UNITED STATES ENVIRONMENTAL PROTECTION AGENCY OFFICE OF PREVENTION, PESTICIDES, AND TOXIC SUBSTANCES WASHINGTON, D.C. 20460

OFFICE OF PREVENTION, PESTICIDES
AND TOXIC SUBSTANCES June 9, 2008

SUBJECT: Factors Which Impact Soil Fumigant Emissions - Evaluation For Use In Soil Fumigant Buffer Zone Credit Factor Approach

PC Codes: 053201-Methyl bromide; 000011-Iodomethane; 029001-1,3-Dichloropropene; 081501-Chloropicrin; 035602-Dazomet; 039003-Metam (sodium) & 039002 (potassium);
068103-MITC CAS No.: 74-83-9/Methyl bromide; 74-88-4/Iodomethane; 542-75-6/1,3-Dichloropropene; 76-06-2/Chloropicrin; 533-74-4/Dazomet; 137-42-8 Metam (sodium) & 137-41-7 (potassium);
556-61-6/MITC

Decision No.: N/A Registration No.: Various

Petition No.: N/A Regulatory Action: Phase 6 Risk Assessment Type: Special Project Case No.: N/A

TXR No.: N/A DP Barcode: 306857

MRID No.: Various – See bibliography 40 CFR: N/A

FROM: Jeffrey L. Dawson, Chemist/Risk Assessor Reregistration Branch 1 Health Effects Division (7509P)

Charles Smith, Environmental Scientist/Risk Assessor Reregistration Branch 2 Health Effects Division (7509P)

THROUGH: Jack Housenger, Associate Director Health Effects Division (7509P)

TO: John Leahy, Senior Advisor Special Review And Rere gistration Division (7509P)

The analysis of the possible factor s which may impact soil fumigant emissions from treated agricultural fields is attached. This document not only addre sses these possible factors but also attempts to
characterize how such information c ould be interpreted as well as th e basic methods for generating both field- and laboratory-based emissi ons data. First principles mode ling (e.g., use of USDA CHAIN 2D)
has also been included in this document.

[Acknowledgements: Faruque Khan (EFED), Gabe Rothman (EFED), Charles Peck (Versar, Inc.)]

# FACTORS WHICH
IMPACT SOIL
FUMIGANT EMISSIONS

`


# U.S. Environmental Protection Agency Office of Pesticide Programs Health Effects Division (7509P) Jeffrey L. Dawson, Chemist/Risk Assessor Charles Smith, Env. Scientist/Risk Assessor Date: June 9, 20083 

### Table of Contents

1.0 Introduction

....................................................................................................................... 4
2.0 Analysis Of Soil Emissions Factors

................................................................................. 6
2.1 Tarp Use

......................................................................................................................... 6
2.1.1 Nomenclature

........................................................................................................ 7
2.1.2 Laboratory Performance Based Standards For Categorizing Films

............... 9
2.1.3 Field Study Based Standards For Categorizing Films

.................................... 16
2.1.4 Mechanistic Study Based Standards For Categorizing Films

........................ 24
2.1.5 Summary of Emission Reduction Potential For Tarps

................................... 30
2.2 Soil Amendments

........................................................................................................ 33
2.2.1 Mechanistic Study Based Standards For Categorizing Soil Amendments

.... 34
2.2.2 Field Study Based Standards For Categorizing Soil Amendments

................ 56
2.2.3 Summary of Emission Reduction Potential of Soil Amendments

.................. 60
2.3 Application Methods

................................................................................................... 61
2.3.1 Mechanistic Based Standards For Categorizing Application Methods

......... 62
2.3.2 Field Study Based Standards For Categorizing Application Methods

.......... 73
2.3.3 Summary of Emission Reduction Potential of Application Methods

............ 80
2.4 Field Conditions

.......................................................................................................... 81
2.4.1 Soil Categorization

.............................................................................................. 81
2.4.2 Biological Activity

............................................................................................... 90
2.4.3 Soil Temperature

................................................................................................ 92
2.4.4 Localized Weather Conditions

........................................................................... 97
2.4.5 Summary of Emission Reduction Potential of Field Conditions

................. 102
2.5 First principles modeling

.......................................................................................... 104
2.5.1 Model Selection

................................................................................................. 104
2.5.2 Basic Model Structure

...................................................................................... 105
2.5.3 Model Performance

.......................................................................................... 109
2.5.4 Sensitivity Analysis To Quantify How Changes In Conditions Impact
Emissions

........................................................................................................................... 114
2.5.4.1 Injection depth

.............................................................................................. 118
2.5.4.2 Soil Moisture

.................................................................................................. 124
2.5.4.3 Organic Matter via K d Value

....................................................................... 131
2.5.4.4 Soil Type

........................................................................................................ 138
2.5.4.5 Bulk Density

.................................................................................................. 145
2.5.4.6 Mass Transfer Coefficient (via tarp type)

.................................................. 149
2.5.4.7 Agency Sensitivity Analysis Conclusions

.................................................... 151
3.0 Characterization

........................................................................................................... 152
3.1 Uncertainty and Variability

..................................................................................... 153
3.2 Data Quality Objectives

........................................................................................... 153
3.3 Co-critical Factors

.................................................................................................... 153
3.4 Standardization

......................................................................................................... 154
3.5 Representativeness and Purposively Designed Research Plan

............................. 155
3.6 Lack of Critical Database Elements

........................................................................ 156
3.7 Methodology

.............................................................................................................. 157
3.8 Summary of Considerations

..................................................................................... 157
4.0 Conclusions

.................................................................................................................... 158
5.0 References

...................................................................................................................... 1604 

### 1.0 Introduction

It is important for the Agency in the developmen t of a viable risk management strategy for soil fumigants to understand the basic factors whic h may impact emission rates from treated agricultural fields. Figu re 1 below illustrates the chemicals which are the predominant focus in
this document.

A number of efforts have been undertaken in order to elucidate possible factors which may
influence emissions. These have included:

• calls with researchers who focus on issues related to fumigants,

• extensive literature searching and information gathering, and

• interaction with stakehol ders on many meetings (including the Methyl Bromide Alternative Outreach meetings in 2005, 2006 and 2007, field trips, and other public stakeholder meetings..5 Based on these efforts, the Agency has identifie d a list of possible factors which may primarily be responsible for regula ting the rate at which administered fumigants volatilize from treated agricultural fields. The analyses of the factor s which have been identified are presented in Section 2.0: Factors Analysis based on the use of empirical da ta (Sections 2.1-2.4) and the use of a modeling approach (Section 2.5). Sections 2.1 through 2.3 are believed to address factors which growers could readily control through thei r choices of field preparation and cultural practices. Section 2.4 addresses fa ctors which are more inherent to specific field situations which are not easily manipulated by growers. The modeling which was completed addresses many factors from Sections 2.1 through 2.4. Th e following describes th e general types of
information included in this section:

• Tarp Use (Section 2.1)

o Nomenclature;

o High barrier films;

o Film thickness;

• Soil Ammendments (Section 2.2)

o Use of reactive adjuvants & water seals;

o Organic matter levels;

o Soil moisture;

o Field Preparation;

o Soil compaction;

• Application Methods (Section 2.3)

o Application methods;

o Application implements;

• Field Conditions (Section 2.4)

o Soil texture;

o Biological activity;

o Soil temperature;

o Localized weather conditions;

• First principles modeling (Section 2.5)

o GUI (Graphical User Interface) evaluation

o Inputs Verification

o Calibration to existing emissions data

o Sensitivity of emissions relative to changes in parameters

In addition to the factors that may impact em issions above, a number of related issues are discussed including research need s and possible directions (Secti on 3.0); characterization of the results of this analysis (Section 4.0); and a su mmary of the key conclusi ons that can be drawn from this analysis (Section 5.0). Each of the f actors introduced in section 2 will be evaluated and discussed in the subsequent sections of this document. As appropriate, specific recommendations pertaining to each category are provided.

It is the overall goal of th is document to describe critical elem ents of the state of the science, identify key uncertainties related to the use of the data and analyses included herein, and
summarize trends in the data and research whic h could assist in deve lopment of additional research strategies in the future to better understand the impact of the various measures available
to reduce emissions.6 

### 2.0 Analysis Of Soil Emissions Factors

This section of the document de scribes the analysis and data reviews completed by the Agency in its efforts to evaluate how specific factors, or combinations of fact ors impact emissions, and ultimately exposures. Sections 2.1 through 2.4 describe information which was retrieved from the scientific literature on these subjects. Tarp Use (Section 2.1) presents information on the use of tarps (sometimes referred to as agricultural films) and how varied tarp characteristics can
influence emissions. Soil Amendments (Section 2.2) presents information pertaining to how soil amendments can influence emissions including th e use of water seals and adjuvants such as potassium thiosulfate, as well as how organi c matter levels can influence emissions. Application Methods (Section 2.3) addresses how varied applicati on methods and equipment can alter
emissions. Field Conditions (Section 2.4) describes many of the proper ties that are inherent in a specific field setting which may influence emissions such as soil texture. This section also
addresses certain factors that ar e inherent in field conditions but that may be manipulated in different degrees through cultura l practices (e.g., moisture leve ls) thereby altering emissions. Finally, First Principles Modeling (Section 2.5) describes the efforts to evaluate the sensitivity of various factors using a m odeling context (i.e., CHAIN2D).



