute response 	   20
            parathion use history  	   22
            AChE activity versus
            AChE activity versus
            AChE activity versus
            AChE activity versus
            AChE activity versus
            AChE activity versus
time for  1% daily delAChE  ...   23
time for  27. daily delAChE  ...   24
time for  4Z daily delAChE  ...   25
time for  8Z daily delAChE  ...   26
time for 167. daily delAChE  ...   27
time for 0 residue variability  .   28
            Wylber simulation program  	  A-l
            PC simulation program  	  B-l
            PC health analysis summary program 	  C-l
            Example results of PC health analysis summary program  D-l
-------
                 The Effects of Organophosphate Pesticide                  ^ *- '
                Residue Variability Upon Reentry Intervals
Abstract;
     A stochastic simulation program was written to study the importance of
residue   variability   in  preventing  anti-cholinesterase   overexposure,
excessive  chronic AChE inhibition,  or acute illness.   A range  of  daily
inhibitions and residue variability were explored, and the simulated cohort
response  was compared to the historical record of chronic and acute  data.
The  resulting  chronic  and  acute  AChE  response  patterns  are  largely
displayed in Tables IV and V, respectively.

     It  was concluded that residue variability has only a slight effect on
the  mean  AChE health status of a working cohort,  in comparison  to  mean
daily  inhibition.   Residue variability has a slightly  stronger  although
still  secondary  effect upon the overall variability of the cohort's  AChE
activity.   The  fraction  of  the cohort whose  seasonal  AChE  inhibition
exceeded  a relatively arbitrary chronic threshold of 50% was again largely
controlled  by  the  daily mean inhibition,  but at 4% per  day  and  above
virtually  the  entire  cohort exceeded that limit and  below  4%  per  day
residue variability had an increasing important role.

     On  the other hand,  residue variability appears to have a very strong
effect  upon the uniformity of the AChE activity within the  cohort.   This
nonuniformity has both chronic and acute AChE implications.   For instance,
for residue variability even below a geometric deviation of 2.0,  the crews
seasonal  AChE  inhibitions are not expected to  be  statistically  uniform
(with 997. confidence) until half way through a season;  and for variability
of  3.0  and  above,  the crews will be dissimilar throughout  the  season.
Thus,  it  is possible to have several crews below an  administrative  AChE
inhibition  threshold (or even potentially with clinical symptoms) while the
cohort  mean is only marginally different from normal.   This  pattern  has
similar implications to both the design and interpretation of epidemiologic
surveys among this population.

     Acute  responses,  in terms of individual and group AChE responses  in
excess of potential clinical symptoms, exhibit a fairly clear boundary as a
function  of both residue mean and variation.   In the low mean range of 1%
to  2% per day,  no acute individual or group incidents were predicted  for
geometric  variations  below  2.5;  however,  a  set  of  random  parathion
commercial application residues collected on days 2,  9,  and 16 all showed
variations  of  around  2.6,  just  sufficient  to  induce  sporatic  acute
responses.

     This pattern of acute effects under conditions of low chronic response
and  the  practical  boundary of a geometric deviation  of  2.5  (+  150%),

suggest  that consideration to both the mean anti-cholinesterase effect and
the  variability of the foliar residues should be considered  when  setting
administrative reentry intervals.
                                Draft                            ph2.doc
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                                                                            422
Introduction;

     The 35 year history of harvesters being poisoned while working in a
field "recently"-sprayed with an organophosphate (OP) insecticide has been
sporatically documented from scattered case reports during their earliest
use beginning circa 1950 [1-4] to whole summaries in the last 10 years
[5-10].  The consensus of opinion is that these poisonings have resulted
from excessive field residues, most likely on the foliage of the plants
being harvested rather than on the crop, per se.  The focus of interest for
the control of this occupational health hazard has been to require an
adequate time period between insecticide application and crop harvest (or
other activity) for the residue to "decay" to levels not harmful to the
harvest workers.  This time period has come to be referred to as the
"reentry interval".

     One particular review of the history and development of reentry
intervals for OP pesticides [9] resulted in a conceptual and quantitative
model which will be the basis for the study described herein.  This
"Unified Field Model" could predict short-term anti-cholinesterase effects
from either single exposures or a series of uniform exposures to
insecticides of known dermal toxicity, and was proposed to be used to set a
foliar residue limit or "reentry interval" associated with a preselected
and acceptable anti-cholinesterase threshold.  One of the key limitations
of this original proposal was the uncertainty of setting an "acceptable"
administrative threshold for daily cholinesterase inhibitions, given the
fact that real exposures are neither daily nor uniform.

     This report describes a study using this Unified Field Model to
explore the effects of variations in both the levels and frequencies of OP
exposure upon the cholinesterase activity of a harvesting cohort throughout
a harvest season.  The method used in this exploration has been Monte Carlo
simulation.  This report first reviews the background of the model; then
the methods used in these explorations; and finally their findings and
implications.

Background;

     A comprehensive report describing the reentry problem and integrating or
"unifying" the major factors controlling it, was first published in 1982 as
the Unified Field Model [9].  This basic model originally had three submodels
[9 p.132], as shown below.   Later elaborations and clarifications of this
model [11] broke out a fourth submodel [9 p.180, 11], essentially Equation
3 as follows:

                                             —k T
     Residue Decay:                R « R  exp  r                     (1)

     Dose Deposition:             D1 = k.Rt                          (2)

     Dose Absorption-Distribution: D = k  D1 / m                     (3)

     Toxic Response:         delAChE « 1 - exp"keSum(Di/LD50:i)      (4)
                                     2         2
     where R  = foliar residue, ug/cm  or ng/cm , at some point in time T
                after application.
                  Draft Report Page 2 of 28                     ph2.doc
-------
     R  = initial foliar residue, ug/cm  or ng/cm  at the time of      423
      0   application.                                 ,
     k  « pesticide exponential decay coefficient, day  ; in
          practice, multiple coefficients may be used [12], or other
          than exponential submodels or a non-mathematical (e.g.
          graphical) method may be used [9].
     T  * the time (usually in days) after application of the
          pesticide; viz. the reentry interval.
     D1 • topical dermal dose, ug of pesticide (which in a mixture
          would be subscripted i).
     k , = a crop (and possibly activity) dependent dosing
          coefficient, whose units are dependent upon those of R, D,
          and t; e.g. if units of R were ug/cm , D were ug, and t
          were hours, then k. would be cm /hr [see ref. 11],
     t  = the length of exposure, hours (assumed herein to be a
          nominal 8 -hour workday).
     k  = a dermal absorption coefficient presented in most
          pesticide literature as a percent of deposited dose [9]
          but is toxicologically more related to a permeation rate
          coefficient [13]; however, use of the latter would require a
          more detailed scenario of the total time the dose would
          remain unwashed upon the skin.  Use of the model herein
          (with submodel Equation 4 and k =6) assumes k «1.
     m  = the mass of the exposed person, kg (nominally 70 kg).
     D  = the toxicological, dermally deposited dose, mg/kg.
     k  = an enzyme coefficient relating the degree of red blood
      e   cell (RBC) acetylcholinesterase (AChE) activity to the LD5Q
          of an OF-  This submodel is an extension of an original
          finding by Grob and Harvey [14].  K  » 6 was used herein
          with a rat dermal LDcn-            e
     cg * the dose (mg/kg) of cnemical necessary [or sufficient] to
          kill 50% of the exposed animals; this submodel expects the
          use of a dermal LDcn> the subscript i for LDcn and dose D
          implies that the ACRE response to multiple, simultaneous
          OPs is additive in this fashion.
deLAChE = the fractional, acute change in AChE enzymatic activity
          relative to a pre-exposure baseline, i.e.

              delAChE = (E  . - E ) / E  .                     (5a)
                          n-l    n     n-l

                      " <*n-l 'W  -(En/En-l>           (5b)
              delAChE •      1         - (E  / E  .)           (5c)
                                           n    n~l
          and

              E /E  . - 1 - delAChE                            (5d)
               n  n-l

where E • the measurable AChE enzyme activity on post-exposure day n
          and the pre-exposure activity on day n-l.  Note also that
          the previous day (n-l) may or may not necessarily be the
          person's unexposed baseline (see later discussion circa
          Equations 12-16).
            Draft Report Page 3 of 28                      ph2.doc
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                                                                             42'
     While not essential to the development of this particular study, a
number of alternative submodels were discussed in the original document [9]
and its later elaboration [11].  For instance, exponential decay (submodel
1) is a very common temporal pattern of environmental decay for pesticides,
but it may not always apply; procedures were in fact outlined therein for
dealing with graphical or even tabular decay data.  A more complex (and
potentially far-reaching) variation upon submodel 4 may be necessary to
deal with non-cholinergic effects; one suggestion to extend the Unified
Field Model to non-cholinesterase pesticides was to use the allowable
absorbed daily or chronic dose which is typically established for all
pesticides [11 p.337].  While the quantitative Dose-Response submodel for
risk assessment might employ any of several criteria, such as a
carcinogenic extrapolation risk assessment, a generalized submodel for
acceptability (replacing Equation 4) could be viewed as follows:


     _  .  _               .  ,.,..      dermally absorbed            /•,>.
     Toxic Response:  acceptability «	*	           (.o)

                                          allowable

     where acceptability is simply the ratio of actual to allowable daily
           dose (e.g. its NOEL) for each pesticide (or analogue subscripted
           i as above); this approach is equivalent to the ACGIH TLV and
           OSHA's PEL procedure for mixtures [15,16].

     Of more direct relevance herein are the consequences of repeated
variable residues upon AChE and the associated incidents of clinical
poisoning.  The consequences of daily repeated equal residues and of
variability in single daily residues upon AChE were originally discussed
separately on pages 137-140 and 186-191 [9].  A later manuscript [11]
described the temporal pattern of chronic (seasonal) AChE response in more
detail but was still restricted to constant residues and doses.  As it is
the purpose of this report to describe the effects of repeated, variable
residues and doses, the mathematical description of the methods used herein
shall begin with the model and terminology as developed in the original
report and presented above.

Methods;

     The historical focal point of health studies on people exposed to
anti-cholinesterase chemicals has been the enzymatic activity of AChE in the
body, which is most conveniently and routinely measured in the blood by
various laboratory procedures [17].  In health research settings, the effect
on a person's AChE is commonly expressed as a change relative to the study
subject's individual baseline activity, viz. delAChE as shown in Equation 5a
or 100 x delAChE expressed as a percent [6-8].  This concept reflects the
fact that individuals differ in their enzymatic activity but that health
effects accrue from changes in their activity relative to either their
normal (unexposed E ) or pre-exposure baseline (E  ,).  Note also that a
"pre-exposure baseline" implies that there may have been exposures with some
enzyme inhibition prior to the exposure in question and that normal and pre-
exposure levels are only equal for the first exposure (dose) of the season.

     An alternative to delAChE which is mathematically and biochemically
more convenient, is to look directly at the individual's relative activity
En^En-l °r En^Eo where En» En-l' and Eo are the Person's current, pre-


                  Draft Report Page 4 of 28                     ph2.doc
-------
                                                               29 Jul 86
exposure, and personal normal baseline activities, respectively [14,17].
One might think of this ratio as the person's health status (in fact this
shall later be called "H").  The relationship defined in Equation 5d
permits the direct use of the basic Unified Field Model Equations 1-4 to
also predict E /E _..  Thus, Equations 2 through 5 may be combined into one
complete equation, as follows:
     E /E   . - exp'W s^V" ^50, i)  - 1 - delAChE             (7)
      n  n~l

It may be convenient at this time to introduce the fact that values of many
of the model's variables and coefficients will differ among the chemical
components  within a residue (i), among the people within the cohort (j),
and from day to day (n).  These values are subscripted in the following
rewrite of  Equation 7:

      E.  /E.   . - wtp^duWn suffl(Ri,n ' nj LD50:i)              (8)
       j »n  j ,n i
     where  the following subscripts when used with subscripted coefficients,
     follow a colon:
            i = chemical component of the residue,
            j » person within the cohort,
            n = day within the season

     As noted above in Equations 5d, 7 and 8, the enzyme "health" status
ratio E.  /E,   . is relative to a generalized pre-exposure E  1 enzyme
activity 'valuenwnich may be either a true "baseline" (E ) or an already
partially inhibited level of activity.  Clinical health°effects are often
related to  this acute change in activity [14,17], but focus for standards
requiring infrequent biological monitoring of AChE is chronic or cumulative
seasonal changes relative to a person's unexposed baseline, E  [9].  As a
notational  convenience to aid the reader, the acute fraction E.  /E.   , for
a given person "j" on a given day "n" as defined by Equation 8Jw?llJ '
hereafter be noted by K,   (similar to a K notation without subscripts
used in the original repdrt [9]), as follows:
                                                     »J LD50:i)      (9)
Thus,

     EJ.n ' KJ.n EJ.n-l
And as another notational convenience, the fractional activity relative to a
normal baseline E.  /E,   will be noted by H.    which can easily be related
to the acute pre-exposufi effect K,   via Eqia?ion 10, as follows:
                                  j»n
     Hj.n-EJ.n/I!J.o   ' «,.„ WVo  -  KJ.n (Ej.n-l'Ej.o>
     But before using Equations 9-11 to predict long-term (i.e. cumulative
seasonal) changes in E, two biochemical and physiologic processes must be
included within the model.  These are (a) reversible inhibition or
reversion, the fact that recently inhibited enzymes are bound somewhat
reversibly, and that over a few hours a generally small fraction of the
enzymes will revert or return to their active state; and (b) regeneration,
the normal process of erythropoiesis (red blood cell production) which will
result in the generation of new RBC AChE and the accompanying process of


                  Draft Report Page 5 of 28                     ph2.doc
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                                                                            126
removal of a proportional fraction of inhibited RBC AChE.  In the original
report [9], these two processes were termed o1 and o, respectively.

     Although the detailed process and temporal pattern of reversion is not
completely understood, it may proceed for 6 to 30 hours or so after
exposure.  Values for o1 range from 0 to 10% for the strong inhibitors such
as TEPP and Sarin studied by Grob [14], to circa 15 to 20% for parathion
[9], to nearly 100% for most carbamates [15].  The modeled effect of
reversion is that the post-exposure enzyme activity E/E  is increased
after one day (24 hours) to a new fraction E'/E  by the reversion of a
certain fraction of the recent acute inhibition:

     reversion = o1 (1 - KR) - c-' delAChE                            (12)

     where o1  * the fraction of recently inhibited enzymes which becomes
                  unbound.

     The process of regeneration of RBCs is believed to be independent of
exposure and inhibition, as is the accompanying process of removal of old
RBC by the liver which is assumed to remove cells with both active and
inactive AChE indiscriminately.  Values of o for man range from 0.008 to
0.012 per day (average RBC life times of 85 to 130 days) [18].  In the
context of enzyme activity, the overall effect is one of replacement of
some old, inhibited enzymes with fresh, uninhibited enzymes.  The net effect
on the long-term model is that each day a fraction (o) of new RBC and
associated AChE are produced and an equal fraction of old RBC are removed
along with a somewhat lesser amount of active AChE proportional to the bulk
blood activity (or o x E /E ).  Here a mathematical approximation is made to
the model to account for the fact that E /E  may be changing throughout the
day due to the on-going process of reversion of recently inhibited enzymes.
As a convenient first-order approximation, the linear average relative
enzymatic activity over this period is assumed.  Thus,

     regeneration = o - (o/2 (E /E  + E'/E ))                        (13)
                    \\nono

     where E1 - E  + a slight improvement after 24 hours by virtue
            n    of reversion, see Equation 12.

     These processes were modeled within the original report only to
predict the net effect of repeated equal exposures [9 p.137-140].  Herein,
they are used to predict the entire pattern of the cumulative seasonal
response (E.  /E.  ) which would result from a random series of
variable reslaues'?R.  ).  To predict the cumulative effect of variations
in repeated unequal occupational exposures, one begins with responses
(K.  ) predicted from Equation 9 and the post-exposure enzyme health status
predicted by Equation 11.  Subsequent to each exposure and predicted acute
response, the body will undergo a level of recovery so that the next day's
"pre-exposure" baseline is slightly improved over that at (or shortly
after) the end of the previous day's work.  This improved enzyme activity
shall be defined as H' n (or E'. n/E.  ).  Therefore, to the initial post-
exposure health status'predicted by^Equation 11 shall be added the effects
of reversion (Equation 12) and regeneration (Equation 13) with the
appropriate person subscripts "j" inserted as needed:
                  Draft Report Page 6 of 28                     ph2.doc
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     Ej.n/Ej.o
Or.
     Ej.n/Ej.o - "WE3.o(1 - */2> + Vj<> * Kj,n>
                                                  2 (Ej.n/Ej,o>
And now solving for E*  /E.   which appears on both sides of Equation 14:
                     J »n  j ,o
     EJ.n"j.o(1 + ?/2) - Ej,n/Ej,o(1 -
  /E.  (1 - o/2) + o!(l - K.
*S.J*2 ..... i ...... & ...... 4iD
                                                   ) +
      J'n  J'°                   (1 +o/2)

     As indicated circa Equation 5d, it is this slightly recovered enzyme
activity level of Equation 16 which becomes the pre-exposure value for
succeeding exposures (E.   ./E.   in Equation 11), or
                       J»n-i  j,o

     H. n-1 - E. n.1/E. Q - E! n/E. Q - H'n_1                        (17)


     The above iterative sequence of daily inhibition (Equation 9-11) and
partial recovery (Equation 16) lends itself nicely to a digital program
which re-iterates this calculation, day-by-day throughout the season.  A
stochastic or Monte Carlo simulation computer program was written to permit
the parametric random variation of many of the factors described above (see
Appendices A-C).  In principle, the Monte Carlo simulation process could
create a heterogeneous population of values for any model parameter
distributed about a mean value with virtually any specified variability [19J.
Table I summarizes the terms within this study which are varied and fixed,
their notation within the text, and the subscripts assigned to those
parameters permitted to vary during this study.

     A number of modeled factors were either not varied or not used at
all, in particular, the residue application and decay coefficients.  The
initial plan for this project intended to begin these variations from the
point of residue application, i.e. to randomize R  and the decay
coefficients (k ) as used in a multicomponent decay model [12].  The
intended decay model for paraoxon follows:

                   K|K-                 Ky K«
     R         , —_i.±i9_- exp-klT + —S-fi2. exp-k3T +
      paraoxon    .     ,                .     .
                  K5 " Kl              K5 " *3


                       (R_	*---	12__) exp-k5T        (18)
                         3'°    k  - k      k  - k
                                K5    1      5    3
                  Draft Report Page 7 of 28                     ph2.doc
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     where the subscripts it to                                              "eg
            k,, indicate one of five k  coefficients used in this particular
             " model [12], and       r
          R..   indicate one of two portions of the initial paraoxon residue
           ff'° [12].

Considerable difficulty with this approach resulted from the existence of a
"pole" in these equations (i.e. when the denominator approaches zero, the
value of the  residue becomes impossibly large).  When the residue at
selected points in time was calculated using the above decay submodel with
randomly varying coefficients based on field data [12], such impossibly and
unrealistically high  values sporadically resulted.  To avoid this
difficulty, the residue at initial reentry R was randomized directly;
therefore neither k  nor T was used within this simulation, as noted in
Table I.           r

     For other reasons k  was also not used within this simulation (since a
dermal LD_Q value was aslumed, the absorption coefficient k  would have been
set to unity as intended by the original Unified Field Model.  Various
other parameters in the Unified Field Model were used but not permitted to
vary.  For example, Values for dermal toxicity were not varied since only
one generic OP was examined and variations in interpersonal susceptibility
was already accounted for by k  ., m., o1, and o.  As a first approximation
and to obtain a more clear understanding of the effects of variability
caused only by people and their daily residue, the length of the workday
was assumed constant.  Later elaborations of this study could include more
daily variations or even an interaction as from a hypothetical effect of
decreased enzyme activity upon the harvester's physical ability to sustain
an 8-hour workday (t) and a uniform work-rate (equivalent here to the
dosing coefficient k,).

     The concept of Monte Carlo simulation is that any factor can be varied
at random in any of a wide range of possible distributions [19].  This
particular study assumed that the factors listed as "varied" in Table I
were assumed to be either normally or log-normally distributed (the
logarithms of log-normally distributed values are themselves normally
distributed).  The mean and variability of each of the varied terms is
listed in Table II.  Most of these factors are assumed to be log-normal,
although for small deviations (equivalent coefficients of variation less
than 50%) it makes little difference whether normal or log-normal
variability is assumed.

     The particular form and magnitude of the parametric characterization of
R was selected from experimental experience with environmental samples
generally and foliar residue samples in particular [9],  Such samples are
invariably distributed in a skewed manner approximating a log-normal
distribution.  The largest available set of multiple foliar residue samples
collected on the same day(s) post-application but from a variety of
commercial applications is listed in Table III.  It is clear from the
relative size of the standard deviations compared to the arithmetic mean
values, that these data are not normally distributed.  The acceptability of
fit of these data to a log-normal distribution was confirmed by a Univariate
Analysis on the pooled data.  The pooled geometric deviation for all six
sets of samples is 2.6 or 160%, a point of reference for later comparisons.
                  Draft Report Page 8 of 28                     ph2.doc
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                                                                    --  429
     Two versions of the simulation program (Appendices A and B) were
utilized during this study.  Version A was designed to run on a large
mainframe computer (viz. an IBM 370/168 (with VM/CMS operating system)) and
utilized two commercially available subroutines (IMSL).  This version can
only be operated- in the batch mode and generates its model parameters by
randomly drawing separate sets of personal characteristics and field
residues for each crew from pseudo-infinite, parametrically distributed
universes for each respective model parameter.  The benchmark runs were made
with Version A, primarily because of its earlier availability.

     To expand portability to other computers and add further flexibility to
the exposure patterns which could be simulated, a second version was written
for Fortran-77 which can be (and was) run on an IBM - PC.  It utilizes two
published subroutines [20-21] to create parametrically distributed sets of
personal characteristics for the entire cohort and all field residues, then
randomly distributes this cohort and field residues among the crews.

     There are a number of differences between these two approaches: the
latter version consistently creates an entire study population of harvesters
or residues which fits the desired parametric distribution virtually exactly
(versus an F(°°,n) sampling error in the cohort created by the former version
which is a function of the cohort size, n; for 216 harvesters the 99%
confidence limit on achieving the parameter variability requested is + 15%
and for 916 field residues is + 6%).  At the same time, the variability of
each crew created by the latter method is only slightly larger than the
former (a sampling error which is a function of both the cohort and crew
sizes; for the population cohort of 216 and crew size of 12 used within
this study, the crew sampling error for both approaches is about 2-fold
(+ 98% versus + 99%, respectively)).

     Operationally, the simulation program (Appendix B) first creates a
cohort of harvesters.  Each harvester is randomly characterized in terms of
work habits (k.), weight (m), enzyme susceptibility (k ), and reversion-
regeneration coefficients.  This population is then distributed among
"ncrews" with "npersons" per crew.  An array of parametrically distributed
residues at reentry is also created and randomly assigned to each field.
For this series of simulations, each harvest crew works alternately two or
three days for 8 hours within two fields each week (five days per week).
Initially H.   • H.   • 1.  Each day the acute K.   is determined by
Equation 9,J4nd H.J'°by Equation 11.  At the startnof the next exposure day,
H1.   is determined'By Equation 16; and the process, beginning with acute
inhibition, is repeated.  Non-exposure days are handled in much the same
manner (except of course K  =1.0 for all j).  The program records the
enzyme health status (H) fcr each harvester within each crew from field to
field for a 26 week (six month) harvest season.

     Finally a second program (Appendix C) was written to analyze this
detailed personal health history to determine the following:

     0AM and OGM = the overall arithmetic and geometric mean delAChE of the
         cohort, respectively.
     PSD and PGD = the pooled standard and geometric deviations of the
         cohort, respectively.
     F * an analysis of variance F test statistic characterizing the
         uniformity or dispersion of delAChE within the crews versus among


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                                                                              43C
         the crews.  It was expected that the crews will be significantly
         different from each other as a function of residue variability but
         will become more uniform with passing time.
     Poi// * the daily incidence of individual OP "poisoning" cases based on
         one of four threshold criteria:
          (1) acute response, a delAChE of at least 507. in 1 day,
          (2)   "      "      "   "     "  at least 65%  " 2 days,
          (3)   "      "      "   "     "  at least 75%  " 3 days,
          (4) chronic  "      "   "     "  at least 50% versus E.  .
                                                                J»°
An example of the print-out from this program is shown in Appendix D.  The
first three of the above poisoning counters are also summarized by the total
number of poisoning cases, the number of days (nominal number of fields) in
which the above cases occurred, and the number of potentially reportable
incidents (assumed here to require at least one-half of the crew to have
exceeded one of the above criteria in one day).  In practice the 1-day acute
and seasonal chronic "poisoning" will be the primary focus of discussion.

Results and Discussion;

     A depiction of the cohort mean delAChE response throughout the season
is presented in Figures 1-5 grouped by daily mean inhibition (with residue
variability as a co-factor) and in Figure 6 grouped by variability equal to
1.0 (with daily mean inhibition as a co-factor).  A summary of the end-of-
season (chronic) AChE conditions are tabulated in Table IV.  By comparing
the vertical spread in the cohort mean response lines within Figures 1-5
with those in Figure 6, it can be stated that variations in the daily mean
inhibition (l-K ) have a greater impact on the chronic state of inhibition
than does residue variation, per se.  The rate at which these simulated
cohorts approached their seasonal lows varied from "half-response" times of
about 2 months for low daily inhibitions (1%) to about 2 weeks for high
daily inhibitions (16%).

     These chronic response patterns are similar to earlier predictions [11,
p. 330] except that they assumed uniform exposures 7-days per week.
Another feature not seen before is the weekly cycle of inhibition and
partial recovery as the level of inhibition approaches 60% or greater; for
relatively high daily inhibitions (circa 8 to 16%), the combination of
reversion and regeneration results in recoveries of as much as 4% of the
baseline normal over a weekend, which becomes more visible on the expanded
scale near the bottom of these figures.

     Based on the first row of data in Table IV, it appears that the only
practical daily inhibition (i.e. residues with any variability at all) for
which seasonal inhibition will be less than 50% is 1% per day.  This is
similar to the prediction in reference 9 (p. 140) of 1 to 2% per day for
40% seasonal inhibition given a 7-day per week exposure pattern.  The
variability in the seasonal inhibition is indicated by the second row of
data, the geometric deviation.  It appears that the relative variability in
the seasonal low AChE activity is reduced primarily as the daily
inhibitions increase and secondarily as the variability in the residues
increases.  This pattern is explainable by the overwhelming importance of
the magnitude of the daily inhibition in relationship to the speed of
enzyme reversion and replacement.  The impact of variability on the
fraction of the cohort whose seasonal inhibition exceeds 50% is indicated


                  Draft Report Page 10 of 28                     ph2.doc
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                                                               29 Jul 86


by the third row of data  in this table: the trade-off between low levels or -^ '
daily inhibition and relatively high levels of variability causes there to
be some fraction of the cohort which exceeds 50% under all conditions,
although again this fraction increases primarily with increasing levels of
daily inhibition.

     As it will be discussed in relation to acute responses, the
significance of these simulated chronic responses in relation to future
human experience is best  judged in relation to past experience.
Unfortunately the history of past chronic cholinesterase studies among
harvesters has hardly been reported.  One rather unique study of one East
coast migrant crew by Owens and Owens [24] was referenced in the
elaboration of the Unified Field Model [11] showed seasonal inhibitions of
30 to 40%, but the more detailed analyses of their data has not been
accomplished.  Another known report was a student master's thesis of some
822 California central valley harvesters in 1970 which showed seasonal
inhibition of 5 to 8Z [25].  In both studies neither the intensity nor
frequency of residue exposure was known; therefore, direct comparisons of
the chronic response between this simulation and human experience is barely
possible.

     In general, such chronic longitudinal studies are difficult to
initiate, organize, maintain and analyze.  Of practical importance is the
effect on data analysis of the relative size (and numbers) of individual
crews into which the whole cohort is distributed.  Included in the results
printed by the data summary program is the analysis of variance F statistic
indicating the uniformity between the crews versus within the crews (and
the cohort as a whole).   Initially these test statistics are quite large,
indicating statistically  greater variation between crews than within crews
(except of course, when the variability in the residues is 0 (geometric
deviation of 1.0).  Over  time, the crews become more and more consistent,
and the daily F statistic decreases.  A value of about 2.0 indicates
statistical uniformity with 99% confidence.  The fourth row of Table IV
indicates the number of days into the season necessary for such inter-crew
uniformity to be established.  From this data it is clear that residue
variability is the dominant factor characterizing inter-crew uniformity and
that statistical uniformity is not achieved within a six-month season for
levels of variability of  3 or more.  This conclusion implies that an
adequate interpretation of harvester cholinesterase survey data must take
into account the grouping of that data by crew; similarly, some crews
should be expected to be  significantly more inhibited than others or than
the group as a whole.

     The preceding discussion of chronic response variability is directly
relatable to acute "clinical" responses which represent the high "tails" of
the daily statistical distribution.  A summary of acute response data are
depicted in Table V only  (rather than in figures).  The first row of this
table indicates the frequency with which individual harvesters might have
RBC AChE shifts sufficient to induce clinical symptoms.  It appears that
while the incidence of chronic seasonal inhibition is high under many of
the scenarios investigated, the incidence of clinically acute responses is
undetectable under a much broader range of residue conditions.  Practical
conditions without incidents begin at 8% per day if the variability can be
kept at or below 1.5; at  4% per day, below 1.5; 27. per day of at or below
2.0; and 1% per day if variability approaches 3.0.  It also appears that


                  Draft Report Page 11 of 28                     ph2.doc
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once conditions of daily mean or residue variability are sufficiently
to cause one individual acute case, multiple cases (the second row) and
incidents involving at least half the crew (the third row) begin to occur;
this pattern is relatable to the inter-crew versus intra-crew uniformity
discussed above.•  Therefore, a reasonable administrative target is to
prevent any individual cases of anti-cholinesterase poisoning sufficient to
cause clinical symptoms.

     Fortunately the significance of these simulated acute responses in
relation to future human experience is more readily judged in relation to
past experience than was true for chronic cholinesterase effects.  Several
tabulations of "reported" poisoning incidents from the literature are
available to provide a first estimate of past field experience [3,6,8].
These data indicate an incidence of about 25 harvesters and 1 "incident"
reported in California per year from 1948 to 1977; although two other
independent reports indicate the above values "represent perhaps 1% of of
the persons who suffered less severe symptoms, received medical treatment,
but remained unreported" [9].  Most (but not all) of these reported cases
involved parathion.

     The denominator for these cases can be estimated from the roughly
74,000 acres of Valencia oranges in California [26] (the variety harvested
during the April-September prime parathion application period; see Table VI)
and reported individual harvest rate data [27], which indicate about 7400
harvesters are actively engaged in harvesting the seasonal crop most
commonly involved in reported residue poisoning incidents [3,6,8].  The
denominator for these incidents can be closely approximated from California
Pesticide Use Reports, Table VI, which for the early 1970s was about 2000
fields and circa 60,000 acres of oranges (of which about 50% was summer
valencias) sprayed per year [28].  Thus, on an annual average over the
thirty year history, about 25/7400 = 0.34% of the orange harvesters in
California have been involved in a reported residue poisoning per year and
1/1000 = 0.1% of the field applications have resulted in reported incidents.

     Recall also that the variability in parathion residues on citrus was
2.6 (Table III); furthermore, the equivalent anti-cholinesterase power of
the residues on day 9 was about 8% and on day 16, about 4%.  Since
California had, prior to 1970, an administrative harvest - reentry interval
for parathion of 14 days, a mean daily inhibition of 4% is the worst
expected scenario to simulate historical experience.  Although it is
unlikely that every field was harvested as soon as its reentry interval had
elapsed, it is possible that a great many of these fields could have been
harvested between that time and 2 weeks later (or about 2 half lives and
l/4th the AChE effect later).

     Locating these conditions in Table V indicates a reasonably good
agreement between the simulation and historical record.  The simulation
indicates a somewhat higher incidence of individual overexposures than the
historical record shows, but well within the higher range estimated by the
"critics" of the established reporting system.  On the other hand, the
simulation indicates an incidence of "potentially reportable" group
incidents somewhat lower than reported.  There are many possible
explanations for this latter difference, among them are (1) a difference in
the ratio of number of crews and crew size from that modeled, (2) an overly
conservative definition of "incident" (i.e. at least 50% of the crew having


                  Draft Report Page 12 of 28                     ph2.doc
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an AChE shift of 50% or more), and (3) other than a log-normal distribution  A ? 7
of foliar residues (i.e. a bimodal distribution with unexpectedly more
frequent high residues under some relatively unique combination of
environmental and/or application conditions, or possibly some combination of
the above.  Whatever the reason, the differences are relatively slight;
proving the value of this stochastic simulation procedure.

Conclusion;

     A stochastic simulation program was written to study the importance of
residue variability in preventing anti-cholinesterase overexposure,
excessive chronic AChE inhibition, or acute illness.  A range of daily
inhibitions and residue variability were explored, and the simulated cohort
response was compared to the historical record of chronic and acute data.
The resulting chronic and acute AChE response patterns are largely displayed
in Tables IV and V, respectively.

     It was concluded that residue variability has only a slight effect on
the mean AChE health status of a working cohort, in comparison to mean daily
inhibition.  Residue variability has a slightly stronger although still
secondary effect upon the overall variability of the cohort's AChE activity.
The fraction of the cohort whose seasonal AChE inhibition exceeded a
relatively arbitrary chronic threshold of 50% was again largely controlled
by the daily mean inhibition, but at 4% per day and above virtually the
entire cohort exceeded that limit and below 4% per day residue variability
had an increasing important role.

     On the other hand, residue variability appears to have a very strong
effect upon the uniformity of the AChE activity within the cohort.  This
nonuniformity has both chronic and acute AChE implications.  For instance,
for residue variability even below a geometric deviation of 2.0, the crews
seasonal AChE inhibitions are not expected to be statistically uniform (with
99% confidence) until half way through a season; and for variability of 3.0
and above, the crews will be dissimilar throughout the season.  Thus, it is
possible to have several crews below an administrative AChE inhibition
threshold (or even potentially with clinical symptoms) while the cohort mean
is only marginally different from normal.  This pattern has similar
implications to both the design and interpretation of epidemiologic surveys
among this population.

     Acute responses, in terms of individual and group AChE responses in
excess of potential clinical symptoms, exhibit a fairly clear boundary as a
function of both residue mean and variation.  In the low mean range of 1% to
2% per day, no acute individual or group incidents were predicted for
geometric variations below 2.5; however, a set of random parathion
commercial application residues collected on days 2, 9, and 16 all showed
variations of around 2.6, just sufficient to induce sporatic acute
responses.

     This pattern of acute effects under conditions of low chronic response
and the practical boundary of a geometric deviation of 2.5 (or + 150%),
suggest that consideration to both the mean anti-cholinesterase effect and
the variability of the foliar residues should be considered when setting
administrative reentry intervals.
                  Draft Report Page 13 of 28                     ph2.doc
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                                                                             434
References;

 1. Abrams, H.K., and A.R. Leonard: Toxicology of Organic Phosphate
    Insecticides. Calif. Med. 73:183-186, 1950.
 2. Ingrain, F.R.: Health Hazards Associated with Use of Airplanes for
    Dusting Crops with Parathion.  Amer. Ind. Hyg. Assoc. Quart. 12:165-
    170, 1951.
 3. Quinby, G.E., and A.B. Lemmon: Parathion Residues as a Cause of
    Poisoning in Crop Workers.  J. Amer. Med. Assoc. 166:740-746, 1958.
 4. Milby, T.H., F. Ottoboni, and H. Mitchell: Parathion Residue Poisoning
    Among Orchard Workers.  J. Amer. Med. Assoc. 189:351-356, 1964.
 5. Milby et al.: Occupational Exposure to Pesticides.  A Report of the
    Federal Working Group on Pest Management, Washington DC, 1974.
 6. Spear, R.C., W.J. Popendorf, J.T. Leffingwell, and D. Jenkins:
    Parathion on Citrus Foliage: Decay and Composition as Related to Worker
    Hazard.  J. Agr. Food Chem. 23:808-810, 1975.
 7- NIOSH: Proceedings; Pesticide Residue Hazards to Farm Workers. U.S.
    Dept. HEW Pub. No. (NIOSH) 76-191, 1976.
 8. Gunther, F.A., Y. Iwata, G.E. Carman, C.A. Smith: The Citrus Reentry
    Problem: Research on its Causes and Effects, and Approaches to its
    Minimization.  Residue Reviews 67:1-139, 1977.
 9. Popendorf, W. and J.T. Leffingwell: Regulating OP Pesticide Residues
    for Farmworker Protection.  Residue Reviews 82, 125-201, 1982.
10. Dermal Exposure Related to Pesticide Use; Discussion of Risk
    Assessment, ed. by R.C. Honeycutt, G. Zweig, N.C. Ragsdale. American
    Chemical Society, Washington DC, 1985.
11. Popendorf, W.P.: Advances in the Unified Field Model for Reentry
    Hazards, p. 323-341, op cit., 1985.
12  Popendorf, W. and J.T. Leffingwell: Natural Variations in the Decay and
    Oxidation of Parathion Foliar Residues.  J. Agric. Food Chem. 26:437-
    441, 1978.
13. Scheuplein, R.J.: Percutaneous Absorption After Twenty-five Years: or
    "Old Wine in New Wine Skins." J. Invest. Dermat. 67:31-38, 1976. (not
    his latest).
14. Grob, D. and A.M. Harvey: Effects in Man of the Anti-cholinesterase
    Compound Sarin (Isopropyl Methyl Phosphonofluoridate). J. Clin. Inv. "
    37:350-368, 1958.
15. ACGIH Threshold Limit Values and Biological Exposure Indices for 1985-
    86. TLV for Mixtures, p. 47, Amer. Conf. Governmental Industr. Hyg.
    Cincinnati, OH, 1986.
16. OSHA Safety and Health Standards (29 CFR 1910.1000) U.S. Dept. Labor
    Pub. No. (OSHA) 2206, Washington DC, 1983.
17. Wills, J.H., and K.P. Dubois: The Measurement and Significance of
    Changes in the Cholinesterase Activities of Erythrocytes and Plasma in
    Man and Animals. CRC Critical Reviews in Toxicol. 1:153-202,1972.
18. Geigy Scientific Tables Vol. 3, 8th Ed., edited by C. Lentner, Ciba-
    Geigy, W. Caldwell NJ, 1984.
19. Rubinstein, R.Y.: Simulation and the Monte Carlo Method. John Wiley &
    Sons, New York, 198T~~
20. Odeh, R.E. and J.O Evans: The Percentage Points for the Normal
    Distribution, AS 70. Appl. Statist. 23:96-97, 1974.


                  Draft Report Page 14 of 28                     ph2.doc
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                                                               29 Jul 86


21. Wichmann, B.A. and I.D. Hill: An Efficient and Portable Pseudo-random     435
    Number Generator, AS 183. Appl. Statist. 31:188-190, 1982.
22. Gage, J.G.: The Significance of Blood Cholinesterase Activity
    Measurements.  Residue Reviews 18:159-173, 1967.
23. Gaines, T.B.: Acute Toxicity of Pesticides. Toxicol. Appl. Pharmacol.
    14:515-534. 1969.
24. Owens, E.W. and S.Y. Owens. Science, submitted 1984.
25. Ray, R. Occupational Exposure to Organophosphate Pesticides among
    Agricultural Workers and their Families.  MS Thesis, University of
    California, Berkeley CA, 1972.
26. California Crop and Livestock Reporting Service, 1981.
27. Popendorf, W. and R.C. Spear: Preliminary Survey of Factors Affecting
    the Exposure of Harvesters to Pesticide Residues.  Amer. Industr. Hyg.
    Assoc. J. 36:374-380, 1974.
28. Pesticide Use Reports, California Dept. Food and Agriculture,
    Sacramento CA, for the years noted (1972-1982).
                  Draft Report Page  15 of  28                      ph2.doc
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                                                                             436
Table I.  Equivalence of the terms and notation used in the text of this
report versus the notation used in the programs listed in Appendix B and C.
Terms are listed according to the subscript by which they were varied (or
if in parentheses, by which they could have been varied).
Terms not used:
  initial residue
  reentry interval
  absorption coef.
                          m
                    text subscript
                          text
                         notation
terms varied:
  reentry residue
  daily field residue
  dosing coef.
  body weight
  enzyme coef.
  reversion coef.
  regeneration rate
terms not varied:    .
  residue decay coef.
  work day
  dermal tox.
  enzyme measurement
                           r
                          LD
                            50 :
                          T
                          k.
                                    chem.
                                     i
                                    (x)
                                            crew
(x)a

(x)



(x)a

(x)
                  program subscript  mm
                                    chem.
               person
                 j
                                             x
                                             x
                 X
                 X
                 X
                 X
                 X
                                             i
                                            crew
 (x)

  j
person
         day
          n
          x
          xa
         (x)
         (x)a
                         k
                        day
program
notation

  res
   r
   kd
   wt
   ke
   PP
   P

(x)

(x)

(x)

xb
half
hwpd
Id50
vlab
 program
 notation
  footnote:
     a) term effectively varied by variations in R  above.
     b) capability to vary laboratory measurement technique was built in to
        program, but unused throughout the majority of the  study.
                  Draft Report Page 16 of 28
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                                                                            437
Table II.  A summary of the mean and deviation for each model parameter
used within this study.  Deviations followed by "x" denote geometric
deviations (multipliers and dividers of the mean); those with units are
geometric deviations (the normal distribution).
terms varied:

  reentry residue

  dosing coef.
  body weight
  enzyme coef.
  reversion coef.
  regeneration rate

terms not varied:    .
  residue decay coef.
  work day
  dermal tox.
  enzyme measurement
                           i.n
                           ., ,

                           '"
                          t
                          LD
                                   mean
                            50:i
5.1 cm
70  kg
6.0
0.15 day-1
.893 Z/day
  7  days
  8  hours
 10. mg/kg
            deviation
1.0 to 4.0 x

 1.41  x
+ 7 kg
"1.3   x
 1.01  x
 1.046 x
                reference
[6,12]

[9]
[9]
[9]
[9,14,15]
[18]
                  [12]
                  [15,16]
                  [9,22]
                  [9,17,23]
  footnote:
     a) initial mean reentry residue levels were determined from the Unified
        Field Model based upon the mean daily delAChE response specified by
        the user of the program; each of the following combinations of mean
        residue and residue variability were investigated:
        geometric deviation 1.0
                            1.5
                            2.0
                            3.0
                            4.0
                                  mean delAChE
                                   1%   2%   4Z
               8Z   16Z
                  Draft Report Page 17 of 28
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Table III .  Summary of foliar-dislodgeable parathion (thion) and paraoxon
(poxon) concentrations, ng/cm  from commercial citrus groves sampled on the
same day post-application [reference 6].
                                                                              438
       day  thion    poxon
day  thion    poxon
day  thion    poxon








































arth
std.

geom
geom
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
.mean =
dev. =
N =
.mean =
. dev . =
264.1
398.5
412.6
1434.
180.8
170.9
149.7
190.7
49.75
252.3
186.7
103.5
174.7
294.7
89.13
240.5
688.5
229.2
483.4
1297.
172.6
438.6
152.8
417.3
771.0
386.7
617.8
363.1
199.7
482.8
893.0
562.9
216.0
1724.
303.6
327.2
418.5
193.9
234.6
607.1
419.3
365.1
40
316.0
2.12
18.63
32.07
29.71
109.2
89.60
71.44
108.9
10.54
6.272
33.72
52.82
21.41
35.13
33.72
23.06
20.91
25.46
51.64
166.5
227.3
327.7
396.1
15.66
24.99
365.5
11.44
320.7
32.07
145.0
325.7
276.8
38.02
24.70
92.55
159.7
262.6
62.19
90.48
96.08
210.7
111.2
114.6
40
63.31
3.08
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9











29.94
55.17
95.49
311.2
22.85
17.38
6.319
13.49
7.451
26.64
20.77
22.73
27.59
32.77
19.64
43.62
48.34
57.77
12.05
119.8
21.06
42.68
7.710
38.20
69.32
42.91
67.20
20.30
24.05
15.51
45.39
15.59
92.84
80.76






46.31
54.40
34
31.65
2.33
12.61
33.01
11.67
64.84
25.46
20.42
25.94
2.759
1.252
19.92
16.98
9.290
14.05
12.43
6.201
18.13
11.13
24.99
53.76
135.1
65.78
71.91
4.551
16.41
51.17
15.51
115.3
13.16
22.40
65.31
95.79
9.549
27.62
40.67






33.38
32.75
34
21.16
2.83
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16










9.526
24.52
49.75
113.9
7.474
9.148
5.965
3.466
1.250
9.785
7.144
7.946
10.87
6.272
13.46
8.205
11.48
11.95
5.376
39.14
32.54
2.358
3.843
3.631
20.84
11.93
24.05
4.409
8.583
13.03
10.49
3.773
18.04
35.96
24.73





16.42
20.44
35
10.51
2.52
6.956
8.983
10.12
49.99
7.286
7.875
9.974
1.582
.6413
10.09
6.508
7.309
9.384
6.696
1.846
7.215
7.097
11.51
21.41
21.08
20.58
17.97
2.499
4.951
25.46
4.716
58.00
7.026
13.16
90.19
19.54
4.362
20.45
26.23
29.77





15.96
18.04
35
10.00
2.76
Pooled geometric deviation = 2.61
                  Draft Report Page 18  of 28
                                  ph2.doc
-------
                                                               29 Jul 86
                                                                         439
Table IV. Chronic Response to exposures 5 days per week distributed among 18
crews of 12 persons each working alternately 2 and 3 days per field (see
also Figures 1-6):
   1) The typical mean pseudo-equilibrium AChE activity expressed as a
      percent of normal baseline of each individual for the harvester cohort
      as a whole  in mid- to late in the last week of the season.
   2) The geometric deviation for the above data, expressed as a percent +
      of the mean.
   3) The prevalence (expressed as percent) of harvesters who have exceeded
      50% inhibition of RBC AChE by the end of the season.
   4) The number of days into the 182 day (26 week) season necessary for
      the analysis of variance F statistic to decrease to or below 2.00
(the critical F
                     ._
                     ii i
Geometric

Deviation
1.0
1.5
           2.0
           3.0
4.0
                            test statistic for 99% confidence of uniformity).
          1%

          35
         +30*
         "6-7%
           0

          37
         +29%
         "6-8%
          38

          40
         +27%
         12-18%
         117
  54
 +17%
 69-74%
>182
                               Daily    Mean   delAChE
                               27.        4%        87.
            50
           +20%
           53-56%
             0

            52
           +19%
           58-63%
            39
            65
           +12%
           "97-99%
             0

            66
           +11%
           98-99%
            38
                                                   75
                                                 +7%
                                                 "lOO%
                                                    0

                                                   76
                                                 +6%
                                                 100%
                                                   37a
55
+17%
69-74%
119
68
+10%
> 9g%
140b
77
+ 6%
100%
>182
47
+22%
41-46%
175
61
+13%
90-94%
>182
71
+ 8%
> 98 %
>182
78
+ 5%
100%
>182
  66
 +10%
 95-97%
>182
                                         74        80
                                        +6%    +4%
                                        99-100% " 100%
                                       >182      >182
                                                                  167.

                                                                   82
                                                                  +4%
                                                                  "lOO%
                                                                    0

                                                                   82
                                                                  +3%
                                                                  1002
                                                                   35°

                                                                   83
                                                                  +3%
                                                                  100%
                                                                 >182

                                                                   84
                                                                  + 3%
                                                                  100%
                                                                 >182

                                                                   85
                                                                  3%
                                                                  100%
                                                                 >182
      footnotes:
         a) not consistently uniform thereafter.
         b) infrequently uniform thereafter.
            Draft Report Page 19 of 28
                                                                 ph2.doc
-------
                                                                       440
Table Va. Acute Response (in absolute numbers)  to exposures 5 days per week
distributed among 18 crews of 12 persons each working alternately 2 and 3
days per field:
   1) The number of harvesters experiencing  a 1-day AChE inhibition of >50%
      (believed capable of inducing clinical symptoms);  values in excess of
      216 indicate individuals multiply exposed to high  levels of residue.
   2) The number of days throughout the season  in which  the above
      inhibitions occurred (this will be between 1 and 2 times the number
      of fields, depending upon the frequency and severity  of the
      "poisoning").
   3) The number of sprayed fields  per season in which the  above
      inhibitions were clustered into potentially reportable "incidents"
      (involving at least one-half  of the crew).
    Geometric

    Deviation
1.0
                 1.5
                 2.0
                 3.0
                 4.0
                              Daily   Mean   delAChE
                           1Z        2%        4%
 0
 0
 0

 0
 0
 0

 0
 0
 0

 1
 0
 0

20
 5
 2
 0
 0
 0

 0
 0
 0

 0
 0
 0

12
 5
 1

82
22
 5
  0
  0
  0

  0
  0
  0

  1
  1
  0

 84
 26
  6

288
 36
 18
 8Z

  0
  0
  0

  0
  0
  0

 39
 16
  2

383
 48
 32
                                                        42
16Z

  0
  0
  0

 46
 22
  3

317
 79
  5

 *
 *
"76

 ft
 ft
 ft
                 2.6
           0
           0
           0
           2
           2
           0
          28
          10
           1
                  Draft  Report Page 20 of 28
                                                ph2.doc
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                                                                  JUJ. OO
                                                                  .      441


Table Vb. Acute Responses (in rates per hundred) to exposures 5 days per
week distributed among 18 crews of 12 persons each working alternately 2 and
3 days per field:

   1) The incidence (percent of 216) of harvesters experiencing a 1-day
      AChE inhibition of >50Z (believed capable of inducing clinical
      symptoms); values in excess of 100% indicate individuals multiply
      exposed to high levels of residue.
   2) The percent of 182 days throughout the season in which the
      above inhibitions occurred (this will be between 1 and 2 times the
      number of fields, depending upon the frequency and severity of the
      "poisoning").
   3) The percent of the 916 sprayed fields per season in which the  above
      inhibitions were clustered into potentially reportable "incidents"
      (involving at least one-half of the crew).

                              Daily    Mean   delAChE
                           1%        2%        4Z        8Z       16Z

    Geometric    1.0        0         0         0         0         0
                            00000
    Deviation               00000

                 1.5        0         0         0         0        21%
                            0         0         0         0        12%
                            0         0         0         0        .3%

                 2.0        0         0        .5%       18Z      147Z
                            0         0        .5Z        9Z       43Z
                            0         0         0        .2%       .5%

                 2.6        0        .9%       13%
                            0         1Z        5Z
                            0         0        .17.

                 3.0        .1Z       6%       39Z      177Z       *
                            0         37.       14%       26%       *
                            0        .1%       .7Z      3.5Z     "8.3%

                 4.0        9Z       38Z      133Z       *         *
                            3%       12Z       20Z       *         *
                           .2Z       .5Z      2.0Z     ~4.6%       *
    footnote *) frequencies become unreliably too high to be representative.
                  Draft Report Page 21 of 28                     ph2.doc
-------
                                                                        442
Table VI. Reported use history of  parathion on oranges in California
[Source Pesticide Use Reports, California Dept. Food and Agriculture, for
the years noted}.
     Year
 Number of
Applications
Founds
Acres
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
Tulare County
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
1982
2206
2135
1554
921
1000
744
638
443
476
681

548
Use Report,
0
1
271
128
165
260
182
158
83
67
21
5
160 377
194 061
161 233
124 049
77 956
90 240
57 111
69 722
50 051
55 677
54 171

49 848
Oranges, 1972.
0
1
22 045
6 867
15 640
25 335
11 975
11 174
20 778
6 462
1 520
559
52 855
65 777
63 574
45 189
30 742
29 106
21 765
23 314
14 199
14 446
21 845

17 579

0
1
9 905
3 092
4 662
7 086
4 357
3 682
2 919
1 900
1 520
155
Pounds/Acre
                                                      2.84
                                                      2.23
                                                      2.22
                                                        .35
                                                        .58
                                                        ,75
                                                        .03
                                                        ,12
                                                        .40
                                                      3.55
                                                      3.60
                                     3.
                                     3.
                                     2.
                                     3.
                                     7.
                                     3.
           totals   1444
               122  737   38  199
                       3.21
                  Draft  Report Page 22 of 28
                                               ph2.doc
-------
I
NJ
     o
     
                                                                                                                      f-60  -
                                                                                                 to
                                                                                                 ac.

                                                                                           f-BO  u.
                                                                                                 o
                                                                                                                             o:
                                                                                                                             UJ
                                                                                                                             Q-
                                                                                                                        •90
                                                                                                                182
-------
                   DAYS
WITHIN    SEASON
Figure 2.  Plot of AChE activity on the Y axis versus seasonal time in
           days on the X axis for 2% daily delAChE, with residue geometric
           deviation increasing progressively from the top to bottom line
           from 1.0 to 1.5, 2.0, 3.0, and 4.0, respectively.
-------
100-

 BO-

 60-


 4O-
    rt

    UJ
I    UJ
N)   v>
        10
                 —T
                  14
                   —T
                    28
—T
 42
—T
 56
70
                                               DAYS
—T
 B4
98
—T~
 112
126
140
154
—I—
 168
                                                   WITHIN   SEASON
                            Figure 3.  Plot of AChE  activity on the Y axis versus  seasonal time in
                                       days on the X axis for 4% daily delAChE, with residue geometric
                                       deviation increasing progressively from the top to bottom line
                                       from 1.0 to 1.5, 2.0, 3.0, and 4.0 respectively.
                                                                                                                   •o

                                                                                                                   •20

                                                                                                                  -4O
                                                                                                                  _     OQ
                                                                                                                        I
                                                                                                                  -60  -
                                                                                                                         X
                                                                                                                         o
                                                                                                                        CO
                                                                                                                        Of

                                                                                                                  •BO  u.
                                                                                                                        o
—T
 182
                                                                                                             go
                                                                                                                 CD
-------
                                                                                         30
                                                                                      182
                    DAYS
MlTHIN    SEASON
Figure 4.  Plot of AChE activity on the Y axis versus seasonal time in
           days on the X axis for 8% daily delAChE, with residue geometric
           deviation increasing progressively from the top to bottom line
           from 1.0 to 1.5, 2.0, 3.0, and 4.0, respectively.
-------
1CM
                                        DAYS
WITHIN    SEASON
                      Figure 5.  Plot of AChE activity on the Y axis versus seasonal time in
                                 days on the X axis for 16% daily delAChE, with residue geomtric
                                 deviation increasing progressively from the top to bottom line
                                 from 1.0 to 1.5, 2.0, 3.0, and A.O, respectively.
-------
                   DAYS
WITHIN    SEASON
Figure 6.  Plot of AChE activity on the Y. axis versus seasonal time in days
           on the X axis for 0 residue variability, with daily delAChE
           increasing progressively from the top to bottom line from 1% to 2%,
           4%, 8%, and 16%, respectively.
                                                                                           00
-------
                                                               25 Jul 86

                                                                           449
Appendix A: listing of Fortran simulation program (with comments) to be run
            on an IBM 370 with VS operating system.  Lines following
            //GO.FT01F001 DD *  are input data for batch mode operation.

// JOB (,25),'F1-FREY',TIME=(002),MSGLEVEL-(1,1)
/'PASSWORD PPSU8
/*ROUTE PRINT WYLBUR
// EXEC FORTVCLG,FVREGN«1200K,GOREGN-1024K
//FORT.SYSIN DD *
c
c Reentry Simulation Program , IBM 360 Version 1
c
c input used for this simulation problem:
c
c  i.   means and sd (geometric means and sd for kd,p,pl,res.eq.coeff.)
c       1. rmp, regeneration of rbc and their enzymes [usually 1-2%]
c          rmpl, reversion of rbc  [usually 0-15]:
c          rmp,sdp,rmpl,sdpl                            (4F10.5)

c       2. kd dose coefficient, ke enzyme coefficient:
c          rmkd.sdkd, rmke.sdke                         (4F10.5)

c       3. mass (body weight) of workers:
c          rmwt,sdwt                                    (2F10.5)

c
c  ii.  parameters determing pattern of field exposure
c       1. nf = number of fields worked per crew:       (i3)
c       2. nc = number of crews to be placed in fields:  (i3)
c       3. nm = number of members per crew:             (20i3)
c       4. te « time (days) of initial re-entry into each field
c               after spraying:                         (flO.5)
c       5. tw * time (hours) worked per day :           (flO.5)
c       6. Id50 = LD50 for parent and each oxon:        (4fl0.5)
c       7. nd = number of days to complete each field:  (i3)
c
c  iii. input residues of parent [and oxon]:
c       1. read mean and geometric deviation of parent and ditto for oxons
c                                                       (4F10.5).
c
c  iv.  other input variables
c       1. dseed = an integer value (1-2147483647) typed in double
c                  precision, to be used to generate random number:
c                                                       (flO.O)
c
      CHARACTERS  NMC(20)
      CHARACTER*40 INPUT,OUTPUT,RESDUE
      DOUBLE PRECISION DSEED
      REAL  P(18,12),P1(18,12),KK(18,12,100),LD50(10),
     +     KD(18,12),KE(18.12),WT(18,12),DEL(18,12,100),
     +     RP(301),RKE(301),RKD(301),RWT(301),RAA(301),RP1(301),
     +     R(18,18),H(18,12,100),H1(18,12,101),HE(12),
     +     HM(18,100),HSD(18,100)

c     lun is a vestigal parameter from a F77 version directing output:
      LUN -  4
      READ  (1,10) RMP,SDP,RMP1,SDP1
      READ  (1,10) RMKD.SDKD,RMKE.SDKE


                   Appendix A  Page 1 of 6
-------
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
   READ  (1,10) RMWT.SDWT

   print means and sd

   WRITE (LUN.20)  RMKE,SDKE,RMWT,SDWT,RMP,SDP,RMP1,SDP1,RMKD,SDKD
20 FORMATdX,/,
  +   IX,'MEANS AND DEVIATIONS GIVEN ON INPUT'   /
  +   7X,'FOR NORMALLY DISTRIBUTED VARIABLES'    /
  +  1OX,'ENZYME COEFFICIENT KE1,    T45.2F10.5  /
  +  10X,'WORKERS WEIGHT',           T45.2F10.5  /
  +   7X,'FOR LOGNORMALLY DISTRIBUTED VARIABLES '/
  +  1OX,'REGENERATION, P1,          T45.2F10.5  /
  +  1OX,'REVERSION,PI1,             T45.2F10.5  /
  +  10X,'DOSING COEFFICIENT KD',    T45.2F10.5  /)

   input other parameters describing field
   READ  (1,25) NF
   READ  (1,25) NC
   READ  (1,30) NM
   READ  (1,10) TE
   READ  (1,10) TW
   READ  (1,10) LD50(1)
   READ  (1,30) ND
   READ  (1,10) RESSS,REGED
   READ  (1,40) DSEED
   input formats
10 FORMAT(4F10.5)
15 FORMAT(8F10.5)
25 FORMAT(I3)
30 FORMAT(20I3)
40 FORMAT(FIO.O)

   output parameters describing field
   WRITE (LUN.35) NC.NF.ND.TW.TE,DSEED,LD50(1),RESSS,REGED
35 FORMAT( IX,'OTHER PARAMETERS GIVEN ON INPUT1,
  +       lOX.'NO. OF CREWS',
  +       lOX.'NO. OF FIELDS PER CREW',
  +       lOX.'NO. OF DAYS TO COMPLETE FIELD1,
  +       10X,'NO. OF HOURS WORKED PER DAY',
  +       10X,'TIME OF RE-ENTRY INTO FIELDS',
  +       10X,'INPUT VALUE FOR DSEED1,
  •f       10X,'LD50 FOR PARENT1,
  +       10X,'MEAN RESIDUAL VALUE1,
                                                                        450
                                                             .I3
                                                             .I3
                                                             .I3
                                                             .F10
                                                             .F10
                                                             .E13
                                                             .F10
                                                      T41
                                                      T41
                                                      T41
                                                      T40.
                                                      T40.
                                                      T60.
                                                      T40.
                                                      T60.F10.5 /
                                                                    /
                                                                    /
                                                                    /
                                                                    /
                                                                 .5 /
                                                                  5 /
                                                                  7 /
                                                                  5 /
             1 OX, 'GEOMETRIC DEVIATION FOR RESIDUAL VALUE1 ,T60,F10.5)

      notation and documentation of arrays
     i.  for crew i, member j
           P  (i,j)  per cent regeneration
           pi (i,j)  per cent reversion
           rkd(i.j)
           rke(i.j)
           wt
ii.
         for crew i, member j, day 1 (field k)
         del(i,j,l)  represents the percent of enzyme activity inhibited
                   Appendix A  Page 2 of 6
-------
                                                               25 Jul 86


c                    after one dose
c        kk (i,j,l)  ratio of active enzyme after 1 dose « 1- del(i,j,k)
c        h  (i,j,l)  health (ratio to initial of enzyme remeaining after
c                    the kth dose)
c        hi (i,j,l)  ratio of enzyme after recovery of 1-1 doses
c                    and therefore also the ratio before the 1th dose
c                    and health at beginning of the kth day
c
c   iii. for coefficients of residue equation
c        cf(n,k)  n coefficients for the k fields worked per crew
c        r(i,l)  is residue to parent (i-1) or oxon (i»no. oxon+1)
c                 for day 1
c
c     get characteristics for each individual from using either a
c     lognormal or normal random no generator (imsl routine)
c
c     get random values for wt, and ke using the normal dist.,  imsl ggmnl
c
c
      DO 500 1=1,NC
         CALL LGNM (DSEED.NM.RMP, SDP, RP)
         CALL LGNM (DSEED,NM,RMP1,SDP1,RP1)
         CALL LGNM (DSEED,NM,RMKD,SDKD,RKD)
         CALL GGNML(DSEED.NM.RWT)
         CALL GGNML(DSEED.NM.RKE)
c
c        now assign each member of field a random no.  chosen above
c
         DO 400 J « l.NM
            KD(I,J) - RKD(J)
            KE(I,J) « RKE(J)*SDKE+RMKE
            WT(I,J) - RWT(J)*SDWT+RMWT
            P (I,J) - RP (J)
            P1(I,J) - RP1(J)
  400       CONTINUE
  500    CONTINUE
c
c     for each crew member get the ratio of the percent of active
c     enzyme to the initial amount of active enzyme after nf fields
c     have been harvested.
c
c     parameters used here
c
c     h(i,j,l) fraction of depleted enzyme after 1 days (in kth field)
c        for crew i,member j, field 1 (health after 1 days in kth field)
c     hl(i,j,l) fraction of active enzyme 24 hours after the 1th dose
c        or the fraction of active to start the (l+l)th day (kth field)
c        for crew i, member j, 1 days in field k
c
c     set health for initial to 1  (i.e. hli.j.lM for all i,j)
      DO 700 I • l.NC
         DO 710 J • l.NM
            H1(I,J,1) «= 1
  710       CONTINUE
  700    CONTINUE
                   Appendix A  Page 3 of 6
451
-------
                                                               25 Jul 86


c                                                                        452
c     print table discribing events
c
      DO 1000 I i l.NC
         WRITE (LUN.1001) I
         WRITE (LUN.1010) (WT(I,J),J=1,NM)
         WRITE (LUN.1020) (KE(I,J),J=1,NM)
         WRITE (LUN.1030) (KD(I,J),J=1,NM)
         WRITE (LUN.1040) (P(I,J),J-1,NM)
         WRITE (LUN.1050) (Pl(I.J),J=1,NM)
 1001 FORMATUX ,////,
     +
     +  5X,
     +  T33, 'l',T42,
     +  TlOZ.'S'.TllZ,
 1010 FORMAT(T28,'WT«
 1020 FORMAT(T28,'KE=
 1030 FORMAT(T28,'KD=
 1040 FORMAT(T28,'  P*
 1050 FORMAT(T28,'P1«
 RESULTS OF ANALYSIS FOR CREW ',13,
 WHERE INFRACTION OF ENZYME INHIBITED1,//
 FIELD CONDITIONS',T28,'CREW MEMBERS'/
2',T52, '3',T62 ,'4',T72/ '5',T82, '6',T92,
9',T122,'10I // )
 F6.2.T41.F6.2,  8(4X,F6.2))
 F7.3.T41.F7.3,  8(3X,F7.3))
 F7.3,T41,F7.3,  8(3X,F7.3))
 F9.5.T41.F9.5,  8(1X,F9.5))
 F9.5.T41.F9.5,  8(1X,F9.5))
         DO 850 K = l.NF
            NIII = 50
            CALL LGNM (DSEED,NIII,RESSS,REGED,RAA)
            IF (RAA(l).GE.RESSS) FPER=3
            IF (RAA(l).LT.RESSS) FPER=2

            DO 777 L «= l.ND
c     calculate dose for each residue (from parent and oxon)
c     note r(mm,l) is residue for 1th day for parent(mm=l) or
c                             oxon number mm-1
c
c     determine residue for each of parent and oxons
               DO 803 MM » 1,1
                  IDDD    - L*5+MM
                  R(MM,L) = RAA(IDDD)
                  IF (L.EQ.3) R(MM,L)*R(MM,2)*EXP(-0.074)
                  IF (L.EQ.4.AND.FPER.EQ.3) R(MM,L)-R(MM,2)*EXP(-0.148)
                  IF (L.EQ.5.AND.FPER.EQ.2) R(MM,L)=R(MM,4)*EXP(-0.074)
                  IF (L.EQ.6.AND.FPER.EQ.2) R(MM,L)=R(MM,4)*EXP(-0.148)
                  IF (L.EQ.6.AND.FPER.EQ.3) R(MM,L)=R(MM,5)*EXP(-0.074)
                  IF (L.EQ.l) R(MM.L) - 0.
                  IF (L.EQ.7) R(MM,L) - 0.
  803             CONTINUE

               DO 800 J = l.NM
                  SUM = 0
                  DO 801 MM - 1,1
                     D   = (KD(I,J)*R(MM,L)*TW)/(WT(I,J)*1000.)
                     SUM - (D/LD50(MM))+SUM
  801                CONTINUE
                  DEL(I,J,L) = 1-EXP(-KE(I,J)*SUM)
                  KK(I.J.L)  = l-DEL(I.J.L)
                  H(I.J.L)   = KK(I,J,L)*H1(I,J,L)
                  P2         - l.
                   Appendix A  Page 4 of 6
-------
                                                               25 Jul 86
                  P3
                  Rl
                  R2
                  TOP
   800
                  H(I,J.L)
                  HE(J)
                  CONTINUE
                               H(I,J,L)*P2
                               P1(I,J)*DEL(I,J,L)
                               R1+R2+P(I,J)
                               TOP/P3
                               l.-H(I.J.L)
                               H1(I,J,L-H)
                                                                          453
   802
               SUMH   - 0.
               SUMH2  = 0.
               DO 802 J - l.NM
                  SUMH  - SUMH+H(I,J,L)
                  SUMH2 « SUMH2+H(I,J,L)*H(I,J,L)
                  CONTINUE
               HM(I,L)  - SUMH/NM
               HSD(I.L) = ((SUMH2-HM(I,L)*SUMH)/(NM-1))**0.5
               WRITE (LUN,1060)K,L,R(1,L),(H(I,J,L),J=1,NM).
                               HM(I,L),HSD(I,L)
  1060
  777
               FORMATUX, 'W ,13, 'D1,13,Til, 'RESIDUE-1 ,F8.3,T28, 'H=',
                      F6.4,T37,F6.4,12(1X,F6.4))
               CONTINUE
            DO 888 J « l.NM
               H1(I,J,1) - HE(J)
  888          CONTINUE
  850       CONTINUE
 1000    CONTINUE
      END

      SUBROUTINE LGNM(DSEED,NF,A,B,X)
      DOUBLE PRECISION DSEED
      DIMENSION X(301)
      S  - ALOG(B)
      XM « ALOG(A)
      CALL GGNLG(DSEED,NF,XM,S,X)
      RETURN
      END

//GO.FT04F001 DD DSN=WYL.CMD.SIM.AAA,UNIT=DISK,VOL*SER=WYLB02,
// DCB=(RECFM=FB,LRECL«133,BLKSIZE«6118),
// SPACE=(TRK,(27,2)),DISP=(NEW,CATLG)
//GO.FT01F001 DD *
                       0.15000   1.01000
                       6.00000   1.30000
 0.0089286
   5.10000
  70.00000
026
018
012
   7.00000
   8.00000
  10.00000
007
 119.00000
3857100000
 1.0457
1.41000
7.00000
             2.00000
                   Appendix A  Page 5 of 6
-------
Appendix B: Listing of the simulation program run on the IBM-PC with       454
            Microsoft Fortran compiler.  Lines following the last "end" are
            a separate input file called "simlib" .

      program simS
c     unified field model simulation program , "version 5.2" [23 Jul 86]
c     comprising submodels 2-3 with variability in the H measurement added
c        submodel 1: R   * Ro exp(-Kr T)
c        submodel 2:D   = R Kd t / m
c        submodel 3: del « 1 - exp(Ke sum(D / LD50))
c     therefore      R   « -LD50 ln(l - del) m / Kd Ke t
c     This version groups and prints (with no option) all crews by day to
c        facilitate operation of summary program pp5, and staggers the working
c        pattern experienced by each crew.

c  batch file input for this simulation program in a file to-be-named:
c
c  i. input the field residue conditions: mean daily inhibition,
c     geometric deviation of that residue, and short-term decay rate:
c      1. percent daily inhibition "delbar"                            (flO.2)
c      2. geometric deviation of the residue "gdres"                   (flO.2)
c      3. half-life [days] of residue during harvest "half"            (flO.2)
c      4. AChE measurement (interday intraperson) variability          (flO.2)
c  ii. means and sd of worker parameters:
c      5. gmp, regeneration of rbc and their enzymes, usually 1-2%
c         gmpp, reversion of rbc, usually 0-15%:
c         "gfflpV'gdpV'gmppV'gdpp"                                   (AflO.5)
c      6. kd dose coefficient, ke enzyme coefficient:
c         "gmkd","gdkd","gmke","gdke"                                 (4fl0.5)
c      7. arithmetic mean and standard deviation of mass of worker:
c         "rmwtV'sdwt"                                               (2fl0.5)
c  iii. parameters determing field exposure
c      8. nw    = number of weeks worked per crew:                        (13)
c      9. nc    • number of crews to be placed in fields:                 (i3)
c     10. nm    = number of members per crew:                           (2013)
c     11. dpa   = time (days) of initial re-entry into
c                 each field after (post) spraying:                       (13)
c     12. hwpd  = time (hours) worked per day :                           (13)
c     13. nd    = number of days to complete each field
c                 (a repeatable pattern of from 1-10 values
c                  ending in 0; any negative values are accepted
c                  as specifying days with no additional exposure):     (1013)
c     14. Id50  = LD50 for parent (and each of up to 3 oxons):        (4fl0.5)
c  iv.  other input variables
c     14. iseed = three integer values (1-2147483647)
c                 to be used to generate random numbers:
c  to input and output data to and from this program:
c     1) the program will ask the user the name of the file which has
c        data for input (assigned as file 1);
c     2) the program asks the user if output is to be written on the
c        termianal or to be saved as an output file;
c     3) the file number and name are then given by the user.
c        If user enters 0 then the terminal receives the output;
c        If  "     "    1 and a file name, health only output is saved  f6.4


                   Appendix B  page 1 of 10
-------
                                                              23 Jul 86

                                                                           455
         If  "     '     2 and a file name, more complete output is saved f5.3

      character*14 input, output
      integer      iseed(3),nd(10),dpa,hwpd,filel,file2,
                   ipat(18),ires(18),m(18),noff(18)
      real         res(2000),    r(4),ld50(4),
                    p(18,12),pp(18,12),wt(18,12),kd(18,12),ke(18,12),
                   rp(216), rpp(216), rwt(216), rkd(216),  rke(216),
                   kk(18,12),
      filel  « 1
      file2  - 2
c     indicate the name of the file to find input data
      write  (*,101)
  101 format ( ' Enter name of input file :   '  )
      read   (*,102) input
  102 format (a)
c     indicate the name of the file to receive the worker health output.
*     write  (*,103)
* 103 formatC Enter either 0 for the output to return to the terminal1,
*    +  /,8x,' or 1 if health status  only  is to be saved as a file1,
*    +  /,8x,' or 2 if more complete output is to be saved as a file:1)
*     read   (*,104) lun
      lun    - 1
  104 format (il)
      if     (lun .eq. 0) go to 106
      write  (*,105)
  105 format (' Enter the name of the health status output file :  '  )
      read   (*,102) output
      open   (file2,file»output, status* 'new1 )
      rewind  file2
  106 open   (filel, file=input)
      rewind  filel

c     read average daily inhibition, its variability, and
c     the within field residue decay rate (half life);
c     followed by other means and deviations
c     (geometric for lognormal distributions p, pp, kd, ke, and (arith) wt);
c     and finally other input parameters describing field.
      nr     « 1
      mnp    • 10
      read  (1,10) delbar,gdres,half , gdlab
      read  (1,10) gmp,   gdp,  gmpp, gdpp
      read  (1,10) gmkd, gdkd,  gmke, gdke
      read  (1,10) rmwt, sdwt
      read  (1,25) nw
      read  (1,25) nc
      read  (1,30) nm
      read  (1,25) dpa
      read  (1,25) hwpd
      read  (1,30) nd
      read  (1,10) (Id50(nres),nres=l,nr)
      read  (1.45) iseed
   10 fonnat(4f!0.5)
   25 format(i3)
                   Appendix B  page 2 of 10
-------
   30 fonnat(20i3)
   40 fonnat(flO.O)
   45 format(4ilO)

      npp   = 0  '
      ndinp = 0
      do 50 i « l,mnp
         ndinp  * ndinp + abs(nd(i))
         if     (nd(i) .gt. 0) npp * npp + 1
         if     (nd(i) .eq. 0) go to 51
   50    continue
   51 np    = i-1
      nf    = nc * (npp * (nw * 5) / ndinp)
      npop  = nm * nc
*     nlab  = npop * (nw * 7)
      gmres = -Id50(l)*alog(l.-(delbar/100.))*nnwt / (gmke*gmkd*hwpd)
      if    (gdlab .le. 1.0) gdlab -1.0
      do 53 i - l.nc
         ipat(i) - mod(i.np) + 1
         ires(i) = i
         m(i)    = 1
         noff(i) * 0
   53    continue
c     internal variables defined above:
c     mnp   = maximum number of "within field harvest" patterns.
c     nd    - (array) repeatable pattern of the number of days to complete
c             each field.  Note nd<0 means to work in field with no anti-AChE
c             residues; weekends (iday = 6 and 7) are also no-exposure days.
c     np    = number of patterns specified by user.
c     npp   = number of positive pattterns (fields with residues) per  cycle.
c     ndinp = total number of working days within all patterns.
c     nf    = total number of fields required to be simulated.
c     npop  - total size of population (cohort) required to be simulated.
c     ires  = (array) residue (or field) counter stepping res() up to  nf.
c     nlab  = number of simulated laboratory measurements (assumed to  be each
c             day of season) and associated measurement variations.
c     noff  * (array) number of weekend days (off) during which residue decays
c     gmres = geom. mean residue corresponding to the mean delta AChE  specified

c     get characteristics for each individual in population and
c     field in the entire season by using
c     either a lognormal or normal random number generator (subroutine) :
  500 write (*,*) 'Creating body weights'
      call   nml  (iseed,npop,rwt,rmwt,sdwt)
      write (*,*) 'Creating dosing coefficients'
      call  Ignml (iseed,npop,rkd,gmkd,gdkd)
      write (*,*) 'Creating enzyme coefficients'
      call  Ignml (iseed,npop,rke,gmke,gdke)
      write (*,*) 'Creating RBC regeneration rates'
      call  Ignml (iseed,npop,rp ,gmp ,gdp )
      write (*,*) 'Creating RBC reversion rates'
      call  Ignml (iseed,npop,rpp,gmpp,gdpp)
      write (*,*) 'Creating residues'
      call  Ignml (iseed.nf,res, gmres,gdres)
                   Appendix B  page 3 of 10
-------
                                                              23 Jul 86


      if    (lun .eq. 0) go to 600                                     457
      write (*,*) 'Beginning simulation:'                               < ------
      write (*,*) 'Day     of     Crew     Residue      of
c
c  notation and documentation of subsequent arrays
c    for crew i, member j (day k in season, day m(i) in field)
c     P  (i»j) " per cent regeneration / 100.
c     pp (i,j) « per cent reversion  / 100.
c     rkd(i,j) » dosing coefficient
c     rke(i,j) = enzyme response coefficient
c     wt (i,j) = body weight (mass)
c
c     kk (i,j) « enzyme activity after any one dose • 1- del(j),
c                relative to pre-dose activity
c     h  (i,j) * health (enzyme activity remaining after the kth dose
c                relative to initial activity)
c     hi (i,j) « relative enzyme activity after 24 -hr recovery of kth dose
c                and therefore also the pre-dose activity before the k+lth
c                dose
  600 CONTINUE
      k    - 0
      do 1000 iwk - l.nw                                                WEEKS

c        A "pattern" do loop could not be constrained either internal to
c        or external from the week loop (used to define weekends off).
c        Thus, each new ipat is set prior to line 870 to another field
c        (ires) whose initial reentry residue (day m-1 in the field) is
c        selected from the random array of nf residues created earlier
c        but which also undergoes decay after each (m) harvest-working
c        day in that field plus any intervening (noff ) days off at the
c        half-life specified until the harvest has been completed
c        (m » number of days in nd(ipat(i))).