### 2.1 Tarp Use

According to Laverde (2002) [1] total sales of ag ricultural films in the United States in 1998 was 180 ktonnes of which 37 percent were used for mulc h film (i.e., ~67 ktonnes). This article also
notes that most films are between 0.75 and 3.0 mi ls depending upon the area where it is installed
and that most films are between 31 and 90 inch es depending upon crop type . For sterilization and fumigation, Laverde notes that films are between 4 and 10 mils thick and from 5 to 17 meters wide which does not appear to be consis tent with many recent observations the Agency has had with a variety of stakeholders. These inte ractions appear to indicate that films less than 3 mils thick are more commonly used today for soil fumigation purposes.

Based on this and what is known about produc tion practices in agri cultural crops where fumigants are used, it is clear that the use of plastic films is a key factor related to sustaining the production of many key vegetable crops such as tomatoes, peppers, strawberries, squash and eggplant since much of these crops are produced in raised bed cultu re. Similar systems are also used in ornamental production and forest seed ling production. Tarps can be used for many purposes but cultural systems can be categorized by whether or not fl at fume (broadcast or strip) applications are made to flat fields or if rais ed beds of some design are used. In broadcast applications, the primary function of tarp use re lative to fumigant appl ications is to reduce emissions and retain more fumigant to achieve higher concentrations over time in order to achieve higher efficacy, perhaps us ing lower application rates in so me circumstances. In raised bed culture, tarp use is more complex in that it is typically used to control temperature (e.g., white or black films), reduce water use (i.e., by reducing temperature an d reducing water vapor loss), control emissions by serving as a barrier, ac t as a structural component of the raised bed over a growing season (or many subsequent crops in the same bed), serve in pest management programs (e.g., metalized film and white flies in Florida), and serve as a weed germination barrier. These factors would all be considered by growers in the selection of a tarp for use in a raised bed cultural system.7 The focus of this analysis is clearly that tarp use does serve the purpose of reducing emissions at the boundary layer (i.e., soil surf ace) with the caveat th at the performance of such tarps depends on the material from which they are made, temper atures, and the identity of the fumigant to which they are subjected. Of course, the other issues described above ar e also important because they directly impact the types of films growers select and use in their cultural systems. Tarp use
can also influence subsurface factors that can im pact emissions but the impact they may have on reducing total or cumulative emissions is also desirable. These types of impacts are also addressed below as appropriate.

Many investigators such as Yates, Gao, Chellim i, Wang, Ajwa and others have demonstrated that tarps reduce emissions and also that thei r performance varies depending upon conditions. For example, when Barry (1999) [2] examined emission ratio groupings for methyl bromide, it was found that [Note: This analysis does not address strip fumigation permit conditions.]:

“These results indicate that for both the unadj usted and the recovery adjusted emission ratios, for broadcast applicati ons, there are only two distingu ishable groups: non-tarp and tarp.”

It should also be noted that in the California Department of Pe sticide Regulation (DPR) permit conditions for methyl bromide, the distinct differences are assigned (i.e., based on emission ratios for similar application techniques) based on whether or not a tarp is used (pg 2 from http://www.cdpr.ca.gov/docs/legbills/ru lepkgs/03-004mbr/03004buffer_zones.pdf).



### 2.1.1 Nomenclature

Historically, one of the problems with the interpretation of emissions data for tarps has been one
of nomenclature because there is a lack of standard ization pertaining to how tarp products are described and there is also a lack of performan ce criteria for tarps under varied conditions that
can be used to describe true in-f ield performance. For example, it is widely believed that the determination of mass transfer coefficients (M TCs) using laboratory based studies such as column tests are good predictors of the permeability of a tarp under the specific conditions of the test. However, relating the conditions of such te sts to field performance is elusive because key parameters are difficult to quantify (e.g., impact of stretching films during laying process in field impacts permeability and tensile forces/process vary by user). Ajwa (2007) [3] also recognized this at the 2007 Methyl Bromide Alternatives Ou treach conference (hence forth referred to as MBAO) in his description of activities related to plastic film permeability to fumigants that was presented as part of the USDA-ARS Pacific Ar ea Wide Project. In this presentation, he described several categories of f ilms including: VIF (Virtually Impermeable Film), that is essentially a layer of polyamide sandwiched by two layers of polyethylene (Figure 1); SIF (Semi-Permeable Film), that is essentially a layer of semi-permeable polymer polyethylene sandwiched by two layers of low density polyeth ylene (Figure 2); metalized film, which is essentially a layer of polyethylene with a thin la yer of metal coating adhered to its surface; and TIF (Totally Impermeable Film), that is a sandwic h of outer layers of polyethylene with 2 layers of ADMERTM adhesive and an inner layer of E VALTM resin (Figure 3). In this presentation, both high-density (HDPE) and low-density poly ethylene (LDPE) are mentioned but no further descriptions of these materials were provided. The American So ciety for Testing and Materials (hereafter referred to as ASTM) in its Sta ndard D4635-01 describes low density polyethylene8 materials (based on standard D4976) as having a de nsity not necessarily equa l to the density of a molded plaque from the same resin and when pol yethylene is blended with ethylene vinyl acetate that the density does not exceed 0.929 grams/cc [4]. Additionally, no information describing other standard factors such as impact strength, nominal thickness or coe fficient of friction as
described in ASTM standard D2103-05 that descri bes specifications for plastic sheeting were provided [5]. Yates reported [6] slightly differe nt definitions of LDPE and HDPE films. These
are described in Table 1 below.

Figure 1: Virtually Impermeable Film (Ajwa MBAO Definition, PE=polyethylene)

Figure 2: Semi Impermeable Film (Ajwa MBAO De finition, PE=pol yethylene, LD=Low density)

Figure 3: Totally Impermeable Film (A jwa MBAO definition , PE=polyethylene)

Table 1: Bulk density of polyethylene films Linear Low Density Polyethylene Film (LLDPE) 0.900 to 0.939 g/cc Low Density Polyethylene Film (LDPE) 0.916 to 0.925 g/cc Medium Density Polyethylene Film (MDPE) 0.926-0.940 g/cc High Density Polyethylene Film (HDPE) 0.941 to 0.965 g/cc Reported by Yates [6] (http://www.blueridgefilms.com/page2.htm)9 The Ajwa definitions described above are one po ssible approach for establishing a systematic nomenclature for films used in agriculture. These may or may not ultimately be the most appropriate ways to categorize ta rp materials. There are altern ative approaches which are based on performance rather than composition which may be preferable for many reasons. Yates [6] introduced the concept of a performance-based approach for categorizing films based on the following descriptors [Note: TIF is not included in this scheme as in the Ajwa approach.]:

• HDPE (High Density Polyethylene): A logical choice for the st andard material used to define when a film meets the definition of VIF and SIF.

• SIF (Semi-impermeable film): An agricultural film that offers significant resistance to fumigant diffusion across the membrane when co mpared to a standard material but not as
much resistance as a VIF.

• VIF (Virtually-impermeable film): An agricultural film that offers significant resistance to fumigant diffusion across the membrane when compared to a standard
material.

In addition to these proposals, th ere are likely many other factors th at could be used to describe plastic films based on how the industry describe s materials. To date, no researchers have extensively used them for such purposes and it is unclear based on current research that specific compositional factors can be linke d to film performance. Howe ver, the following sources of information should be considered as a possible future basis for providing further details about films. The plastics industry is extremely large and diverse wi th many associated professional organizations with many possible ways to cate gorize or otherwise describe plastic products including films that could be used in ag riculture. Some of these include ASTM (www.ASTM.org); Society of the Plastics I ndustry (http://www.socplas.org); American Chemistry Council - Plastics Divi sion (http://www.americanchemis try.com/plastics); and Society of Plastics Engineers (http://www.4spe.org). Ad ditionally, other organizations such as the American Society of Agricultural and Biological Engineers (http://www.asabe.org) geared more toward agricultural issues also have developed bodies of research on films and their use in agriculture. As an example, ASTM Committ ee D20 on Plastics was formed in 1937. This committee currently has jurisdic tion over 480 standards published in the Annual Book of ASTM Standards, Section 8 on Plastics. D20 has 23 te chnical subcommittees that maintain these
standards. Information on this subcommittee structure and its a ssociated portfolio of approved
standards and work items under deve lopment is available from their List of Subcommittees, Standards, and Work Items . One of the standards de veloped by D20: ASTM D883-07 Standard Terminology Relating to Plastics could be useful for serving as the basis for providing a more systematic way of describing films in use in agriculture today and in the future.