         do 880 iday « 1,7                                              DAYS
            k  • k + 1
            do 870 i - l,nc                                             CREWS
               if ((iday .eq. 6). or. (iday .eq. 7)) noff(i) » noff(i) + 1
*              if (lun .eq. 0) go to 630
               write (*,1065) k,nw*7,i.ires(i),nf
  630          do 800 j - l,nm                                          MEMBERS
                  if ((iwk  .ne. 1) .or. (iday .ne. 1)) go to 700
                        ) = rwt(((i-l)*nm)+j)
                        ) « rkd(((i-l)*nm)+j)
                        ) = rke(((i-l)*nm)+j)
                  P (i.j) »  rp(((i-l)*nm)+j)
                  pp(i,j) • rpp(((i-l)*nm)+j)
                  set initial health to 1  (i.e. hl(i,j) « 1 for each j)
  700             sum     * 0.0
                  if      ((iday .eq. 6) .or. (iday .eq. 7)) go to 702
                  if      (nd(ipat(i)) .It. 0) go to 702
                  r(l)    = res(ires(i)) / (2.**((m(i)+noff (i)-
                  do 701 mm * l,nr                                      CHEMRES
                     d       - (kd(i,j)*r(mm)*hwpd) /
                     sum     * (d/ld50(mm)) + sum
                   Appendix B  page 4 of 10
-------
  701                continue
  702             kk(i,j) « exp(-ke(i,j)*sum)
                  p2
                  p3
                                     I p3
c                 process validation print test
*                 write (*,739) k,i,j,m(i),nd(ipat(i))
*    6            ,ipat(i),noff(i),ires(i),res(ires(i))
*    6            ,r(l),d,sum,kk(i,j),h(i,j),hl(i,j)
*    6            ,p2,p3,p(i,j),pp(i,j),wt(i,j)
*    6            ,kd(i,j),ke(i,j)
*739              format(/lx
*    6            , 'day, cr//, mem//, m,nd(i),i,,noff, ires -' ,/lx,8i3,/lx
*    6            ,'res,r(l),d,sum,kk,h,hl «'       ,/lx,7f 11.7,/lx
*    6            ,'p2,p3,p,pp,wt,kd,ke «'          ,/lx,7fll.7)
c                 end print test

c                 an expected level of laboratory AChE measurement variability
c                 has been integrated into version 4 and later updates, which
c                 affects output via "h" but not the actual health via "hi".
                  vlab    * gauinv(random(iseed),ifault)
                  if     (ifault .eq. 1) stop 'ifault error in lab var. '
                  h(i,j)  - 1. - (h(i,j)*(gdlab**vlab))
  800             continue
* 820          if    (lun .eq. 1) go to 821
*              write (lun,  1060) i,iwk,iday,r(l),(h(j), j-l,nm)
*              go to 830
  821          write (file2,1061) i,iwk,iday,r(l),(h(i, j),j-l,nm)
  830          if    ((iday .eq. 6) .or. (iday .eq. 7)) go to 870
               m(i)    • m(i) + 1
               if    (m(i) .le. abs(nd(ipat(i))))       go to 870
               ipat(i) = ipat(i) + 1
               if    (nd(ipat(i)) .It. 0)               go to 870
               if    ((ires(i)+nc) .le. nf) ires(i) = ires(i) + nc
               m(i)    « 1
               noff(i) - 0
               if    (ipat(i) .gt. np) ipat(i) - 1
  870          continue
  880       continue
 1000    continue
c     print residue and toxicologic means and deviations
c     followed by other parameters describing field
      write (file2,1054) rmwt,sdwt,gmkd,gdkd,gmke,gdke,
     +                   gmp , gdp , gmpp , gdpp , delbar , gdlab , gmres , gdres
      write (file2,1055) nm,nc,npop,nw,nf .nd.hwpd.half ,(ld50(i),i=l,3),
     +                   iseed
c     print individual crew descriptors unless the condensed
c     output file (lun=l) was requested.
      if (lun .eq. 1)  go to 2000
      do 1100 i • l,nc
         write (file2,1001) i, ( j , j=l,nm)
         write (file2,1010) (wt(i, j) ,j=l,nm)
         write (file2,1020) (ke(i,j), j=l,nm)


                   Appendix B  page 5 of 10
-------
                                                              23 Jul 86


         write (file2,1030) (kd(i,j),j-l,nm)
         write (file2,1040) ( p(i,j),j=l,nm)
         write (file2,1050) (pp(i,j),j=l,nm)
 1100 continue
 2000 stop ' Normal termination of Sim5'

c --- THE LAST PORTION OF THIS PROGRAM SPACE IS RESERVED FOR FORMAT STATEMENTS
 1054 format(lx,/,lx,' Means and deviations given on input1/
     +   7x,'for normally distributed variables:1/
     +  lOx,'workers weight1,                 t45,2f!0.5/
     +   7x,'for lognormally distributed variables:  '    /
     +  lOx,'dosing coefficient kd1,          t45,2f!0.5/
     +  lOx,1enzyme coefficient ke1,          t45,2f!0.5/
     +  1Ox,'regeneration, p1,                t45,2f!0.5/
     +  lOx,1reversion, pp1,                  t45,2f!0.5/
     +  lOx,'mean daily inhibition, X1,       t45,lfl0.5/
     +  lOx,'variability in AChE measurement1,t55,If10.5/
     +  lOx,'reentry residue, ug/cm2',        t45,2f!0.5)
 1055 formatdx,1 Other parameters given on input:',    /
     +  lOx,'number of members per crew ',    t46,i3    /
     +  lOx,'number of crews',                t46,i3    /
     +  lOx,'total size of population    ',   t46,i3    /
     +  10x,'no. of weeks worked per crew',   t46,i3    /
     +  lOx,'total no. of fields sprayed1,    t39,ilO    /
     4-  lOx.'no. of days to complete field',  t46,10i3  /
     +  lOx.'no. of hours worked per day1,    t46,i3    /
     +  lOx,'subsequent half-life, days',     t45, flO.5/
     +  10x,'LD50 for OP pesticide(s)',       t45,3f!0.5/
     +  lOx,'input values for iseed',         t39,3ilO  )
 1001 formatdx ,//lx,
     +'Fraction Acetyl Cholinesterase inhibition for crew *,i3,
     +/lx,'Field conditions:',t22,'Crew members:',
     +/,t22,36(i2,3x)
1010 formate tlS.'wt*
1020 formate t!8,'ke-
1030 formate t!8,'kd=
1040 formate t!8,'p «
1050 formate t!8,'pp«
1060 formate'C',i2,'W
1061 formate'C',i2,'W
                           36ef5.1,0x))
                           36ef5.2,0x))
                           36(f5.2,0x))
                           36ef5.4,0x))
                           36ef5.4,0x))
                                            f6.4,tl9,'H«',14ef5.3,Ox))
                                            f6.4,tl9,'H-',14ef6.4,lx))
 1065 formatei+Day'ti4,' of',i4,' Crew'.iS,1  Residue',i5,'  of',i5)
      end
^^^ — ^ — — — — — — — — — — — «•• • • • • V • V • V •• V * V • V • V • V • •*• •
      subroutine nml (iseed,nx,x,a,b)
      dimension  iseed(3),x(2000)
      s    - b
      xm   -a
      call mcarloeiseed,nx,x,xm,s)
      return
      end
*                         ••••••
      subroutine Ignml (iseed,nx,x,a,b)
      dimension  iseed(3),x(2000)
      s    * alog(b)
      xm   = alog(a)
                   Appendix B  page 6 of 10
-------
      call mcarlo(iseed,nx,x,xm,s)
      do 1 i = l.nx
         x(i) = exp(x(i))
    1    continue.
      return
      end
C [[[
      subroutine mcarlo (iseed,nx,x,a,b)
c     creates a random sequence of 'nx1 values of 'x* which taken as a whole
c     are EXACTLY normally distributed between 0 and 1 in steps of 1/nx
c     (see line 001 taken from U.S. HEW (NIOSH) Publ. //77-173)
      real      x(2000),  y(2000)
      integer   iseed(3),iy(2000)

      do 10 i - l,nx
         y(i) - random(iseed)
   10    continue
      call    rankc (nx,y,iy)
      do 20 i = l,nx
  001    p    = (float(i) - 0.5) / nx
         y(i) = gauinv(p.ifault)
         if    (ifault .eq. 0) go to 20
         write (*,1001)  i.a.b
         stop 'Due to fault error in INVGAU from MCARLO1
   20    continue
      do 30 i = l.nx
         x(i) - a + (b*y(iy(i)))
   30    continue
      return
 1001 format  (' A fault error resulted when processing the ',15,
     +     'th value with mean of ',gl2.4,' and deviation of ',g!2.4)
 1002 format  (' A no-match error resulted when searching for "min"1
     +         ,/,' of ',15,' values with mean of ',g!2.4,
     +            ' and deviation of ',gl2.4)
      end
C [[[
      FUNCTION RANDOM(iseed)
*      FUNCTION RANDOM(IX,IY,IZ)

C     ALGORITHM AS 183 modified FROM B.A. WICHMANN and I.D. HILL
C     An Efficient and Portable Pseudo-random Number Generator.
C     APPL. STATIST. 31:188-190, 1982.
C     incorporating the amendment proposed by A.I. McLeod
C     APPL. STATIST. 34:198-200, 1985.

C     On input IX.IY.IZ are seed integers between 1 and 30,000
C     RETURNS A PSEUDO -RANDOM NUMBER RECTANGULARLY DISTRIBUTED
C     BETWEEN 0 AND 1.

-------
  -                                                           ^J JUl 86


       iz *  iseed(3)

       IX =  171 * MOD(IX,177)  -  2*(IX/177)
       IY -  172 * MOD(IY,176)  - 35*(IY/176)
       IZ -  170 * MOD(IZ,178)  - 63*(IZ/178)

       IF (IX  .LT. 0) IX - IX  + 30269
       IF (IY  .LT. 0) IY - IY  + 30307
       IF (IZ  .LT. 0) IZ - IZ  + 30323

C      IF INTEGER ARITHMATIC UP OT 5212632 IS AVAILABLE,
C      THE PRECEDING 6 STATEMENTS MAY BE REPLACED BY

*      IX -  MODU71 * IX, 30269)
*      IY -  MOD(172 * IY, 30307)
*      IZ -  MOD(170 * IZ, 30323)

C      ON SOME MACHINES THIS MAY SLIGHTLY INCREASE THE SPEED,
C      THE RESULTS WILL BE IDENTICAL.

       iseed(l)=ix
       iseed(2)=iy
       iseed(3)=iz
       RANDOM  = AMOD(FLOAT(IX)/30269.+FLOAT(IY)/30307.+FLOAT(IZ)/30323.,
     +             1.0)
C      McLeod  amendment follows:
       IF (RANDOM.GT.0.0) RETURN
       RANDOM  * DMOD(DBLE(FLOAT(IX))/30269.DO + DBLE(FLOAT(IY))/30307.DO
     +             + DBLE(FLOAT(IZ))/30323.0DO,1.0DO)
       IF (RANDOM.GE.1.0) RANDOM - 0.999999
       RETURN
       END
C	
       FUNCTION GAUINV (P, IFAULT)

C      ALGORITHM modified FROM R.E. ODEH and J.O. EVANS
C      The Percentage Points of the Normal Distribution. AS 70
C      APPL. STATIST. 23:96-97. 1974.

C      Note  the additional following reference provides a polynomial
C           for (0.8 .le. p .It. infinity) if needed:
C      M.E.  Tarter:
C      Inverse Cumulative Approximation and Applications.
C      Biometrika 55:29-41, 1968.
C      See also AS 111, APPL.  STATIST. 26:118-121, 1977 for an alternative form.

C     AS 70 was originally called GAUINV:
C      GAUINV  FINDS PERCENTAGE POINTS OF THE NORMAL DISTRIBUTION
C       P on  input is the lower tail area p
C       IFAULT is a fault indicator :
C         ?' - 1 and gauinv «  0 if (E-20 .le. p .le. 1.- E-20)
C             (ERROR: the requrested x exceeds the outer limits of p
C                     for which the inverse is accurate to within E-7)
C         " « 0 and gauinv *  0 if (p .eq. 0.5)
C             (the true value but not calculable from the polynomial)


                   Appendix B page 8 of 10
-------
                                                                        462
          " = 0 in all other cases.

      DATA            PO,   PI,             P2,                 P3
     +   /-.322232431088, -1.0, -.342242088547,  -.204231210245E-1/
      DATA       "        P*,               QO,             Ql
     +   /-.453642210148E-4, .993484626060E-1,  .588581570495/
      DATA            Q2,            Q3,               Q4
     +   I .531103462366, .103537752850,  .38560700634E-2/
      IFAULT - 1
      GAUINV =0.0
      PS     = P
      IF     (P .GT. 0.5)      PS - 1.  - PS
      IF     (PS .LT. l.OE-20) RETURN
      IFAULT - 0
      IF     (PS .EQ. 0.5)     RETURN
      YI     • SQRT(ALOG(1./(PS*PS)))
      GAUINV • YI + ((((YI*P4+P3) *YI+P2) *YI+P1)  *YI+PO)
     f            / ((((YI*Q4+Q3) *YI+Q2) *YI+Q1)  *YI+QO)
      IF     (P .LT. 0.5)      GAUINV - -GAUINV
      RETURN
      END

      subroutine rankc (nx,x,ix)
      dimension x(2000),ix(2000)

      do 1 i = l,nx
         ix(i) - 0
         continue
      do 3 j = l,nx
         ilast - 2000
         xmin  - 10.**30
         do 2 i * l,nx
            if ((x(i) .It. xmin) .and.  (ix(i)  .eq.  0))  then
            ix(ilast) « 0
            xmin      * x(i)
            ilast
            end if
    2       continue
    3    continue
      return
      end
Input file, typically called simlib:
      4.00      3.00      7.00      1.0 >delbar,gdres,half,gdlab
  .0089286    1.0457       .15      1.01>gmp (regen.),gdp,gmpp (revers.),gdpp
      5.10      1.41      6.00      1.30>kd,gdkd,ke,gdke
     70.        7.                      >wt,sdwt
 26>nw eeks
 18>nc rews
 12                           >nm embers per crew
 14>dpa (days post-application)
                   Appendix B  page 9 of 10
-------
                                                          23 Jul 86

                                                                      463
8>hwpd (hours worked per day)
2-3  3-2                 >nd (pattern of number of days in each field)
   10.00                             >LD50
   25784       985     12919          Mseed
                 Appendix B  page 10 of 10
-------
                                                                           464
Appendix C: Listing of the daily health status analysis and summary program
            written to be run on an IBM-PC with Microsoft Fortran compiler.
            Lines following last "end"  are an external input file typically
            called "pplib" .
C
C
C
C
C
C
C
C

C
*
*
*
ft
*
*
ft
ft
ft
ft
ft
ft
*
ft
ft
*
ft
ft
ft
A
ft
ft
ft
ft
*
ft
ft
ft
ft
ft
ft
ft
ft
ft
ft
*
*
Program PP2
Phasell Program 2, Version 5
to read simulation (SIMS) output option //I files;
  (1) scan daily through all crews for "illness" using four
      threshold criteria as specified in a separate "pplib" file,
  (2) tabulate and calculate daily summary health statististics, and
  (3) conduct analysis of variance among crews for health status
      in both arithmatic and geometric modes, optional to do
      every day or only on each clinic day of each week (see inclin)

Defined variable list follows:
CPOI   counter for poisoning cases within day (see also fpoi and inpoi)
DEL    delta AChE
F      F test statistic for arithmatic distribution
FILEO  assigned file: keyboard and screen
FILE1     "      "  : input data
FILE2     "      "  : output of analysis of variance
FILE3     "      "  : input library of run parameters (pplib)
FILE4  unused
FPOI   counter for fields in which poisoning cases occurred
G      F test statistic for geometric distribution
GD     geometric deviation for group
GM     geometric mean for group
GX     log-transformed values of x
H      health (enzyme activity) = 1-x
I      index for days
IDAY   day of week within input sequence
II     "del" counter
IWK    week within input sequence
INCLIN (from pplib) day of the week crews are in clinic for "h" samples
INPOI  counter for incidents of poisoning (defined as .gt. 1/2 of crew)
INPUT  character name of input file
J      index for crew
K      index for person within crew
LINE   line of narrative text; a maximum of 79 characters
M      index for running cumulative days, equivalent to nthres
Nl     degrees of freedom for numerator   of arithmatic F
N2     degrees of freedom for denominator of arithmatic F
N3     degrees of freedom for numerator   of geometric  F
N4     degrees of freedom for denominator of geometric  F
NCREW  crew number as listed within data
NCREWS (from pplib) number of crews included within the study design
       (from pplib) day of the programmed "week" exposure starts
       total number of days in study = nwks*7
       overall number of geometric  data points
NDAYO
NDAYS
NG
NLINE1 (from pplib) number of narrative lines to be copied at end
        of input file
NLINE2 (from pplib) number of narrative lines to be skipped at end
        of input file
NN     overall number of arithmatic data points
                Appendix C  page 1 of 7
-------
                                                              23 Jul 86
*
ft
ft
ft
ft
*
*
*
*
ft
*
*
*
ft
ft
*
*
ft
*
*
ft
ft
ft
NPERC
NTHRES
NWKS
NX
0AM
OGM
OUTP2
PDAY
PGD
PINC
PSD
RES
S2SG
S2SX
SD
SIMAN
SSG
SSG2
SSX
SSX2
TCPOI
THRES

X
XM
(from pplib) number of people per crew
(from pplib)
(from pplib) number of weeks in study
                                                                          465
overall arithmatic mean
overall geometric  mean
character name of anova output file » file2
integer number of previous days within study (max=nthres)
pooled geometric deviation
counter for poisoning cases within a crew-day (see also cpoi)
pooled arithmatic deviation
residue for each exposure day (not manipulated)
(see anov)
(see anov)
standard deviation for group (array)    >
external library of program run parameters
(see anov)
(see anov)
(see anov)
(see anov)
counter for seasonal total cases of poisoning within each  category
(from pplib) quantitative poisoning criteria for 1-AChE (see
 "Parameters from pplib" description below)
level of inhibition (read from input)
arithmatic mean for group (array)
      character*14 input,outp2
      character*79 line
      dimension
      dimension
      integer
      integer
      logical
             h(4,18,12),x(12).gx(12),thres(4)
             xm(18), sd(18),gm(18), gd(18)
             fileO,filel,file2,file3,day,pday
             cpoi(4),fpoi(4),inpoi(4),tcpoi(4),pinc(4)
             negx
C»>»   INPUT FORMAT FROM SIMS option 1
 1000 format (a79)
 1001 format (4x,i2,lx,il, 3x,f6.4,3x,14(f6.4,lx))
*     +      ,(/,20x,14(f6.4,lx)),(/.20x,14(f6.4,lx)))
C 1W.1D1 R« .1986 H- .0817  .0781 -.0195  .0429  .2360
                                                  .1964  .1505  .0969  .304
      fileO
      filel
      file2
      file3
      file4
        0
        1
        2
        3
        4
                                                           user
                                                           input
                                                           output
                                                           pplib
                                                           unused
C.... ask for and read the name of input and output files.
  100 continue
      write  (fileO.1005)
      read   (filed,1004) input
      if     ((input .eq. 'quit').or.(input .eq. 'exit').or.
     +       (input .eq. 'stop').or.(input .eq. 'end')) go to 999
      write  (fileO,1007)
      read   (fileO,1004) outp2
                Appendix C  page 2 of 7
-------
      open   (filel, file=input)
      rewind  filel
      open   (file2, file=outp2, status='new')
      rewind  file2
      open   (file3, file-'pplib')
      rewind  file3
      write  (fileO,1009) input,outp2

C	 READ the operating PARAMETERS FROM PPLIB (file3)
C     ncrews • number of crews included within the study design;
C     nperc  = number of people per crew;
C     nwks   « total number of weeks expected to be read from "input";
C     ndayO  « day of the week exposure starts, assuming 1 equals Monday;
C     inclin » day of the week blood samples are taken in the clinic (0=all);
C     thres  - criteria for 1-AChE ( or x(k) within this program) at
C              which person i considered "overexposed".  Criteria are
C              initially set based on    Daily Inhibition
C                    Simple      day 1  .5     .4     .35
C                    Cumulative  day 2  .75    .64    .58
C                    Effect      day 3  .875   .78    .725
C              therefore, respective values of thres in array
C              (1) a single daily inhibition, x .ge. 0.5
C              (2) a 2-day cumulative  "    , x .ge. 0.65
C              (3) a 3-day     "       "    , x .ge. 0.75
C              (A) a cumulative reduction in h below 0.5
C               m subscript used
C               to store h      and    to calculate del h
C               1: today's h              single daily inhibition (h2-hl/h2),
C               2: yesterday's h          2-day cumulative  "     (h3-hl/h3),
C               3: two day's ago h        3-day     "       "     (h4-hl/h4),
C               4: three day's ago h      cumulative reduction in h
C     nlinel = number of narrative lines to be copied at end of file (input)
C     nlineZ = number of crew narrative lines to be skipped

      read   (file3,3001) ncrews,nperc,nwks,ndayO,inclin,nthres,thres
     +                   ,nlinel,nline2,nline3

C     type input parameters, output table, and screen headers
      write  (file2,3001) ncrews,nperc,nwks,ndayO,inclin,nthres,thres
     +                   ,nlinel,nline2,nline3
      write (file2,2002) 'Wk','Day1,'0AM1,'PSD','F','nl1,'n2','OGM',
     +                   'PGD','G','n3','n4','pol','po2','po3','po4'
      write (file2,2002) •--•,• —•.• — •, • —', •-•, •„•, t__. f ,___i §
     J.                   t___l l_l l__l l__l •___! I	I I   II   I
     T                        »•»»»»   ~ ,
      write (*,*) 'now on crew//    and day     of

C	 set health array (m,j,k) equal to 1.0 (each individual's "normal")
C     subscript i = day within study (season)            limit : ndays = nwks*7
C               j - crew                                         ncrews
C               k = person within crew                           nperc
C               m = running day (1 = today, 2 = yesterday, etc)  nthres
      do 152 j = 1,ncrews
         do 151 k = 1,nperc
            do 150 i « 1,nthres
               h(i,j,k) = 1.0


                Appendix C  page 3 of 7
-------
                                                              23  Jul  86

                                                                        467
  150          continue
  151       continue
  152    continue
      do 153 m « .1 .nthres
         fpoi(m)  - 0
         tcpoi(m) • 0
         inpoi(m) - 0
  153    continue

C - - Begin daily analysis of all crews: ----------------
      iday     - 7
      ndays    • nwks * iday
      do 450 i - 1,ndays                                               DAY >
         negx  » .false.
         iday  • iday + 1
         if    (iday .gt. 7) iday - 1
C        zero the poisoning cases for that day counter
  210    do 215 m » 1,nthres
            cpoi(m)  « 0
  215       continue
C        initialize anova arrays if anova is to be run for the day
         if    ((iday .ne. inclin)  .and. (inclin .ne. 0)) goto 220
         call  anovO (ssx,ssx2,s2sx,nn,ngpn)
         call  anovO (ssg,ssg2,s2sg,ng,ngpg)
C        day i for crew "j" read "k»l,nperc" daily health values
  220    do 300 j * l.ncrews                                           CREW >
            write (*,1111) j,i,ndays
  222       read  (filel,1001,end=501) iwk,iday,res,(x(k),k«l,nperc)
*           write (*,    1001        ) iwk,iday,res,(x(k),k=l,nperc)
            do 223 m « l.nthres
               pinc(m) * 0
  223          continue
C           talley cases in 'Poisonings IN Crew* counter.
            do 6 k = l.nperc                                           MEMB.>
               if  (negx)     goto 1
               if  (x(k) .le. 0.0) then
                   negx = .true.
                   goto 1
                   endif
               gx(k)  = alog(x(k))
    1          do 2 m * l,nthres-l
                  h(nthres-m+l,j,k) « h(nthres-m,j,k)
    2             continue
    3          h(l,j,k) - 1. - x(k)
               do 4 m • l,nthres-l
                  del « (h(mfl,j,k)-h(l,j,k))/h(m+l,j,k)
                  if (del .gt. thres(m)) pinc(m) « pinc(m)+l
    4             continue
    5          del -    (1. -     h(l,j,k))/l.
               if (del  .ge. thres(nthres)) pinc(nthres) » pinc(nthres)+l
    6          continue                                                 
-------
                                                                 Jui oo
            do 7 m = l.nthres
               cpoi(m) * cpoi(m) + pinc(m)
               if (pinc(m) .gt. 0) fpoi(m) • fpoi(m) + 1
               if (float(pinc(m))/nperc .ge. .5) inpoi(m) - inpoi(m) + 1
    7          continue
C           if desired (inclin .ne. 0) anova may be run only on clinic days
            if ((iday .ne. inclin) .and. (inclin .ne. 0)) goto 300
            call  anovl (x, nperc,xm,sd,ssx,ssx2,s2sx,nn,ngpn)
            if    (negx) goto 300
            call  anovl (gx,nperc,gm,gd,ssg,ssg2,s2sg,ng,ngpg)
  300       continue                                                    
-------
                                                              23 Jul 86


 2003 format (13,i2,lx, 2(2x,2f8.5,2x,f5.2,i3,i4), 2x,4i4)
 2004 format (i3,12,lx, I(2x,2f8.5,2x,f5.2,i3.i4),34x,4i4)
 2005 format (13,i2,lx, 2(32x                   ), 2x,4i4)
 2006 format (lx///,39x,'Total number of poisoning cases: '. 3i4,/,
     +              36x,'Days on which a poisoning occurred: ',3i4,/,
     +              38x,'Potentially reportable incidents: *,3i4)
 3001 format (6i4,4f4.2,3i4)
      end
c	
      subroutine anovO ( ssx,ssx2,s2sx,nn,ngp)
c     sx   » sum of x within group
c     sx2  « sum of x**2 within group
c     ssx  * sum of sums of x within group
c     ssx2 » sum of sums of x**2 within group
c     s2sx *
c     nn   • cumulative number of observations in groups l,ngp
c     ngp  * cumulative number of groups
      ssx  - 0.
      ssx2 » 0.
      s2sx - 0.
      nn   - 0
      ngp  « 0
      return
      end
C	
      subroutine anovl (x,nx,xm,sd,ssx,ssx2,s2sx,nn,ngp)
      dimension  x(nx),xm(ngp),sd(ngp)
c     x (i)= array of x values within group ngp
c     nx   = number of observations within group j
c     xm(j)= mean within group j
c     sd(j)« std. dev. within group j
c     sx   * sum of x within group
c     sx2  = sum of x**2 within group
c     ssx  * sum of sums of x within group
c     ssx2 = sum of sums of x**2 within group
c     s2sx =
c     nn   * cumulative number of observations in groups l.ngp
c     ngp  = cumulative number of groups
cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc
c                                                          c
c  Note :  it is possible for nx (no. of prsons) to equal  c
c          zero, which in turn would cause division by     c
c          zero in mean and s2sx variables.  If there is   c
c          such a possibility, if could be fixed with an   c
c          "IF...THEN"  statement.                         c
c                                                          c
cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc
c
      sx   - 0.
      sx2  * 0.
      do 1   i • 1, nx
         sx    = sx+x(i)
         sx2   - sx2+(x(i)**2)
    1    continue
      nn   • nn+nx
                Appendix C  page 6 of 7
-------
      ngp   = ngp + 1
      fn   - float(nx)                                                 47Q
      ssx   « ssx+sx
      ssx2  * ssx2+sx2
      s2sx  - s2sx+((sx**2)/fn)
      xm(ngp)* sx/fn
      sd(ngp)» sqrt((sx2-((sx**2)/fn))/(fn-l.))
     2 return
      end
C	
      subroutine anov2  (ssx,ssx2,s2sx,nn,k,oam,psd,f,nl,n2)
c     k     « total number of groups  [ngp in anovl]
c     ssx   * sum of sums of x within group
c     ssx2  = sum of sums of x**2 within group
c     s2sx  *
c     oam   = over-all-mean
c     psd   «= pooled standard deviation
c     f     » F statistic
c     nl    = numerator  degrees of freedom, k - 1 * ngp - 1
c     n2    = denominator degrees of freedom, nn - k
      oam    « ssx/float(nn)
      totss - ssx2-((ssx**2)/float(nn))
      betss » s2sx-((ssx**2)/float(nn))
      witss - totss-betss
      betms = betss/float(k-l)
      witms » witss/float(nn-k)
      psd    • sqrt(witms)
      f      = 9999.99
      if (witms  .ne. 0)  f = betms/witms
      nl     • k-1
      n2     « nn-k
      return
      end
Listing of input file pplib:
  18  12  26   1   0   4 .50 .65 .75 .50  23 114
ncrwnpernwksdayOinclnthrthrs   2   3   41inllin2
                Appendix C  page 7 of 7
-------
                                                             27 Jul 86
Appendix D:  listing of a typical output  from  the health analysis summary
            program (Appendix C); this particular run was for a daily mean
            response of 4% with a residue variation of 3.0 (plotted as the
            second line from the bottom  in Figure 3).
                                                                                 471
 Wk Day    0AM
PSD
nl  n2
OGM
PGD
G   n3  n4   pol po2  po3  po4
1 1
1 2
1 3
1 4
1 5
1 6
1 7
2 1
2 2
2 3
2 4
2 5
2 6
2 7
3 1
3 2
3 3
3 4
3 5
3 6
3 7
4 1
4 2
4 3
4 4
4 5
4 6
4 7
5 1
5 2
5 3
5 4
5 5
5 6
5 7
6 1
6 2
6 3
6 4
6 5
6 6
6 7
7 1
7 2
7 3
7 4
7 5
7 6
.03603
.06132
.11174
.17631
.24283
.22551
.22351
.26962
.29544
.32077
.34635
.36641
.35558
.35241
.36939
.37835
.39645
.45244
.47523
.45783
.45376
.48126
.49771
.52123
.54419
.55989
.54372
.53889
.56026
.56846
.57354
.58424
.59814
.58172
.57654
.59733
.60104
.61392
.62627
.63371
.61678
.61130
.63185
.63794
.64973
.65380
.65166
.63887
.01988
.03254
.05850
.08352
.09752
.08994
.08917
.10039
.10221
.10551
.11053
.11393
.10940
.10843
.11314
.11392
.11553
.12241
.12176
.11608
.11506
.11940
.11983
.12135
.12285
.12216
.11613
.11511
.11840
.11741
.11612
.11670
.11592
.11036
.10938
.11272
.11036
.11063
.10989
.10823
.10310
.10220
.10590
.10426
.10203
.10062
.09948
.09625
19.65
20.37
15.86
16.32
19.88
18.86
18.85
24.60
28.26
23.56
18.18
16.26
17.06
17.06
15.42
15.14
16.38
13.06
15.79
14.34
14.34
15.06
14.55
14.76
9.83
9.14
9.61
9.61
9.11
8.94
8.80
8.20
8.29
8.23
8.23
9.78
9.98
8.90
7.70
7.14
6.97
6.97
5.26
5.11
7.78
8.59
6.67
6.31
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
17 198
.02608
.04489
.08421
.13742
.19112
.17892
.17733
.21348
.24231
.27309
.30271
.32424
.31452
.31172
.32915
.33864
.35519
.41731
.43807
.42399
.42022
.44724
.46482
.48866
.51783
.53540
.52029
.51567
.53713
.54613
.55204
.56371
.57812
.56301
.55800
.57701
.58135
.59574
.60978
.61842
.60248
.59712
.61861
.62536
.63620
.64034
.63942
.62734
1.57011
1.55964
1.53532
1.50343
1.47487
1.47140
1.47139
1.45621
1.44281
1.42929
1.41401
1.40100
1.39808
1.39808
1.39214
1.38479
1.37696
1.35599
1.34167
1.33822
1.33822
1.33211
1.32171
1.30919
1.29359
1.28051
1.27598
1.27597
1.27244
1.26592
1.26034
1.25225
1.24382
1.23949
1.23946
1.23727
1.23152
1.22561
1.21847
1.21168
1.20741
1.20741
1.20606
1.20067
1.19554
1.19174
1.18870
1.18619
28.68
28.68
31.22
31.56
38.00
36.97
36.97
40.27
29.99
23.58
20.31
19.20
19.64
19.64
18.57
18.37
19.31
12.68
14.83
13.91
13.91
13.07
12.72
12.96
9.11
8.55
8.87
8.87
8.48
8.36
8.16
7.82
8.10
7.96
7.96
9.41
9.60
8.31
7.06
6.44
6.46
6.46
4.81
4.57
6.24
6.60
5.63
5.39
17 198
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11
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0 0
0 1
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0 17
0 10
0 10
0 22
0 25
1 33
0 38
0 44
0 42
0 40
0 44
0 48
0 54
1 80
5 92
0 85
0 84
0 92
0 102
0 114
0 126
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                Appendix D  page 1 of 4
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.63318
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.63861
.65286
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.67431
.65403
.64821
.65964
.66059
.67096
.67860
.68954
.66900
.66305
.67675
.68077
.68229
.68312
.68311
.66969
.66373
.67367
.67417
.68439
.68765
.68540
.67174
.66576
.67827
.68072
.68977
.69414
.69613
.68074
.67469
.68659
.68713
.68651
.68931
.69022
.67655
.67054
.67358
.67428
.67816
.68484
.68636
.67134
.66537
.68046
.68286
.68617
.69337
.69376
.09541
.09654
.09585
.09750
.09723-
.09480
.08864
.08785
.09082
.08973
.08978
.08880
.08815
.08267
.08194
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.08394
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.08110
.07786
.07718
.08035
.07974
.08111
.07995
.07791
.07409
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.07696
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.07109
.07047
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.07235
.07170
.06818
.06760
.06914
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.07026
.07160
.07072
.06626
.06569
.07031
.07002
.07002
.07022
.06930
6.31
6.57
6.52
7.25
7.31
8.05
6.62
6.62
6.78
6.84
6.90
5.96
7.65
6.51
6.51
8.59
8.73
7.50
7.49
7.63
7.28
7.27
6.60
6.55
7.45
8.13
7.21
7.32
7.31
4.66
4.29
3.52
3.10
3.53
2.99
2.99
3.57
4.59
2.87
3.05
3.38
3.10
3.10
3.27
3.43
3.71
4.14
5.05
4.23
4.23
2.68
3.29
3.75
4.10
4.82
17 198
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.62175
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.67601
.66999
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.68190
.68177
.68451
.68540
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.67334
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.66684
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.67829
.68151
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.68891
1.18620
1.18639
1.18477
1.18097
1.17597
1.17004
1.16466
1.16465
1.16609
1.16346
1.16089
1.15713
1.15319
1.14835
1.14835
1.14998
1.14807
1.14577
1.14411
1.14249
1.13967
1.13968
1.14198
1.14017
1.13925
1.13649
1.13373
1.13020
1.13021
1.13209
1.12985
1.12901
1.12653
1.12442
1.12094
1.12094
1.12278
1.12105
1.12061
1.12061
1.11946
1.11609
1.11611
1.11798
1.11794
1.11904
1.11945
1.11789
1.11374
1.11376
1.11789
1.11652
1.11576
1.11543
1.11394
5.39 17
5.54 17
5.53 17
6.15 17
5.95 17
6.41 17
5.44 17
5.44 17
5.57 17
5.65 17
5.93 17
5.25 17
6.61 17
5.72 17
5.72 17
7.25 17
7.38 17
6.57 17
6.53 17
6.62 17
6.36 17
6.35 17
5.94 17
5.95 17
6.74 17
7.31 17
6.72 17
6.72 17
6.72 17
4.37 17
4.10 17
3.31 17
2.89 17
3.24 17
2.80 17
2.80 17
3.24 17
3.99 17
2.69 17
2.81 17
3.09 17
2.86 17
2.86 17
3.01 17
3.18 17
3.47 17
3.89 17
4.66 17
3.96 17
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Appendix D  page 2 of 4
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                                         Jul 86
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.67845
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A. 00
3.99
5.39
3.80
3.77
A. 58
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A. 25
A.2A
3.95
A. 50
A. 82
9.25
11.00
7.55
7.53
8.52
8.56
10.18
11. 5A
1A.A6
11. OA
11.03
9.23
9.23
10.12
10.18
7.36
7.81
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6.92
7.08
7.1A
6.39
6.62
5.82
5.81
6.2A
7.06
7.06
7.A7
7.79
6.78
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5.6A
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7.93
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1.11009
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1.10990
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1.10941
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1.10826
1.10828
1.10476
1.10061
1.10064
1.10376
1.10324
1.10453
1.10567
1.10218
1.09475
1.09476
1.09836
1.09742
1.09700
1.09658
1.09590
1.09309
1.09311
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1.09584
1.09655
1.09836
1.09787
1.09401
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1.09831
1.09855
1.09743
1.09242
1.09245
1.09788
1.09730
1.09700
1.09596
3.76 17
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3.43 17
3.40 17
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4.84 17
3.77 17
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3.51 17
3.98 17
A. 32 17
8.00 17
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6.68 17
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12.98 17
10.19 17
10.19 17
8.91 17
9.05 17
9.92 17
9.38 17
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6.7A 17
6.75 17
5.78 17
5.90 17
5.28 17
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5.60 17
6.25 17
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6.53 17
6.79 17
5.99 17
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.68964
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6.05
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6.87
7.56
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5.68
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5.58
5.58
7.84
9.64
17.02
16.44
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9.78
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1.09628
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1.09814
1.09742
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1.09694
1.09200
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1.09655
1.09523
1.09504
1.09489
1.09422
1.09072
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5.49
5.55
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6.14
5.32
5.18
5.23
5.23
7.36
8.87
13.18
12.69
13.08
8.35
8.34
.9.75
10.24
10.30
9.15
9.48
9.22
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1
0
0
0
0
0
0 213
0 213
0 213
0 213
0 213
0 213
0 213
0 213
0 213
0 212
0 212
0 211
8 213
8 214
7 214
0 214
0 214
0 214
0 213
0 213
0 213
0 213
0 213
0 213
                                      Total number of poisoning cases:
                                   Days on which a poisoning occurred:
                                     Potentially reportable incidents:
Means and deviations given on input
     for normally distributed variables:
        workers weight
     for lognormally distributed variables:
        dosing coefficient kd
        enzyme coefficient ke
        regeneration, p
        reversion, pp
        mean daily inhibition, %
        variability in AChE measurement
        reentry residue, ug/cm2
Other parameters given on input:
        number of members per crew
        number of crews
        total size of population
        no. of weeks worked per crew
        total no. of fields sprayed
        no. of days to complete field
        no. of hours worked per day
 70.00000   7.00000
5.10000
6.00000
.00893
.15000
4.00000

.11673
1.41000
1.30000
1.04570
1.01000

1.00000
3.00000
 12
 18
216
 26
936
  2
  8
332000000
              Appendix D  page 4 of 4
-------
Pesticide Exposure of Harvesters  of Blueberries,
  Blackberries, and Raspberries
        Research performed by

        University of California
        Richmond, CA  94804

        October 15, 1985
-------
                                                               476
                            Abstract
During the 1983 growing season in California three field studies
were conducted on the exposure of harvesters of berry crops to
pesticide residues.  These studies were designed to compare dermal
exposure to children (10-12 years old) and adults harvesting
these crops.  In all three studies, methiocarb on blueberries,
benlate on blackberries and benlate on raspberries, a consistent
relationship between age and exposure was observed.  When exposure
is measured as total weight of pesticide deposited per hour
worked, there is an increase of exposure with age.  When the
exposure measure is normalized by body weight there is no
difference in exposure as a function of age.  One study where
work rate was available, provided evidence that differences in
total exposure with age are attributable to differences in work
rate.
-------
                                              Section No. 2
                                              October  15, 1985
                                              Page  1 of  2
                      2.  TABLE OF CONTENTS
Section                                    Page Reference
   1.  Title  page	1  page
   2. Table of contents	2  pages
   3. Project description and
        administrative summary	2  pages
   4. Harvester Exposures in Blueberries	19  pages
       4.1 Introduction	1  of 19
       4.2 Study site and experimental  design....2  of 19
       4.3 Sample storage/  handling  and
           preparation	5  of i9
       4.4 Analytical procedures	5  of 19
       4.5 Crew characteristics	7  of 19
       4.6 Results	9  of 19
   5. Harvester Exposures in Blackberries	18 pages
       5.1 Introduction	1  of 18
       5.2 Study site and experimantal  design....2  of 18
       5.3 Sample storage,  handling  and
           preparation	6  of 18
       5.4 Analytical procedures	«	7  of 18
       5.5 Crew characteristics	8  of 18
 c
       5.6 Results	10  of 18
   €. Harvester Exposures in Raspberries	17  pages
       6.1 Introduction	1  of 17
       6.2 Site and  experimental design	2  of 17
       6.3 Sample storage,  handling  and
           preparation	8  of 17
-------
                                                            478

                                               Section  2
                                               October  15,  1985
                                               Page  2 Of  2
       6.4 Analytical procedures	..8  of  17

       6.5 Crew characteristics	8  of  17

       6.6 Results	9  of  17


Appendices

   A. Methods: Quality Assurance Project  Plan....52 pages

   B. Correspondence regarding  urinary
      metabolite analyses	17 pages
   C. Data files	22
pages
   D. Consent forms and  participant
      instructions	.... .9 pages
-------
                                                                 479
                                               Section  No.  3
                                               October  15,  1985
                                               Page 1 of  2
        3. PROJECT DESCRIPTION AND ADMINISTRATIVE  SUMMARY
     During the 1983 growing season the California PHAP Project
conducted three field studies on the exposure of the harvesters
of berry crops to pesticide residues.  These studies were design-
ed to compare the dermal exposure of children (10-12 years  old)
and adults harvesting these crops.  Secondly an attempt was to be
made to compare dermal exposures as measured by the patch techni-
que and glove monitors with exposures quantified by the urinary
excretion of the pesticides and their metabolites.