### 2.1.2 Laboratory Performance Based Sta ndards For Categorizing Films

Along with providing a possible appr oach for describing different t ypes of films detailed above, Ajwa 2007 [3] described a laboratory-based perf ormance-based standard upon which to gauge their efficacy as a boundary layer barrier for reducing fumigant emissions based on relative differences in mass transfer coefficients (MTC s). [Note: The performance results are not necessarily intuitive based on the descriptions of the films. In this context, MTCs should be compared directly.] The categories he proposed based on the definitions described above and the empirical data described below (Figures 1 through 3) are:10 o High >4cm/hr

o Low <4 cm/hr

o Impermeable <2 cm/hr

o VIF <0.2 cm/hr

These categories were defined ba sed on laboratory-based column te sts at ~20oC us ing 4 different fumigants (i.e., iodomethane, methyl bromide, 1,3-dicholorpropene, and chloropicrin) and a variety of film materials. The results for specific products he presented include:

o High permeability - Pliant black embosse d (1.25 mil) and Pliant white/black embossed (1.25 mil)

o Low Permeability - PolyPak Std. (1.5 mil), Poly Pak SIF (2 mil), Metalized stripe (1.3 mil), metalized shiny (1.3 mil)

o Impermeable - Pliant (B-A)(1.25 mil), C& B micro-embossed (1.25 mil), T&F microembossed (1.25 mil)

o VIF - IPM clear VIF (1.38 mil) , Bromostop VIF (1.38 mil),

o TIF - Eval/Mitsui film (1.38 mil)

The results for these analyses are presented be low for each film category (Tables 2 through 5). Figure 4 illustrates the device that was used by Ajwa to define MTCs. Film is placed between chambers and fumigant is applied to the lowe r chamber then measured in the top chamber. Figures 5 through 7 illustrate diffusion over time so the shape of an emission profile can also be altered depending upon the rate of diffusion. Th e results indicate good agreement within MTC categories and specific fumigants, except in a few cases (e.g., Poly Pak SIF provides consistently lower MTCs in the low permeability category; however, it is thicker (i.e., 2 mil) than the rest in the category (i.e., 1.3 or 1.5 mil)). It should be noted that ther e is disparity in MTC trends between the different fumigants as seen in Tabl e 2, below, where 1,3-D MTCs are almost 4 times as high as those measured for methyl bromide. Interestingly, this difference is significantly altered for low permeability films (Table 3) where in some cases they are equivocal or the factor is ~2 times greater for 1,3-D. Ajwa did conc lude that the average MTC for iodomethane was generally small for all films except embossed high permeability films.

Table 2: MTCs (cm/hr) High Pe rmeability Films (Ajwa 2007 MBAO)

Film type MB IM Cis 1,3-D Trans 1,3-D CP

Pliant black embossed (1.25 mil) 4.57 4.11 18.12 18.94 10.95

Pliant white/black embossed (1.25 mil) 4.48 3.09 17.21 17.51 10.7511 Table 3: MTCs (cm/hr) Low P ermeability Films (Ajwa 2007 MBAO)

Film type MB IM Cis 1,3-D Trans 1,3-D CP

PolyPak Std (1.5 mil) 4.95 0.46 3.80 5.28 1.60

PolyPak SIF (2.0 mil) 0.82 0.42 1.42 1.50 0.67

Metalized /stripe (1.3 mil) 3.53 0.55 3.84 5.68 1.67

Metalized /shiny (1.3 mil) 2.61 0.50 4.82 6.02 2.24

Table 4: MTCs (cm/hr) Impe rmeable Films (Ajwa 2007 MBAO)

Film type MB IM Cis 1,3-D Trans 1,3-D CP

Pliant (B-A) (1.25 mil) 3.60 0.02 1.17 2.28 0.22

C&B, micro-embossed (1.25 mil) 4.42 0.02 0.86 1.65 0.11

T&F, micro-embossed (1.25 mil) 3.05 0.04 1.18 1.51 0.21

Table 5: MTCs (cm/hr) Virtuall y Impermeable Films (Ajwa 2007 MBAO)

Film type MB IM Cis 1,3-D Trans 1,3-D CP

IPM clear VIF (1.38 mil) 0.13 0.01 0.17 0.36 0.001

Bromostop VIF (1.38 mil) 0.18 0.02 0.16 0.32 0.05

Eval/Mitsui film (1.38 mil)
TIF 0.20 < 10 -13 < 10 -13 < 10 -13 < 10 -1312 Figure 4: Ajwa Diffusion Chamber For Measuring Fumigant MTCs

Figure 5: MB Diffusion Through LDPE (R =receiving or top of cylinder, S=source)

Figure 6: MB Diffusion Through Metalized Film (R=receiving or top of cylinder, S=source)13 Figure 7: MB Diffusion Through Bromostop VIF (R=receiving or top of cylinder, S=source)

The United States Department of Agriculture Salinity Laboratory [7] also reported mass transfer coefficients in 2005, generated using column test s, for varying thicknesses of HDPE and silver Mylar films for 3 chemicals (Table 6). These va lues indicate films thickness and the material composition impact emissions as does the identity of the fumigant.

Table 6: Mass transfer and sorpti on coefficients for fu migants diffusing through plastics at 20 °C

Methyl Bromide Propargyl Bromide Chloropicrin

1-mil HDPE

h (cm h -1 ) 0.37±0.02 1.48±0.08 0.65±0.06

α (h -1 ) 0.5±0.4 0.4±0.2 0.9±0.3

kp (cm) 0.3±0.1 1.2±0.02 1.8±0.2

4-mil black HDPE

h (cm h -1 ) 0.14±0.01 0.48±0.02 0.25±0.03

α (h -1 ) 0.21±0.09 0.13±0.04 0.32±0.06

kp (cm) 1.3±0.2 1.3±0.2 3.8±0.3

silver mylar†

h (cm h -1 ) <4.4×10-6 <1.3×10-5 <2.3×10-4

α (h -1 ) 2.1±0.9 1.2±0.4 0.8±0.3

kp (cm) 1.09±0.07 5.2±0.4 11±2

Source: USDA [7]14 Gamliel [8] also reported permeability estimates which are presented in Table 7 for various films against methyl bromide using a column type of test.

Table 7: List of Films Tested By Gamliel and Their Properties

Researchers such as Yates [6] and Wang have al so reported varied measures of permeability defined by laboratory testing. In fa ct, Yates [6] suggested a varied approach that uses a variable
referred to as the "R-value" which is a measure of the capacity of a material, such as a plastic film to impede fumigant flow with increasing va lues having increasing capacity to reduce flow.
These values can be calculated as follows:

R = 1/h (where h = mass transfer coefficient)

or Yates [6] suggested perhaps a more robust valu e of (R) can be calcula ted using the ratio of mass transfer coefficients or flux density (P) of a film of interest relative to values associated
with a standard HDPE.

There are a variety of researchers who have report ed mass transfer coefficients or other similar measures of permeability in the literature. However, linking the performance of films in a laboratory setting to actual fiel d performance is a critical issue which remains uncertain at this time. This is supported by the presentations of Ashworth and Yates [9] and Yates [10] at the 2007 Methyl Bromide Alternatives Outreach confer ence. Ashworth and Yates [9] noted that there are potential problems resulting inherent in scaling from field to laboratory-based research because accounting for differences in degradation rates has not been addressed. They also indicated that laboratory data can be used to simulate fi eld experiments and that there is generally good agreement but that there is uncertainty. Yates [ 10] considered laboratory, field plot and large field research designs and conclu ded that each has attribut es as illustrated in Figure 8. Essentially, Yates noted that laboratory studies are chea per and more reproducible but they often represent idealized c onditions. Conversely, large field re search is more expensive but it is more representative yet highly variable.15 Figure 8: Yates Illustration of Relationship s Between Different Scales Of Research Design

The use of film permeability measurements of one sort or another (e.g., MTCs) can clearly be useful when defining the general performance of a film in the field. For example, when MTCs from typical LDPE and HDPE films are compared with MTCs from films that are expected to reduce emissions, such as Hytibar or Bromostop, it is clear that the la ter significantly reduce emissions. It is also clear that their performa nce can be impacted by a number of factors (some of which are explored below) and the identity of the fumigant chemical to which they are subjected. In many cases, emission reductions ac tually reduce flow by as much as 90 percent and in some cases even by orders of magnitude. This type of performanc e in the field, under real world conditions, is just not lik ely for any number of reasons, but most importantly, as Yates [10] indicated, that laboratory te sts are idealized. As such, the Agency does not believe that laboratory-based tests should be used in a manner which quantitatively predicts emission reductions by just considering the ratio of single factor, such as an MTC (e.g., VIF to HDPE) because this type of research does not account fo r factors such as film stretching, atmospheric condition changes, and changes in field conditions relative to the perfor mance of a film in the field. Instead, the results of laboratory based re search should be considered as the basis for qualitatively ranking the potential performance of films in the field in context with results from other types of research designs, discussed belo w, for a more comprehensive estimation of the reduction potential of various agricultural films. For example, it is clear that certain films described by Ajwa, Wang, Gao, and Yates are significantly less permeable to most fumigants than other more typical films such as < 2 mil LD PE or HDPE. As such, if alternative films are used by growers such as Bromostop or other VIFs , then a “credit” towards buffer zone reduction should be applied. The magnitude of the credit , given the current scie ntific knowledge, should ultimately be defined based on a consideration of varied types of data that attempt to quantify such factors (see conclusions based on all types of tarp research below for further discussion). 

### ScaleIncreasing

0 25 50 75 100

Laboratory Field Plot Large Field Regional Variability & UncertaintyRepresentativeness

50% ± 25

35% ± 1016 

### 2.1.3 Field Study Based Standar ds For Categorizing Films

Along with laboratory based research programs described above, a va riety of field-based research has been completed which can be describe d similarly to Yates [10] as being either of field plot scale or large -field type of research. Both types are described in this section but the major focus in on the evaluation of field scale da ta. Generally, there are lower numbers of these studies available because of costs and logistical difficulties. These data become even more
limited with the use of modern films that are considered to be high barrier.

It is important to consider the following when reviewing the information presented in this section. As indicated by Yates, field scale data can be highly vari able and costly but at the same time they are the most representative of true emi ssions. In the results described above, the tarps considered have been essentially evaluated base d on their effect solely as a boundary layer even though it is clear that other fact ors can contribute to reductions in emissions (e.g., enhancing longevity of soil moisture retention during and afte r application/irri gation events). There are, of course, obvious exceptions which will be addressed below and in other areas of this document as appropriate. Below, a series of field scale studies are describe d where emissions were quantified and many factors were recorded th at substantiate the conditions of the experiment but, in large part, these factors were not cons trained. Instead, the factors were quantified (e.g., soil type, moisture levels, % organic matter). This type of issue leads to a dilemma when attempting to
evaluate results from field scale studies in that it is hard to qu antify the relative contribution of one factor over another because a systematic research program design was not employed that was robust enough to capture these differences. Inst ead, a purposive selecti on of a study site and condition was made for any number of reasons ra nging from logistics and ease of cooperation from a grower to an assigned repres entativeness of the site by the i nvestigators. It should also be noted that field scale and even small plot rese arch can be prohibitively expensive and it is typically not considered as re presentative of field conditions as larger scale efforts.

The field scale monitoring data used to develop emissions profiles used in the Agency’s risk
assessments were evaluated. Most of the existi ng data were generated using standard cultural practices that involved the typi cal shanking operations or other application methods and typical films such as LDPE or HDPE less than 1.5 mil thick. As part of the recent decision to register iodomethane ( http://www.epa.gov/pesticides/f actsheets/iodomethane_fs.htm ) a series of data were required as a condition of the experiment al use permit that was granted prior to the registration decision. The focus of these data was to evaluate the effectiv eness of different tarp materials for reducing emissions. The data that were generated by the registrant for iodomethane are summarized in Figure 9 [11-16]. [Note: In the Agency’s risk assessments, only a few other studies out of 70 or so used any sort of high ba rrier film. In these cases reductions were not always observed compared to standard polyethyl enes. However, the studies were not based on current film technologies so they have not been presented here.]17 Comparison of Mei Flux, Raised Bed (Normalized to 175 lbs/treated acre)

0 20 40 60 80 100 120 140 160 180

0 10 20 30 40 50 60 70 80

HourFlux (ug/m2-s)

Guadalupe HB 1.5 mil Oxnard, CA Standard 1.5 mil
Plant City, FL Standard 1.25 mil Dover, FL White Metal (0.0013")
Bainbridge, GA Black Hytiblock 7 (0.00125") Hart, MI XL Black Blockade (0.00125")

Figure 9: Iodomethane Emission s Using Various Tarp Materials

In addition to the differences illustrated in Figure 9 above, the studi es conducted under the iodomethane experimental use permit also quantif ied the emissions of chloropicrin when such tarping materials were used. These results are illustrated in Figure 10 below [14-18].

Comparison of Chloropicrin Flux, Raised Bed (Normalized to 350 lbs/treated acre)

0.00 50.00 100.00 150.00 200.00 250.00 300.00

0 5 10 15 20 25 30 35 40 45

HourFlux (ug/m2-s)

AZ Untarped AZ 1.5 mil plastic mulch
Dover, FL White Metal (0.0013") Bainbridge, GA Black Hytiblock 7 (0.00125")

Hart, MI XL Black Blockade (0.00125") Note: The maximum rate for untarped applications is 175 lbs/treated acre. The AZ untarped flux rates have been doubled for comparison purposes.

Figure 10: Chloropicrin Emission s Using Various Tarp Materials

These graphs illustrate emissions from application start time through 2 to 4 days or so after the initiation of application. Figures 9 and 10 clearly illustrate that there is a significant impact on emissions when different tarp materials are used . Also, the emission rates have been normalized to typical non-tarped app lication rates for a more direct compar ison of the impacts of tarp use.18 More typically, it would be anti cipated that these values would be even lower because the rates
would be reduced since the non- typical tarps would retain mo re fumigant and would likely achieve needed levels of pest control at lowe r dosage rates. In th e first 24 hour period, differences as much as an order of magnitude or more are noted in the results. For example, in Figure 10, flux exceeds 100 μg/cm 2 /hour for a 12 hour period with typical tarps from the Arizona study but is much lower (i.e ., on the order of 10 μg/cm 2 /hour or less) with the higher barrier films from the Bainbridge GA and Hart MI sites. Conversely, fl ux reaches levels of approximately 30 μg/cm 2 /hour at the Dover FL site using me talized tarps. It should also be noted that the field sites for these studies and environmental conditions were significantly different except for Dover FL which would be an ticipated to produce results more similar to the
standard studies than the GA and MI sites because the climate is warmer and soils more similar to the existing data. The GA and MI site data , could be confounded, at least for a comparison based solely on tarp performance, because the th ese sites are more temperate and the soils are different in that they likely have higher levels of organic matter.

Other field scale research has been conducted wh ere the objective was to directly evaluate the
reduction in emissions when various techniques are employed and co mpared against one another. Examples of such research were presented by Ajwa [19] at the October 2007 MBAO meeting where the results are summarized in Figur es 11 through 14 for chloropicrin and 1,3- dichloropropene, respectively, using VIF, SIF and K-thiosulfate.

Figure 11: Impacts On Chloropicrin Emission Profile From Varied Surface Treatments Chloropicrin Emission Rates, October 2006

0 10 20 30 40 50 60 70 80 90 100

10/3/2006

6:00 10/3/2006

18:00 10/4/2006

6:00 10/4/2006

18:00 10/5/2006

6:00 10/5/2006

18:00 10/6/2006

6:00 10/6/2006

18:00 10/7/2006

6:00 10/7/2006

18:00 10/8/2006

6:00Emission Rate (ug/m2/sec)

VIF

VIF + K-thiosulfate

Standard Tarp + K-thiosulfate

Standard Tarp

Date/Time after fumigant application19 Figure 12: Impacts On Cumulative Chloropicri n Emissions From Varied Surface Treatments

Figure 13: Impacts on 1,3-Dichloropropene Emi ssion Profile From Varied Surface Treatments Oxnard Off-Field 1,3-Dichloropropene Emission Rates, September 2006

0 10 20 30 40 50 60

9/13/06
6:00 9/13/06
18:00 9/14/06
6:00 9/14/06
18:00 9/15/06
6:00 9/15/06
18:00 9/16/06
6:00 9/16/06
18:00 9/17/06
6:00 9/17/06
18:00 9/18/06
6:00 9/18/06
18:00 9/19/06
6:00

Date/Time1,3-D emission Rate (ug/m2/sec) Standard PE Tarp

Standard PE Tarp + KTS/Water

SIF + KTS/Water

SIF Chloropicrin Emissions Relative to Amount Applied (200 lbs/ac)

0% 5% 10% 15% 20% 25%

1 2 3 4 5 6 7 8 9 10 11 12 13

PeriodCumulative Percentage of Chloropicrin Lost to Applied VIF

VIF + K-thiosulfate

Standard Tarp + K-thiosulfate

Standard Tarp20 Figure 14: Impacts On Cumulative 1,3-Dich loropropene Emissions From Varied Surface
Treatments

Ajwa [19] reported the following conclusions for the VIF experiment using chloropicrin which
was conducted in Santa Maria California in October 2006.

• VIF reduced early emissions rate by 50 per cent relative to 1.5 mil standard tarp.

• Chloropicrin cumulative emissions from fields covered with VIF was ~50% relative to fields covered with 1.5 mil standard tarp.

• K-thiosulafte water seal reduced emissions by ~50%.

• Issues related to VIF persist: ava ilability, price, stretching, gluing, etc.

Ajwa [19] also reported the following c onclusions for the SIF experiment using 1,3- dichloropropene which was conducted in Oxnard California in October 2006.

• 2.0 mil SIF reduced early emissions rate by 25% relative to 1.5 mil standard tarp.

• K-thiosulafte water seal reduced emissions by ~35%.

• 1,3-D cumulative emissions from fields covered with SIF were 88-90% relative to fields covered with 1.5 mil standard tarp.

• Earlier studies found that 1,3-D (Telone) cumu lative emissions from the SIF field was ~50% relative to emissions from fields covered with 1.25 mil HDPE tarp.

Based on these field studies, it is clear that in the situations ev aluated by Ajwa that both VIF and SIF reduced both peak and cumulative emissions of chloropicrin and 1,3-dichloropropene although it is also clear that the reduction was much more pronounced with the VIF and chloropicrin combination. Anothe r benefit of these field studies over many others is that they incorporated as similar of conditions as possible when evaluating the varied parameters for each tarp/chemical combination (i.e., applications were completed in proximity with one another, Oxnard Off-Field 1,3-Dichloropropene Emission Rates, September 2006

0 5 10 15 20 25 30 35

9/13/06
6:00 9/13/06
18:00 9/14/06
6:00 9/14/06
18:00 9/15/06
6:00 9/15/06
18:00 9/16/06
6:00 9/16/06
18:00 9/17/06
6:00 9/17/06
18:00 9/18/06
6:00 9/18/06
18:00 9/19/06
6:00

Date/Time1,3-D emission relative to amount applied (%)

Standard PE Tarp

Standard PE Tarp + KTS/Water

SIF + KTS/Water

SIF21 samples were collected using direct in-field sa mplers which minimizes cross contamination, and applications were done in similar weather conditi ons). With this attribute, these data likely represent one of the most comparable sets of fi eld study information where the impacts of varied factors can be compared with one another.