     The scope of these studies was limited, where possible, to
the analyses of exposures to the pesticides  applied to  the  crops
most recently before harvesting commenced.  These  pesticides
turned out to be methiocarb on  blueberries and  benomyl  on black-
berries and raspberries.   Approximately twenty  workers  were in-
volved  in each of these studies.  In addition to the collection
of data on dermal exposures, foliar residue  data were also  col-
lected.
                                          *

     The principal failure in achieving the  goals of these
studies is that no urinary metabolite data can  be reported. The
background behind this failure is detailed in the correspondence
included here as Appendix B.  At this time these  samples  remain
in storage awaiting analysis by EPA or its designee. As a result,
the individual study reports, Sections 4,  5,  and  6, deal  with  the
patch, glove and foliar  residue data only.  Included as Appendix
-------
                                                              480
                                               Section No.  3
                                               October 15,  1985
                                               Page 2 of 2
C is the information needed for  subsequent  analysis of the  meta-
bolite results when they become  available.

     A consistent relationship between exposure and age was
observed in all  three  studies.  When exposure is measured as
total weight of pesticide deposited per hour worked, e.g. mg/hr,
there is an increase of exposure with age.   When the exposure
measure is normalized by the body weight of the worker,  i.e.
mg/kg/hr, then there is no difference in exposure as a function
of age.   The blueberry study,  where  information  related to  work-
rate was available, provided some evidence that the differences
in total exposure with age are attributable to  differences  in
workrate between children and adults.
     The staff of the  California PHAP was assisted  in all phases
of the field studies by Dr. James M.  Witt of the Department of
Agricultural Chemistry, Oregon State  University, Corvallis, Ore-
gon,  and Ken Brown, Extension Agent,  Oregon State  University,
Salem, Oregon.  Through  their  knowledge  of  the agriculture  in the
region and their  liason with many growers,   they and their  staffs
were able to locate suitable field sites and enlist the coopera-
tion of the owners.  John Reinhold,  working under Dr. Witt's
aegis, took foliar samples prior to  our  arrival in Oregon and
assisted the PHAP staff in conducting the monitoring studies.  We
are grateful for their  assistance.
-------
                                                             481
                                               Section No. 4
                                               October 15, 1985
                                               Page 1 of 19
*       4.  HARVESTER EXPOSURES TO METHIOCARB  IN BLUEBERRIES
>*
 4.1 Introduction

      In a continuing effort to develop experimental data on the
 exposure to pesticides of fruit harvesters of different ages, a
 study was conducted involving the exposure of blueberry har-
 vesters to residues of the pesticide methiocarb  (Mesurol).  The
 volunteers for this study were divided into  two groups, one
 provided with cotton gloves and dermal patches to measure dermal
 exposures and the other not so provided so that there would be no
 unnatural barrier to their exposure.   Urine samples were  collect-
 ed from all subjects to be analyzed for metabolites of the
 pesticide.

      The purpose of the study was two-fold.  The primary intent
 was to measure the worker's dermal  exposures and determine
 whether a significant difference exists between the exposures
 experienced by children and adults.  Second, an attempt was to be
 made to correlate dermal exposure to methiocarb as determined by
;patch and glove monitors with that  determined by the concentra-
"L
-tion of the metabolite of the pesticide excreted in the urine of
 the exposed workers.   An  additional,  but secondary goal,  was to
 contrast the  absorbtion of the pesticide, as measured by the
 urinary metabolite concentration, between the group wearing
gloves with that not so protected with the object of determining
 the degree of protection afforded.
-------
                                               Section No. 4
                                               October 15, 1985
                                               Page 2 of 19
4.2 Study site and experimental  design

     A fifteen acre plot of the  Bluecrop variety of blueberries,

located about two miles north of Salem, Oregon, was chosen as the

site of the study.   Twenty  five  harvesters,  including men, women

and children, participated  in  the study on a voluntary basis.

They were paid $20 for completing the five day  course of monitor-

ing and urine collection.   Detailed consent  forms and instruc-

tions were handed to each participant before the study began.

The participation of all  minors  required the signed consent of

the parents.


     On Friday,  22  July 1983, methiocarb had been applied to the

blueberries between 0800 and 0900 hours by fixed wing aircraft.
                                                          -—~" ""*"" ~~ *
A 75% wettable powder formulation was applied  at a rate/or 1.5
                                                       ^^^^•^^^^^^
Ib/A.   This plot had been previously  treated with methiocarb at

the same  rate on 5  July 1983.  The harvesters were permitted to

enter the field on Monday,  25 July, the day that the study

commenced.   Weather  conditions were recorded and are given in

Table 4.1.


     The  study was designed to last five days.  None of the

subjects,  as far as  could be ascertained, picked blueberries on

the preceding Saturday or Sunday.  All subjects were instructed

to collect a urine specimen before they entered the field on

Monday to serve as a pre-exposure control.   On  Monday the workers

were divided into two groups,  A  and B.  Group A was provided

light-weight cotton gloves, similar  to those used  in photographic
-------
                                                              483
                                               Section  No.  4
                                               October  15,  1985
                                               Page  3 of  19
                            Table 4.1
                       Meterological Data
           Blueberry Site, 2 miles N of Salem,  Oregon
  Date
DPA
Time
     Temp.  C
RH
Weather
7/22/83
7/25/83
 0
 3
7/26/83   4

7/27/83   5
7/28/83   6
    0925
    0730
    0918
    1105
    1325
    1508
          21.0      70%
          18.0      80
          18.5      70
          19.0      80
          21.2      60
          22.0      60
          18.3      80
1128      21.0      58
    not recorded
0800      18.0      90
1115      21.0      50
              partly cloudy
              heavy overcast
              heavy overcast
              partly sunny
              partly sunny
              sunny
              partly sunny
              partly sunny
              heavy rain
              sunny
              sunny
                                                                   8
-------
                                                               484
                                              Section No. 4
                                              October 15, 1985
                                              Page 4 of 19
darkroom work, to be worn  throughout the workday while picking
berries. During the lunch  break  the gloves were removed and new
gloves  issued after the break.  Also, new gloves were provided if
a pair  became too soiled or wet.
     On Tuesday Group A was outfitted with cotton gauze-patch
monitors, in addition to the cotton gloves, to measure total
dermal  exposure.  Details  of the placement and handling of the
patches and gloves are contained in  Appendix A. Group b wore
neither gloves  nor patches at any  time  during  the study.

     The collection of urine specimens was scheduled to begin on
Wednesday when it was assumed  excretion would have reached some-
thing near steady state.   Starting Tuesday after work all sub-
jects were provided with 1-liter plastic specimen bottles and
intructed to collect their urine for the reminder of the 72 hours
of the study in  the following regimen: night samples to go from
the termination of work to through the next morning's void;  day
samples from that point through  the  termination of work.  On
Wednesday, 26 July,  about  half the subjects did not pick berries
due to a heavy rainfall that commenced about 8 AN and lasted for
about one hour.   The subjects  from group A who did harvest
berries despite the rain were  provided with gloves  ana patches.

     On Thursday, 27 July, all participants worked  in the field.
Group A was provided with  gloves only, but not patches.  None of
the subjects picked  on Friday, 28 July but they did  deliver their
-------
                                                                 485
                                              Section No. 4
                                              October 15, 1985
                                              Page 5 of 19
final overnight urine sample that morning.
     Throughout the study foliar  residue samples were collected
according to the procedures detailed in Appendix A. On three
occasions participants were also  outfitted with personal air
samplers to measure pesticide residues existing as aerosols.
These procedures are also detailed in Appendix A.
4.3 Sample storage handling and preparation
     Urine samples were stored on ice for no more than 4-6 hours
and transported to the laboratory the same day as they were
received from the subjects.   Individual  urine sample volumes were
measured and duplicate 20 mL aliquots  were pipetted into glass
ampules to which 1.5 mL of concentrated hydrochloric acid was
added as a preservative resulting in a final pH of less than
0.5).  The ampules  were sealed and  stored at room  temperature.
     Gloves and patches were collected from participants by  pro-
ject personnel and stored in zip-loc plastic bags. (See section
6.7 of  the  QA  Plan  for sample preservation  procedures.)

4.4 Analytical procedures
     Extraction of samples was as detailed in section 9 of the
Quality Assurance Plan.
     Methiocarb residues were analyzed by reverse-phase HPLC
using a  Waters 6000A dual  pump Solvent Delivery System, a WISP
Model 710A Automatic Sample Processor, a Waters Model 720 System
                                                         1     10
-------
                                                                 486
                                               Section No.  4
                                               October 15,  1985
                                               Page 6 of 19

Controller with a Mode 730 Data Module and a Model  4530  Variable
Wave-length Detector.  A Supelco C^g reverse-phase column (25 cm
x 4.2 mm ID) was  capable  of separating each of  the  three com-
pounds reported to be associated  with methiocarb residues
{methiocarb:  3,5  dimethyl-4-(methylthio)phenyl-N-methyl  carbarn-
ate; methiocarb sulfoxide: 3,5 dimethyl-4-(methylsulfonyl)phenyl-
N-methyl carbamate; methiocarb sulphone:  3,5 dimethyl-4-(methyl-
sulfonyl)phenyl-N-methYl  carbamate} without appreciable interfer-
ence from extraneous materials originating in  the field. The
mobile phase was a mixture of acetonitrile and water and the
optimum chromatographic conditions  for each compound  are listed
in Table 4.2.
                            TABLE 4.2
                   Chromatographic  Parameters
                             for  the
                       Analysis of  Mesurol
                               and
                        Related Compounds

         Column              25 cm  X 4.6  mm C,« Supelco  LC-18-DB
                             with a C^g Guard-PAK precolunm
         Mobile Phase        65%  Acetonitrile,  35%  Water
         Flow Rate           0.9  ml/min
         Detecrtor           Wavelength:   265  nm
         Parameters          Sensitivity:  0.02 AUFS
         Integration         Peak Width:   20
         Parameters          Noise  Rejection:   7.5
                             Chart  Speed:  0.5  cm/min
                             Run  Time:  10 min
                                                       tr
                                                             11
-------
                                                                 487
                                               Section No. 4
                                               October 15, 1985
                                               Page 7 of 19
4.5 Crew characteristics
     The crew characteristics, including sex, height, weight,  age
and computed body surface area are given in Table 4.3.  In additon,
the crew included six Asian workers in Group A and two in Group B.
     The owner of the blueberry farm selected his harvesters
individually, and allowed only approved personnel to work in his
plots; because of this procedure, he maintained a relatively
stable work crew, year-to-year.  The owner adhered to this prac-
tice because the blueberry bush is a perenial that is not cut
back heavily each year like the other types of berries included
in this series of studies; and damage caused by careless workers
could cause long-term losses in productivity for the plants.

     Upon arrival at the study site it was discovered that the
grower had preselected the crew for the exposure study.   These
individuals came from the growers regular crew, and included
relatives and friends of his family.  The entire crew was com-
prised of 24 individuals, ranging in age from 10 to 47 years old.
Four (17%)  were at the age of ten, while ten of the subjects
(42%)  were between 11 and 15 years, and the remaining ten were 16
years or older.  In Group A (the 13 individuals wearing gloves
and patches), three (23%) were ten years old; five (39%) were
between 11 and 15 years; and the remaining five were over 15.
                                                12
-------
                                               488
                              Section No.  4
                              October 15,  1985
                              Page 8 of 19
            Table 4.3

         Blueberry Study

Physical  Characteristics of Crew
IDI
2
3
4
5
6
7
8
9
10
11
12
17
26
1
14
15
16
18
19
20
21
22
23
25
Group
A
A
A
A
A
A
A
A
A
A
A
A
A
B
B
B
B
B
B
B
B
B
B
B
Sex
F
F
M
F
M
M
F
F
M
M
F
M
F
F
F
F
M
M
M
F
M
F
M
M
Age weight (kg)
15
10
14
10
14
20
18
14
16
10
47
11
28
40
14
16
14
15
11
30
17
37
14
10
52.2
31.8
48.5
36.3
54.4
63.5
45.4
45.4
53.5
34.0
45.4
35.8
60.0
61.2
54.4
61.7
47.6
59.4
27.2
61.2
49.9
72.6
79.4
37.6
Height (cm)
160
114
152
147
170
165
152
152
163
140
152 ~*
137
162
168
168
168
168
146
137
165
160
175
187
127
Area (m2)
1.50
1.00
1.45
1.20
1.60
1.65
1.40
1.40
1.55
1.15
1.40
1.15
1.60
1.65
1.65
1.65
1.50
1.55
1.05
1.65
1.50
1.85
2.00
1.15
                                             13
-------
                                                              489
                                               Section No. 4
                                               October 15, 1985
                                               Page 9 of 19
4.6 Results
     As noted above, only on Tuesday was there a complete set of
both patch data and glove data.  On all four days, however, glove
data was collected.  Au important issue then, is the degree of
correlation between the glove exposure and total exposure.  Fig-
ure 4.1 shows the scatterplot of patch exposure vs. glove expo-
sure.  The two variables are positively correlated and the re-
gression is significant, but  the r2 value of 0.40 is not parti-
cularly impressive. However, as will be discussed below, about
50% of the total exposure is to the hands.  Hence, glove exposure
will be a much better predictor of total exposure than of patch
exposure alone.
     It is clear that the degree of exposure to foliar residues
is a function of the residue level and the activity of the work-
er.  Because of the method of payment of the workers in blue-
berries, it was possible to obtain data on the mean daily har-
vesting rate in Ibs/hr for each worker.  Because all workers were
engaged in harvesting it seems reasonable to hypothesize that the
                                           *
mean daily yield should provide a good index of the activity
related effect on exposure. It seems highly likely, however, that
yield is a strong function of age.  Indeed, it seems likely that
the principal effect of age on exposure among workers carrying
out the same task will be through the workrate variable.
                                                                14
-------
                                               Section  No.  4         490
                                               October  15,  1985
                                               Page 10  of  19
     Figure 4.2 and 4.3 show the mean  yield  rate  for  the  entire
period of the experiment vs. age for males and  females  respec-
tively. In both cases there is an increase of yield with  age.
                                                                  15
-------
                                                                                                     491
                                                                          Section 4
                                                                          October 15, 1985
                                                                          Page  11 of 19
                                       Figure  4.1

nsoa s.oo    BunsENur SWOT, FKKH DUOS, us GUVE EXPOS.  («yw)« DM 2   nut B««303-2P  REV* 7

ODttMNDt ILOT
mssac VALUE IMMXMTI uswxss
             0.00000     0.900000     1.60000     2.40000      3.20000     4.00000      4.80000
                  0.400000      1.20000      2.00000     2.80000     3.60000     4.40000
       4.80000  4
       4.70000  4
       4.60000  4
       4.50000  4
       4.40000  4
       4.30000  4
       4.20000  4
       4.10000  f
       4.00000  *
       3.90000  +
       3.80000  +
       3.70000  4
       3.60000  4
       3.50000  4
       3.40000  4
       3.30000  4
       3.20000  4
       3.10000  4
       3.00000  4
       2.90000  4
       2.80000  4
       2.70000  4
       2.599*9  4
       2.49999  4
       2.39999  4
       2.29999  4
       2.19999  4
       2.09999  4
       1.99999  4
       1.69999 4
       1.79999  4
       1.69999  4
       1.59999  4
       1.49999 4
       1.39999 4
       1.29999 4
       1.19999 4
       1.09999 4
      0.999994 4
      0.899994 4
      0.799994 4
      0.699994 4
      0.599994 4
      0.499994 4
      0.399994 4
      0.299994 4
      0.199994 4
   9.99938E-02 4
       0.00000 4
            0.00000    0.800000      1.60000      2.40000      3.20000     4.00000     4.80000
                  0.400000     1.20000     2.00000     2.80000     3.60000      4.40000


                                   c cuvn
                                                                                                      16
-------
                                                                   Section  4
                                                                   October  15, 1985
                                                                   Page 12  of 19
                                                                     492
ABSTAT 3.00

COMMAND: PLOT
                                    Figure 4.2
BLUEBERRY STUDY, MEAN YIELD  (KG/HR) VS AGE> MALES
                                                                            FILE:  8303-PH
0
4
M
E
A
N
Y
L
D
              0.00000     8.33333      16.6667      25.0000      33.3333      41.6667      50.0000
                     4.16667      12.5000      20.8333      29.1667      37.5000     45.8333
20.0000
19.5833
19.1667
18.7500
18.3333
17.9167
17.5000
17.0833
16.6667
16.2500
15.8333
15.4167
15.0000
14.5833
14.1667
13.7500
13.3333
12.9167
12.5000
12.0833
11.6667
11.2500
10.8333
10.4166
9.99998
9.58332
9.16665
8.74998
8.33331
7.91665
7.49998
7.08331
6.66665
6.24998
5.83331
5.41665
4.99998
4.58331
4.16664
3.74998
3.33331
2.91664
2.49998
2.08331
1.66664
1.24998
0.833311
0.416644
0.00000

4
4
4
4
4
4
4
4
4
4
4
4
4 1
4
4
4 1
4
4 1
4
4
4
4
4
4
4
4 1
4
4
4 1
4 1
4
4
4
4
4 11 1
4
4 1
4
4
4 11
4
4
4
4
4
4
4
4
4
_4 	 4 	 4 	 4 	 4 	 4 	 4 	 4 	 4 	 4 	 4 	 4 	
               .00000      8.33333      16.6667      25.0000      33.3333      41.6667      50.0000
                    4.16667      12.5000      20.8333      29.1667      37.5000      45.8333


                                      2 ACE
                                                                             r-
                                                                                     17
-------
                          Section  4
                          October  15, 1985
                          Page 13  of 19
                                            493
Figure 4.3
ABSTAT 3.00
COMMAND: PLOT
0

















0
4


H
C
A
N
T
L
D
























20.0000
19.5633
19.1667
18.7500
18.3333
17.9167
17.5000
17.0833
16.6667
16.2500
15.8333
15.4167
15.0000
14.5833
14.1667
13.7500
13.3333
12.9167
12.5000
12.0833
11.6667
11.2500
10.8333
10.4166
9.99998
9.58332
9.16665
8.74998
8.33331
7.91665
7.49998
7.08331
6.66665
6.24998
5.83331
5.41665
4.99998
4.58331
4.16664
3.74998
3.33331
2.91664
2.49998
2.08331
1.66664
1.24998
0.833311
* 0.416644
0.00000

0.

BLUEBERRY STUDY, MCAH YIELD (KC/BR) VS ACE, FEMALES PILEi 8303-PP
.00000 6.33333 16.6667 25.0000 33.3333 41.6667 50.0000
4.16667 12.5000 20.8333 29.1667 37.5000 45.8333
4
4
4
+
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4 1
4
4
4
4
4
4
4 1
4
4 111 1
4
4
4 1
4 1
4 1 1
4
4 1
4
4
4 1
4
4
4
4
4
4
4
-4— 	 4 	 -4-- 	 4 	 4-- 	 4 	 4- — — 4 	 4 	 4 	 4 	 4 	 4
00000 8.33333 16.6667 25.0000 33.3333 41.6667 50.0000
4.16667 12.5000 20.8333 29.1667 37.5000 45.8333
  2 ACE
                                               18
-------
                                                               494
                                               Section No. 4
                                               October 15, 1985
                                               Page 14 of 19
The age range for females is sufficient to suggest a paateau in
the neighborhood of 8 kg/hr, but no such plateau is seen for
males because of the limited age range of the workers. In any
case there is clear evidence that yield rate is a function of age
and sex.
     If, then, we consider exposure a function of residue level
and yield rate, age and sex are taken into account through their
non-linear effect on yield rate.  Table 4.4 contains the results
of a multiple linear regression of the log of residue and the log
of the yield rate against the log of the glove exposure rate
which we use here as a surrogate of the total exposure rate.  The
logarithmic relationship is suggested by the fact that without
residue there can be no exposure as is the case with work rate.
Hence a multiplicative relation is plausible and the logarithmic
transform appropriate.

     As can be seen in Table 4.4 the F value indicates that both
regression coefficients are non-zero and that these two variables
account for about 60% of the variability in the data.  The foliar
residue is the more important predictor of exposure as indicated
by the standardized coefficients.  These numbers indicated the
change in the dependent variable resulting from a change in the
independent variable of one standard deviation.  Hence, a one
standard deviation change in the log of the yield rate changes
the log of the glove exposure by 0.376 as contrasted with a
change of 0.745 with a one standard deviation change in the log
                                                                19
-------
                                 Table 4.
ABSTAT 3.00   BLUEBERRY STUDY, MULTIPLE REGRESSION, GLOVE VS RESIDUE  &  YIELD

COMMAND: REGR

MISSING VALUE TREATMENT: LISTWISE

                     *** MULTIPLE LINEAR REGRESSION  ***

DEPENDENT VARIABLE:  10 LNGLRATE               34  VALID CASES
COEFP OF DETERMINATION:    0.578869
MULTIPLE CORR COEFF:       0.760835
                        ESTIMATED CONSTANT TERM:      -1.61601
                        STANDARD ERROR OF ESTIMATE:   0.717036
ANALYSIS OF VARIANCE  FOR  THE  REGRESSION:
                      DEGREES OF      SUM OF
SOURCE OF VARIANCE      FREEDOM      SQUARES
      REGRESSION
      RESIDUALS
      TOTAL
           2
          31
          33
VARIABLE
12 LNYLDRAT
13 LNRES

DURBIN-WATSON
 REGRESSION
COEFFICIENT
   0.853255
   0.759902

    2.26362
 MEAN OF
 SQUARES
 10.9541
0.514140
       21.9082
       15.9383
       37.8465
              CORRELATION
STANDARDIZED      WITH
 COEFFICIENT    DEPENDENT
    0.376589     0.218159
    0.745907     0.665919
 F TEST
21.3057
                                  -o o t/>
                                  o» n rt>
                                  to n- o
                                  n> o r»-
                                    cr -*•
                                  —< o> o
                                  01 1 3
                                  o —• .&
                                  -h en
                                                                                     vo
                                                                                     oo
                                                                                     en
                                                                                      LTI
-------
                                                               496
                                               Section No. 4
                                               October 15, 1985
                                               Page 16 of 19
of the residue level.
     A second regression was carried out with the log of age
included as a third predictor variable to see if there was evi-
dence of any independent influence of age other than through work
rate. As shown in Table 4.5 the result is essentially unaffected
as measured by the coefficient of determination (r2).  The appro-
priate F test applied to the residuals in the two  variable vs.
the three variable case indicates that the small increase in r
is only what one would expect from random variation. Here age and
yield rate have a similar influence on the outcome as might be
expected because of the high correlation between the two as
evidenced by Figure 4.2.  Hence, we conclude that there is no
evidence that age is an important variable except thru its in-
fluence on work rate.

     This conclusion is supported by a different analysis in
which each individual's average glove exposure,  normalized for the
weight of the individual (mg/kg/hr), is calculated over the course
of the experiment and regressed against age.  Figure 4.4 shows
the scatter plot.  The F-value is 0.009 and r2 about 10~^
indicating an almost totally random relationship between these
variables.  That is, exposure rate on a per unit weight basis is
virtually unrelated to the age of the worker. This finding sup-
ports the interpretation that workrate is the important variable
and that younger workers, who tend to be smaller, work at lower
rates and thus receive less total exposure than adults.
                                                                 21
-------
                                                                        Section 4
                                                                        October 15,  1985
                                                                        Page  17 of 19               497
                                          Figure  4.4
ABSTAT 3.00      BLUEBERRY BTDDY, WEIGHT NORMALISED CLOVE EXPOSURE VS ME      PILEi Bi8303-Cl   REV! 7

OOmARDl PLOT

NI8SIRG VALUE TREATMBTt LI6WISC

             0.00000      1.33333      I«.«f7     25.0000     33.3333      41.Hit      50.0000
                   4.1CC67     12.5000      20.8333      29.HC7      37.5000      4S.B333
                •4——4-—-4——4——4——4——4——4—--4———4« -—4——4——4
   •.OOOOOE-02  4
   7.87500E-02  4
   7.75000E-02  4
   7.C2499E-02  4
.













































P.49999E-02
.37499E-02
.24999E-02
.12499E-02
.99999E-02
.B7499E-02
.74999E-02
.C2499E-02
.49999E-02
.37499E-02
.24999E-02
.12499E-02
.99999E-02
.87499E-02
.74999E-02
.62499E-02
.49999E-02
.37499E-02
.24999E-02
.12499E-02
.99999E-02
.87499E-02
.749991-02
.C2499E-02
.49999E-02
.37499E-02
.24999E-02
.12499E-02
.99999E-02
.87499E-02
.74998E-02
.62498E-02
.49998E-02
.37498E-02
.24998E-02
.12498E-02
.99998E-02
.87498E-02
.74998E-02
.(2498E-02
.49998E-02
.37498E-02
.24998E-02
.12498E-02
.OOOOOE-02
0.
4
4 1
4
4
4
4
4
4
4
4
4
4
4
4
4 1
4 11
4
4
4 1
4
4 11
4
4 1
4
4
4 1
4 1
4
4 1
4 1
4
4
4
4
4
4
4
4
4
4
4
4
4
4 1
4
00000 B. 33333 1C.CCC7 25.0000 33.3333 41.CCC7 50.0000
                   4.1C6C7     12.5000      20.B333      29.1M7      37.5000     45.8333


                                    3 ACE
                                                                                                   22
-------
ADSTAT 3.00

COMMAND: REGR
                                         Table 4.5
                            BLUEBERRY STUDY,  MULT.  REGR. ,  GLOVE VS RESIDUE,  YIELD & AGE
             MISSING VALUE TREATMENT:  LISTWISE

                                 *** MULTIPLE LINEAR REGRESSION ***
             DEPENDENT VARIABLE:  10 LNGLRATE

             COEFF OF DETERMINATION:   0.595609
             MULTIPLE CORR COEFF:      0.771757
                                             34 VALID CASES

                                       ESTIMATED CONSTANT TERM:
                                       STANDARD ERROR OF ESTIMATE:
                                        -2.24593
                                        0.714256
             ANALYSIS OF VARIANCE FOR THE REGRESSION:
             SOURCE OF VARIANCE
                   REGRESSION
                   RESIDUALS
                   TOTAL
                      DEGREES OF
                       FREEDOM
                          3
                         30
                         33
        SUM OF
       SQUARES
       22.5417
       15.3048
       37.8465
            VARIABLE
            11  LNAGE
            12  LNYLDRAT
            13  LNRES

            DURBIN-WATSON
                REGRESSION
               COEFFICIENT
                  0.348166
                  0.679906
                  0.749882

                   2.37323
STANDARDIZED
 COEFFICIENT
    0.149566
    0.300080
    0.736071
      MEAN OF
      SQUARES
      7.51390
     0.510161

CORRELATION
    WITH
  DEPENDENT
   0.267307
   0.218159
   0.665919
 F TEST
14.7285
                •o o to
                o» o n
                 O ct-
                  cr -••
                —• ro o
                oo -i a
                o —• *>
                -»• tn
                                                                                             vo
                                                                                             oo
                                                                                             en
rv>
                                                                                              oo
-------
                                                               499
                                               Section No. 4
                                               October 15, 1985
                                               Page 19 of 19
     Table 4.6 shows the anatomical distribution of methiocarb
exposure.  Here less of the exposure was to the hands than was
the case in the strawberry studies reported previously.  Since
the blueberry variety involved in these studies grew on bushes as
tall as 1.7 meters, there was a substantially greater potential
for body contact than is the case with strawberries.  There is a
considerable difference in the data for the two different days,
presumably  because of the substantial rainfall on the third  day
of  the  study.   The data on day 4  post-application  should  be
regarded as more representative of the normal exposure situation.
                            Table 4.6
          Anatomical Distribution of Dermal Exposure to
               Methiocarb on Blueberry Harvesters

                                   Days Post-Application
Body Part                             4            5
Head+Neck                        0.28  (7.4)*    0.13 (6.2)
Back+Shoulders  "                 0.13  (3.4)     0.19 (8.8)
Chest+Stomach                    0.23  (6.0) •    0.23 (10.6)
Lower Legs                       0.23  (6.0)     0.34 (15.9)
Upper Arms                       0.22  (5.8)     0.20 (9.7)
Lower Arms                       0.64  (16.8)    0.74 (34.6)
Hands                            2.05  (53.9)    0.31 (14.3)
Total                            3.80           2.14
*Tabled numbers are exposure rates in mg/hr followed by the
percent of the total in parentheses.
-------
                                             Section  5
                                             October 15,  1985
                                             Page 1  of  18         5QQ

       5.  HARVESTER EXPOSURES TO BENLATE IN BLACKBERRIES

5.1  Introduction
     In a continuing effort to develop experimental data on the
exposure to pesticides of fruit harvesters of different ages,  a
study was conducted involving the exposure of blackberry harves-
ters to residues of the pesticide Benlate (benomyl).   The volun-
teers for this study were divided into two groups,  one provided
with cotton gloves and dermal patches to measure  dermal exposures
and the other not so provided so that there would be no unnatural
barrier to their exposure.   Urine samples were  collected  from  all
subjects to be analyzed for metabolites of the pesticide.
     The purpose of the study was two-fold.   The  primary  intent
was to measure the worker's dermal  exposures  and  determine wheth-
er a significant difference exists  between the  exposures  experi-
enced by children and adults.  Second, an attempt was  to  be made
to correlate dermal exposure to benlate  as  determined  by  patch
and glove monitors with that determined by the  concentration  of
the metabolite of the pesticide excreted in the urine  of  the
                                              •
exposed workers;  An additional, but secondary  goal, was  to
contrast the absorption of  the  pesticide,  as  measured  by  the
urinary metabolite concentration, between the group wearing
gloves with that not so protected with the  object of determining
the degree of protection afforded.
                                                                    25
-------
                                             Section   5             501
                                             October  15,  1985
                                             Page  2   of   18
5.2 Study Site and Experimental Design
     The site chosen for an exposure study among blackberry
harvesters was located within a plot of about 30 acres of the
Marion variety near Independence,  Oregon.   Over the growing sea-
son this field had been treated with a variety of  insecticides,
herbicides and fungicides;  and the spray history is delineated in
Table 5.1.  The most  recent treatment  had  been with Lannate and
Thylate applied to various portions of the plot on 6  and  9 July.
However, for several practical reasons,  the 1 July treatment was
chosen as the pesticide application to follow with our measure-
ments.  First,  the entire plot was treated in  one day, offering
the possibility of more homogeneous foliar residues.   Second, in
addition to  Lannate and Thylate,-benomyl had  been applied on
1 July, and this happened to be the same compound followed in the
raspberry study conducted the  previous week, meaning  that the
same residue and metabolite analytical methodology could  be uti-
lized for both  studies.  Third,  no readily detectable  urinary
metabolites  could be expected  from Lannate, and we did not know
what dermal  adsorption and  urinary excretion patterns might be
expected from Thylate;  whereas,  we did know some of the pertinent
toxicological and  metabolic  information  regarding benomyl.
     The study  lasted for five days, running from Monday, 18 July
1983 through  Friday,  22 July 1983.  On-site weather observations
for the study site covering 19 through 22  July are found  in
Table 5.2.  There was one heavy  rainfall early in the  study,
having come  during Monday night.  Although the field was wet on
                                                                   26
-------
                                             Section  5
                                             October 15,  1985
                                             Page 3  of  18
                                        502
                            TABLE 5.1

                      1983 Spray Schedule*
                     Marion Blackberry Field
      Date
 Product
Application Rate
    and Mode	
     3 March

     17-18 March
     28 March

     13 April


     15 April
     6-9 May
     13-16 May

     14 May

     5 June

     1 July

     6, 9 July
   Lime Sulfur
    Spray-Aid
    Parathion
 Karmex (diuron)
    Paraquat
    X77 (sic)
     Dinitro
   WDX77 (sic)
     Roundup
     Guthion
     Dinitro
   WDX77 (sic)
     Ronilan
     Thiraro
     Benlate
     Lannate
     Benlate
     Thiram
     Lannate
Thylate (thiram)
 7.5 gal/A in 100 gal
 0.25 gal/A
 1 Ib/A
 2.4 Ib in 4* band
 1.1 pt
 3.2 oz
 2.0 qt/100 gal
 50 gal/A
 1 pt
 General Burn-back
 0.5 gt on centers
 0.5 Ib/A
 Burn-back
 1 pt
 1.75 lb/100 gal
 1.3125 Ib
 on West 1/2
 8 oz
 3- lb/100 gal
 1 Ib
 0.5 gal/100 gal
 1 Ib
 3 Ib
 0.5 gal
 3.0 Ib
* This table does not contain all  of  the
terminates at the time of the study.
                 1983 spray history,  but
                                                                  27
-------
                                             Section   5
                                             October  15,  1985
                                             Page  4   of   18
                                                     503
                            TABLE 5.2

                      Weather Observations
                     Marion Blackberry Field
   Date
Time
Dry Bulb
  (QC.)
Wet Bulb
  (QC.l
% RH    Description
19. July
1430
   22.8
   19.4     73    Cloudy and
                  partly sunny;
                  had rained
                  the night
                  before.
20 July
21 July
22 July
1300
1408
0730
1008
0925
21.1
23.3
14.2
21.1
21.1
15.8
20.0
13.1
17.2
17.8
57
74
89
67
72
Sunny with
spotty clouds.
*
*
*
* Although no information was recorded for  these days, the
weather was generally clear  and  sunny  for the duration of the
study.

Tuesday morning,  relatively  warm weather that day dried the
foliage within a  reasonable  time, allowing  the crew to work
without dealing with very wet gloves.
     Consent  forms approved  by the Committee for the Protection
of Human Subjects at the  University  of California at Berkeley
(see  Appendix  D) were distributed to each participant along with
detailed instructions  for their  particular  subgroup  (Appendix
D).   in order  for anyone to be allowed to join the Study, they
were  required to  return their consent  form  signed; and if they
were minors, their form had to be signed by their parents, as
                                                                    28
-------
                                             Section  5           504
                                             October 15, 1985
                                             Page 5  of  18
well.  Each participant was paid a bonus of $20.00 for  the  com-
pletion of the study if all urine samples were collected as
instructed.  This was done, not as an inducement to participate,
but to offset the inconvenience and potential aggravation of
providing total urine collection for up to 72 hours.
     As with  the blueberry  study  (See Section 4.2), the volun-
teers were divided into two subgroups.   The first,  Group A,  was
assigned to wear patch monitors and gloves,  while the second were
to have no unusual  impediments to exposure,  which the gloves
might constitute.  Thus,  Group B wore neither gloves nor patches
at any time during the study.   All subjects were instructed to
collect a urine specimen before they entered the field on Monday
to serve as a pre-exposure control.   On  Monday Group A  was  pro-
vided light-weight cotton gloves to be  worn throughout  the  work-
day while picking berries.  During the  lunch break the  gloves
wer.e removed and new gloves issued after the break.   Also,  new
gloves were provided if a pair became too soiled or wet.
     On Tuesday Group A was outfitted with cotton gauze-patch
monitors, in addition to the cotton gloves,  to  measure total
dermal exposure.  Details of the placement and handling of  the
patches and gloves  are contained in Appendix A.
   *
     The collection of urine specimens was scheduled to begin on
Wednesday when it was assumed excretion would have reached  some-
thing near steady state.   Starting Tuesday after work all sub-
jects were provided with 1-liter plastic specimen bottles and
instructed to collect  their urine for the remainder of the  72
                                                                  29
-------
                                            Section  5
                                            October 15, 1985
                                            Page 6  of  18
hours of  the study in the following regimen:  Night samples to go
from the  termination of work to through the next morning's void;
day samples from that point  through the termination of work.
     The  owner of this plot  did not select individuals for his
crew, but allowed anyone who wished to do so to come and pick; he
merely decided which portions of  the  field would be harvested
each day.   In  spite of the fluid nature of the work force,  there
were a number  of family groups that either came from the sur-
rounding  area  or  followed the  hand harvest crops through Oregon
and Washington as migrants,  and these groups made up a relatively
stable population at the study site for the  duration of the
experiment.  This characteristic of the work force also meant
that there were  children available for inclusion in the study who
                              •
were younger than the 12-year  old legal age  limit for children
who can be hired directly by a grower.  Also, the task of re-
cruiting  the participation of  youngsters with parental approval
was greatly simplified when  the parents were also participating.

5.3 Sample  Storage Handling  and Preparation
     Drine samples were stored on ice for no more than 4-6 hours
and transported  to the laboratory the  same day as they were
received  from  the subjects.   Individual urine sample volumes  were
measured  and duplicate 20 mL aliquots  were pipetted into glass
ampules to which 1.5  mL of concentrated hydrochloric acid was
added as  a  preservative  (resulting in a final pH of less than
0.5).  The ampules were sealed and stored at room temperature.
                                                            "     30
-------
                                             Section  5
                                             October 15,  1985     5Q6
                                             Page 7   of   18
     Gloves and patches were collected from participants  by  pro-
ject personnel and stored in zip-loc plastic bags.  (See  section
6.7 of the QA Plan for sample preservation procedures.)

5.4 analytical Procedures
     Extraction of samples was as detailed in Section 9 of the
Quality Assurance Plan.
     Benomyl residues were analyzed by reverse-phase HPLC using a
Waters 6000A dual pump Solvent Delivery System, a  WISP Model 710
A automatic sample processor, a Waters Model 720  System Control-
ler with a Model 730 Data Module  and a Model 4530  Variable Wave-
length Detector.  A nBondapak C18 reverse-phase column (25 cm X 2
mm ID) was capable of separating  benomyl from interference by
substances originating in the field.   The mobile  phase found to
be most effective was 65% acetonitrile and  35% water.  Chromato-
graphic conditions for benomyl analysis are  in Table 5.3.
   ?
     Since benomyl spontaneously  converts to  Carbendazim in  the
environment, it is the latter compound that is found in all per-
sonal and environmental samples,  if present at all.  Benomyl has
a substantially longer retention  time than  does Carbendazim  under
the chromatographic conditions describe above, and it is desir-
   s
able to convert the Carbendazim back to benomyl in order to
better separate the analyte  of interest from interferences.  This
was accomplished by the addition  of 20 microliters of butyl
isocyanate to each sample as  it was  prepared  in its autosampler
vial.   This treatment assured a large,  stable benomyl peak,  in

                                                              '     31
-------
                                             Section  5            en-?
                                             October 15, 1985      ~>U/
                                             Page 8  of  18
standards,  spiked blanks and samples, alike.

                            TABLE 5.3
                   Chromatographic Parameters
                             for the
                       Analysis of Benomyl
          Column              25 cm X 2.0 mm C18 pBondapak with
                              a Ci8  Guard-PAK precolumn
          Mobile Phase        65% Acetonitrile, 35% Water
          Flow Rate           2.0 ml/min.
          Detector            Wavelength:  292 run
          Parameters          Sensitivity:  0.02 ADFS
          Integration         Peak Width:  30
          Parameters          Noise Rejection:  3.0
                              Chart Speed  0.5 cm/min
                              Run Time:   10 min
5.5 Crew Characteristics
   \  Nearly thirty  volunteers  were recruited  from  among the  work
force of more  than  80 which was found already working at the
site.   Twenty-two subjects completed enough of the study to be
included in the  data set;  although,  only sixteen lasted through
the entire study.   The  physical characteristics of the partici-
pants in the blackberry study  are contained in Table 5.4.  As  can
be seen, five  (22.7%) of the volunteers  were  ten years old or
below, and another  five were between ten and fifteen.  Of the
twelve people  in Group  A (wearing gloves and patches), there were
three (25%) who  were ten or less, while  two  (16.7%)  were between
ten and fifteen.
                                                                   32
-------
                                                               Section 5
                                                               October 15, 1985
                                                               Page  9  of 18
                                                                         508
                                      Table 5.4
ABSTAT 3.00
BLACKBERRY STUDY, PERSONAL CHARACTERISTICS OP THE CREW
                                                                        FILE: 8302-1      ftEVI 4
COMMAND* PRINT DATA
VARIABLES*
CASE
1
2
3
4
5
6
7
8
9
10
11
12
13
14
IS
16
17
18
19
20
21
22
1 SUBJECT
1.00000
2.00000
3.00000
4.00000
5.00000
6.00000
7.00000
8.00000
9.00000
10.0000
11.0000
12.0000
13.0000
14.0000
15.0000
16.0000
21.0000
22.0000
23.0000
24.0000
28.0000
29.0000
2 GROUP
1.00000
1.00000
1.00000
1.00000
1.00000
1.00000
1.00000
1.00000
1.00000
1.00000
1.00000
1.00000
2.00000
2.00000
2.00000
2.00000
2.00000
2.00000
2.00000
2.00000
2.00000
2.00000
3 SEX
1.00000
1.00000
2.00000
2.00000
2.00000
2.00000
1.00000
1.00000
1.00000
2.00000
2.00000
2.00000
2.00000
1.00000
1.00000
2.00000
2.00000
2.00000
1.00000
2.00000
1.00000
2.00000
4 AGE
37.0000
31.0000
49.0000
25.0000
10.0000
7.00000
11.0000
9.00000
21.0000
39.0000
19.0000
15.0000
11.0000
8.00000
33.0000
30.0000
45.0000
11.0000
42.0000
28.0000
12.0000
10.0000
5 HGBTKG
113.400
68.0000
93.4000
56.2000
22.7000
20.4000
31.8000
34.0000
68.9000
57.2000
117.900
94.8000
36.3000
31.3000
99.8000
73.0000
68.0000
49.0000
56.7000
59.0000
68.0000
58.1000
6 BEIGHTCM
180.003
190.000
170.000
168.000
128.000
118.000
140.000
143.000
170.000
153.000
162.000
154.000
141.000
129.000
175.000
173.000
170.000
157.000
187.000
162.000
154.000
159.000
7 AREAM2
2.35000
1.85000
2.05000
1.60000
0.900000
0.800000
1.20000
1.20000
1.80000
1.55000
2.30000
2.00000
1.20000
1.05000
2.20000
1.85000
1.75000
1.45000
1.70000
1.60000
1.70000
1.60000
                                                                                       33
-------
                                             Section  5            rrn«
                                             October 15, 1985      JU9
                                             Page 10 of  18
5.6 Results
     All of the dermal patch data was below the limit of detec-
tion for benomyl.  Foliar residues and amounts of benomyl in the
gloves of the workers were, however,  sufficient  to guantitate.
Foliar residues were collected on days 17, 19 and 20 post appli-
cation.  Figure 5.1 is a scatter-plot of these residue values
versus day post-application.   The corresponding regression ana-
lysis, Table 5.5, results in an F value of 4.21 which indicates
that the regression is significant at the 90% level.
     Dsing the regression equation to estimate the decay curve
for all five days allows an investigation of  the  relation between
hand exposure,  as measured by the glove residues, and the residue
on the foliage.   Figure 5.2 shows  a  scatter-plot  of  the  daily
exposure rate,  averaged across the entire crew, and  the  estimated
residue level for that day.   Table 5.6 shows  the  result  of  the
corresponding regression which is significant at above the 95%
level.
     Table 5.7 contains  the glove  exposure rate,  in  mg/hr,  by day
and individual  worker.   The average  exposure  rate was calculated
                                              •
for each worker.  These values are  also  given in Table  5.7.
Figure 5.3 is a  scatter-plot of the  average individual  glove
exposure rate vs.  age which suggests that the hand exposure rate
is less for  young workers than for adults.  Taking age twelve as
the dividing line between children and adults,  for present  pur-
poses,  a Mann-Whitney U test  was  performed to test the  hypothesis
that the distribution  of hand  exposures was the same for children

                                                                    34
-------
                                              Section   5
                                              October 15,  1985
                                              Page 11 of   18            510
and  adults  against  the  alternate  hypothesis that the exposure  of
the  children was less.  The 0 value was calculated to  be  5 which,
for  4 children and 8 adults,  would be observed with probability
0.036 under BQ<  Hence, we conclude that the  children  do  have  a
lower  average  hand  exposure than  the  adults.  In contrast to the
blueberry study,  the absence of yield data makes it impossible to
verify that differences in workrate account for this difference
in exposure, but it seems highly  likely that  this is the  case.
     As was done in the case of blueberries,  the average glove
exposure for each individual was  normalized by body weight,
resulting in a exposure rate expressed in mg/kg/hr.   This value
was  regressed  against age with the corresponding scatter-plot
shown as Figure  5.4.  The value of the F  statistic was 0.355  with
the  corresponding r2 equal to 0.0034,  indicating an  essentially
random relation  between these variables.  Hence, as  in the blue-
berry study, adults have a higher total exposure than children?
but,  on a mg/kg/hr basis, there is no evidence of differences in
exposure as a function of age.
                                                                   35
-------
                                                                   Section  5
                                                                   October  15, 1985
                                                                   Page 12  of 18
                                       Figure  5.1


                    BLACKBERRY  STUDY, RESIDOE DECAY (NG/CM2)  VS  DPA
                                                                              PILE: PDNCH2
XBSTAT 3.00

COMMAND: PLOT

HISSING VALUE TREATMENT: LXSTVISE

             15.0000      16.6667      18.3333      20.0000      21.6667      23.3333       25.0000
                    15.8333      17.5000      19.1667      20.8333      22.5000      24.1667

       500.000  *
       497.917  +
       495.833  +
       493.750  +
       491.667  +
       489.583  +
       487.500
       485.417
       483.333
       481.250
       479.167
       477.083
                                 1
                                                                                        REVI12
I
G
C
H
2
                +
                •f
475.000
472.917
470.833
468.750
466.666
464.583
462.500
460.416
458.333
456.250
454.166
452.083
450.000
447.916
445.833
443.750
441.666
439.583
437.500
435.416
433.333
431.250
429.166
427.083
425.000
422.916
420.833
418.750
416.666
414.583
412.500
410.416
408.333
406.250
404.166
402.083
400.000
             15.0000      16.6667      18.3333       20.0000      21.6667       23.3333       25.0000
                    15.8333      17.5000      19.1667      20.8333      22.5000      24.1667


                                      1 DPA
                                                                                            36
-------
                                 Table 5.5


ABSTAT 3.00          BLACKBERRY STUDY, RESIDUE DECAY  (NG/CM2) VS DPA

COMMAND: REGR

MISSING VALUE TREATMENT: LISTWISE

                    *** MULTIPLE LINEAR REGRESSION  ***

DEPENDENT VARIABLE:  4 NGCM2                  6 VALID CASES
                                                                               FILE:  PUNCB2
COEFP OF DETERMINATION:    0.513016
MULTIPLE CORR COEFF:       0.716252
                                       ESTIMATED CONSTANT TERM:        741.793
                                       STANDARD ERROR OF ESTIMATE:     22.8753
ANALYSIS OF VARIANCE FOR THE REGRESSION:
                      DEGREES OF      SUM OF
SOURCE OF VARIANCE     FREEDOM       SQUARES
      REGRESSION
      RESIDUALS
      TOTAL
                                                 MEAN OF
                                                 SQUARES
                                                 2205.00
                                                 523.277
VARIABLE
 1 DPA

DURBIN-WATSON
                REGRESSION
               COEFFICIENT
                  -15.3704

                   2.71818
1         2205.00
4         2093.11
5         4298.11
                 CORRELATION
   STANDARDIZED      WITH
    COEFFICIENT    DEPENDENT
      -0.716251    -0.716251
 F TEST
4.21383
                                                                                 -O O
                                                                                 U3 c* O
                                                                                 fl> O r*
                                                                                   «T -••
                                                                                 —• n> o
                                                                                 <*> -I 3
                                                                                 o —• in
                                                                                   to
                                                                                   oo
                                                                                   en
                                                                                        U1
-------
                                                                    Section 5                     tr i
                                                                    October 15,  1985             ^ '
                                                                    Page 14 of 18
                                        Figure 5.2
ABSTAT 3.00   BLACKBERRY STUDY, AVERAGE EXPOSURE RATE VS ESTIMATED RESIDOE5    FILEi  PUNCH 3      REVI16

COMMAND: PLOT

HISSING VALUE TREATMENT! LISTWISE

             400.000      416.667      433.333      450.000      466.667      483.333      500.000
                   408.333      425.000      441.667     458.333      475.000      491.667

















0
7


A
V
E
I
P
0
s





















0.300000
0.295833
0.291667
0.287500
0.283333
0.279167
0.275000
0.270833
0.266666
0.262500
0.258333
0.254166
0.250000
0.245833
0.241666
0.237500
0.233333
0.229166
0.225000
0.220833
0.216666
0.212500
0.208333
0.204166
0.200000
0.195833
0.191666
0.187500
0.183333
0.179166
0.174999
0.170833
0.166666
0.162499
0.158333
0.154166
0.149999
0.145833
0.141666
0.137499
0.133333
0.129166
0.124999
0.120833
0.116666
0.112499
0.108333
0.104166
l.OOOOOE-01
*
+
+
+
+
+
+
•f
•+
+
+
+
+
+
+
+
4
+
+
•»•
+
4
+
+
+
+
•f
+
+
+
•f
+
•f
+
+
+
+
+
+
+
+
4.
+
+
+
+
4.
•f
4
             400.000      416.667      433.333      450.000      466.667      463.333      500.000
                   408.333      425.000      441.667     458.333      475.000      491.667
                                     6 EST
                                                                                         38
-------
                                     Table 5.6

ABSTAT 3.00   BLACKBERRY STUDY, AVERAGE EXPOSURE RATE VS ESTIMATED RESIDUES

COMMAND: REGR

MISSING VALUE TREATMENT: LISTWISE

                    *** MULTIPLE LINEAR REGRESSION ***

DEPENDENT VARIABLE:  7 AVEXPOS                5 VALID CASES
                                                                FILE: PUNCH3
COEFF OF DETERMINATION:   0.776790
MULTIPLE CORR COEFF:      0.881357
                        ESTIMATED CONSTANT TERM:
                        STANDARD ERROR OF ESTIMATE:
      -0.839192
      3.423E-02
ANALYSIS OF VARIANCE FOR THE REGRESSION:
                      DEGREES OF     SUM OF
SOURCE OF VARIANCE     FREEDOM      SQUARES
      REGRESSION
      RESIDUALS
      TOTAL
                                  MEAN OF
                                  SQUARES
                                1.223E-02
                                1.171E-03
VARIABLE
 6 EST

DURBIN-WATSON
 REGRESSION
COEFFICIENT
  2.285E-03

    1.33459
1       1.223E-02
3       3.515E-03
4       1.574E-02
                 CORRELATION
   STANDARDIZED      WITH
    COEFFICIENT    DEPENDENT
       0.881357     0.881357
 F TEST
10.4403
                      "O O l/»
                      o> o n
                      10 r* O
                      (D O it
                        cr -••
                      —•mo
                      on 3
                      O —'01
                      -n 01
                                                                                     Co —•
                                                                                      vo
                                                                                      CO
                                                                                      01
                                                                                         c_n
-------
                                          Table 5.7
ABSTAT 3.00
BLACKBERRY STUDY,  GLOVE EXPOSURE DATA, ALL DAYS
                                                                              FILEi  CtB302-P    REVC12
COHHANDi PRINT DATA
HISSING VALUE TREATMENTi INCLUDE
VARIABLES i
CASE
1
2
3
4
5
6
7
8
9
10
11
12


1 SUBJECT
1.00000
2.00000
3.00000
4.00000
5.00000
6.00000
7.00000
8.00000
9.00000
10.0000
11.0000
12.0000



0
0
0
0
0
0
0
7 GL1
.549846
.342769
.223716
.198595
.161276
.245500
.120154
8.4S896E-02
9.240S3B-02
0.604182
0.251091
0.139273




8 GL2
0.469895
0.230706
0.382164
0.214153
0.232982
0.258432
0.161390
0.210842
0.163817
0.432267
0.255733
0.139733


9 GL3
0.342956
0.261391
0.244348
0.130957
6.92174E-02
0.128522
9.79131E-02
6.50435E-02
9.65000E-02
0.207500
8.45000E-02
8.85000E-02


10 GL4
0.139692
0.203692
0.162000
0.143077
0.129231
0.167692
9.92308B-02
7.75385E-02
HISSING
HISSING
HISSING
HISSING


11 GL5 12 AVGLRATB
0.113778
0.231556
0.179755
0.120444
0.109778
0.183778
7.93334E-02
7.95556E-02
HISSING
HISSING
HISSING
HISSING


0.323233
0.254023
0.238396
0.161445
0.140497
0.196785
0.111604
0.103514
0.117574
0.414649
0.197108
0.122502


2
3
2
2
6
9
3
3
1
7
1
1


13 WGLRATB
.85038E-03
.73563B-03
.55242E-03
.87269E-03
.18929E-03
.64631E-03
.50957E-03
.04452E-03
.70644E-03
.24912E-03
.67182E-03
.29221E-03











•o O  O fD
(O r* O
n o rt-
a- -••
— i a> o
Oh 1 3
o — • in
-h in
00 —•
00
en
-------
                                                                      Section  5
                                                                      October  15, 1985
                                                                      Page 17  of 18
                                                                                                  516
                                           Figure 5.3
 ABSTAT 3.00      BLACKBERRY STUDY, AVERAGE EXPOSURE RATE  (MG/HR) VS AGE       FILE: Cz8302-P    REVI12

 COMMAND:  PLOT

 HISSING VALUE TREATMENT: LISTKISE

               4.00000      12.0000      20.0000      28.0000      36.0000      44.0000      52.0000
                     8.00000      16.0000     24.0000      32.0000      40.0000      48.0000

        0.440000  4
        0.432500  4
        0.425000  4
        0.417500  4
        0.410000  -I-                                                        1
        0.402500  4
        0.395000  +
        0.387500  4
        0.380000  4
        0.372500  4
        0.365000  4
        0.357500  4
        0.350000  4
        0.342500  4
        0.335000  4
        0.327500  4
        0.320000  4                                                     1
 1       0.312500  4
 2       0.305000  4
        0.297500  4
        0.290000  4
 A       0.282499  4
 V       0.274999  4
 G       0.267499  4
 L       0.259999  4
JL       0.252499  4                                           1
 A       0.244999  4
 T       0.237499  4                                                                       1
 E       0.229999  4
        0.222499  4
        0.214999  4
        0.207499  4
        0.199999  4
        0.192499  41                  1
        0.184999  4
        0.177499  4
        0.169999  4
        0.162499  4
        0.1S4999  4                                 1
        0.147499  4
        0.139999  4          1
        0.132499  4
        0.124999  4
        0.117499  4                  11
        0.109999  4           1
        0.102499  4        1
    9.49993E-02  4
    8.74993E-02  4
    8.00000E-02  4

               4.00000      12.0000      20.0000      28.0000      36.0000      44.0000      52.0000
                     8.00000      16.0000     24.0000      32.0000      40.0000      48.0000


                                       3  AGE
-------
                                                                                             517
                                                                   Section  5
                                                                   October  15, 1985
                                                                   Page  18  of 18
                                      Figure 5.4
llBSTAT 3.00   BLACKBERRY STUDY, AVERAGE EXPOS. NORMALIZED FOR WIEGBT VS ACE    FILEt C:8302-P   REVI12

BMARDi PLOT

IISSING VALUE TREATMENT: LISTWISE

            4.00000      12.0000      20.0000      26.0000      36.0000     44.0000      52.0000
                   8.00000     16.0000      24.0000      32.0000      40.0000      48.0000












I
J


II
5
L
K
It
I
E





















l.OOOOOE-02
9.81249E-03
9.62499E-03
9.43749E-03
9.24999E-03
9.06249E-03
8.87499E-03
8.68749E-03
8.49999E-03
6.31249E-03
8.12499E-03
7.93749E-03
7.74999E-03
7.56249E-03
7.37499E-03
7.18749E-03
C.99999E-03
C.81249E-03
6.62499E-03
6.43749E-03
6.24999E-03
6.06249E-03
5.87499E-03
S.68749E-03
S.49999E-03
S.31249E-03
5.12499E-03
4.93749E-03
4.74999E-03
4.56249E-03
4.37499E-03
4.18749E-03
3.99999E-03
3.81248E-03
3.62498E-03
3.43748E-03
3.24998E-03
3.06248E-03
2.87498E-03
2.68748E-03
2.49998E-03
2.31248E-03
2.12498E-03
1.93748E-03
1.74998E-03
1.56248E-03
1.37498E-03
1.18748E-03
l.OOOOOE-03
4
*
+
+
+
•«•
+
•*•
+
4
+
+
^
•f
4-
+
4.
4.
•f
+
4
4
4
4
4
4
4
4
4
4-
4
4
4.
4
4
4
4
4
4
4
4.
4
4
4
4

                                                   _ _+ ______ + _____ + ______ + _____ + ______ + _____ +

              00000    *"l27oOOO     20.0000      28.0000      36.0000      44.0000      52.0000
                   8.00000      16.0000      24.0000     32.0000      40.0000     48.0000
                                     3 ACE
                                                                                         42
-------
                                             Section  6
                                             October 15, 1985
                                             Page 1  of  17
       6.  HARVESTER EXPOSURES TO BENLATE IN RASPBERRIES

6.1 Introduction
     In a continuing effort to develop experimental data on the
exposure to pesticides of fruit harvesters of different ages,  a
st.udy was conducted involving the exposure of raspberry harves-
ters to residues of the pesticide Benlate (benomyl).   The volun-
teers for this study were divided into two groups,  one provided
with cotton gloves and dermal patches to measure dermal exposures
and the other not so provided so that there would be no unnatural
barrier to their exposure.   Urine samples were collected from  all
subjects to be analyzed for metabolites of the pesticide.
     The purpose of the study was two-fold.   The primary intent
was to measure the worker's dermal  exposures  and determine wheth-
er a significant difference exists  between the exposures experi-
enced by children and adults. Second, an attempt was  to be made
to correlate dermal exposure to  benlate  as  determined  by patch
and glove monitors with that determined by the concentration of
the metabolite of the pesticide  excreted in the urine  of the
exposed workers;  An additional, but secondary goal, was to
contrast the absorption of the pesticide, as  measured  by the
urinary metabolite concentration, between the group wearing
gloves with that not so protected with the  object of determining
the degree of protection afforded.
                                                                43
-------
                                             Section   6            5 1 Q
                                             October 15, 1985
                                             Page 2  of  17
6.2 Site and Experimental Design
     The site chosen for an exposure study among raspberry har-
vesters was a 50 acre plot of the Canby variety located on a
berry farm in Troutdale, Oregon. It was  late in the season for
this variety, and the plot had  been  picked over  at least one time
prior to the commencement of the study.  This field had been
treated with very few insecticides,  herbicides or fungicides; but
the spray history up to the study date, such  as  it was, is
delineated in Table 6.1.  The most recent  treatment had been with
Lannate on the first and second of June,  some 40 days prior to
our crew's entry.   Even  though  it was 60  days post application,
the benomyl treatment was chosen as the one to use in the moni-
toring study, since no readily  detectable  urinary metabolites
could be expected from Lannate.  However,  we  did know some of the
pertinent metabolic information regarding benomyl.
                            TABLE 6.1

                      1983 Spray Schedule*
                      Canby Raspberry Plot
Date
25 February
5-7 April
15-18 Hay
1-2 June
Product
Orthrex (sic)
Karmex (diuron)
Paraquat
Benlate
Lannate
Application Rate
3 gal/A
1.6 Ib/A
1 qt/A
2 Ib/A
25 gal/A



(sic)
  This table  does  not  contain  all  of  the 1983 spray history, but
terminates at the  time of the study.
                                                                    44
-------
                                             Section   6            con
                                             October 15, 1985      ^u
                                             Page  3  of  17
     The study lasted for four days,  running  from  Monday, 11 July
1983 through Thursday, 14 July 1983.  On-site weather  observa-
tions for the study site covering 11  through  14 July are found in
Table 6.2.   This particular  study was plagued with frequent
rains, cutting the workdays short and keeping the  foliage, the
subjects, and the glove and patch monitors wet much of the time.
Table 6.2 also includes  information about the crew in  addition to
weather data.  For a number of reasons, individual times for
entry to and exit from the field were not recorded, obviating the
assignment of individual exposure times.  This arose,  in large
part, due to the fact that  as part of a school bus operation this
group arrived at the field  all at one time, and the PBAP staff
was obliged to conduct its  business  (collection of forms, taking
down; personal information,  applying patch monitors and gloves,
etc.) to  all  the  subjects at once and in as  small  a time span as
possible; therefore, movement into and  out  of the  field was taken
for the group, altogether.
     On Thursday, July 14th,  the Canby plot had been completely
harvested out soon after the crew began working in it; so the
entire operation was moved to a plot  adjacent to the Canbies that
happened to be the Meriam variety.  This plot was made up of
considerably older plants than the Canbies  and had considerably
less foliage.  Less  foliage  resulted  in lowering the potential
for the workers to contact  and pick up  residues.   In addition, it
was impossible to determine whether this particular plot had
received the same pesticide regimen as  the  Canby plot.
                                                                    45
-------
                                                     Section   6         521
                                                     October  15,  1985
                                                     Page 4   of   17
                                   TABLE 6.2
                        Weather and General Observations
                               of Conditions in the
                               Canby Raspberry Plot

   Date     Time     Dry Bulb   Wet Bulb   % RH       Description of Weather
	(PC.)	(PC.)	and Other Observations
11 July     0630                                  Rain the night before, heavy
                                                  dew on leaves.  School bus
                                                  arrived, collected evening
                                                  (pre-exposure) urines.
            0730                                  Completed patching Group A.
            0735        15.3        14.2     91    Sunny and
                                                  clear, light wind.
            0855                                  Dew beginning to dry.  Workers'
                                                  shirts are wet from dew.
            1200                                   Crew left field.
        r
12 July     0640                                   Crew arrived. Sunny with
                                                  spotty clouds. Heavy dew.
            0655                                   Group A completely patched.
            0915        18.9
            0945                                   Heavy Clouds have moved in.
                                                  Worker 11 arrived.
            1000                                   Drizzle begun.
         1010-1015                                Crew leaves field for 1/2 hour
   v                                              lunch.  Gloves are very wet;
                                                  Lower legs dry; Lower arms wet.
            1250                                   Crew leaves field.
Table 6.2 continued on Page 5.
-------
                                                                  522
                                             Section  6
                                             October 15,  1985
                                             Page 5  of  17
 TABLE 6.2
Weather and General Observations of Conditions in the Canty Raspberry Plot oont.
Date Time
13 July 0640
0947
1020
14 July 0630
0700
0800
0845
1000

r
1115
1130

Dry Bulb Wet Bulb t PS Description of Weather
(°C.) (°C.) and Other Observations
Crew into field. Drizzle.
18.6
Drizzle turns to heavy rain;
crew leaves field for the day.
Crew into field.
Worker 21 arrived.
Canty plot picked out; Crew
moved to an adjacent plot,
Heriaro variety.
15.0 10.0 53 Good weather, no clouds.
Crew leaves field for 1/2 hour
lunch.

18.1 14.7 72 No clouds.
Meriams picked out; crew leaves
and will not return Friday.
     Consent forms approved by the Committee for the  Protection
of Human Subjects at the University of California at  Berkeley
(see Appendix D)  were distributed  to each participant along with
detailed instructions for their particular  subgroup (Appendix D).
In order for anyone to be allowed to join the Study, they were
required to return their consent form signed; and if they were
minors, as was this entire crew, their form had to be signed by
their parents, as well.  Each  participant was paid  a  bonus of
$20.00 for the completion of the study if all urine samples were
-------
                                             Section  6
                                             October 15,  1985
                                             Page 6  of  17
collected as instructed.   This  was done,  not  as  an inducement to
participate, but to offset the  inconvenience  and potential aggra-
vation of providing total urine collection for up to 72 hours.
     As with the blueberry study  (See Section 4.2),  the volun-
teers were divided into two subgroups.  The first,  Group  A,  was            M
assigned to wear patch monitors and gloves, while the second were
to have no unusual  impediments  to exposure, which the gloves
night constitute.  Thus,  Group  B wore neither gloves nor  patches
at any time during the study.   All subjects were instructed to
collect a urine specimen  before they entered  the field on Monday
to serve as a pre-exposure control.   On Monday Group A was pro-
vided light-weight cotton gloves to be worn throughout the work-
day while picking berries.  During the lunch  break the gloves
were removed and new gloves issued after  the  break.   Also,  new
gloves were provided if a pair  became too soiled or  wet.  How-
ever, adhering  to this  policy was difficult in this  particular
study,  because  of the heavy moisture on the foliage  due to the
great deal of dew,  drizzle and  rain experienced  during the week
of 11 to 14 July.
     On Tuesday Group A was outfitted with cotton gauze-patch
monitors, in addition to  the cotton gloves, to   measure total
dermal exposure.  Details  of the  placement and handling of the
patches and gloves  are  contained in Appendix  A.   The initial
distribution of subjects  between the two  groups  was sufficiently
imbalanced (14  to 10) that one  person (Subject 13) was  moved from
Group A to Group B on Tuesday.   Table 6.3  does not reflect this
                                                                         48
-------
                                            Section  6
                                            October 15, 1985
                                            Page 7  of  17
 shift of one subject from one group to another,  but  simply shows
 Subject 13 as part  of Group 2 or B.
     The collection of urine  specimens was scheduled to begin on
 Wednesday when it was assumed excretion would have reached some-
 thing near  steady state.  Starting Tuesday after work all sub-
 jects were  provided with 1-liter plastic specimen bottles and
 instructed  to collect their urine for  the remainder of the 72
 hours of the study in the following regimen:  Might  samples to go
 from the termination of work to through the next morning's void;
 day samples from that point through the termination of work.
     The owners of the raspberry farm  had both a drive-up work
   ;
 force and a school bus operation.  The former is much the same as
 was described for the Marion  blackberry field,  where just about
 anyone who wished to do so could come and pick.  These people
 were kept segregated from the monitoring study and did not work
 in the same plot with our crew at any time.
     The school  bus  crew  was designated to work  with the monitor-
 ing study and was constituted the same as the crews  that had
 worked with this organization in strawberries in Corvallis,  Ore-
 gon,  during 1981  and 1982.  The  grower  contracts with a  Crew Boss
 for a crew.  The  Crew Boss recruits a school bus driver  who
 contracts with the school district to  rent one of  the district's
busses for the summer.  A crew of thirty to forty children is
 recruited from the local  school(s)  (with parental consent,  and/or
urging).  On work days the Crew Boss and Driver  go to  each
 child's house and picks them up  before  work  and returns  them home
-------
                                             Section  6
                                             October 15, 1985       525
                                             Page 8  of  17
 after work.   In  the  field,  the  harvesters are paid piece rate,
 and the  Crew Boss is credited for the produce harvested.
 6.3 Sample Storage Handling and Preparation
     Drine samples were stored on ice for no more than 4-6 hours
 and transported to the laboratory the same day as they were
 received from the subjects.  Individual urine sample volumes were
i
 measured and duplicate 20 mL aliguots were pipetted into glass
 ampules  to which 1.5  mL  of  concentrated hydrochloric acid was
 added as a preservative  (resulting in a final pB of less than
 0.5).   The ampules were  sealed and stored at room temperature.
   •  Gloves  and patches were collected from participants by pro-
 ject personnel and stored in  zip-loc  plastic bags.  (See section
 6.7 of the QA Plan for sample preservation  procedures.)
 6.4 Analytical Procedures
     Extraction of samples was as detailed in Section 9 of the
 Quality  Assurance Plan.  A description of the chromatographic
 analysis of  benomyl  is contained  in Section 5.4, including a
 listing  of the chromatographic  conditions in Table  5.3.