Other researchers such as Gao, Paperienik, and Wang have also evaluated the impacts on emissions of using varied films. For example Ga o [20] evaluated the imp act of varied films and irrigation methods on emissions in field plots (i.e., the Yates definition) using chloropicrin and 1,3-dichloropropene in a Hanford sandy loam in the San Joaquin valley of California in 2005 (Figures 15, 16, 17).

Figure 15: Effect of Va ried Surface Seals On 1, 3-D Hourly Flux Rates22 Figure 16: Effect of Varied Surface Seal s On Chloropicrin Hourly Flux Rates

Figure 17: Effect of Varied Surface Seals On 1,3-D and Chloropicrin Cumulative Flux Rates23 Gao concluded that water seals can reduce emissions but that VIF tarps more effectively reduced emissions compared to water sealing methods. She indicated more research is needed to evaluate VIF performance in fi eld scale trials. Based on the results presented in the above figures it also appears that the use of VIF can reduce peak emissions up to 20 times in some cases. [Note: Water sealing methods w ill be discussed below in more detail.]

Paperienik [21] evaluated the possible impacts of aging film related to permeability where films were subjected to 28 days of California field c onditions then collected at various intervals over that time and subjected to permeability tests. It was concluded that “field aging did not appear to significantly alter the flexibility, color, elastic ity, or other physical char acteristics of the film, even after 1 month's exposure to relatively hi gh temperatures and intense solar radiation.” Additionally, it was concluded “in the present studies, temperatur e and polyethylene film type had the largest impact on mass transfer coefficien ts of fumigant compounds across HDPE films.” This could be important to consider when fields under drip irrigation could receive multiple treatments between sequential crop s in the same field setting. This conclusion is also supported by Wang [22].

Wang [23] also supports the results observed in the field studies described above in that significant emission reductions ar e achieved through the use of less permeable films. Wang concluded that Hytibar tarp is effective in c ontaining methyl bromide, whereas the polyethylene tarp is more permeable to methyl bromide (F igure 18). Wang also noted that the measured cumulative emission loss of approximately 60 per cent is similar to the losses observed in other studies conducted by himself and Yates under similar conditions. In this study methyl bromide applications were made using the hot gas met hod on field plots near Riverside California in
1996.

Figure 18: Effect Of Film Type On Methyl Bromide Bromide Emissions24 The use of field emissions data, like the use of laboratory based measurements of emissions, can clearly be useful when defining the general performan ce of a film in the field. It is also clear the use of less permeable films such as VIFs or SIFs (e.g., Bromostop, Hytibar, etc.) can substantively reduce both peak and cumulative emissions. In fact, some of the research
referenced above indicates that peak emissions can be reduced by an order of magnitude or more such as illustrated in Figures 9, 10 and 18. Conve rsely, when more factors are constrained, such as in the Ajwa research, emissions can be re duced by 50 percent or less for VIF or SIF use (Figures 11 and 13). The apparent differences may be due to many factors but the major differences are likely due to site differences. For example, in Figures 9 and 10 the studies were generated at different locations as noted above such as Plant City Florida fo r iodomethane raised bed emissions compared to Georgia and Michigan in two of the studies. It should also be noted that due to cost and logistical constraints that the results illustrated herein, like other similar studies, have limited replication associated with them for each specific research circumstance (i.e., there is a low number of data points for comparison). This adds significantly to the overall uncertainty with interpreting the results and it does not allow for a true assessment of the variability of the predicted outcomes. Conversel y, it should also be considered that purposive decisions were made in site selection by resear chers, but the motives for those decisions are unclear (e.g., logistical ease or some sort of sp ecific site characteristic), to ensure they were representative for the research intent. Given all of these factors, if alte rnative films are used by growers such as VIF or other high barrier film th en a credit towards buffer zone reduction should be applied based on this research. The magnitude of the credit, given the current scientific knowledge, should ultimately be defined based on a consideration of varied types of data that attempt to quantify such factors (see conclusions based on all types of tarp research below for further discussion).



### 2.1.4 Mechanistic Study Based Standards For Categorizing Films

Along with laboratory and field based research programs described above, a variety of related
research has been conducted that has evaluate d, for lack of a better term, the mechanistic subsurface factors that can possibl y influence emissions. After a fu migant is injected below the soil surface or applied via a topical method such as drip irrigation where it is driven into the soil with additional carrier water, various processes then influenc e the rate at which fumigants approach the boundary layer which is the interface between the so il matrix and atmosphere. For the purposes of this discussion, agricultural films are the primary boundary layer option but others such as water sealing methods or the additi on of organic matter can also be used. [Note: These additional possible boundary layers are disc ussed below since this discussion is focused on agricultural films.] The rate at which fumi gants move to the boundary layer and the amounts relative to the starting mass applied (which can decrease from the app lied via degradation and metabolism) are possibly important factors rela tive the overall emission process because these processes regulate the level of the source concentration over time prior to diffusion through the boundary layer (i.e., in this case plastic film of some sort) and emission into the atmosphere.

In order for a fumigant to achieve a desired level of efficacy, a sp ecific concentration time course must be achieved which is governed by Haber’s la w (i.e., referred to as CxT requirements). If
sufficient concentrations are not available at the ar ea of the target pest, less than desirable control will result for the pest of interest. With this inmind, the goal of mechanistic research, in general,25 has been ostensibly to achieve required CxT regi mens at reduced application rates and for longer periods of time. This has been critically im portant for methyl bromide users governed by the phase out schedules specified by the Montreal Protocol ( http://www.epa.gov/ozone/mbr/ ) and for other fumigant users as well due to increased costs and use restrictions. The use of more retentive agricultural films has b een a part of these efforts and results from several researchers are noted below. Typically, more retentive fi lms increase the retention time in soil which increases the achieved CxT. They can also in crease bandwidth of the effective CxT area in
subsurface soil because more mass is retained over longer periods of time. Other factors as well
can also influence the rate of soil retention (many of these are discussed below) such as proper moisture control, application of additional irrigation wa ter, and the use of other soil adjuvants such as organic matter additions. Much of the re search in this area has focused on use of more retentive films but it also has incorporated other f actors so it makes it difficult to attribute overall increases in CxT effects to just the use of films. Many research ers have conducted this type of investigation including Ajwa, Gamliel, Yates, Wang, Gao, and Papeirnik. Of few examples are provided below. [Note: This phenomena has al so been predicted by Cryer [24] through the first principles soil modeling whic h is discussed below.]

Papiernik [25] investigated how the tarp mate rial could impact emissions by impacting, among other factors, subsurface soil mechanisms that lead to emissions. In this study HDPE and Hytibar were investigated in mesocosm (i.e., basi cally field plots) and field scale studies. Three fumigant materials were used including 1,3-dichloropropene, pr opargyl bromide, and methyl isothiocyanate (MITC). The key result of this study is presented in Figure 19 which illustrates the effective CxT subsurface contour is expanded with the use of a more retentive film (e.g., Hytibar in this case).

Figure 19: Concentration Contour Of cis 1,3-D 2 Days After Application (Arrows indicate flow, Dots are sample points)26 Ajwa [26] evaluated the impacts of using differe nt films, application methods and levels of irrigation on the retention of 1,3-D and various other fumigants (Figure 20). [See below for more detailed discussion related to impacts on rete ntion relative to differing soil moisture levels.] In Salinas sandy loam, the use of VIF significantl y enhanced the CxT area that resulted after shank application. In fact, the effective area appears to have at least doubled and concentrations at were approximately 4 times higher (e .g., at 40 cm the concentration is 1000 under polyethylene instead of 4000 ug/L under VIF). Dr ip application under standard polyethylene slightly expanded the CxT area and a similar enhan ced result would be expected for VIF is used in conjunction with drip applica tion. In Watsonville sandy loam, the impact of using VIF is as predicted where the CxT area is enhanced and c oncentrations at similar depths are enhanced (e.g., at 50 cm the air concen tration is ~100 ug/L under polye thylene versus ~400 ug/L under VIF).

Figure 20: Concentration Contours Of 1-3D (ug/L air) In (A) Salinas Sandy Loam After Shank & Drip Applications Under VIF and Standard Polyethylene and (B) Watsonville Sandy Loam After Drip Application Under VIF and Standard Polyethylene

Gao [20] also evaluated concen tration contours for 1,3-D and chlo ropicrin using a variety of
surface treatments and films as illustrated in Fi gures 21 and 22. [Note: Gao evaluated many combinations of treatments and their impact. The non-film approaches will be addressed below.] Half of the profiles were measured near the injection line (A) and th e other monitoring was completed between drip emitters (B). As expect ed, the VIF contour was large and the resulting concentrations were high. However, HDPE coupl ed with pre-applicati on irrigation provided similar results and the HDPE only profile was simila r. This indicates that soil moisture plays a key role in the concentration contour for both 1,3-D and chloropicrin as well as film type.27 Gamliel [8] investigated the use of high barrier fi lms as well. When these films were used in comparison to typical polyethylene films (i.e., they had a polyamide or ethylene vinyl alcohol layer) they were significantly less permeable. Th ey also expanded the efficacious concentration contour by increasing size and the ab solute concentration values at similar depths. As part of this effort, Gamliel also invest igated the impact of film thickness using LDPE (30 and 100 μm) and found, as expected concentration contours were enhanced with the th icker material because boundary layer diffusion rates are impacted by barrier thickness. The effect of LDPE film thickness in this experiment, however, was swam ped by the overall impact of using a higher barrier film (Baromide in this case). Figure 23 provides of summary of these results. In Figure 23, results for injection depths of 20 and 40 cm are reported based on methyl bromide. These results represent concentration dissipation over time at the speci fic depths for different tarp materials evaluated. [Note: USDA [7] Table 6 a bove also presents results for different tarp
thicknesses which are consistent with Gamliel – th icker tarps reduce diffusi on and retain more.]