 6.5 Crew Characteristics
     This group,  because of  the  fact that it was a school bus
 operation, was made up almost exclusively of youngsters;  only two
 individuals were older than 16 years, and they were friends or
 related to the Crew Boss.  Table 6.3 shows the  physical  charac-
 teristics of  the entire crew of volunteers for  the monitoring
                                                                 .-»•
                                                                       50
-------
                                             Section  6               526
                                             October 15, 1985
                                             Page 9  of  17
 study which consisted of 24 individuals,  of which 13 were
 assigned to Group A (wearing glove and patch monitors) while 11
 made up Group B.
      Taking the crew as a whole,  the ages distributed as follows:
 Ten years or less, 1 (4%); 11 and 12 years, 6 (25%); 13 - 15
 years, 12 (50%); and 16 years and older, 5 (21%).  They were
Ndivided evenly between the sexes, and ranged in weight from 31 to
 86 kg with the bulk  (54%) falling into the 40 - 60 kg range.
      The group wearing patches  (Group A),  numbering 13, ranged in
 age from 9 to 16 and were distributed thus:  Ten years or less, 1
 (8%); 11 and 12 years, 4 (31%); 13 - 15 years, 5 (38%); and 16
 years, 3 (23%).  This subgroup was made up of seven  males and six
 females.  Weights ranged from 32 to 77 kg, again with the great-
 est number of subjects (69%) falling into the 40 to 60 kg range.
 6.6 Results
      Because this field had been treated with benomyl 60 days
 prior to entry,  the  foliar residues  were much lower  than was the
 case in the blackberry study.  As in that  case,  all  of the  dermal
 patch data was below the limit of detection fof  benomyl.   The
 foliar residue data  is shown in Table 6.4.   No clear time trend
 would be expected in this case since  the sampling  interval  was
 over only two days and sampling variability is high  when residue
 levels are very low.   Hence, the residue environment is best
 characterized by the  mean of all sample values which was
 175 ng cm~^»
                                                                      51
-------
                 ABSTAT 3.00
                                         RASPBERRY STUDY, PERSONAL DATA ON THE CREW
FILEI 0301-1
                                                                                                                  REVt 4
                 COMMANDI PRINT DATA
Ln
VARIABLESi
CASE
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
1 SUBJECT
1.00000
2.00000
3.00000
4.00000
6.00000
7.00000
12.0000
15.0000
16.0000
17.0000
18.0000
19.0000
20.0000
5.00000
8.00000
9.00000
10.0000
11.0000
13.0000
14.0000
21.0000
22.0000
23.0000
24.0000
2 GROUP
1.00000
1.00000
1.00000
1.00000
1.00000
1.00000
1.00000
1.00000
1.00000
1.00000
1.00000
1.00000
1.00000
2.00000
2.00000
2.00000
2.00000
2.00000
2.00000
2.00000
2.00000
2.00000
2.00000
2.00000
3 SEX
1.00000
2.00000
1.00000
1.00000
1.00000
2.00000
1.00000
2.00000
1.00000
1.00000
2.00000
2.00000
2.00000
1.00000
2.00000
2.00000
1.00000
1.00000
2.00000
2.00000
2.00000
2.00000
1.00000
1.00000
4 AGE
12.0000
9.00000
IS. 0000
15.0000
16.0000
13.0000
13.0000
16.0000
16.0000
15.0000
12.0000
12.0000
12.0000
15.0000
15.0000
13.0000
14.0000
14.0000
11.0000
14.0000
12.0000
28.0000
19.0000
13.0000
5 MGHTKG
43.1000
31.8000
77.1000
61.2000
77.1000
58.1000
44.0000
52.2000
56.7000
59.0000
48.5000
51.3000
54.0000
45.4000
61.7000
63.5000
43.1000
81.6000
35.4000
49.0000
36.3000
86.2000
77.1000
52.6000
6 HEIOHTCM
142.000
135.000
176.000
175.000
175.000
162.000
157.000
157.000
170.000
173.000
160.000
170.000
165.000
168.000
150.000
160.000
152.000
183.000
141.000
156.000
152.000
162.000
180.000
164.000
7 ARBAM2
1.30000
1.10000
1.90000
1.70000
1.90000
1.60000
1.40000
1 .50000
1.60000
1.65000
1.50000
1.55000
1.55000
1.50000
1.60000
1.65000
1.35000
2.00000
1.20000
1.45000
1.25000
1.95000
1.90000
1.55000















ti O 
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aa «-•• o
n> o r+
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-h en
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en
ro
-------
                                             Section  6
                                             October 15, 1985
                                             Page 11 of  17
528
                            TABLE 6.4
                       Foliar Residue Data
                         Raspberry Study
Sample
Number
8301-P-61.1
. 8301-P-61.2
8301-P-62.1
8301-P-62.2
Total
Micrograms
66.6
45.1
43.0
68.8
Number of
Leaf Disks*
43
43
47
48
Average
s
% RSD
Residue
ng/cm2**
219
148
129
203
175
43
25
* 3.0 cm diameter.
** Single side basis.
     As in the blackberry study, the measure of exposure is the
residue found in the gloves.  Table 6.5  contains  the  glove expo-
sure rate, in pg/hr, by day and individual worker.  The average
glove exposure rate was calculated for each worker for the entire
course of the study. These values are also  shown in  Table 6.5.
Figure 6.1  is a  scatterplot  of  this  average exposure  rate vs age,
which is mildly suggestive of the same positive correlation
between exposure and age which was seen in both the blackberry
and blueberry studies.   The regression results are given in Table
6.6  and  indicate an F value  on  the verge  of  significance,  but a
very low r2 value  of 0.27.
     As  in the blueberry and blackberry studies,  even this weak
correlation between exposure and age disappears if the exposure
measure is normalized by the body weight of the individual
-------
                                            Section  6
                                            October 15f 19B5
                                            Page 12 of  17
worker.   Figure 6.2 shows the scatterplot of exposure in
ng/kg/hr, versus  age.  As expected, the F value  declines to  0.40
with an  r2  of  0.17 as shown  in Table  6.7.  Note  that these
exposure  values are given in micrograms rather than in milligrams
as in the previous studies.  Therefore, despite the very low
foliar residues and the adverse weather conditions encountered in
this study, the conclusion is the same:  Older workers tend to be
exposed to  a greater total residue than younger workers, but when
exposure is measured on a mg/kg basis, there is no evidence of an
age-related difference  in  exposure.
                                                                       54
-------
                                                          Table  6.5
                 ABSTAT 3.00
                                       RASPBERRY STUDY,  GLOVE EXPOSURE DATA, ALL DAYS
riLEs 8301-P     REVI22
                 COMMANDi PRINT DATA
                 MISSING VALUE TREATMENTi INCLUDE
VARIABLESi
CASE
1
2
3
4
5
6
7
8
9
10
11
12
13
14
1 SUBJECT
1.00000
2.00000
3.00000
4.00000
6.00000
7.00000
12.0000
13.0000
15.0000
16.0000
17.0000
18.0000
19.0000
20.0000
7 GL1
51.2000
27.2000
22.0000
112.200
83.0000
145.000
34.0000
53.8000
92.0000
72.4000
23.2000
37.8000
67.8000
38.6000
8 GL2
19.4445
7.11111
21.8519
30.7408
MISSING
22.0370
23.3333
MISSING
0.00000
0.00000
22.7778
14.0000
21.1111
HISSING
9 GL3
37.0270
20.3514
25.4324
33.5135
35.1351
14.0541
17.6487
HISSING
73.2433
79.1892
22.3243
25.3514
22.2703
47.2973
10 GL4
32.0000
35.5556
82.0000
22.8889
54.0000
28.4444
28.8889
MISSING
40.6667
32.8889
71.5556
26.4444
49.5556
36.6667
11 AVGLRATE
34.9179
22.5545
37.8211
49.8358
57.3784
52.3839
25.9677
53.8000
51.4775
46.1195
34.9644
25.8990
40.1842
40.8547
12 NGLRATE
0.810160
0.709262
0.490546
0.814310
0.744207
0.901616
0.590175
1.51977
0.986159
0.813395
0.592617
0.533999
0.783319
0.756568








"O O t/»
o> o rt>
«o n- o
m o n-
o- -••
— '(0 O
CO -I 3
O — •• (T>
-+i en
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10
00
en

en
-------
                                                                     Section  6
                                                                     October  15,  1985
                                                                     Page 14  of 17        531
                                          Figure 6.1


ISTAT 3.00         RASPBERRY STUDY, MEAN EXPOSURE RATE  (UU/HR)  VS AGE         PILE:  8301-P      REV«22

HWANDi PLOT

;SSIHG VALUE TREATMENT] LISTWISE

           • .00000      9.50000      11 .'0000      12.5000       14.0000       15.SOOO      17.0000
                  8.75000      10.2500      11.7500      13.2500     14.7500       16.2500

   .  SB.0000  4
     57.2500  *                                                                      1
     56.5000  4
     55.7500  *
     55.0000  4
     54.2500  4
     53.5000  *                          1
     52.7500  4
     52.0000  4                                            1
     51.2500  +                                                                      1
     50.5000  4
     49.7500  4                                                              1
     49.0000  4
     48.2500  4
     47.5000  4
     46.7500  4
     46.0000  4                                                                      1
     45.2500  4
     44.5000  4
     43.7500  4
     43.0000  4
     42.2500  4
     41.5000  4
     40.7500  4                                   1
     40.0000  4                                   1
     39.2500  4
     38.5000  4
     37.7500  4                                                              1
     37.0000  4
     36.2500  4
     35.5000  4
     34.7500  4                                   11
     34.0000  4
     33.2500  4
     32.5000  4
     31.7500  4
     31.0000  4
     30.2500  4
     29.5000  4
     28.7500  4
     28.0000  4
     27.2500  4
     26.5000  4
     25.7500  4                                   11
     25.0000  4
     24.2500  4
     23.5000  4
     22.7500  4
     22.0000  4         1

           8.00000      9.50000      11.0000      12.5000       14.0000       15.5000      17.0000
                  8.75000      10.2500      11.7500      13.2500     14.7500       16.2500
                                    3 AGE
                                                                                                56
-------
                               Table 6.6


ABSTAT 3.00              RASPBERRY  STUDY,  MEAN EXPOSURE RATE VS AGE


COMMAND: REGR

MISSING VALUE TREATMENT:  LISTWISE


                     ***  MULTIPLE LINEAR REGRESSION ***


DEPENDENT VARIABLE:  11 AVGLRATE               14 VALID CASES
COEFF OF DETERMINATION:    0.270291
MULTIPLE CORR COEFF:       0.519896
ANALYSIS OF VARIANCE  FOR THE  REGRESSION:
                      DEGREES OF      SUM  OF      MEAN OF
SOURCE OF VARIANCE      FREEDOM       SQUARES      SQUARES
      REGRESSION           1          447.116      447.116
      RESIDUALS          12          1207.08      100.590
      TOTAL              13          1654.20
                                            CORRELATION
                REGRESSION   STANDARDIZED      WITH
VARIABLE       COEFFICIENT   COEFFICIENT    DEPENDENT
 3 AGE             2.70261       0.519895     0.519895
                                               8301-P
ESTIMATED CONSTANT TERM:       4.91215
STANDARD ERROR OF ESTIMATE:    10.0295
                        F TEST
                       4.44492
                                                                                •o o v>
                                                                                0> O rt>
                                                                                »Q r* O
                                                                                rt> O rt-
                                                                                  O" -••
                                                                                —« CD O
                                                                                O1 -1 =>

                                                                                O —* 
-------
                                                                   Section 6
                                                                   October 15,  1985
                                                                   Page  16 of 17                 533
                                        Figure 6.2


ABSTAT 3.00   RASPBERRY STUDY, EXPOS. RATE NORM. FOR WGHT. (OG/KG/HR)  VS AGE   PILE.  8301-P      REVI22

COMMAND* PtOT

MISSING VALUE TREATMENTI LISTWISE

             8.00000      9.50000      11.0000      12.5000      14.0000      15.5000      17.0000
                    8.75000      10.2500      11.7500      13.2500      14.7500      1C.2500

       l.COOOO  4
       1.S7500  4
       1.55000  4
       1.52500  4
       1.50000  *                          1
       1.47500  4
       1.45000  4
       1.42500  *
       1.40000  4
       1.37500  4
       1.35000  4
       1.32500  4
       1.30000  4
       1.27500  4
       1.25000  4
       1.22500  4
       1.20000  4
1      1.17500  4
2      1.15000  4
       1.12500  4
       1.10000  4
W      1.07500  4
C      1.05000  4
I      1.02500  4
It     0.999998  4
A     0.974998  4                                                                    1
T     0.949998  4
B     0.924998  4
      0.899998  4                                           1
      0.874998  4
      0.849998  4
      0.824998  4
      0.799997  4                                   1                         11
      0.774997  4                                   1
      0.749997  4                                   1
      0.724997  4                                                                    1
      0.699997  4         1
      0.674997  4
      0.649997  4
      0.624997  4
    .  0.599997  4
    :  0.574997  4                                           11
      0.549997  4
      0.524997  4                                   1
      0.499997  4
      0.474997  4                                                             1
      0.449997  4
      0.4249*7  4
      0.400000  4

             8.00000      9.50000      11.0000      12.5000      14.0000      15.5000      17.0000
                    8.75000      10.2500      11.7500      13.2500      14.7500      16.2500
                                      3  AGE
                                                                                            58
-------
                                          Table 6.7

         ABSTAT  3.00    RASPBERRY STUDY,  EXPOS. RATE NORM.  FOR WGHT.  (UG/KG/HR)  VS AGE

         COMMANDS REGR

         MISSING VALUE  TREATMENT: LISTWISE

                              *** MULTIPLE LINEAR REGRESSION ***

         DEPENDENT  VARIABLE:  12 WGLRATE               14 VALID CASES
                                        FILE: 8301-P
         COEFP OF  DETERMINATION:   3.226E-02
         MULTIPLE  CORR COEFF:       0.179615
ESTIMATED CONSTANT TERM:       1.06819
STANDARD ERROR OF ESTIMATE:   0.258549
         ANALYSIS  OF VARIANCE FOR THE REGRESSION:
                                DEGREES OF     SUM OF      MEAN OF
         SOURCE  OF VARIANCE     FREEDOM      SQUARES      SQUARES
               REGRESSION          1       2.674E-02    2.674E-02
               RESIDUALS          12        0.802173    6.684E-02
               TOTAL              13        0.828915
                                                     CORRELATION
                          REGRESSION   STANDARDIZED      WITH
         VARIABLE        COEFFICIENT    COEFFICIENT    DEPENDENT
          3 AGE           -2.090E-02      -0.179615    -0.179615
                        F TEST
                      0.400043
                                                                                         "OO 
                                                                                         o» o n>
                                                                                         IO r+ O
                                                                                         fl> O «-»•
                                                                                           cr -*.
                                                                                         —•mo
                                                                                         -•4 -J 3
                                                                                         o —• o*
                                                                                         -•> 01
                                                                                           vo
                                                                                           00
                                                                                           in
LTI
-------
                                                     535
         APPENDIX A
QUALITY ASSURANCE PROJECT PLAN
                                                   60
-------
                                                          •w W I J I VII  I1U «
                                                         Date:   'November  10. 1982^
                                                         Page      1     "bT
                                                                        536
                  PESTICIDE EXPOSURE BY HARVESTERS
                   OF BUSHBERRIES, TRAILING-  AND
                             CANEBERRIES
                                by

                        John T.  Leffingwell
                          Hugh R.  McLean
                          Gunther  Zweig
                   Quality Assurance Project  Plan
                 Pesticide Hazard Assessment  Project
                       University of California
                        Berkeley, California
Approval:
Principal Investigator:	Date
Project QA Officer:	Date
EPA Project Officer:	Date_
EPA QA Officer:	Date
                                                                             61
-------
                                                                       re-
                                                      Section No.	2
                                                      Revision No.	0
                                                      Date:    November 10.  1982
                                                      Page    i     of    3
                      2.  TABLE OF CONTENTS

Section                                                       Page Reference
    1.  TITLE PAGE	   1 page
    2.  TABLE OF CONTENTS	
    3.  PROJECT DESCRIPTION	   2 pages
    4.  PROJECT ORGANIZATION	   2 pages
    5.  QUALITY ASSURANCE OBJECTIVES 	   3 pages
    6.  SAMPLING PROCEDURES                                      6 pages
        6.1  Dermal Exposure to Pesticides	   1 of 6
        6.2  Personal Aerosol Monitors   	   2 of 6
        6.3  Area Aerosol and Vapor Monitors	   2 of 6
        6.4  Foliar Samples	   2 of 6
        6.5  Soil Samples	   3 of 6
        6.6  Urine Samples	   4 of 6
        6.7  Sample Preservation	   5 of 6
        6.8  Forms, Notebooks,  Recordkeeping 	   6 of 6
    7.  SAMPLE CUSTODY                                           5 pages
        7.1  Field Sampling Operations 	   1 of 5
        7.2  Laboratory Operations	   4 of 5
    8.  CALIBRATION PROCEDURES  AND FREQUENCY                     4 pages
        8.1  Field Equipment	   1 of 4
        8.2  Laboratory Equipment	   2 of 4
    9.  SAMPLE PREPARATION                                       4 pages
        9.1  All Samples	   1 of 4
        9.2  Gauze Pads	   1 of 4
        9.3  Glove Samples	   1 of 4        62
-------
Section No.
538



Section
9 4 Aerosol Samples 	
9 5 Leaf Punch Samples 	
9 6 Soil Samples 	
9.7 Urine Analysis 	
10. DATA REDUCTION, VALIDATION AND REPORTING .
11. INTERNAL QUALITY CONTROL CHECKS 	
12. PERFORMANCE AUDITS 	
13. PREVENTIVE MAINTENANCE
13.1 Field Equipment 	
13.2 Laboratory Equipment 	
13.3 Spare Parts Inventory 	
14. SPECIFIC ROUTINE PROCEDURES USED TO ASSESS
PRECISION, ACCURACY AND COMPLETENESS
14.1 Field Sampling 	
14.2 Laboratory Analyses 	
14.3 Further Reduction 	
15. CORRECTIVE ACTION 	
16. QUALITY ASSURANCE REPORTS MANAGEMENTS . .
APPENDICES
A. Field Personnel Sampling Form 	
B. UCB Sample Identification Code 	
C. Laboratory Sample Log 	
D. Dermal Data Sheet 	
E. Monthly Laboratory Status Report 	
F. Study/ Investigation Monthly Status Report
G. Aerosol Data Form 	
Revision No. 0
Date: November 10, 1982
Page 2 of 3
Page Reference
	 1 of 4
	 2 of 4
	 2 of 4
	 3 of 4
	 5 pages
	 2 pages
	 1 page
2 pages
	 1 of 2
	 1 of 2
	 2 of 2
DATA -
2 pages
	 1 of 2
	 1 of 2
	 2 of 2
	 1 oaae
	 1 page
	 1 oaae
	 l nage


.... 1 oaae
	 1 page
	 1 cage
-------
Section
APPENDICES (Continued)
    H.  Leaf Punch Data Sheet .
    I.  Soil Sample Data Sheet
    J.  Crop Sample Data Sheet
    K.  Meteorology Data Sheet
                                                                        2
                                                                        0
Section No._
Revision No.."	
Da te:   	November 10, 19R2
Page	3     of   3
     Page Reference

        1 page
        1 page
        1 page
        1 page
53'
                                                                             64
-------
                                                                                    54(
                                                          Section No.	
                                                          Revision No.
                                                          Date: November 10, 1982
                                                          Page   1   of	2
                          3.  PROJECT DESCRIPTION
     The purposes of the proposed field studies to be conducted by the
California PHAP during the summer of 1983 are several fold:  1) The harvesting
activities of children in the cane-, bush- and trailing berry crops in all
likelihood presents a different pesticide exposure pattern to pesticides than
does strawberries because of substantial differences in the physical charac-
teristics between the former set of crops and strawberries.  Therefore, as an
extension of the "youth- in-agri culture" line of monitoring studies we will
attempt to determine the nature and extent of pesticide exposure suffered by
children or youths harvesting these crops.  This information will be compared
and contrasted with like information for corresponding groups of adults.
2) It is of considerable general interest to compare the excretion of pesticide
metabolites with whatever measures of dermal exposures that can be made on
workers.  Toward that end, two groups of subjects will be monitored in each
crop type.  One group will be monitored for dermal exposure to one or more
pesticides, while the other group will give urine samples for metabolite analy-
sis.  In each study both groups will engage in both activities so that they will
act as their own controls.  As usual cognizance will be taken of age, height,
weight and sex of the individual subjects in these studies.
     Since the pesticides under consideration are generally compounds with low
volatility (e.g., captan), exposures are expected to result from dermal contact;
consequently, this route of exposure will be given major emphasis in the con-
templated field studies.  Dermal dosimeters currently in use in this laboratory
consist of 12-ply, 3x3 inch surgical sponge (gauze pad) backed by a polythylene
                                                                                    65
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                                                                                  541
                                                          Section No.      3	
                                                          Revision No.     0
                                                          Date:  November 10,  1982
                                                          Page   2   of   2	
moisture shield enclosed in a paper envelope that has a circular face  opening
               2        2
to expose 28 cm  (4.3 in ) of the gauze pad.  This configuration allows for
rapid assembly of the dosimeter and easy attachment to carrier garments such
as T-shirts.  The potential for inhalation exposure will not be ignored,
however.  Both aerosol and vapor samplers will be placed in the field  for
general  monitoring.   At least two of the subjects will carry "breathing zone"
personal aerosol monitors.  These monitors will  be rotated among the subjects
at two hour.intervals.
     Since urine grab samples are of limited utility in interpreting metabolite
outputs, complete urine collections will be requested of the cooperating sub-
jects over the desired monitoring period.  These will be done in as close to
12 hour blocks ("day" and "night") as the subjects can manage.
     Environmental  samples will be comprised of foliar leaf discs (48  x 3 cm
diameter punches per sample), soil samples (6 scoops of 8 x 10  x 1 cm  deep
making in all  some 600-800 g of soil) representative crop samples and  area
aerosol  collections  which are backed up with charcoal tube vapor traps.  All
environmental  samples will be collected in the vicinity of the  workers  who are
being monitored.
     All samples will be placed on dry ice immediately after they have  been
collected.   At the completion of sample collection they will be transported on
dry ice  to the laboratory, transferred to a freezer and maintained at  -10°C or
colder until just prior to extraction.
                                                                                  66
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                                                          Section No.
                                                                                542
                                                                             o
                            Revision No.
                            Date:   November 10, 1982
                            Page   l
                                                                        of
                          4.   PROJECT ORGANIZATION
     The following chart shows the organizational  structure  for  developing and
implementing QA Project Plans within the Berkeley PHAP.   Acceptable  QA  procedures
will be developed in cooperation with, and under the supervision of, the OPP Quality
Assurance Officer.  It is understood that the EPA project officer (Dr.  C.W. Miller)
will function as the Quality  Assurance Officer in  terms  of:
     1)  providing guidance to the project on QA,
     2)  facilitating interlaboratory QA, and,
     3)  approving QA project plans.
The responsibility for the design and implementation of  acciptable QA procedures
lies with the Project Director of the Berkeley PHAP.
                             Quality Assurance
                              Officer OPP/HEB
                            EPA  Project Officer
                                NPHAP/HEB
                            Project Director
                             Berkeley PHAP
       Committee for the
 Protection  of Human Subjects
     Chief Chemist
Statistician
Field Studies
  Supervisor
                                                                                    67
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                                                          Section No.       4
                                                          Revision No.      0
                                                          Date;   November 10, 1982
                                                          Pa ge    2     of    2
        The Chief Chemist,  with  guidance  and concurrence of the Project Director,
is responsible for the design  and  implementation of QA procedures within the
laboratory.  He is responsible for supervising the laboratory's participation
in any inter!aboratory quality control  programs.  He also must design, schedule
and evaluate interlaboratory QA  procedures  to  insure confidence in the methodology
and facilities used for all  analyses.
                                                                                     68
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                                                                            544
                                                          Section No.      5	
                                                          Revision No.     0
                                                          Date;  November 10,  1982
                                                          Page  1    of    3
                       5.  QUALITY ASSURANCE OBJECTIVES
     The measurements to be attempted in this study center on the estimation
of dermal exposure of pesticides undergone by workers harvesting cane-, bush-
and trailing berries.  Since there is no standardized methodology for accom-
plishing this task, it is impossible to estimate accuracy.  However, components
of the method can be tested for precision and accuracy by employing the device
of spiked blanks.  These components include recovery of pesticide from gauze
patches, gloves, aerosol and vapor samplers, and the analytical  procedures used
to assay the recovered residues.
     For environmental samples, accuracy can be gauged by the taking of repli-
cate samples.  Spiked blanks are required here, as well, to determine precision
and accuracy.  Table 1.5 shows the best current estimates for the QA objectives
for the measurements to be made during the berry harvester studies.
     In reference to Table 1.5, since this study does not focus  on only one
pesticide or class of pesticides, the detailed methods of extraction and
analysis cannot be foretold, and, in fact, can be developed and  validated only
after the exact combination of pesticides applied to a given field (to be
monitored) have been reported.  In general, however, the following references
are applicable to Dermal Dosimeters:
     1)  McLean, H. R.; Futagake, S.; and Leffingwell, J. T.
         Loss of Paraoxon in aqueous acetonitrile extractions.  Bull.
         Environ.  Contam. & Toxicol.  18:247, 1977.
     2)  Popendorf, W.
         Exploring citrus harvesters exposure to pesticide contaminated
         foliar dust.  Am. Industr. Hyg. Assoc. 0.  41:652, 1980.
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                                                                           545

                                                         Section No.      5
                                                         Revision No.     o
                                                         Date; November 10, 198?
                                                         Page    2   of    3
to Aerosol  Monitors and Vapor Traps (for total  dust  and  various  pesticides):

     National  Institute for Occupational Safety &  Health
     NIOSH  Manual  of Analytical  Methods, 2nd Ed.

         vol.  1   DHEW (NIOSH) Publ.  No. 77-157-A
               2     "            "       77-157-B
               3     "            "       77-157-C
               4     "            "       78-175
               5     "            "       79-141
               6     "            "       80-125

to Soils:

     1)  Spencer,  W.  F., Cliath, M. M.,  Davis,  K.  R., Spear, R.C., and
        Popendorf, W.  J.
        Persistence of Parathion and its oxidation  to paraoxon  on the soil
  ?      surface as related to worker reentry into treated crops.  Bull.
        Environ.  Contain. Toxicol. 14:265,  1975.

     2)  Spencer,  W.  F., Iwata,  Y., Kilgore,  W.  W.,  and  Knaak, J. B.
        Worker reentry into treated  crops II:   Procedure for the deter-
        mination  of pesticide residues  on the  soil  surface.  Bull.  Environ.
        Contam.   Toxicol.   18:656, 1977.

to Foliar Surface  Residues:

 *   1)  Iwata, Y., Knaak,  J.  B., Spear, R.  C.,  and  Foster, R. J.
        Worker reentry into pesticide treated  crops I:  Procedure for the
        determination  of dislodgeable pesticide residues.  Bull.  Environ.
        Contam. Toxicol.  18:649, 1977.

     2)  Popendorf, W.  J.,  and Leffingwell,  J.  T.
        Procedures for the determination of dislodgeable dust on foliage as
        related to worker reentry hazards.   Bull.   Environ.  Contam.  Toxicol,
        18:787, 1977.

to Body  Surface Area:

     1)  Popendorf, W.  and  Leffingwell,  J. T.
        Regulating OP  pesticide residues for farmworker protection.  Residue
        Reviews 82:125, 1982.
                                                                                   70
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             TABLE  1.5

Quality Assurance of Objectives
Measurement EPA Experimental
Parameter Reference Conditions
Dermal
Dosimeters
Gauze
Gloves
Aerosol Monitors
Vapor Traps
Soils
Foliar Surface
Residues
Urine
Body Surface
Area
Time Of
Exposure

Spiked Blanks
Spiked Blanks
Spiked Blanks
Spiked Blanks
Spiked Blanks
Spiked Dust
Spiked Blanks


Pr(RSD)°n Accuracy Completeness

±5%
+ 5%
t 5%
is*
i 10% - 10%
+ 5*
i 10%
1 20%
1 5 Min. - 15 Min.
(2%) (5%)
                                                                                     •O O 30 t/>
                                                                                     Dl OJ  (T> 
                                                                                    U3 <-<• < O
                                                                                     (T> fD  —'• r*
                                                                                          -i. O
                                                                                          O 3

                                                                                       z   z
                                                                                       O Z O
                                                                                       < O •
                                                                                       tt>
                                                                                       n>
                                                                                              C_T
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                                                                                 b4/
                                                           Section  No.	6	
                                                           Revision No.       0
                                                           Date:    November  10, 1982
                                                           Pa ge    1     o f   6	
                          6.  SAMPLING PROCEDURES

6.1  Dermal  Exposure to Pesticides
     This project employs a gauze pad as  a  dermal  dosimeter for all areas of
the worker's body except the hands.   This dosimeter consists of a  12-ply 3x3
inch gauze surgical  sponge.  A polyethylene "moisture  barrier" is  placed on the
side of the pad facing the skin of the subject.  All of this is held  in a glossy
paper envelope.  The side of the envelope facing away  from the skin has a circular
                                     2
hole 60 mm in diameter exposing 28 cm  of the gauze pad.
     Body locations for mounting the gauze pad dosimeters are as follows:
     Head — mounted on the side of the head, roughly  over one ear, either stapled
             to a stretchable head band or taped to the inside of  the brim of the
             subject's hat (1);
     Chest — mounted over sternum between the pectoral muscles  (1);
     Back — mounted over backbone between the scapulae (1);
     Upper Arm ~ mounted over the deltoid muscles (2);
   l'  Lower Arm ~ mounted roughly midway between the elbow and wrist  on the
                  dorsal surface (2);
     Upper Leg — mounted roughly midway between the hip and the knee on the
                  anterior surface (2);
     Lower Leg — mounted roughly midway between the knee and  the  ankle on the
                  anterior surface (2).
     For ease of mounting and dismounting the patches, the  chest,  back and  upper
arm dosimeters are stapled to T-shirts which are dispensed  to  the  subjects  at
the beginning of the work period and collected at  the  end.   The  T-shirts  are to
be worn next to the skin of the subject so that the dosimeters will  be exposed to
                                                                                     72
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                                                                                548
                                                           Section No.__6____
                                                           Revision No.       0
                                                           Date:    November 10.  1982"
                                                           Page    2     of    6.

only that portion of the residues that would eventually reach the skin.  Lower
arm and upper and lower leg patches are taped directly to the skin in the appro-
priate location after the T-shirt is donned.  The subjects then wear whatever
clothing they would normally wear while working, except that if they were to strip
to the waist (which some male workers in California do) they would continue to
wear the T-shirt bearing the dosimeters.  A hand dosimeter consists of a light-
weight cotton glove.
6.2  Personal Aerosol Monitors
     Personal breathing zone air samples will be taken on at least two subjects
each working period of two hours.  An open-face, 37 mm Milltpore cassette
(0.8 y pore size) is mounted in a subject's breathing zone and is aspirated at
2.0 t 0.2 Lpm with a belt-mounted portable pump.
6.3  Area Aerosol and Vapor Monitors
     An open face 37 mm Millipore cassette (0.8 w pore size) will be stationed
centrally in an area of the field under harvest by our cooperative subjects.
This cassette will be backed up by a charcoal tube vapor trap as insurance against
vapor break-through from the filter.*
6.4  Foliar Samples
     Foliar sampling is accomplished by using a leaf punch equipped with a 3 cm
diameter die.  The punch-through action of the device pushes the leaf disk into
a 4-oz wide-mouth jar which is attached to the punch and which subsequently serves
as the sample storage container.  The punch is also equipped with a resettable
counter.
*It should be noted that the experience of this laboratory is that Millipore filters
possess an appreciable capacity for absorption of pesticide vapors.  However, since
the vapor retention characteristics of the filter for the particular pesticides1'
being studied here are unknown, a back-up charcoal tube (#226-01, SKC-West, Inc.)
is employed.
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                                                           Section No.	6_
                                                           Revision No.       0
                                                           Date:    November 10.  1982
                                                           Page    3     of   6
     Sample collection is accomplished by striking out diagonally across the area
to be sampled, stopping every three or four rows to take a single leaf sample.
The sampling points are to be distributed throughout the areas of the plants
that the harvester will actually contact.  This includes all parts of the plant,
both outer and inner canopy leaves, from either side of the row, and from the
center.   The standard sample size is 48 disks.  If the edge of the field is reached
before the full complement of leaf disks has been obtained, the person collecting
the sample merely reflects back into the field on a new diagonal.
6.5  Soil  Samples
     The soil sampling device has two parts: a three-sided form 10 cm long by
8 cm wide and 8 cm high, which when pressed into the soil surface blocks out an
          2
area 80 cm ; and a small rectangular shovel which fits just inside the form and
has a 1  cm high rim around the sides and back.  See Figure 6.1
     Sampling is accomplished by first shoving the form into the soil several
centimeters.  The shovel is then inserted vertically into the soil at the open
face of the form, likewise, several centimeters; and the soil is scraped away
from the form with it to a depth of several centimeters.  The shovel is then run
into the form horizontally so that it picks up a layer of surface soil the area
of the form and 1 cm deep.  The sampling pattern utilized for
taking soil samples is the same as for taking foliage samples, being collected
on a diagonal across the area being harvested by the cooperating pickers.  How-
ever, since there are only six "scoops" taken, rather than the 48 sampling sites
for the leaf punches, samples are taken every 8 to 10 rows.   Furthermore, samples
are taken in the area between the planted rows, which is where pickers are
physically located, and where they contact the soil.  Soil samples  are held in
a paper sack (#4 lunch bag) which is in turn placed in a plastic bag  to  reduce ___
moisture loss in frozen storage.
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                                                          Section  No.
                      550
                                                                             o
Revision No.
Date:    November 10, 1982"
Page	A
                                                                         of
Figure 6.1:  Soil Sampler
6.6  Urine Samples
     Subjects are requested to give all  of their urine in  approximately 12  hour
segments for at least 72 hours after exposure has terminated.   Initially they  are
supplied with three wide-mouth, 500 ml Nalgene bottles:  1) One  for the first period
of collection; 2) one for the next 12 hour period; 3)  and  one back-up for the  event
that it is ever needed.  The bottles are labeled with  the  subject's name, and
those that are intended for a specific time period have  the proper date and the
designation "day" or "night" on them.  In addition, there  is label  space for the
subject to write the actual times of first and last use  on the  label.  Thus, each
subject should have a full 24 hour's worth of bottles  and  a back-up to start out.
     The subjects are instructed to fill out the label on  each  bottle as it is
used in its appropriate time period and to either bring  the bottle to project
staff or to store it .refrigerated until  they can do so.  Each subject is given
a new set of bottles to replace the ones brought in to project  personnel  until -
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                                                           Section  No.	6	
                                                           Revision No.       0
                                                           Date:    November 10,  198T
                                                           Page     5     of   6
the 72 hour time  period is exhausted.
     Bottles are prepared by washing fn hot water with a strong laboratory
detergent.   After appropriate water rinsing, the bottles may be rinsed with
dilute hydrochloric acid if visual Inspection shows deposits.  Finally, they
are rinsed with acetone to remove traces of soluble organic material, air-dried
and returned to service.
6.7  Sample Preservation
     All  samples, regardless of type, are placed on dry ice as sampling is
completed.   They are kept thus in ice chests until they are delivered to the
laboratory, where they are transferred to a standard freezer chest maintained
at -10°C.   Samples are generally hand delivered to the laboratory by the field
study personnel  (which often includes laboratory personnel).   If the samples
cannot be  inventoried by the laboratory personnel  immediately upon arrival, the
samples are stored until the next working day in a different freezer from the
one customarily used for sample storage.  This procedure minimizes the possibility
of not logging a sample entering the sample storage freezer.
     In the unexpected event that samples are shipped by common carrier rather
than being  hand carried by project personnel, arrangements will be made between
the person(s)  shipping the samples and the laboratory to reduce the chance of
losing samples en route.  For short distances, our laboratory has found that
the "next bus  out" service of Greyhound Package Express is reliable and provides
overnight  (cool) service from anywhere in Northern California.  For longer distances
an appropriate air courier service is chosen.  Once the shipment has been made  and
the laboratory has been telephoned to confirm that face, the Chief Chemist arranges
for someone to meet the shipment at its expected time of arrival.
i
                                                                                     76
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                                                                                   55
                                                           Section  No.	6" '
                                                           Revision No.       0
                                                           Date:    November TO.  1982^
                                                           Page    6      of   6
     Samples are removed from the storage freezer only for the purpose of
inventorying and analysis.  During inventories, small numbers of samples are
removed at one time to prevent their thawing during identification and counting.
When samples are removed for analysts, they are withdrawn from the freezer in
groups only the size of the numbers of samples to be extracted, allowing the
minimum thawing time needed for room temperature equilibration.
6.8  Forms, Notebooks, Recordkeeping
     A form has been developed in this laboratory to record pertinent field
data on cooperating harvesters, and is included here as Appendix A.   This form
records a subject's personal data such as age, height, weight, his/her clothing,
work habits, and production (yield).  The form also provides for accumulation of
exposure data (contact time, times and flow rate for air samplers, and for
comments on irregularities in sampling, or subject's work).
     A field notebook is maintained as well  as individual  data sheets on the
subjects.  This book records the time, location, and quantity of all  environmental
samples taken, including data on all foliar, soil, and area air samples, regardless
of whether they were taken before, during, or after a harvester exposure study.
                                                                                77
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                                                          Section No.       7    553
                                                          Revision No.      0
                                                          Date:   November 10, 1982
                                                          Pa ge    1     of    5
                             7.   SAMPLE  CUSTODY
7.1   Field  Sampling Operations
     A.   Preparation of Sampling Supplies
         1)  Preparation of the  dermal dosimeters is described in Section 3
             of this document.   The  gauze sponges are preextracted by soxhlet
             in order to eliminate possible  interferences which this laboratory
             has previously encountered  with some batches of the sponges.  Once
             the samplers are assembled  they are labeled with their sample identify-
             ing code and stapled, when  appropriate, to the T-shirt carrier.   All
             head,  lower arm, and leg patches must be attached to the subject as
             he/she is about to  enter the study.  Lower arm and leg patches are
             taped  in place. Head patches are either taped to the subject's  hat,
             if one is worn, or  stapled  to a project-supplied hat or head band
             which  is worn as a  carrier.
         2)  The Millipore filters used  both for personal samplers and for area
             monitoring are prepared as  follows;
             Pre-weighing Procedure
             —Prior to use in the field, the filters are weighed in order to
             establish initial weights.  Six control filters are weighed at the
             same time in order  to quantify  weight gained due to moisture in the
             laboratory.
             —The  test filters  are  removed  from the sealed packaging and allowed
             to sit out on the laboratory bench for approximately 10 to 20 minutes,
             in order to equiibrate  with room moisture.  A paper is placed over
                                                %
             the filters to prevent any dust  from settling on them.  The control
             filters are prepared for weighing in the same manner.  The cassette
                                                                                    78
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                                              Section No.	7    554
                                              Revision No.	0
                                              Date:    November 10, 1982^
                                              Pa ge   2      o f    5
holders are prepared at this time and an identification number is
assigned to each one.  Once the filters are placed in the cassettes
they will be referenced by this number.
—An automatic electro balance having a digital readout in milli-
grams with four significant figures is used to weigh the filters to
1 5 micrograms.  The balance is calibrated using a standard 20 mg
weight prior to any filter weighing.  The calibration is repeated
intermittently during the weighing of both the control and test
filters to assure an accurate reading.  The filters are handled with
blunt-tipped forceps.  The weights for both the test and the control
filters are recorded and logged into a laboratory notebook.  The
test filters are entered and referenced by the number given to the
cassette in which they will be placed.  The control filters are
referenced by a number on the petri dish in which they are stored.
After the weight has been recorded the filters are assembled into
the cassettes and are then ready for field use.
Transport From Field
--At the completion of aerosol sampling the cassettes are placed
upright in a styrofoam holder to prevent Toss of dust from the
filter.  The holder is placed fn an ice chest with dry ice which
is then transported to the laboratory.
Post Weighing
—The post-weighing procedure is identical to the pre-weighing
procedure with the exception that the filters are placed in
individual, dessicating jars which contain anhydrous calcium sulfate.
                                                                    79
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                                                                           ;     55l
                                                      Section No.	7
                                                      Revision No.       Q
                                                      Date;    November 10,  1982~
                                                      Pa ge     3     of    5
        After removal of the moisture caused by the refrigeration process,
        the filters are placed on the laboratory bench and allowed to equili-
        brate with the room moisture.  The control  filters are prepared in
        the same manner.  The difference in weight  between pre- and post-
        weighing are logged into the notebook for each filter.  The same is
        done for the control filters.  The resulting dust weights are obtained
        by the difference in the pre- and post-weight minus the average weight
        gain for the six control filters.  The filters are then placed back
        in their cassettes and held in the freezer  to await chemical  analysis.
    3)  Leaf punches are cleaned of the build-up of leaf debris and dirt
        which accumulates on the punch and die while a sample is being
        collected.  This cleaning is performed with a household scouring pad
        made of either copper or stainless steel turnings.  Cleaning  is per-
        formed between samples and reduces chances  of any cross contamination
        between samples.
    4)  Vapor samplers are obtained commercially and are opened only  as they
        are to be used.  They are resealed immediately after sampling with
        the caps provided by the supplier.
B.  Procedures and forms for Recording Sample Acquisition Data
    This information  is put forth in Section 6.
C.  Documentation of Specific Sample Preservation Methods      ^  ^7
    Sample preservation methods are described in SectionsS of this document.
D.  Sample Labeling
    Each study is preassigned a "field" or study number.  This number,
    combined with the year, yields a unique study number.  Appendix B is
    the document supplied to the field personnel describing label generation.
                                                                                  80
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                                                          Section No.       7   556
                                                          Revision No.	0
                                                          Date    November  10, 198T
                                                          Page    4     of   5
     E.  Field Tracking Forms
         The subject data sheet and the field notebook act  as  field tracking
         devices.
7.2  Laboratory Operations
     A.  Laboratory Sample Custodian
         The Chief Chemist is the laboratory sample  custodian  and  as such  is
     responsible for inventorying samples  held for analysis or other processing
     and for maintaining all  necessary documents  pertaining to sample custody.
     These documents include  any shipping  receipts or  bills of lading and  a
     sample log book (see Appendix C).
         The sample log book  provides  for  the notation of sample identity,  date
     of sampling if known, date recieved,  date extracted, and  date analyzed.
     Sample numbers used within the laboratory are those assigned  in the field
     since those identifiers  are unique and  specify  useful  and pertinent data
     about the sample as well  (i.e., days  post application, sample type).
         Sample flow through  our laboratory,  and  thereby sample custody, follows
     the general pattern discussed hereinafter: when samples first arrive  at the
     laboratory they are placed in a specified freezer, "A", until  they can be
     logged into the laboratory log book.  After  they  are logged in they are trans-
     ferred to a second freezer, "B".   The  person responsible for extraction pulls
     batches of samples from  freezer "B",  noting  this  in the log book, performs the
     appropriate extraction procedure,  prepares the  sample  for analysis, and turns
     the batch over to the analyst.  The analyst  is  responsible for proper documenta-
     tion of the chromatograms and maintenance of sample integrity while he/she
     has possession of them.   The anaylst  also must  note the date  of analysis in
     the log book.   When analysis of the batch is complete, the samples are  r
                                                                                   8
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                                                     Section No.	7
                                                     Revision No.      0
                                                     Date:   November 10, 1982
                                                     Page    5     of   5
returned to the person who performs  the extraction and the samples are

prepared for archival  storage  and  replaced in freezer "B".
                                                                            82
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                                                          Section  No.	8    558
                                                          Revision NoT0
                                                          Date:    November 10, 1982
                                                          Page    1     of    4
                  8.  CALIBRATION PROCEDURES AND FREQUENCY

8.1  Field Equipment
     A.  Personal Sampling Pumps
         The personal sampling pump shall  be calibrated  before  and  after each
     day of sampling.
     1)  Allow the pump to run 5 minutes prior to voltage  check and calibration.
     Check the voltage.
     2)  Assemble the polystyrene cassette filter holder using  the  appropriate
     filter for the sampling method.   Ensure that the  luer adaptor  does  not come
     in contact with the back-up pad.
     3)  Connect the collection device,  tubing,  pump and calibration apparatus.
     The length of hose should be the  same as that used  in the  sampling  train
     (approximately 1 meter).
     4)  Check the seals on all Tygon  tubing connections.
     5)  Wet the inside of a 1-liter buret with  a soap solution.
     6)  Turn on the pump and adjust the pump rotometer  to the  appropriate flow
     rate setting.
     7)  Momentarily submerge the opening  of the buret in  order to  capture a
     film of soap.
   ;  8)  Draw two or three bubbles up  the  buret  in order to ensure  that  the
     bubbles will complete their run.
     9)  Visually capture a single bubble  and time the bubble from  0 to  1000 ml.
    10)  The timing accuracy must within t 1  second of the time corresponding to
     the desired flow rate.
                                                                              83
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                                                          Section No.        8       559
                                                          Revision No.       0
                                                          Date:   November  10,  1982~
                                                          Page   2	o f   4	
    11)   If the time is not within the range of  accuracy, adjust the flow  rate
    and  repeat steps 9 and 10 until  the correct  flow  rate is achieved.
    12)   Steps 9 and 10 shall be performed at least twice.
    13)   While the pump is still  running,  mark the position of the pump rotometer
    14)   A calibration curve can then be calculated.  The flow rate in LPM is
    placed on the x-axis and the rotometer reading on the y-axis.
     B.   Other Field Equipment
         In general, the relatively crude  measurements taken in the field, such
     as  height and weight of subjects, and time  worked, are effected with  devices
     whose calibration are not checked.  This includes carpenter's rule, a bath-
  ;   room scale, and various researchers'  wrist  watches.  The thermometers of the
     psychrometers, the thermo-anemometer  and any other meterological instruments
     are used generally uncalibrated, as well, since their data is used only for
     background information to describe the quality of the working conditions.
8.2   Laboratory Equipment
     A.  Balances
     1)   Electrobalance: the electrobalance used to weigh membrane filters for
     aerosol  monitoring is calibrated as described in Section 7 of this document.
                                                      •
     2)   Analytical  Balance:  the balance used to weigh analytical standards and
     smaller samples (SlOg),  when necessary,  is  a Mettler Model B-6 which  is good
  v*
     on  its vernier to at least 0.02 mg.   It  is  kept in a balance room out of
     normal  traffic flow and  laboratory activities.  The laboratory which  houses
     PHAP activities contracts for an annual  cleaning and calibration from a
     private  contractor.
     B.   Volumetric Glassware
         Whenever practical or necessary,  volumetric  glassware  is  Class  A or
     Class A serialized quality.  Further  calibration is  not  attempted.   In
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                                                                         _    56C
                                                      Section No.	8	
                                                      Revision No.       0
                                                      Date;    November 10,  1982
                                                      Pa ge    3     o f   4
non-critical applications such devices as tilt-dispensers and graduated
cylinders are employed.
C.  Analytical  Instruments
    The chromatographs are checked continually as they are used by injection
of standards.   When manual injections are done, more frequent standardization
is required (every three to five samples) than when a mechanical injection
device is used.  Furthermore, the analyst  is  required to choose standards
that clos:ely match the samples being injected.  When automatic injection is
being done,less frequent standardization is called for (every ten samples).
D.  Calibration Standards
    Chromatographic standards are obtained, whenever possible, from the
EPA repository  at Research Triangle Park, NC.  Failing that, they are
obtained from the following sources in the order of preference listed:
1)  Commercial  suppliers (e.g., Chem. Services, Westchester, PA) as
    crystalline solids or neat liquids.
2)  Pesticide manufacturers as crystalline solids or neat liquids.
3)  Commercial  suppliers as a solution in organic solvent (e.g., Nanogens,
    Supelco, Van'an Associates, etc.).
4)  Pesticide manufacturers as technical grade materials.
5)  Pesticide manufacturers as formulated pesticides.
    Standards,  unless obtained as a solution, are weighed out with an accuracy
sufficient to give at least four significant figures.  Dilutions are made in
hydrocarbon solvents, at least for the concentrated standards.  This is done
to minimize the possibility of chemical reactions between solvent and solute.
If reactive solvents, such as ketones, alcohols, or nitrogen containing
compounds, are  required by the analytical method being used, they are used
only to make up the working standard.
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                                                     Section No.	8   56 I
                                                     Revision No.       0
                                                     Date;    November 10, 1982"
                                                     Pa ge    4     o f   4	
    As few dflution  steps as possible are used to obtain a working standard,

thus reducing errors inherent  in such operations.
                                                                           86
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                                                                                   56;
                                                           Section No.	9
                                                           Revision No.      0
                                                           Date;    November 1o, 1982"
                                                           Page    1     of   4
                      9.  SAMPLE PREPARATION PROCEDURE

9.1  All Samples
     Sample preparation date and extraction volume are entered in the spaces
provided in the sample log book.
9.2  Gauze Pads
     Samples consist of one or two pads (one for main trunk or two for limb sample
sites) stapled inside paper application packs and stored in an RPE Zip-lock bag
in the freezer.  After the staples are removed, the pad together with the plastic
moisture barrier is transferred to a 125 ml LPE wide-mouth bottle.  Thirty ml of
toluene, some of which is used to rinse the bag, are added and the sample is
shaken at about 200 Hz for one hour.  A ten ml aliquot of the analyte  is reserved
in a glass polyseal vial and an auto-sampler vial is prepared from the remainder.
9.3  Glove Samples
     These are treated in a manner similar to the patches.   The gloves are frozen
one pair to a sample bag and 100 ml of toluene is used to extract a pair.  If a
subject wore more than one pair, the solvent is increased only to the extent that
an aliquot can be easily removed and the actual amount of solvent is noted.
9.4  Aerosol Samples
  S  These are Millipore disposable cassettes with 37 mm membrane filters.  An
extraction bottle is chosen so that the filter can be dropped directly in from
the cartridge without handling.  Loose dust is washed off the cassette with hexane
because the plastic is soluble in toluene.  The hexane is allowed to evaporate and
the pesticide extracted with 30 mL of toluene.  The sample is stored as above
under the procedure for gauze pads.
                                                                              87
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                                                           Section No.       9
                                                           Revision No.      0
                                                           Date:    November 10, 1982
                                                           Pa ge    2     o f   4
9.5  Leaf Punch Samples
     The standard leaf punch sample consists of 48 - 3 cm leaf disks frozen in
a glass jar with a water-tight plastic cap.  After a half-hour thawing period,
the leaves are dumped into a one pint square Mason jar with the standard lid and
ring closure.  Six drops of a 20 mg/L solution of dioctyl sodium sulfosuccinate
is added to the sample jar.  The surfactant and dust are transferred quantitatively
to the Mason jar with 100 ml of distilled water.  The leaves are shaken for 30 min.
on a shaker table at about 140 Hz and the liquid decanted into a 500 ml separatory
funnel containing 50 ml methylene chloride (dichloromethane), using a narrow-stemmed
funnel to avoid transferring leaves.  The process of washing the leaf disks with
surfactant and water is repeated twice more.   Then the leaves are counted and
discarded.  The separatory funnel is shaken 30 times and the organic layer is
drained through a small funnel plugged with glass wool and filled with anhydrous
sodium sulfate into a 500 ml round-bottomed f 24/40 flask.  The extraction proced-
ure is repeated twice more with fresh 50 ml aliquots of solvent and the combined
solvent is rotary evaporated to about 1 ml.  Ten ml of toluene is added and
evaporated.   This process is repeated twice more.  The final residue is quantita-
tively transferred to a 10 mL volumetric flask and made up to volume.  The sample
is ready for gas chromatographic analysis.
     The dust which was washed from the leaf surfaces remains in the interfacial
layer in the separatory funnel.  This material is filtered onto a pre-weighed
glass fiber  filter, dried at 110°C overnight and cooled in a dessicator.  Post-
weighing of  the filter yields the foliar dust weight.
9.6  Soil  Samples
     The sample is weighed and sifted through a #10 sieve (2 mm hole size) to break
up lumps and remove rocks, twigs and leaves.   The trash is weighed. The sifted    Qg
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                                                           Section  No.        9
                                                           Revision No.       0
                                                           Date:    November 10.  198
                                                           Pa ge     3     o f   4
sample is mixed and a 250 ml portion 1s transferred to a tared beaker and weighed;
the beaker is then placed in an ambient-temperature vacuum desiccator and dried
for 24 hours or more and reweighed until the residual  moisture is less than 0.5%.
A glass soxhlet thimble is prepared by placing 1.5 cm of acetone washed sand in
the bottom to protect the extra-course frit from fouling by soil fines.  The
thimble is tared and about 30 gm of desiccated soil is added, the weight being
taken to four significant figures.  The thimble is then placed in a 250 mL soxhlet
extractor and cycled for four hours.  The solvent is azeotropic acetone-hexane
C59* - 41%).  After extraction the solvent is removed by rotary evaporation and
replaced by a solvent compatible with the analytical method to be used - toluene
for GC only, acetronitrile for GC plus HPLC.  This method is applicable only to
those low-volatility chemicals expected to be found during this study.
9.7  Urine Analysis
     A.  Captan
         Tetrahydrophthalimide (THPI) is the major urinary metabolite of captan,
     with tetrahydrophthalamic acid being a closely related minor metabolite.
     There are two analytical methods for THPI.  The former Idaho PHAP project
     developed a method (Brokopp, c.D.   Annual Progress Report, Idaho Epidemiologic
     Studies Program, EPA Cooperative Agreement, #78-CX-0363, April 1980) and a
     newer version is being published by Schoen and Winterlin (Schoen, S.R. and
     W.L.  Winterlin. "Gas Chromatographic Determination of the Captan Metabolite
     Tetrahydrophthalimide (THPI)in Urine," submitted for publication, 1982.
         The major difference between the methods is the fact that the THPI is
     derivatized for EC analysis in the former, while in the latter, the metabolite
     is analyzed as underivatized THPI on a nitrogen-phosphorous detector equipped
     gas chromatograph.  The clean-ups for the two methods diverge somewhat due to
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                                                      Section No.	9      565
                                                      Revision No.       0
                                                      Date     November 10,  1982
                                                      Page     4     of   4
the facts that they were developed for analysis on different detectors and
that they were developed in different laboratories.  We will develop the
Shoen-Winter!in method in our laboratory first, given that the elimination
of a derivatization step simplifies the procedure.
B.  Organophosphate Insecticides
    There are six alkyl phosphate metabolites commonly observed as a
consequence exposure organophosphate pesticide.  One or two of these are
possible from any given OP compound, depending on its structure.  Reid
and Watts have published a method for derivatizing these metabolites without
the use of the explosive and powerfully carcinogenic reagents (diazoalkanes)
required by older methods [Reid, S.J. and R.R. Watts.  "A Method for
Determination of Dialkylphosphate Residues in Urine."  J. Anal. Toxicol.,
5, 126 0981)].
                                                                              90
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                                                                                   c//
                                                            Section No.       10    JOD
                                                            Revision No~0
                                                            Date:    November 10,  1982
                                                            Page     i     of   5
                         10.  DATA REDUCTION, VALIDATION
                                   AND REPORTING
 10.]. Agricultural Chemical Samples
     Most samples yield four (4) data items:
     1)  Sample vol., is the final  amount of  solvent in which the
         entire amount of analyte from the sample is contained.  This
         number is determined by the technician who prepares  the sample
         and records it with the extraction date in the sample log.
     2)  Injection vol., is the amount of sample actually injected
         into the GC or HPLC.   This number is recorded  by the instrument
         operator along with the sample number on the recording tape of
         the HP3390A (GC) or the Water's Data Module (LC) Integrator,
         i.g., 2 yL of a 1:1000 dilution are  injected,  the operator
         records, inj.  vol. = 0.002 yL.
     3)  Counts per injection:  This is the area under  the peak in yV-sec.
         as determined by the integrator for  each injection.
     4)  Response factor:  This is  an arithmetical  expression of the sensitivity
         of the entire analytical system to the injection of  a standard of
         known volume and concentration and has the units- R  = C/ng.   If C~  is
         the average count for 2 or more identical  standard injections with a
         RSD < 5% then R = C~ * inj. vol. * std. cone, proof:
                          __L-x   M  v ML  = J_
                            inj  *    yL  * ng    ng
     At the conclusion of a day's run the integration record  is logged in thus:
The values for each standard are entered, averaged  and  the RSD verified.  The
response value is calculated.
                                                                                  91
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                                                            Section  No.    10
                                                            Revision No.       0
                                                            Date:    November  10.  1982
                                                            Pa ge      2    o f   5
    The sample  number,  Injection volume and the replicate peak areas are
recorded,  then the sample number 1s looked up 1n the sample log and the analysis
date recorded there, finally the extraction volume 1s transferred from the log
to the laboratory notebook.  Since we want the final result to be 1n yg of
analyte per sample the following formula 1s used:
                                                                ug
                T inj volume  T  resp factor x ext volume =   sample
Note that prior to multiplication by the extraction volume ngA± convert £§r_
acidens to yg/mL.  Proof:
     The principal criteria used to validate data integrity will be agreement
 between the records and calculations and transfer checks done by the analytical
 chemist on randomly selected samples,
     For the purpose of this study, an outlier  is defined as an analytical
 data set with a relative standard deviation greater  than 10%.  Because  several
 samples are taken from different parts of each  subject's body, the  unacceptable
 sample is held until the rest are completed and the  whole body dose calculated.
 The effect of the uncertainty on the whole body dose can then be estimated  and
 a proper decision made as  to whether the sample must be cleaned and concentrated.
     The following data flow and reporting scheme refers to  the Sample/Data
 Flowchart, Figure 10.1.
                                                                                 92
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Figure 10.1:    Sample/Data Flowchart
                                                                            568
                                                                    ro
                                                                      0
                                 Section No.
                                 Revision No."	
                                 Date: November 10. 1982
                                 Page    3     of   5
    Subject
     Data
    Sheets

    Field
  Notebooks
Sample Collection
       and
Number Assignment
        I
                  Sample
                 Storage
                 Sample
                Extraction
               Chemical
               Analysis
               Chemical
             Calculations
              Industrial
              Hygiene
              Evaluation
            Statistician
                  JL
                Samples
                              Sample
                              Log
                                              Laboratory
                                               Notebook
                            Data
                            Sheets
                              J
                          Computer

                          Work-up
                                 1
                            Final Report
Laboratory
 Archives
                                                                          93
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                                                            Section No.     10
                                                            Revision No"!       5
                                                            Date : _ November 10, 1982
                                                            Page      4    of  5
     The process begins in the field when the Industrial hygienist fills
out a field data sheet on each worker and assigns a subject number to him/her.
A specimen field data sheet is in Appendix A.  As each sample is collected a
number is assigned to it in accordance with the protocol described in Appendix
B.  The samples are placed in dry-ice chests for the trip to the laboratory.
The field data sheets and the spray history are given to the senior scientist
for later use.
     Upon arrival at the laboratory, the samples are transferred to the deep-
freeze for storage and the sample log is filled out through the storage date
column.  The Chief Chemist and the analytical chemist later verifying the log
against the samples with a physical inventory.  The samples are turned over to
the technician who organizes them by type for extraction.  As each sample is
extracted the date and volume is entered in the log and the extracts are turned
over to the analyst.  After the extracts have been run, they are returned to the
technician for storage.   The raw data is transferred from the instrument print-
out to the laboratory notebook,and the printout is dated, coded and filed.  The
extraction volume and any special data (e.g. crop weight) are entered into the
laboratory notebook from the sample log and the analysis date entered in the log
book.  The calculations are performed and the results entered in the laboratory
notebook and the data sheets (see Appendix D).  The data sheets are checked
against the laboratory notebook by the analytical chemist and given to the senior
scientist who uses the field data sheets to perform additional calculations.  The
completed data sheets are turned over to the statistician who enters  the data
on the computer and does the statistical work-ups.  The computer output and all
the data sheets finally go to the Project Director for use in preparing the
final report.                                                                _(
                                                                                   94
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                                                                              570

                                                          Section No.    10	
                                                          Revision No.      0
                                                          Date:    November  10, 1982]
                                                          Page     5     of  5	
     The field notes, data sheets, and laboratory notebooks are retained by

the senior project chemist or/and project director for seven years.
                                                                             95
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                                                            Section  No.   n     g 7 i
                                                            Revision No"!       o^ ' '
                                                            Date:    November 10.  1982"
                                                            Page       i   of  ?
                      11.  INTERNAL QUALITY CONTROL CHECKS

     As  many samples as possible are collected in duplicate.  However, the
majority of the personal monitoring samples are unique.  Leaf punch, and soil
and airborn pesticide samples are regularly collected in duplicate or more.
     In  the laboratory, method checks are conducted by analysis of replicated
spiked samples and blanks.  These method checks are employed during routine
analysis, as well  as regular calibrations described in Sections 7 and 8 of
this document.  Reagent (solvent blanks) are also a regular feature of the
internal checks employed by this laboratory.
     Replicates and frequent calibrations are the backbone of our quality
assurance program  for chromatographic analysis.  At least duplicate injections
are used whether manual or automatic sample introduction is used.  The analyst
is required to check results for each sample for reproducibility and if the
relative standard  deviation exceeds 10% (5% is the laboratory goal) then
additional  injections are required until satisfactory results are achieved.
     Spiking leaf  punches, soil  samples, and to some extent, dermal dosimeters,
will be  accomplished by the use  of pesticide-laden dust.  This material will
be formulated by dosing a quantity of pre-sieved soil dust which has been
checked  for freedom from residues of the compound(s) of interest.  In this way
a supply of dust carrying a known and verified concentration of pesticide(s)
can be kept on hand for spiking  into the various substrates which will be analyzed.
A supply of unspiked dust from the same source will  be kept on hand for blanks.
     This technique offers advantages over spiking pesticides carried in organic
solvent, especially in the case  of checking foliar dislodgeable residue
methodology.
                                                                                  95
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                                                                                  572
                                                           Section No.    n
                                                           Revision NoT      0
                                                           Date :   November 10, 198
                                                           Page _ 2_of__2
     The schedule of internal quality assurance samples to be run varies
for different types of samples.  The bulk of the samples to be analyzed are
dermal pads.  They are run in batches of 30 samples at a time and two of
those samples will be either a blank and a spiked blank or two spikes.  Since
aerosol samples are extracted and analyzed in an identical fashion as gauze
pads, they will be included with the gauze pads for quality assurance purposes.
     Since soil sample processing is not consistently a batching process,  a
target of 10% of those processed will be QA samples.   Leaf punches are run in
batches of up to fifteen samples in number, and one of each batch, at least,
will be either a blank or a spiked blank.  Since only two or three vapor traps
are extracted at a time, at least one spiked blank will  have to be included in
the process.
     The results of these quality assurance sample analyses will  be accumulated
in a laboratory notebook especially set aside for this purpose.   A summary of
these results will be reported monthly along with the regular monthly reports
forwarded to the EPA project officer.
                                                                                  96
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                                                                           573
                                                          Section No.    12
                                                          Revision No.      0
                                                          Date:    November 10.  198T
                                                          Pa ge	]_p f   i	
                            12.  PERFORMANCE AUDITS

     A plan  for external performance audits 1s currently being developed
with the  cooperation of the chemists at the Pesticide Hazard Assessment Project
located at the University of Iowa, and under the guidance of our Project
Officer.  When this plan 1s complete*it will be forwarded for inclusion in
this document.
                                                                                  97
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                                                           Section No.     13     574
                                                           Revision No.      0
                                                           Date;   November  TO, 1982"
                                                           Pa ge	L__° f_2	
                           13.  PREVENTIVE MAINTENANCE
13.1  Field Equipment
     Generally, cleaning and recalibration (for air sampler pumps) provides
project personnel an opportunity to inspect field sampling equipment for
deficiencies before use.  Leaf punches require sharpening at long intervals
(dnce every two or three seasons) in order that they continue to cut the tough
fibers in xeomorphic leaves (e.g., citrus).  This is performed  on an as
needed basis.
13.2  Laboratory Equipment
     The analytical balance used by the project received an annual cleaning
and recalibration (see Section 8).
     Gas chromatographs are maintained in the following ways:  All carrier
gases are passed through moisture and oil traps to prevent fouling the columns
and detectors, and the trap elements are changed every four (250 SCF) cylinders.
Carrier gas intended for electron capture detectors is passed through an oxygen
scrubber, as well, which is changed after six cylinders of gas have passed
through it.  Electronics are checked by performing an electrometer zero and
noise check monthly or whenever a new method is set up.  The voltage profile
for the ECD is also checked and recorded.
     The digital integrator-printer-plotters used by the project perform a self
check every time they are turned on.  They require a cleaning of paper debris
each time a new role of plotter paper is installed.
     The auto sampler is operated on compressed air which is passed through
a particulate and water trap before use.  The performance of the auto sampler
is checked daily by careful examination of results obtained from calibration
standards and solvent blanks which are interspersed among samples.           •-
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                                                           Section No.   13
                                                                               I	71"
                                                           Revision No.      o J / J
                                                           Date:   November 10, 1982"
                                                           Pa ge     2    o f2	
     The liquid chromatograph automatically does a self diagnostic test when
powered up.   Solvents are prefiltered through a 0.22 y filter to prevent
particulate  damage to the system.
13.3  Spare  Parts Inventory
     Air monitoring pumps use rechargeable battery packs, which periodically
fail, so spare battery packs are kept on hand.
     The gas chromatographs have spare EC power supplies (3), a fan motor,
                                                                     \
heater cartridges for detectors, and injectors and an FPD igniter coil.   There
are three spare tritium ECD cells of unknown condition.  Regular supplies such
as septa, columns, and packings, are procured as necessary on an ongoing basis.
     The integrators have ho spare parts, except the thermal  printer/plotter
paper they consume.
     The auto sampler is backed up with extra syringes and needles, air filter
cartridges and consumable (vials, caps, and septa).
     The liquid chromatograph has a spares kit supplied by the manufacturer
which is kept up when pieces are used.
                                                                                99
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                                                                               576
                                                           Section No.   14	
                                                           Revision No.      0
                                                           Date:   November 10. 198T
                                                           Pa ge	L__° f   ?	
              14.  SPECIFIC ROUTINE PROCEDURES USED TO ASSESS DATA
                       PRECISION, ACCURACY AND COMPLETENESS

14.1  Field Sampling
     Standardized sampling procedures are employed to assure accurate
environmental monitoring (i.e., respirable dust and pesticides,  dislodgeable
residues, soil residues, crop residues, and meteorological  conditions).   The
patch technique used as a dermal dosimeter is unproven, although it is^ generally
accepted as the best technology available at present.   Therefore, the accuracy
of the data generated by the technique is unknown.
14.2  Laboratory Analyses
     All chromatographic analyses are run at least in duplicate.  Relative
standard deviations are calculated and any samples giving greater than 10% RSD
are re-analyzed.  At the discretion of the analyst, a lower RSD  criterion may
be used.  The general precision goal for the laboratory is  five  percent.
Standards are weighed by the most experienced chemist in the group.  Working
standards are cross-checked against one another.
     Calculations are recorded in the laboratory .notebook  assigned to the
project and are double checked by the analyst as he/she performs them by  re-
calculating every third or fourth sample.  The data is then triple-checked by
another person by their recalculating 10% of the samples.
     An absolute quality criteria is used independent of any statistical  test,
viz., any recovery and duplicate analyses not meeting the objectives specified
in Section 5 will cause the corrective actions described in Section 15 to be
initiated.
                                                                             100
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                                                           Section No.   14     577
                                                           Revision No.       0
                                                           Date:    November 10,  1982
                                                           Page    2      of   2
14.3  Further Data  Reduction
     Results  1n  term of mass of target chemical  (pesticide)  reported  by  the
laboratory are then transcribed into computer-readable  format  for  further
calculations.  This operation is also double  checked  either  on a 10%  or  100%
basis depending  upon the computer employed.   Finally,the  results are  tran-
scribed onto  appropriate data sheets and forms  as  indicated  by Appendices F
through J  for aerosol,  leaf punch, soil, crop,  and meteorology data,
respectively.  The  dermal  data reduction sheet  was previously  shown in
Appendix D.
                                                                            101
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                                                           Section No.    15       578
                                                           Revision No.      0
                                                           Date:   November 10. 198JT
                                                           Page	i   of   i	
                              15.  CORRECTIVE ACTION
     Whenever any evidence appears to indicate trouble or insufficiency  1n
sampling, analytical or data reduction activities or equipment,  efforts  are
mounted to trouble shoot and correct any problem to the satisfaction  of
responsible project personnel.   Specific procedures depend upon  the nature
of the problem and the ability of the personnel  dealing with  it  to interpret
the symptons.
     Acceptable limits for the performance of equipment, and  thereby  the data
produced by it, is described in other sections of this document.  The res-
ponsible person for initiating corrective action is that person  who detects
the problem, be it malfunctioning equipment or data that does not appear
correct or consistent.   Of course,  the ultimate  responsibility lies in the
hands of the Chief Chemist and  the Project Director.
                                                                                  102
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                                                           Section  No.    16      579
                                                           Revision No.      0
                                                           Date:    November 10. 1982
                                                           Pa gei	of    i	
                16.   QUALITY ASSURANCE REPORTS TO MANAGEMENT

    Quality  assurance  results will be accumulated  1n a QA notebook.  These
results will  be  summarized  1n the  Monthly  Report format which  1s sent to the
PHAP Project  Officer (see Appendix E).  Monthly reports are prepared jointly
by the Project Director and the  Chief Chemist.
    The  final report will  Include a QA section which summarizes the Monthly
Reports and discusses any QA Issues which  come from an analysis of the'QA
data.
I
                                                                             103
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                                                                    Date:   10 November  1982
                                                                    Page      1     of   1
                                   University of California
                                   School of Public Health
                        Biomedical  and  Environmental Health Sciences
                                    Berkeley, CA  94720
                                                                                        580
                                Field  Personnel Sampling Form
une and Number of Experiment:

tte of Experiment:

             Subject  Name
                                             Subject  Number     Sex    Age    Weight    Height
iration  of Exposure:

      Time In:           Time Out:
                                           Time  In:
                                                            Time Out:
Total Hours:
lothing Description  (type,  parts  of body not covered, hat, etc.):
    Habits:   (Working  position, observed foliar contact, etc.)
lusual  Observations:
amp!ing Patches (Nos.):
                                                              Pump No.:
ead       	
hest
ack       	
pper arm  	
Dwer arm  	
pper leg  	
Dwer leg
jnds (glove?]
ther
                (L)
                (L)
                          (R)
                          (R)
                                                              Collector Type

                                                              Flow Rate:
                (L)        (R)
                                                                                      m3/hr
                                                       Name of Recorder (Observer)

                                                                   .     104
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                                                           section HO. Appendix  B
                                                           Revision No.     0
UCB SAMPLE IDENTIFICATION CODE                             Date:  10 November 1982
                                                           Page     1     of   1
                                                                                  581,
Year (a)      Plot (b)       Sample  Type  (c)     Days Post-     Replicate (e)
                                               Appl. (d)
(a)  Last two digits  of year,  e.g.,  80,  81, or 82
(b)  Plot number (2 digits)  assigned by  project supervisor
(c)  Sample Type
     A = aerosol
     C = crop sample
     P = punch sample
     S = soil
     V = charcoal  or  other vapor collector

     Patches, etc.
     H = head                     Ch =  chest
    UA = upper arm                UL =  upper leg
    LA = forearm                  LL =  lower leg
    St = stomach                  Sh =  shoulder
                                  LB =  lower back
(d)  Days post application,  0  =  day  of application, 1 * first day after,  etc.,
     (PA is preapplication).
{e)  Replicate number for  duplicate  sample, different people, etc., as necessary
     and recorded  in  field notes.
                                                                           105
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               t—i

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              Laboratory Sample
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-------
                                                               Section No.  Appendix  p
                                                               Revision No.     0
                                                               Date:  10_Noyember 1982
                                                               Page _
1
of   1
(7/81) DERMAL DATA SHEET
                                                                                   583
STUDY:
DATE:
LAB LOG:
(b)
chen. mass



*
pads



(c)
time
COMPOUND:

Pad Area:
(d)=(a)(c) (e=(b)/(d)
hr-cm2 rate density

cm2 (a)
(f)
LOC AREA

(h)=(e)(f)
rate
{cation/ ID
• HD
• neck
*
' SH
• BA
**BA
' CH
St
**CH
hips
UL
LL
«>
*
feet
UA
LA
GL
( 9)

















n

















hours



































( q/hr.cm2)

















cm2
1075
230
*1300
1305
1540
**2190
1540
720
**2190
1750
3460
2590
*6050
1230
1860
1290
1075
( 9/hr)
•
















    Indicates area as  sum of previous two locations.

    Areas to be used where no shoulder or stomach pads included.
                                                                               107
-------
                                                                   Date:  10 November 1982
                                                                   Page     1     of   1
                                               Date Report Prepared:
                             MONTHLY LABORATORY STATUS REPORT
                                                                                          584
-ticipating  Pesticide  Hazard Assessment Projects
ile of Study/Investigation:	
-1od of Report:	
to
Substrate
(List chemical residue
to be analyzed for
or
test to be performed)







Number
of
Samples
on
Hand






\
Number
of
Samples
Received
or
Collected
During

Report
Period


Total
Number
of
Samples
to be
Analyzed






Number
of
Sampl es
Completely
Analyzed
During
Report
Period




Numbers
of
Samples
Remaining
to be
Analyzed





j
-------
                                                                 section no. appendix
                                                                 Revision No.     0
                                                                 Date:  10 November  1982
                                                                 Page     1     of   1
                      STUDY/INVESTIGATION MONTHLY STATUS REPORT


*Title of Study/Investigation:	
 Participating  Pesticide Hazard Assessment Project
 Period of Report	to .	Prepared by

 Cognizant HEB  Staff Member	
iI.  Ltst Tasks  Completed During Report Period:
II.  List Tasks Started on Schedule During Report Period:
 II.  Tasks Behind Schedule:
 IV.  Why are Tasks  Behind Schedule? - Account for each Task:
 V.  Action  or Assistance Needed to Complete Tasks - Include New Start and/or
    Completion Dates.
                                               Date Report Prepared:	585
I
                                                                                      109
-------
                                                     	10IX
                                           Revision  No.	
                                           Date:   10 November 1982
                                           Page      1     of
                          1
          University of California
          School of Public Health
Biomedical  and  Environmental Health Sciences

            Aerosol Data Form
                           586
Study ID#
/Chemical
VOL
PERSONAL ID# SAMPLE ID# TIME AIR (L)
"







































DUST
CONC.
Wt(mg) mg/m3




















PESTICIDE
CONC.
Wt( g) g/m3




















POOLED
GROUPS

>








                                                             110
-------
                                                                Section No.  Appendix  H
                                                                Revision No.     0
                              University of California           Date:   10 November 1982
                              School  of Public  Health            Page
                     Biomedical  and Environmental Health Sciences
                                       T
     of    1
                                                   587
                                 Leaf Punch  Data  Sheet
                    Study ID*
                          /Chemical
                             (Area of 3 cm punch  =  7.07 cm)
I.  Preapplication Samples:

                        f OF
    SAMPLE ID#   DATE  PUNCHES
AMOUNT OF              99
PESTICIDE  CONC.  yg/cirT   DUST mg/cnT
CONC. ON
DUST, ppm
II. Post Application
1. Interim Decay Samples
# OF AMOUNT OF ? ? C0fi;c- ON
SAMPLE ID# DATE PUNCHES PESTICIDE CONC. pg/cnT DUST mg/cnT DUST, ppm




























2. Field Study Samples
f OF AMOUNT OF 22 CONC' OI>!
SAMPLE ID# DATE PUNCHES PESTICIDE CONC. yg/cuT DUST mg/cirf DUST, ppm













•














3. Post- Field Study Samples
f OF AMOUNT OF 22 CONC* ON
SAMPLE ID# DATE PUNCHES PESTICIDE CONC. yg/cnT DUST mg/cm DUST, ppip



	 	























11
-------
                                                                                         T
                                                                   Revision No.      0
                                    University of California       Date:   10 November 1982
                                    School of Public Health        Page
                          Biomedical and Environmental Health Sciences
                                        1
                                    of
                          Study  ID#_
                                       Soil Sample Data Sheet
                                     (Area per scoop = 77.4 cm )
. Pre Application Samples
NO. OF SOIL * AMOUNT OF CONC.
SAMPLE ID# DATE SCOOPS WT.(g) H20 PESTICIDE vg/cm2 PPM











,




'II.  Post Application
     1.  Interim Decay Samples
                            NO. OF   SOIL
     SAMPLE  ID#       DATE  SCOOPS  WT. (g)
            %
           H20
       AMOUNT OF
       PESTICIDE
                 CONC.
                vg/cm2
                  PPM
      2.  Field Study Samples

                            NO. OF
     SAMPLE ID#      DATE   SCOOPS
 SOIL
WT. (g)
 %
H20
AMOUNT OF
PESTICIDE
 CONC.
yg/cm2
PPM
      3.  Post Field Study Samples
NO. OF SOIL % AMOUNT OF CONC.
SAMPLE ID# DATE SCOOPS WT. (g) H20 PESTICIDE vg/cm2 PPM





-


























                                                                                        112
-------
                                                             Section No.  Appendix  j
                                                             Revision No.      0
                           University of California          Date:   10 November 1982
                           School of Public Health           Page     1      of    1
                  Biomedical and Environmental Health Sciences
                              Crop Sample Data Sheet

                    Study  ID#	/Chemical
589
Preapplication  Samples

                         NO.  OF
SAMPLE ID#       DATE     FRUITS     FRUIT WT.  (g)     PESTICIDE (  g)  yg/cm2
 PPM
Post Application
1.  Interim Decay Samples
NO. OF
SAMPLE ID# DATE FRUITS FRUIT WT. (g) PESTICIDE ( g) yg/cm2 PPM




























2.  Field Study Samples
NO. OF
SAMPLE ID* DATE FRUITS FRUIT WT. (g.) PESTICIDE ( g) yg/cm2 PPM




























                                                                              113
-------
                       University of California
                       School of Public Health
           Biomedical and Evironmental Health Sciences
                    Meteorology Data Form
                                                                          0
           Study ID#
           Section No._
           Revision No.
           Date:    November 10.  198T
           Page	
                                                                      of
                                590
      M     D    Y

DATE     /    /
                         Wind
Temperature
AVG.
TIME *DIRECTION SPEED





















RANGE
FPM







HUMIDITY
WET F .DRY F %














\






SUN CON-
DITION







    *NOTE  relation to work operation
                                                                      114
-------
          APPENDIX B



CORRESPONDENCE REGARDING URINARY



      METABOLITE ANALYSES
-------
                                                                592
                                      14 December 1984
Dr. Geraldine Fristrom
FSSP Laboratory Coordinator
EAB/HED/OPP  (TS-769-C)
United States Environmental Protection Agency
401 N Street, SW
Washington, D.C.  20460                           RE:  CR-810691-02-0

Dear Gerrie:

      Your letter of 10 October 1984 was appreciated; it is essen-
tially correct.    Here is a summary of progress to date.

      Save for  some  ironing out of a few  minor  quality assurance
issues, I feel  that we have run the benomyl environmental  and person-
al samples in sufficient numbers to  know exactly  what  we have.  All
of the 8301 and 8302 glcve samples were positive, though low.  The
8301 samples ranged  from about 50 to 1000 micrograms,  while the 8302
?loves were 200 to 2000.  Recall  that the gloves from  the  strawberry
 ields where benomyl was  applied ran in the vicinity of 10 to 30
milligrams.   A selection of 48  patches from the 8302 study  (40%)
resulted  in  no detectable (not  "too low to quantitate") residues.
All the air  samples from  both  the  studies  gave the same result.
Finally,  all the  leaf punch samples from  the two benomyl studies
(8301 and 8302) have been completed, along with all the  associated
quality assurance  samples.

      I feel  that we could safely expect that  there is  no  further use
in analyzing any more of the patches.  The indications  to  me are that
there will probably be no detectable metabolites in  the urines, as
well.  However, as we have discussed by telephone,  John Tessari's
laboratory  will select a few samples based  on  the glove data and
verify this prediction.

     The  mesurol urinary  metabolite method that we have been putting
together  has proved itself reasonably reliable with spikes and stan-
dards, so far; and, although it  is getting  close  to the deadline, I
remain optimistic  that we will be able to complete that  task satis-
factorily.  Only one experiment  remains to be  done before the actual
samples go into  the system.  That one is  to  determine whether or not
we must hydrolyze  conjugates as  a first step in the sample work-up.