Figure 21: Distribution Of 1,3-D In Soil After Va rious Surface Treatments And Drip Application28 Figure 22: Distribution of Chloropicrin After Various Soil Treatments And Drip Application29 Figure 23: Methyl Bromide Dissipation Rates After Injection At Either 20 or 40 cm Depth For Different Film Types (CxT Values Calculated By Investigators Are Also Included)30 As with the laboratory-based column studies which evaluated the boundary layer component of emissions and field scale research, it is clear that the use of highe r barrier tarp materials clearly have an impact on the subsurface processes which occur after application pr ior to emission at the soil boundary layer. To a lesser ex tent, film thickness also plays a role in reducing emissions but information is limited on this factor at this point to standard polyethylene materials. It is also
clear that thickness does not reduce emissions as much as the use of higher barrier films with a laminate layer between sheets of polyethylene (e .g., polyamide). It appears that high barrier films enhance concentration contours by expanding their size and increasing absolute fumigant concentrations at similar soil depths for longer periods of time. This information is critical because it provides a basis for theorizing how high barrier films coupled with lower application rates could achieve pest control efficacy. For example, in Figur e 23 the CxT for 100μm LDPE at 40 cm injection depth is 1007 g-h/m 3 and it is 4650 g-h/m 3 for Barromide film. If LDPE provides efficacy, it follows that applicati on rates could be sign ificantly reduced under
Barromide to achieve similar results. Of cour se, the caution is that such adjustments are not necessarily linear in nature and will vary be tween field conditions. Therefore, as indicated above, the results based on mechanistic studies shoul d be considered in context with results from the other types of research to arrive at factors which may be considered as appropriate credits for specific agricultural practices.



### 2.1.5 Summary of Emission Reduction Potential For Tarps

Enhanced agricultural films can be any mate rial which reduces di ffusion rates at the soil/atmospheric boundary layer compared to films commonly used in agriculture such as 0.7 to
1.5 mil LDPEs and HDPEs. These baseline conditi ons are even difficult to quantify because as described above, there is minimal standardization in this industry, relative to fumigant issues, there are many manufacturers and processes, an d the “common” practice of growers, even on small regional levels, often varies widely. Given this premise, it is also clear that the existing research of all types of films indicates that hi gher barrier films of varied composition, or even
thicker LDPE and HDPE films to a lesser exte nt, can reduce emissions and increase CxT parameters by enhancing the size an d absolute concentrations in s ubsurface contours. Therefore, application rates can be reduced without sacrificing efficacy. In many cases, the apparent numerical impact of using such high barrier films can be quite dramatic. For example, in selected laboratory-based permeability tests the emission reduction achieved is essentially quantitative but as Yates points ou t (see Figure 8), while these type s of data pose less uncertainty and variability than other research, they are le ss representative under actual field conditions. Similarly, field studies conducted under varied cond itions such as illustrated in Figures 9 and 10
above indicate dramatic differences when hi gh barrier films are used for iodomethane and chloropicrin, respectively. However, these data we re not collected at sites in proximity to each other. Consequently, it is difficu lt to directly compare what variab les have the greatest impact in reducing emissions. When field re search is constrained, such as illustrated in Figures 11 and 13 above by Ajwa, the impact of using high barrier tarps and other emission reduction methods is reduced (e.g., 50% emissions re duction under VIF and only 25% or so under SIF). Subsurface mechanistic processes also impact emissions but thes e are anticipated to be very site specific so a general quantitative assessment is not likely because so many factors can impact specific

contours.31 There are many factors that should be consider ed in the ultimate determination of possible
emission reduction credits relate d to varied tarp uses as an emission reduction tool. These basically have to do with research design, the lack of replication in the available data, the number of possible factors which are intertwined with each other in different ways that can impact overall emission rates, and uncertainty as well as va riability. It is important to consider that the
situations which have been evaluated in all t ypes of research on tarp materials have been essentially defined through a purposive selection of the conditions and not based on a random or even partially random selection process. This process has not been generally documented which
makes it unclear how results or even monitored situations represent fumigant use patterns. Additionally, there are low numbers of replication and the research was not specifically designed to elicit how individual parameters may impact overall emissions. These are clearly issues with key investigators in this area as well related to how specific factors might impact emissions. Chellemi [27 & 28] described research objectiv es for the USDA area-wide funding program that is currently ongoing to characterize how factors such as film type and field conditions can impact emissions. Yates [10] in Figure 8 above also succinctly summarized the concessions associated with specific types of research. Wang [29] also illustrated the possible repercussions of relying on a single source of information to describe film performance. In Figures 24 and 25 Wang
indicated that there is a discre pancy between predicted film performance-based on the types of studies conducted. In all examples cited, laboratory data over-predicted the ability of a film to reduce emissions when compared with field results.

1-mil HDPE

0 1 2 3 4 5 6 7

Methyl Bromide Chloropicrin 1,3-DTransfer coefficient (cm/h)

Lab

Field

1-mil Bromostop

0 0.1 0.2 0.3 0.4 0.5

Methyl Bromide Chloropicrin 1,3-DTransfer coefficient (cm/h) Figure 24: Comparison of Laboratory and Fi eld Based Emissions Predictions Using HDPE

Figure 25: Comparison of Laboratory and Field Based Emissions Predictions Using 1-mil Bromostop32 It has been shown in research ra nging from laboratory studies to large scale field tests that the use of agricultural films can play an important ro le in reducing emissions from fumigant use. There has not been the rigorous testing with side by side plots to evaluate the many different
field and weather conditions that could contribute to the extent of the reduction th at agricultural films may achieve. This is clearly a future rese arch need in order to mo re clearly understand the many factors that could impact the level of redu ction (e.g., soil type, soil temperature, moisture content, etc) . Despite the wide range of results observed in the studies available, from as little as 25 percent or so difference as noted with SIF by Ajwa up through quan titative reductions of emissions in varied laboratory tests, and given th e uncertainties and variability with the research to date, the assignment of any sp ecific credit which would apply whenever an agricultural film is used, is extremely difficult. While such a cred it can be informed by the current science, much
more research is needed to eliminate the uncer tainties and variability that exists today.33 

### 2.2 Soil Amendments

This section addresses methods and techniques other than th e use of tarps, application reductions, and limiting applicati on to certain equipment and mt hods that growers can control
relative to their field conditions to amend or manipulate soil conditions in ways that will both reduce emissions and improve efficacy. In Section 2.1: Tarp Use above, the impact of tarps as a boundary layer as well as the impact tarps have on subsurface processes that impact concentration profiles over time were consider ed. The impact of application equipment and
related decisions are discussed in Section 2.3: Application Methods below. The overall goal is
similar to those sections is to describe the potential impact that each of the different soil amendments could have on reducing emissions and improving efficacy. Specifically, the types
of factors which have be en identified include:

• Use of reactive adjuvants & water seals;

• Organic matter levels;

• Soil moisture;

• Field Preparation; and

• Soil compaction.

In some cases, growers will be able to intentio nally manipulate all of these factors to some degree or another. In other cases, growers will only be able to manipulate a single factor or none at all. There are a variety of reasons for this related to the availab ility of resources (e.g., irrigation water or organic amendments), costs (e.g., some reactive adjuvants may reduce emissions but they are expensive), cropping syst em impacts (e.g., too much water addition could alter planting schedules) and fiel d conditions (e.g., organic matter levels would be difficult to manipulate in a field with very low levels of existing organic matter). This section also introduces much of the “art” of fumigation into the discussion because although many of these
factors have been identified a nd discussed, and there is genera l consensus that they impact emissions (e.g., compaction and moisture,, it is no t clear how to quantify them. As discussed above, different types of research are available as descri bed by Yates [10] in Figure 8. The same type of characterization applies in that more laboratory scale re search is available than field studies designed to evaluate specific factors.

There is also some clear overlap in the factors ad dressed in this section and factors addressed in other areas of this document. For example, soil mo isture and water seals are differentiated in this context. Water seals are typica lly done during application or af ter application is complete by growers in order to establish a boundary layer on the soil surface or to ensure fumigants reach required depths to achieve efficacy. On the other hand, soil moisture levels in this context refer
to the conditions of the soil prior to an application to en sure proper working conditions pertaining to soil workability exist and to ensu re desired CxT is achieved. In some cases, irrigation water can be applied by growers. In other cases, it is determined by natural phenomena such as rain or lack thereof or th e depth of the water table in a specific area.34 In Section 2.1: Tarp Use above, three distinct types of research were evaluated that were differentiated as follows: laboratory based stud ies assessing boundary layer permeability, field based research that account for boundary and su b-surface conditions leading to emissions, and mechanistic research focused on both boundary cond itions and the subsurface processes that lead to emissions. In this section, there is less di fferentiation because the la boratory-based research evaluated in this section is similar in scope to the mechanistic data presented above. Based on this premise, this section provi des an evaluation of research fo cused on the 5 key factors growers can manipulate above based on mechanis tic and field study data per Figure 8.



### 2.2.1 Mechanistic Study Based Standard s For Categorizing Soil Amendments

The factors that the Agency believes can possibl y be manipulated by growers in varied degrees include the use of adjuvants and water seals, altering organic matter levels, establishing good field preparation practices, adju sting subsurface moisture levels (or, for example, purposively timing applications to coincide with proper rain fall), and using proper soil compaction methods. This analysis evaluates these factors. In many cases, the research was not directly designed to evaluate the sensitivity of emission rates to such specific factors as noted in Section 2.1 above with tarps so in most cases, multiple factors will be discussed at a time.

In Section 2,1, several pieces of research were presented which illustrated the impact of tarps but, in many cases, the research al so addresses several other fact ors. In Figures 21 and 22, Gao [23] assessed not only the impact of tarps but al so the impact of varied irrigation methods on subsurface, mechanistic processes. In this st udy, pre-application irrigation and the addition of water after fumigation appeared to enhance the concentration contour in both size and intensity.

Allaire et al [30] evaluated the effect of soil moisture and irri gation of propargyl bromide [3BP] emissions. In this study, multiple emission chambe rs packed in a raised bed style, covered by HDPE, with sandy loam soil were used and three types of treatments were studied including: no irrigation (M-0) but soil was mois t, a single 5-h surface irrigation 24 h after fumigation (M-5), and a 2-h daily surface irrigation fo r 4 days (M-M). [Note: D-5 is similar to M-5 except the data collection was incomplete and it was dry soil prior to start of experiment.] The impact on hourly
and cumulative emissions based on M-5 and M-M ir rigation is illustrated in Figure 26 which indicates similar profiles are ac hieved for both practices. A mass balance was also calculated for this study which is presented in Table 8. Th ese results indicate that either irrigation method significantly reduces the amount of propargyl bromide volatili zed. Based evaluations of volatility in this study, the i nvestigators concluded:

• Propargyl bromide volatilization tend s to behave as methyl bromide;

• Repeated irrigation lowers volatility sim ilarly to single prolonged irrigation event using less water;

• There is spatial vari ability in the volatilization (i.e., diffe rent amounts come from varied parts of the furrow);35 • HDPE is not as effective as a layer of water for reducing emissions but a thin layer of water on either side of HDP E increases its effectiveness; and

• Partial coverage of fields by grower s is not sufficient for controlling volatilization, either furrow irrigation should be applied or total fields must be
covered.

Figure 26: Propargyl Bromide Volatilization Ra tes Over Time With I rrigation Treatments

Table 8: Mass balance of 3B P for different treatments.

Treatment

D-5 M-0 M-5 M-M

%

Volatilization – 21 3.0 6.3

C

g {concentration gas phase} 0.02 2.6 2.1 1.0

C

l {concentration liquid phase) 15.9 35.3 26.2 41.1

Br –

{concentration bromide ion} 16.9 32.1 48.7 51.0

Total
– 91.0 80.1 99.536 Allaire et al [30] also investigated several m echanistic elements including concentration contour impacts from varied irrigation practices and subs urface water distributions (Figures 27 and 28). They also evaluated pressure distributions and gas phase concentrations associated with the direction of flux flow (Figure 29). Based on th ese analyses, the inves tigators reached several conclusions: most propargyl bromide dissipate s from soil in 24 hours, without irrigation the
movement is radial and diffusi ve, irrigation causes convective fl ux, irrigation increases duration in soil and reduces emissions, irrigation impacts th e shape and intensity of concentration contour, distributions in one phase can only roughly predict mass in ot her phases, and distribution patterns of degradates do not follo w distribution patterns of water.

Figure 27: Bromide Distribution 216 hours After Fumigation For Several Treatments

Figure 28: Water Content Distribution In The Profile 216 hours After Fumigatio n For Different Treatments37 Figure 29: Pressure Distribution In The Gas Phase and Gas Phase Co ncentration Associated With The
Direction of Their Flux In Treatment M- 5 At the Beginning of the Irrigation
Ashworth et al [31] evaluated the impact on 1,3-dichloropropene emissions of irrigation in two sandy loam soils (1cm water per day for 5 days after application), one with relatively low organic matter (LOM at 2.09%) and one with high er levels of organic matter (HOM at 3.16%). It was noted that irrigation led to a limiting of upward diffusi on of the fumigant and to the maintenance of higher soil gas concen trations (Figures 30 and 31).

Figure 30: Mean cis 1,3-D soil gas concentrations with depth over time for LOM soil38 Ashworth also evaluated emissions from the LOM and HOM soils with and without irrigation (Figure 32). It is clear that higher organic material levels in soil reduce 1,3-D emissions and that irrigation does as well. Cumula tive emissions in LOM were redu ced from 33 to 17 percent with irrigation and from 6 to 3 percent with irri gation. The authors ra ised concerns about implementing such an irrigation scheme in commer cial agriculture. They also suggested that
either thiourea or thiosulfate amendments could effectively reduce emissions as an alternative. Figure 31: Mean cis 1,3-D soil gas concentrations with depth over time for HOM soil

Figure 32: 1,3-D Volatilization Fluxes Observed For LOM & HOM Irrigated and Non-Irrigated Soils39 Dungan et al [32] evaluated the impact of temp erature, organic amendment rate, and moisture content on the degradation of 1,3-dichloroprop ene in an Arlington sandy loam soil. The
amendments included:

• Composted cow manure;

• Dewatered biosolids from municipal waste stream;

• Composted chicken manure; and

• Composted forest products.

Temperatures in the experiment ranged from 20 to 40 C. Moisture levels were adjusted to 25, 50, and 75 percent of the water holding capacity of the soil. Composted cow manure reduced
emissions the most of 4 amendments tested (Figure 33 and Table 9). In unamended soils, degradation rate constants increased with temper ature as expected while increases in moisture levels did not significantly impact results (Figure 34). It is also clear that biological activity contributed significantly to 1,3- D degradation (i.e., between 20 and 40%) when compared with unamended soils where degrada tion is around 10 percent.

Figure 33: Disappearance of 1,3-D From Arlington Sandy Loam Soil at 20C (Amended & Unamended)

Table 9: First Order Rate Cons tants For 1,3-D In Arlington Sandy Loan Soil at 20C (Amended & Unamended)40 Figure 34: 1,3-D First Order Rate Constants In Arlington Sandy Loam Soil at Varied Temperatures (Amended & Unamended)

Gan et al [33] conducted a similar experiment wh ere the impact of organic waste application on the emissions of MITC and methyl bromid e in an Arlington sandy loam. Similar soil amendments were used as described in Dungan [3 2]. Temperature and moisture effects were not evaluated to the same degree as in Dungan [32]. Degradation re sults are presented in Figure 35 and impacts on emissions are presented in Ta ble 10 and Figure 36. Gan concluded that the degradation of both compounds was significan tly accelerated in amended soils but the enhancement was greater for MITC than methyl brom ide. Gan also indicated that differences in dissipation kinetics between non-ster ile and sterile soils illustrate methyl bromide dissipation in amended soils is a chemical process while MITC dissipation was essentially a microbial process.

Figure 35: Degradation of Methyl Bromide and MITC In Arlington Sandy Loam Soils (Amended & Unamended)41 Table 10: Cumulative Losses Of Methyl Bromide and MITC In Arlington Sandy Loam Soil (Amended & Unamended)

Figure 36: Volatilization of MITC From Arlington Sandy Loam Soil (Ammended & Unammended)

Gao et al [34] conducted a column study to assess impacts of varied combinations of water seals and tarps on 1,3-D using a Hanford sandy loam. Scenarios which were evaluated considered different irrigation regimens and the use of HDPE and VIF films as well. The results of this study indicate that each water application abruptly reduced 1,3-D emissions but they returned to typical levels after a few hours. Peak emissions were substantively reduced but impact on total
emissions was small. In a non-ta rped control, emissions were 51 percent of applied. Irrigation reduced emissions to between 41 and 46 percen t of applied. HDPE fi lm use also reduced emissions to 45 percent of applie d with water on top of HDPE furt her reducing total emissions to 38 percent of applied. The use of VIF reduced emissions to 10 percent of applied. Surface water application and HDPE use were equated by the i nvestigators. See Tabl e 11 and Figures 37 and
38 for more information.42 Table 11: Peak 1,3-D Flux And 1,3-D Cumulative Emissions For Varied Trea tments (Hanford Sandy Loam)

Figure 37: Peak 1,3-D Emission From Soil Column Experiments (Hanford Sandy Loam)

Figure 38: Cumulative 1,3-D Emission From Soil Column Experiments (Hanford Sandy Loam)43 Gao [20] also evaluated concen tration contours for 1,3-D using a variety of surface treatments and films similar to that illustrated in Figure 21, where the results are from a field study (Figure 39). Results were similar to the field study in that water addition enhanced CxT parameters in the column experiment. Gao also reported the moisture levels and amount of soil air volume which is a measure of compaction (Figure 40).

Guo [35] evaluated the impact of various fact ors on the hydrolysis of 1,3-D and found that organic matter promotes degradation and that, as expected, higher moisture contents enhance hydrolysis (Figures 41 and 42). Other researcher s such as Xu [36], Qin [37], and Zhang [38] have evaluated mechanistic factors which can impact emissions and/or concentration profiles. In each case, increases in organic matter or increased irrigation can decrease emissions in varied degrees.