      I looked into  the  issue of instrument rentals.   It turns out
that there is one major purveyor of rental equipment  in this area,


                                                               116
-------
                                                                  593
Dr.  Geraldine  Fristrom
Page 2
14 December  1984
named USI;  and they handle most major manufacturers.   When one  looks
at short-term  rentals, as opposed to leases of a year or more (usual-
ly with purchase options),  there are some interesting,  though  logi-
cal,  restrictions applied.  If one goes into a long-term arrangement,
you can get a  system configured any way you want,  because  the lessor
will go out and buy exactly what you stipulate.  On the other  hand,
the short-term renter takes what they have "on the shelf."   What they
stock for rental customers  is the  top of the line equipment.  This
way, they minimize their inventory; the fanciest equipment is the
most versatile,  and that minimizes their  capital outlay. Anyway, the
upshot of all  this is that it would cost us approximately  $1700 per
month to rent  a High Pressure Liquid Chromatograph to do the analyses
we presently are carrying out. That is at the academic rate of  5% of
the retail price per month instead of the 10% charged to industrial
clients.

      That is  generally quite a  bit more than I have been  paying in
for the upkeep of the present  equipment.   I do not see  being able to
afford such an expense,  and  the machines have hung in there the last
few months with only minor troubles.

      I spoke  with Dr.  Zweig some time back,  now,  and he informed me
that he will  not be working under Dave Severn;  rather,  he is now
assigned to a one-year project in Registrations.   That  creates a
problem here in the final report department, because, as I pointed
out previously, there  is no way of our  getting these analyses all
completed in time to have final reports written prior  to  the termina-
tion date.  I  am sure that no one has a taste for these data  to go
uninterpreted.  Dr. Spear and I have discussed this issue,  and we
have agreed to submit a request  for a no-cost extension under sepa-
rate  cover.

     Bowever,  the funds are,  in fact,  coming out close to the budget,
and it certainly would  improve the  situation here if there could be
some supplementary  funding from  the PHAP program to facilitate the
last steps of  finishing the reports  and seeing to the transfer of
equipment to Jim Seiber's shop.

                             Sincerely
                             John T. Leffingwell, Manager
                             California PHAP
cc   B.C. Spear
                                                               117
-------
                                                                 594
                                       17 December  1984

Dr. Geraldine FriStrom
FSSP Laboratory Coordinator
EAB/HED/OPP  (TS-769-C)
United States Environmental Protection Agency
401 M Street, SW
Washington, D.C.  20460                        RE: CR-810691-02-0
Dear Gerrie:
      Attached  is a preprint of a manuscript titled, "The Rela-
tionship Between  Dermal  Pesticide  Exposure  By Fruit Harvesters
And Dislodgeable Foliar  Residues."  It will  be appearing shortly
in The. Journal o_f Environmental Science and Health. P_ajLt B.  It
contains a wrap-up of much of the work that  we have conducted
under the aegis of the Youth in Agriculture program, summarizing
most  of  the dermal exposure data collected over  the last four
years.
      Incidentally,  the Mesurol  (methiocarb) dermal  data  is
included in this paper, and the write-up will  serve as the basis
of our final report on the 1983 blueberry study.
                             Sincerely
                             John T. Leffingwell, Manager
                             California PHAP
                                                                118
-------
                                                                  595
                                      11 January 1985
Dr.  Geraldine Fristrom
FSSP Laboratory Coordinator
EAB/HED/OPP   (TS-769-C)
United States Environmental Protection Agency
401  M Street, SW
Washington, D.C.  20460                       RE: CR-810691-02-0

Dear Gerrie:

     Developments in the matter of mesurol metabolite analysis
are  such that I deem it desirable to report them to you in some
detail and seek relief in the deadlines we  face at present.

     As you know from previous telephone conversations and cor-
respondence, we were required in 1983 to change the proposed work
plans which we had submitted to the NPHAP staff to  drop the idea
of doing more  work  on strawberry harvesters in favor of workers
in three other berry crops.  This meant that we would have to
focus on populations of workers and  crops that  were quite distant
from our laboratory and of which we knew  almost nothing.  The
consequence was that  almost all control  over  such  critical fac-
tors as the  choice of the field site  and the  timing of  our
studies in relationship to pesticide applications  was lost  to  us.
In fact, when we  left for Oregon in July 1983, we even had not
been able to determine what pesticides we would be working  with.

     As good fortune  would have it, the blueberry grower who was
cooperating with us happened to be treating one plot of his farm
with mesurol just days before we were to begin our  study, and he
allowed our group of  harvesters to work in that newly treated
plot (it was  a legal  reentry).  In  due  course we collected the
urine samples according  to the approved  protocol,  along with
other personal and environmental samples needed  to complete the
study.

     Since we did not  know what pesticide(s) to expect, we could
not  have methods of  analysis developed and  in place at our labor-
atory before  the  collection of samples.  Consequently, we pre-
served the  urine samples in a manner consistent with our previous
experience; namely, we took 20 mL aliquots of the urines, dosed
them with  1.5  mL  of concentrated hydrochloric acid and sealed
them in 25  mL ampules.  This  style of preservation had worked
excellently for urines containing p-nitrophenol.
                                                             119
-------
                                                                596
 Dr. Geraldine Fristrom      Page 2              11 January 1985


      Alternate methods of preserving  urine samples were  not
 acceptable to roe.  The use of  sodium fluoride would cause severe
 problems  with subsequent acid  hydrolysis of conjugates.   The  use
 of .thymol promised  to give  massive  interference in the analysis
 of  phenolic metabolites  (such  as  those of mesurol).  And, we
 could not handle the  logistics of  freezing, shipping and storing
 the hundreds of urine samples that  were to be  generated in  the
 1983  studies.

      This last Fall, as you already know, we began development
 work  on an analytical method to deal with the realities  of roes-
 urol metabolism  {It leads  to three related phenols:  3,5-dimethyl-
 4-methylthiophenol,  3,5-dimethyl-4-methylsulfinylphenol  (the
 sulfoxide) and 3,5-dimethyl-4-methylsulfonylphenol  (the sulfone),
 reflecting step-wise oxidation of  the  sulfur.} and  the  (acid)
 state of  the one hundred plus samples on hand.  My approach was
 to oxidize the three metabolites all to  the last one and analyze
 for it by HPLC.

      The development process started  from  the  sulfone; with
 standards from the manufacturer in hand,  we first  confirmed that
 we  could analyze that compound by HPLC.  then,  we worked on
 oxidation procedures that  would take the methylthioether  and  the
 sulfoxide to the sulfone.   Finally, having done that, we had to
 anticipate that these phenols could be present in the urines as
 conjugates with  glucose or  sulfate and would require  hydrolysis
 prior to analysis.   This is the stage where we were just prior to
 the Christmas break.

      It  was possible that  conjugate hydrolysis had already  oc-
 curred spontaneously during the samples' standing for over one
 year at a pH of less than 0.1.  The only way we could test for
 the necessity of a  hydrolysis step was to heat an aliquot  of one
 or more actual samples and compare them  to aliquots that had not
 been subjected to this treatment.  The results of that  experiment
 was zero  in all  samples, even the spikes run  for quality assur-
ance.   Subsequent experiments  with  spiked  samples,   including p-
 nitrophenyl-p-D-glucuronide  and  p-nitrophenyl sulfate as  surro-
 gates, showed that  the hydrolysis of the conjugates works per-
 fectly;  however, serious,  if not complete, loss of  the sulfoxide
and sulfone occurred (0 to 40% recoveries,  respectively).

      A review of the chemistry of this  type of sulfur compound
 indicates that  treatment  with strong acid causes the aromatic
 carbon-sulfur bond to  be hydrolyzed,  such that we could expect to
have 3,5-dimethylphenol as  a product  (either conjugated or not).
To confirm this hypothesis, samples of  acid-treated standards
have been submitted to the analytical services laboratory  of  the
College  of Chemistry  on the Berkeley Campus for GC-MS analysis.
We are expecting results back by the  middle of this  month.
                                                                  120
-------
Dr. Geraldine Fristrom       Page  3               11 January 1985


     The set-back that this development represents means that  we
are nearly back  to  square one in the analysis of the mesurol
metabolite  samples.   If our  hypothesis holds,  and 3,5-dimethyl-
phenol  is our analyte,  there is a whole new set of parameters  to
pin down.   What method  of chromatography (gas or liquid)?   If
capillary gas chromatography is the separation method of choice,
what  detector?  This compound has  no substituents that are amena-
ble to detection with the BCD, FPD or NPD,  and would require
privatization with  a  halogen  or  phosphorous containing reagent.
Will  clean-up be required to  remove interferences?

     To resolve these questions and  run the pending  samples,  we
will need time beyond our current  ending date of 31 January  1985,
as well as  salary and supply  money  for  the laboratory personnel
who will be needed to complete the work.   If all  the steps fell
into place, the minimum time  to implement a method would be a
month form now.   Analysis of the  samples would take another
month.  A reasonable estimate of  completion of these  tasks  would
be the  end  of March 1985.

                            Sincerely
                            John T. Leffingwell, Manager
                            California PHAP
cc  R.C. Spear
    Marion Lentz
                                                                   121
-------
                                                                598
                                       24 January 1985
 John Tessari, Manager
 Colorado Pesticide Hazard Assessment Project
 Colorado State University
 Fort Collins, CO
 80523

 Dear John:

      Enclosed are three standards for your use in working with
 the analysis of Benomyl urinary metabolites:   Carbendazim and
 benomyl from the  EPA Standards Repository,  and 5-hydroxy-2-benz-
 imidazolecarbamate from du Pont.   The  later is  the metabolite of
 interest,  although,  I am  surprised that the carbamate moiety is
 still intact at the 2 position.

      The first two are different forms of  the active  fungicide;
 benomyl  decomposes  rapidly in  the environment and  in protic
 solvents,  as well, to carbendazim.   Carbendazim  can be converted
 to  benomyl by reacting  it with  n-butylisocyanate. This technique
 is  useful  in manipulating  these compounds for HPLC analysis;
 benomyl elutes substantially later  than carbendazim on a reverse
 phase C18 column, and  changing the elution time of the analyte
 has been useful  in eluding interfering  peaks.   I  presume  that the
 same reaction would go with the 5-hydroxy-2-benzimidazolecarbam-
 ate.  For  the carbendazim/benomyl analysis, we use an acetoni-
 trile-water mixture (65:35) @ 2.0  ml  per minute on a 25 cm Cj
 column.  The butylisocyanate has to be present in the sample a
 about 2 to 5 ppm  to maintain the compound as benomyl.  Detection
 is by UV at  292 nro.
                                             •
     Good  luckl

                             Sincerely
                             John T. Leffingwell, Manager
                             California PHAP
cc:  G. Fristrom
Tessari2.1tr                                                     122
-------
                                                                  599
                                       25 January 1985
Dr. Geraldine  Fristrom
FSSP Laboratory  Coordinator
EAB/HED/OPP  (TS-769-C)
United States  Environmental Protection Agency
401' M Street,  SW
Washington, D.C.  20460           RE:  Mesurol Metabolite Analysis

Dear Gerrie:

      The answer has come back from  the GC/MS  lab that the only
thing found in the  treated standard we took them was a dimethyl-
phenol.   Given that we started with 3,5-dimethyl-4-methylsulfin-
ylphenol,  it is  reasonably  safe to  infer that the product of the
decomposition  is 3,5-dimethylphenol.

      Since we have been following  Reed  and Watts'  method for
urinary  alkyl  phosphates  using pentafluorobenzylbromide  (PFBB) as
a derivatizing reagent in conjunction with our work on the fluor-
escent tracer  project, and  since  PFBB is reputed to be an excel-
lent derivatizing  agent  with  phenols  for electron capture detec-
tion, we  have undertaken  to develop a  method utilizing  this
reagent.

  •  As way of background, 10 mL of the acidic urine samples will
be diluted with 5 mL of  water and heated to liberate any conju-
gates.   The 3,5-dimethylphenol will be extracted into methylene
chloride.   The derivatization  reaction is carried out in a polar
solvent, such  as an alcohol, acetone or acetonitrile; so, we will
convert the methylene chloride to acetonitrile.  Catalysts for
the reaction are solid anhydrous potassium carbonate and 18 crown
6 ether; the former generates the potassium salt of the phenol,
while the  later  solvates the  potassium ion  away from the phenol-
ate ion, promoting its reactivity  toward the benzylbromide.

      Before he  started,  Mr.  McLean contacted a technical person
at Supelco, our  supplier  of PFBB, to  obtain advice on approaches
to removing the  unused derivatizing  reagent from a sample after
reaction with  the analyte of  interest has  been  completed.   PFBB
is a  neutral molecule; and,  as such,  cannot be separated from the
derivative of  the phenol by commonly employed  extraction tech-
niques,  such as  manipulation of  pH or use of adsorbents to dif-
ferentiate  between  polar  and  non-polar species.   Furthermore, it
is highly  electron  capturing  (response factor of about 10 counts
                                                                    123
-------
                                                                    600
Dr. Geraldine Fristrom       Page  2               25  January 1985


per femptogram on our GC at present), and it will be present in
great excess over the analyte of interest.  Therefore, an unusual
approach is needed in order to  utilize this reagent with an BCD
that was not needed with the alkyl phosphate analyses using the
flame photometric detector.

     The Supelco person  recommended that we use a strong cation
exchange resin in the acid form.  The free PFBB would react with
the benzene sulfonate  groups on the resin and would  be, thereby,
removed from the sample.  The Supelco person had in mind that we
would use that company's disposable solid phase  extraction/clean-
up cartridges loaded with HPLC-type strong cation exchange pack-
ing.   Since  we  did not have this item  in  hand ($2.00  ea.  in
packages of 50), but  did have a  good quality cation  exchange
resin  (Bio-Rad AG  50W-X8).  For ease  of  sample handling,  we
decided to  try the resin as a slurry, rather than  in  a column.

     It did not work.   No matter  how much resin was put in the
sample, no reduction in the quantity of PFBB was seen.  We even
set up a resin column,  and saw no reduction.  So,  back  to our
contact at  Supelco, and his reaction was that he had assumed that
we would be working in an aqueous medium.  Now,  he and another
person at Supelco have given us  a  new procedure.

     The treated sample in  acetonitrile  is to be diluted  with
water and extracted with hexane.  The hexane  extract  is to be put
onto a silica gel  extraction/clean-up cartridge,  eluted  with
additional hexane to remove all  PFBB and then eluted with 20%
hexane in toluene to elute the derivatized phenol.  Mr. McLean is
attempting  this new procedure as of this writing.

     If you think that  there is  a particularly knowledgeable
person within EPA or the NPHAP group,  I would be glad to  talk
with them by telephone.  I am afraid that by the  time copies of
this letter get around, and interested  parties take time  to
respond *ith their  ideas,  it will be  much too  late for  us to
effectively put them into practice. Give me a call when you have
read through this  and have an idea or  two about  people I might
contact directly for additional  suggestions.   Thanks.

                             Sincerely
                            John T. Leffingwell, Manager
                            California PHAP
                                                                 124
-------
                                                                601
                                       14 March 1985
Dr. Geraldine Fristrom
FSSP Laboratory Coordinator
EAB/HED/OPP   (TS-769-C)
United States Environmental Protection Agency
401 M Street, SW
Washington,  D.C.  20460                       RE: CR-810691-02-0

Dear Gerrie:

      It has been some time,  now, since we last talked by tele-
phone.   I did promise you an update on the Kesurol  urinary meta-
bolite methods development work.

      As explained  in  earlier correspondence,  we had developed a
method  for  the  analysis of  mesurol metabolites  based on the
literature,  which indicated that the compounds to be anticipated
were threefold:   Mesurol phenol (3,5-dimethyl-4-methylthiophe-
nol),  the sulfoxide (3,5-dimethyl-4-methylsulfinylphenol) and the
sulfone (3,5-dimethyl-4-methylsulfonylphenol).  The  last step in
the method development showed that not only did real samples lack
any of these phenols, but spiked blanks  lacked  them,  as well.  A
review of the chemical literature on sulfur containing phenols
led us to believe  that  the strong acid  conditions we used to
preserve the samples  cleaved  the  sulfur-carbon bond at the ring.
Subsequent GC/MS analysis of  a  treated spike  confirmed that
hypothesis.

      Work on a second method was undertaken during January, but
had to be terminated when Mr. McLean had to be  laid off due to
lack of funds. The  approach we were taking was to derivatize the
3,5-dimethylphenol with something  that would  allow  it  to be
analyzed  with a  gas chromatograph equipped with an element-
specific  detector, eg. electron  capture or flame photometric
detectors.   The ultra-violet  spectrum of  3,5 dimethylphenol did
not give any distinct peaks at  which we could set the variable
wavelength detector for the HPLC,  rendering that analytical tech-
nique of  questionable applicability for  this problem.

     Since we have  had relatively good success  with the use of
pentafluorobenzylbromide in conjunction with the malathion uri-
nary metabolite work  that we have been doing of late, we decided
to give that a try; we were fairly familiar with its chemistry
and we had a stock  of  the necessary  reagents on  hand.  However,
                                                                  125
-------
                                                           V  602

Dr. Geraldine Fristrom       Page  2                 14 March 1985


contrary to the malathion metabolite work,  which was done on the
flame photometric detector,  this work would be done on the elec-
tron capture detector; and we would have to devise a scheme to
purge the samples,  once treated, of  excess reagent,  which, itself
is extremely electron capturing, and renders the detector useless
for hours if passed through it in  any  quantity.

      As described in earlier correspondence, we sought advice
from our supplier  (Supelco) on approaches to handling this prob-
lem, since they tout the material as a good derivatizing agent
for phenols.   Most  of  the  problems  were  worked out successfully
using blanks and spikes, and we had a reasonably  good clean-up
for clean  samples  with about a 90% recovery.  However, at the
point where we had to terminate our work we were experiencing
disturbing  occurrences of excessive contamination in  a large
proportion  of our blanks.   The few  spiked urines we had attempted
to run through the procedure had chroroatograms similar to the
contaminated blanks making it impossible  to tell whether  the
urines were contaminated  as well, or whether the  procedure was
simply inadequate for treating urine samples.

      Under the circumstances, the only position that  I  could
take was that we might have a usable method, but that it is quite
possible that  more or different  clean-up  would  be necessary.
Worse yet,  an entirely new approach  to the problem might be
required i_f what we saw in the spiked urine was not an artifact
similar to what had appeared in many of the blanks.

      At this point, I think it is  urgent  that we discuss whether
or not I will be taking  the  samples to John Tessari in Colorado.
Time is running short for  me in the near  future, since  I  will be
leaving  for  a field study  late  in May.   I  have most of  the
reagents in hand for continuation  of the  Mesurol methodology, if
the chemists at the Colorado PHAP  wish  to  continue along  that
line.

                             Sincerely
                            John T. Leffingwell, Manager
                            California PHAP
cc:  John Tessari
                                                               126
-------
                                                               603
                                       26 April 1985

John Tessari,  Manager
Colorado Pesticide  Hazard Assessment Project
Colorado State University
Fort Collins,  CO
80523
Dear John:
      Enclosed is a copy of the provisional mesurol metabolite
method, as it stood at the  point where we had to quit working on
it.  If it works out that I shall bring the samples to you, I do
have PFBB and 18 crown 6 ether (hexaoxacyclooctadecane)  that I
could bring along, as well as potassium carbonate and the phenol,
itself.  However, as my letter to Gerri Fristrom points out,  this
method may yet be a blind alley, and an entirely new approach may
be required,  to include  the  possibility of running  these samples
on a capillary/FID set-up.  I do not think that the uv spectrum
of the 3,5-dimethylphenol  is good  enough to  do it by HPLC;  al-
though, I would not shy away from re-running  the spectrum of it
again, just in case my crew blew it in preparing the standard for
the spectrophotometer.
                             Sincerely
                             John T.  Leffingwell
                             University of California
                             Bldg.  112, Richmond Field Station
                             47th & Hoffman Blvd.
                             Richmond, CA  94804
                                                                   1 27
Tessari3.1tr
-------
                 Mesurol Urinary Metabolite Meth
                                                                604
                    MESUROL URINARY METABOLITES

 fifltfifi

      The pertinent analytical methods in the literature were for the
 simultaneous determination of Mesurol and its two oxidized forms as
 Mesurol sulfone.  Our goal was to apply this type of analysis to
 Mesurol phenol and its two oxidized forms as they would be found in
 urine.

      Our first findings indicate that the potassium  permanganate
 oxidation method used by Mobay quantitatively destroys  MP  (mesurol
 phenol), MPX (mesurol phenol sulfoxide)  and MFN (mesurol phenol
 sulfone).

      We next found that the m-chloroperoxybenzoic acid (mCPBA) oxi-
 dation  method recovered both MP and MPX as MPN.   The  recovery was
 low,  but no  lower than for MPN, itself, which led us to suspect the
 extraction rather than the oxidation.

      The pH  of the extraction solution turned out to be the problem.
 Mesurol has  no ionizable molecular site,  so that  drastic methods
 could be used to prevent co-extraction of unwanted acidic  materials.
 Up to 40% of the  MPN can be lost at the high pH values used for
 extraction of oxidized mesurol.   By using magnesium  sulfate solution
 to slightly  lower the pH, recoveries very close to 100% have now
 been  obtained.

 Analytical Method

      Equipment and Reagents

 a)  Tilt dispensers:  1 X 10 mL
                      1 X 15 mL   •
                      2 X 20 mL
                      1 X 25 mL
 b)  1 mL high speed pipettor (Brand).
 c)  Rotary evaporator with water  bath.
 d)  13 X 100 mm screw-top culture tube with teflon lined cap (1 per
    sample).
 e)  Test tube rack for 13 mm tubes.
 f)  125 mL separatory funnel with LPE stopper and teflon plug (1 per
    sample).
 g)  Separatory funnel rack.
 h)  250 mL, S 24/40 Round-bottom  flasks  (2 per  sample).
 i)  « 24/40 Stoppers (2 per  sample).
 j)  117 mm OD X 60 mm ID Cork rings  (1 per sample).
 k)  35 mm Polypropylene analytical funnel (2 per  sample).
 1)  Small volumetric flasks with LPE stoppers (1 X 10 mL Qi 1 X 5 mL
    per sample).
m)  4 Dram vials  with teflon lined caps  (1 per  sample).


                               Page  1              6 September 1984
-------
                         Urinary Metabolite  Method


n)   10 mL Volumetric pipefcs (1 per  sample).                 "~
o)   Wash bottle for methylene chloride.
p)   Wash bottle and dropper bottle  for acetonitrile.
q)   Glass wool.
r)   Pasteur pipets (2 per sample).

    Reagents

All solids ace Analytical Grade; all  liquids are HPLC Grade.

a)   CH2Cl2 ~ Methylene Chloride.
b)   CH3CN - Acetonitrile.
c)   H20 - Class ?? Reagent Grade Water  (Milli-Q).
d)   mCPBA - 1% in CH2C12-
e)   MgS04 - 250 g/L In H20.
f)   NaHCC>3 - Aqueous Saturated Solution.
g)   Na2sc>3 - Aqueous Saturated Solution.
h)   Na2SC>4 - Anhydrous crystals.
i)   Dry ice and coolant (2-propanol)  for rotary evaporator.

Prpcedure

     Pipet 10 mL of prepared urine* into a 125 ml separatory funnel;
then add 15 ir.L MgS04 and 20 ml NaHC03. Extract sequentially with 3
X 25 mL portions of CH2Cl2r filtering each through a 35 mm funnel
plugged with glass wool and containing Na2SC>4 into a 250  round
bottom flask, stopper and set aside for rotary  evaporation.

     Rotovap sample to ~  0.25  to 0.50  mL final  volume.  Transfer  to
a culture tube with a pasteur pipet using 4  X 1 mL portions of 1%
mCPBA to rinse the flask.   Seal  the tube and allow the contents to
react at room temperature for at least 20 minutes, but no more than
30  minutes.

     Quantitatively  transfer  the tube contents to a 125 mL separa-
tory funnel containing  20 mL Na2SC>3 solution and shake well to
destroy any  remaining  organo-peroxide.  Then add 15 mL saturated
Na2S04 and 20 mL NaHC03.   Extract with CH2C12 as described above.

     Rotovap  to * 0.5 mL and add 10 mL CH^CN.  Repeat twice more.
Transfer  with CHjCN and pasteur  pipet  to a small volumetric flask (5
or  10 mL)  and make up  to volume. Shake well and put in a 4 dram
vial with proper label.  Turn  the sample over to the HPLC operator
for analysis  of total  MPN.
                              Page 2               6 September 19
-------
DRAFT                     Page 1
                   Mesurol Urinary Metabolite
                        Analytical Method

                   A Provisional Method Using
                    Pentafluorobenzylbr oroide
                         to Derivatize
                       3 , 5-Dimethy Iphenol
 introduction
      While  at the field  study site,  I  decided to store  the
urines in a fashion that had served us well in the past when we
were dealing with phenolic urinary metabolites:  We placed dupli-
cate 20  roL aliguots of  each  subject's  urine  samples in 20 mL
ampules,  adding 1.5 mL of concentrated hydrochloric acid as  a
preservative.   This approach to storage obviates the need  for
refrigeration/  eliminates leak-prone screw-cap closures,  and sets
the sample up for future  hydrolysis of conjugates.  However, good
evidence  exists to show  that the storage  of urine samples con-
taining metabolites of mesurol  [mesurol phenol  (3,5-dimethyl-4-
methylthiophenol) ,  the sulfoxide (3,5-dimethyl-4-methylsulf inyl-
phenol)  and the sulfone (3,5-dimethyl-4-methylsulfonylphenol)J in
strong acid  causes these compounds  to undergo a reaction that
cleaves the sulfur-carbon bond,  leaving the product,  3,5-dimeth-
y Iphenol.

      In an attempt to produce an analyte that could be quanti-
tated by gas chromatography,  I  decided to derivatize  with  penta-
fluorobenzylbromide (PFBB)  and utilize the electron  capture  de-
tector.   I recognize that this approach  is fraught with many
pitfalls;  ECD's are notoriously sensitive to many extraneous
materials that might be  present in the urine samples, and rea-
gents used to hang a handle on an otherwise non-capturing mole-
cule usually derivatize many unwanted compounds,  as  well.  It is
possible  that a  good quality flame ionization detector coupled
with a capillary column  would give sufficient sensitivity  for
underivatized  3,5-dimethylphenol to eliminate the need for  the
BCD and its attendant hypersensitivities.   I did  not  have  such  a
set-up;  so, decided to press on with the  PFBB procedure.

Materials and Methods

      The first step is taken to hydrolyze any remaining conju-
gates of the phenol.  Ten mL of urine is transferred to a 20 mL
ampule and 5 mL of water  is added.  The ampule is sealed  and
heated under pressure (15 Ib. in a standard household pressure
cooker  or 20 Ib.  in a laboratory or clinical autoclave) for 2  hr.
The ampule is opened and the contents are quantitatively  trans-
ferred to a 125 mL separatory funnel with about 40 mL of  water.
Extract  three times with  about 25 mL of methylene chloride.

     At  this point the  methylene  chloride can  be removed  and
replaced  with acetonitrile by rotary evaporation.  (With  care  not
to let the  solvent go completely  dry,  good recoveries of  the


Mesurol  Urinary Metabolite Analytical Method       26 April 1985
-------
                                                         DRAFT


relatively volatile  phenols can  be  realized doing  a  solvent
volume  reduction on  a rotary  evaporator.)   It has not  been
tested, yet,  to determine  whether or not the  derivatization
reaction can be carried out in  the roethylene chloride, either
with or without a prior drying step.  At this  point, the conver-
sion to acetonitrile  is compatible with the subsequent  clean-up
procedure utilizing hexane/water  partitioning.

     Whether the  reaction is to  be  carried out in  methylene
chloride or in acetonitrile (It will also go equally  well in
acetone.),  the  volume of  the sample should be brought to about
1 roL.  Add  20  pL of pentafluorobenzylbromide, 20 mg  potassium
carbonate and 20 mg 18 crown 6 ether.  Seal (We use Teflon-lined,
screw-capped culture  tubes.)  and heat 2 hours at  100° c.   Add
10 mL of hexane and shake well.   Add 5 mL of  water and, again,
shake thoroughly; then allow to settle.

     Pre-clean a silica gel Sep-Pak by running 10 roL of HPLC or
pesticide grade hexane through  it.  Draw off a 3 mL aliquot of
the hexane  layer of the sample with a gas tight syringe and push
very slowly through  the pre-cleaned  silica gel Sep-Pak.   Follow
it with 5 mL of clean hexane. Discard the eluate;  it contains
about 90% of the  residual PFBB.   Finally,  elute the Sep-Pak  with
5 mL of a toluene/hexane  mixture  (70/30 v/v).  Collect  the el-
uate; bring it to a convenient volume; and analyze for the deriv-
ative of the phenol by EC gas chromatography.   We found the HP-
5880A, using a 50 micron Megabore capillary  column,  gave us a
very nice peak with 4 to 10 feroptograms of the derivative.   Un-
fortunately, the same holds true  for the PFBB,  as  well; so, it is
important to have most  of  the  reagent removed before the sample
hits the BCD.
Mesurol Urinary Metabolite Analytical Method        26 April 1985       _.
-------
                                             608
       APPENDIX C
   DATA FILES  RELEVANT TO.
URINARY METABOLITE ANALYSES
                                                 132
-------
                                                                   609
                                         Section  Appendix C
                                         October 15,  1985
                                         Page  1   of  22
                       APPENDIX C

                      Drine Volumes
                         TABLE 1

       Raspberry  Study, Pre-Exposure Urine Samples*

Sample ID        Subject ID          Group           Volume
8301-U-60.
8301-U-60.
8301-0-60.
8301-D-60.
8301-D-60.
8301-D-60.
8301-D-60.
8301-0-60.
8301-D-60.
8301-D-60.
8301-U-60.
8301-U-60.
8301-U-60.
8301-U-60.
8301-0-60.
8301-0-60.
8301-0-60.
8301-0-60.
8301-0-60.
8301-0-60.
8301-0-60.
8301-0-60.
8301-0-60.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
A
A
A
A
B
A
A
B
B
B
B
A
A
B
A
A
A
A
A
A
B
B
B
170
208
630
230
380
300
177
190
209
480
680
310
186
230
630
380
310
252
186
214
300
146
210
     morning void prior to entry  into the field, 11 July 1983.
                                                                   133
-------
                                             Section  Appendix C    / 1 n
                                             October 15, 1985       61 U
                                             Page 2  of  22
                 -.            TABLE 2

         Raspberry Study, First Urine Sample Collection*

   Sample ID        Subject ID          Group            Volume
8301-0-61/62.
8301-0-61/62.
8301-0-61/62.
"8301-0-61/62.
8301-0-61/62.
8301-0-61/62.
8301-0-61/62.
8301-0-61/62.
8301-0-61/62.
8301-0-61/62.
8301-0-61/62.
8301-0-61/62.
8301-0-61/62.
8301-0-61/62.
8301-0-61/62.
8301-0-61/62.
8301-0-61/62.
8301-0-61/62.
8301-0-61/62.
8301-0-61/62.
8301-0-61/62.
1
2
3
4
5
6
7
8
9
10
11
12
14
15
16
17
18
19
20
21
22
A
A
A
A
B
A
A
B
B
B
B
A
B
A
A
A
A
A
A
B
B
790
305
790
620
380
680
550
234
530
740
740
730
710
450
380
515
620
415
290
290
335
* Overnight samples, Day Two to Day three, 12 to 13 July 1983.
-------
                                             Section  Appendix C      611
                                             October 15, 1985
                                             Page 3  of  22
                             TABLE 3

       Raspberry Study, Second Urine Sample Collection*

  Sample  ID        Subject ID          Group            Volume
8301-0-62.
8301-0-62.
8301-0-62.
8301-0-62.
8301-0-62.
8301-0-62.
8301-0-62.
8301-0-62.
8301-0-62.
8301-0-62.
8301-0-62.
8301-0-62.
8301-0-62.
8301-0-62.
8301-0-62.
8301-0-62.
8301-0-62.
8301-0-62.
8301-0-62.
8301-0-62.
8301-0-62.
1
2
3
4
5
6
7
8
9
10
11
12
14
15
16
17
18
19
20
21
22
A
A
A
A
B
A
A
B
B
B
B
A
B
A
A
A
A
A
A
B
B
194
104
216
100
124
N/S
N/S
N/S
N/S
224
280
172
114
150
410
175
N/S
N/S
N/S
N/S
N/S
* Samples collected during working hours of Day Three, 13 July 1983.


                                                                       135
-------
                                            Section  Appendix C
                                            October 15,  1985
                                            Page 4  of  22

                            TABLE 4

         Raspberry Study, Third Drine Sample Collection*

   Sample ID        Subject ID          Group            Volume
612
8301-0-62/63.
8301-0-62/63.
8301-0-62/63.
8301-0-62/63.
8301-0-62/63.
8301-0-62/63.
8301-0-62/63.
8301-0-62/63.
8301-0-62/63.
8301-0-62/63.
8301-0-62/63.
8301-0-62/63.
8301-0-62/63.
8301-0-62/63.
8301-0-62/63.
8301-0-62/63.
8301-0-62/63.
8301-0-62/63.
8301-0-62/63.
8301-0-62/63.
8301-0-62/63.
1
2
3
4
5
6
7
8
9
10
11
12
14
15
16
17
18
19
20
21
22
A
A
A
A
B
A
A
B
B
B
B
A
B
A
A
A
A
A
A
B
B
770
345
710
270
470
221
153
138
300
700
570
655
435
550
285
730
865
240
281
274
Lost by Subject
* Overnight sample,  13  to 14 July 1983.
                                                                   136
-------
                                            Section   Appendix C     & 1 7.
                                            October  15,  1985         ° 'J
                                            Page 5   of   22
                           TABLE  5

      Raspberry Study, Fourth Urine Sample Collection*

 Sample ID        Subject  ID          Group             Volume
8301-U-63.
8301-0-63.
. 8301-0-63.
8301-0-63.
8301-0-63.
8301-0-63.
8301-0-63.
8301-0-63.
8301-0-63.
8301-0-63.
8301-0-63.
8301-0-63.
8301-0-63.
8301-0-63.
8301-0-63.
8301-0-63.
8301-0-63.
8301-0-63.
8301-0-63.
8301-0-63.
8301-0-63.
1
2
3
4
5
6
7
8
9
10
11
12
14
15
16
17
18
19
20
21
22
A
A
A
A
B
A
A
B
B
B
B
A
B
A
A
A
A
A
A
B
B
123
190
280
92
138
400
265
80
170
430
380
72
232
156
190
170
250
132
170
214
218
Samples collected during working hours of Day Four, 14 July 1983.


                                                       137
-------
                                             Section  Appendix  C
                                             October  15,  1985        L \ A
                                             Page 5   of   22         ° ' H

                             TABLE 5

        Raspberry Study, Fourth Drine Sample Collection*

   Sample ID        Subject ID          Group             Volume
8301-0-62.
8301-0-62.
8301-0-62.
8301-0-62.
8301-0-62.
8301-0-62.
8301-0-62.
8301-0-62.
8301-0-62.
8301-0-62.
8301-0-62.
8301-0-62.
8301-0-62.
8301-0-62.
8301-0-62.
8301-0-62.
8301-0-62.
8301-0-62.
8301-0-62.
8301-0-62.
8301-0-62.
1
2
3
4
5
6
7
8
9
10
11
12
14
15
16
17
18
19
20
21
22
A
A
A
A
B
A
A
B
B
B
B
A
B
A
A
A
A
A
A
B
B
123
190
280
92
138
400
265
80
170
430
380
72
232
156
190
170
250
132
170
214
218
* Samples collected during working hours of Day Four, 14 July 1983.
                                                                  138
-------
                                            Section  Appendix C      , ,  ,_
                                            October 15, 1985         6 I  5
                                            Page 6  of  22
                            TABLE  6

          Blackberry  Study, Pre-Exposure Urine Samples*

   Sample ID       Subject ID          Group            Volume
8302-D-17.
8302-0-17.
8302-D-17.
8302-0-17.
8302-0-17.
8302-0-17.
8302-0-17.
8302-0-17.
8302-0-17.
8302-0-17.
8302-0-17.
8302-0-17.
8302-0-17.
8302-0-17.
8302-0-18.**
8302-0-18.**
8302-0-17.
8302-0-17.
8302-0-17.
8302-0-18.**
8302-0-17.
8302-0-17.
8302-0-17.
8302-0-17.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
21
22
23
24
28
29
A
A
A
A
A
A
A
A
A
A
A
A
B
B
B
B
***
***
B
B
B
B
B
B
615
390
740
235
277
306
224
275
930
950
515
178
264
204
335
450
110
254
395
255
430
366
260
390
* First morning  void prior to entry  into the field, 18 July 1983
** Subjects  15,16,  and  22 joined the study on Day 2.
*** Subjects 17  and 18  were dropped  from the study.
                                                                  139
-------
                                             Section Appendix C
                                             October 15,  1985
                                             Page  7   of   22

                             TABLE 7

        Blackberry Study, First Urine Sample Collection*

   Sample ID        Subject ID          Group           Volume
8302-0-18/19.
8302-U-18/19.
8302-0-18/19.
8302-0-18/19.
8302-0-18/19.
8302-0-18/19.
8302-0-18/19.
8302-0-18/19.
8302-0-18/19.
8302-0-18/19.
8302-0-18/19.
8302-0-18/19.
8302-0-18/19.
8302-0-18/19.
8302-0-18/19.
8302-0-18/19.
8302-0-18/19.
8302-0-18/19.
8302-0-18/19.
8302-0-18/19*
8302-0-18/19.
8302-0-18/19.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
21
22
23
24
28
29
A
A
A
A
A
A
A
A
A
A
A
A
B
B
B
B .
B
B
B
B
B
B
930
780
750
400
148
266
182
224
800
584
1040
242
130
160
720
380
470
580
960
326
455
480
* Overnight samples,  Day Two to  Day  Three,  19 to 20 July 1983
6\i
                                                                    140
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                                             Section  Appendix C      £ 1 7
                                             October 15, 1985         ° ' '
                                             Page 8  of  22
                             TABLE 8

       Blackberry  Study,  Second Urine Sample Collection*
                  \
  Sample ID         Subject ID          Group            Volume
8302-D-19.
8302-U-19.
8302-D-19.
8302-D-19.
8302-0-19.
8302-U-19.
8302-0-19.
8302-0-19.
8302-0-19.
8302-0-19.
8302-0-19.
8302-0-19.
8302-0-19.
8302-0-19.
8302-0-19.
8302-0-19.
8302-0-19.
8302-0-19.
8302-0-19.
8302-0-19.
8302-0-19.
8302-0-19.
1
2
3
4
5
6
7
8
9
10
11