In addition to the impacts of organic matter a nd varied irrigation schemes on emissions and soil gas concentrations as illu strated above, there are also reactive chemical layers that can be used to accomplish similar effects. The most common reactiv e chemical layer that has been investigated in the literature is ammonium thiosulfate (ATS ). Sodium diethyldithiocarbamate (Na-DEDTC)
has also been evaluated in limited fashion. Like the studies described above, typically multiple factors are evaluated in the same research (e.g., ATS and tarps).

McDonald et al [39] investigated the impacts of thiosulfate and manure amendments with water and tarps on 1,3-D emissions. In this column study, 6 types of treatment scenarios were evaluated including a cont rol (dry soil 5% w/w), water seals (9mm water added), ATS (1:1 and 2:1); ATS and HDPE (1:1); manur e alone; as well as manure and HDPE. Cumulative emissions
were calculated and the relative emissions were compared with the following results: control emitted 52% of applied; HDPE with ATS or manu re emitted 24 & 16%, respectively; treatments of ATS and manure without tarp reduced emissions to 29 -39 %, respectively; and water seals alone reduced emissions to 43% (Figure 43). All tr eatments compared to the control also seemed to reduce peak emissions significantly (Figure 44). Concentration contours were also determined and expected enhanced degradation occurred with all treatments (Figure 45). The investigators also indicated these results may not be directly tr anslated to the field as conditions didn't vary.44 Figure 39: Distribution of 1,3-D In Soil Gas Phas e From Column Experiments (Hanford Sandy Loam)45 Figure 40: Soil Water Content and Air Distribution In Hanford Sandy Loam

Figure 41: Chloride Ion Release From 1,3 Hydrol ysis In Hanford Sandy Loam Soil At Different
Moistures

Figure 42: Chloride Release From 1,3-D Hydrol ysis In Unamended And Amended Hanford Sandy
Loam46 Figure 43: Cumulative Emissions of 1,3-D From Soil Column Treatments Over 2 Weeks (Hanford Sandy Loam)

Figure 44: Emission Rate of 1,3-D In Soil Column Studies (Hanford Sandy Loam) With Water Seals and Manure (A) and ATS (B)47 Figure 45: Soil Gas Phase Concentration of 1,3-D From Column Ex periment Over 2 Weeks (Hanford Sandy Loam)48 In another column study conducted by Qin et al [40] using a Hanford sandy loam, the impact of drip application of ATS on degr adation of 1,3-D and chloropicr in was evaluated. This study
indicates that ATS can accelerate de gradation and reduce emissions. In the control, the half-life was 86 hours for 1,3-D and 16.3 hours for chloropi crin. The most rapid degradation was achieved in this study with a 4:1 ATS: fumigant ratio which decreased half -like degradation rates to 9.5 and 5.5 hours respectively fo r 1,3-D and chloropicrin (Figur e 46). Qin also calculated concentration-time indices for the different system s which were evaluated which are presented in
Table 12.

Figure 46: The effect of drip applic ation of ATS solution on average 1,3-D and CP concentrations in the soil-gas phase in soil columns. Symbols are the mean value of six (three depths, duplic ate samples) concentration values and error bars represent standard error.49 Table 12: Concentration–Time Index of 1,3-D and CP (mg h L − 1 ) in the Column Study

treatment 1,3-D CP CK 532 (27) 52 (1.2) W1 503 (24) 51 (0.5) ATS1:1/W1 382 (18) 48 (0.3) ATS2:1/W1 368 (9) 45 (1.0) ATS4:1/W1 210 (12) 40 (1.5) ATS2:1/W2 325 (14) 43 (1.7)

Zheng et al [41] conducted research which also evaluated the reduction in emissions achieved through the use of thiourea (Fi gures 47 & 48, Table 13). Als o, degradation products were identified from treatments of 1,3-D soil with ATS [ 42]. Zheng et al [43] also evaluated the use
of ATS and Na-DEDTC as a react ive barrier as well as compe titive degradation between 1,3-D and chloropicrin. In [41] Zheng concluded 1,3- D volatilization (both peak and cumulative) was significantly reduced using thiourea amendment a nd that subsurface distribution in the profile was not impacted in this case. It was also c oncluded that tarps and thiourea had a synergistic effect. In [42] Zheng concluded that degrada tion products could cause sulfur-like odors that result from thiosulfate use depending upon soil co nditions which could be problematic for those who happen to be in proximity to applications . In [43] Zheng conc luded that if 1,3-D was present the degradation rate of chloropicrin was accelerated but, conversely, trans 1,3-D degradation was inhibited by chloropicrin possi bly due to decreased microbial activity. The degradation rate of both 1,3-D a nd chloropicrin were both signifi cantly enhanced by the presence of ATS or Na-DEDTC (Figures 49 & 50).

Figure 47: Volatilization of cis -1,3-D from soil columns with different rates of thiourea amendment: (A) instanta neous flux; (B) cumulative vola tilization. The fumigant was injected at 30 cm depth.50 Table 13: Effect of Fumigation Depth on Cumulative Emission Loss of 1,3-D over 14 Days from Soil Columns
with and without Surface Amendment of Thiourea

total emission of 1,3-D (% of applied)

unamended soil column soil column amended with thiourea (mol ratio of thiourea/1,3-D = 8:1) relative reduction (%) `treatment

cis trans cis trans cis a trans a

20 cm 67.3 ± 6.4 64.0 ± 7.1 32.3 ± 3.5 40.5 ± 3.9 52.0 ± 11.9 36.7 ± 13.3
30 cm 56.6 ± 5.4 51.0 ± 5.8 29.7 ± 2.6 32.2 ± 3.5 47.5 ± 11.3 36.8 ± 13.1
40 cm 50.5 ± 3.7 42.5 ± 4.5 27.8 ± 0.5 28.4 ± 1.2 45.1 ± 8.0 33.1 ± 15.2

a Nonsignificant between diff erent fumigation depths at p 0.05 using a t test.

Figure 49: Degradation of chloropicrin (CP) in unamended and amended Arlington sandy loam (amendments added at 0.5 mmol/kg). Th e points are the means of three measurements (±standard errors). Figure 48: Effect of thiourea application rate on the relative reduction of 1,3-D emission loss. The fumigation depth was 30 cm belo w the soil column surface.51 Figure 50: Competitive degradation of 1,3- dichloropropene (1,3-D) isomers in amended Arlington sandy loam (amendments added at 0.5 mmol/kg). Initial chloropicrin (CP) and 1,3-D applied concentrations were 0.1 and 0. 4 mmol/kg, respectively. The points are the
means of three measurements (±standard errors).

It is clear that other f actors, in addition to the use of irri gation, reactive adj uvants such as ATS and increasing levels of organic matter, can im pact emissions and subsurface CxT contours. Based on discussions with many field research ers (e.g., Culpepper and Noling) and fumigant applicators (e.g., Godbehere), the predominant factors which have been identified include field preparation and the level of compaction. [Not e: This issue has been raised by several stakeholders in meetings with USEPA. For more information, see docketed synopses of such meetings are included on the docke ts for respective fumigant chemi cals.] With regard to both of these factors, there has been substantive disc ussions around a means to categorize and quantify the "art" of proper field prepara tion and ensuring proper levels of compaction during applications to both reduce fumigant emissions and, where raised bed culture is practice d, to construct raised beds which will persist. Based on these discussions , it is clear that these factors are consistently interpreted using terms such as "fine tilth" and "free from clods and othe r debris." Compaction discussions are typically focused around the need to properly make beds or to properly eliminate chisel traces or chimney effects by sealing or co mpaction immediately foll owing application. In essence, the Agency views these factors as minimum standards of care which should be implemented by applicators during all application events. Assigning a credit structure to these factors is inappropriate at this time because there is insufficient information with which to do so based on the available empirical data and, as noted, these practices should be used in all52 applications. [Note: Some factors in the sens itivity analysis below in Section 2.5 using CHAIN 2D address compaction. This issue will be furt her discussed quantitatively there and in the conclusions of this document.] The key for the pur poses of this analysis has been to establish an approach for describing how to achieve such a requirement. The Soil Science Society of America [44] maintains a glossary of terms that could potentially be used to describe the conditions of a field prior to application (Tables 14 & 15). The result of this could be, based on input from various stakeholders that fine tilth and preparation could mean that, for example only, applications should not be made unl ess soils are at least moderately hard and have the texture of coarse pebbles or less. USDA [45] also has guidance for describing soil moisture levels based for different textures. This guidance instead of that provided in Table 14 could also be considered equally viable as a means for semi -quantitative descripti on of field conditions.

Table 14: Terms For Describing Soil Consisten ce of Blocklike Specimens (Rupture Resistance)53 Table 15: Classification of Rock Fragments

One of the other key "arts" that are involved in proper fumigant application is achieving a proper level of compaction as described above. De fining a quantitative approach or even semiquantitative approach for descri bing proper levels of compaction is unresolved at this time. There is certainly a level of intuition and expe rience which fumigators and researchers use to define these levels but it appear s to be subjective and related to the conditions in individual fields during individual applications. It can also vary because of the specific pieces of equipment being used in an application. For example, the spec ific shape of a bed shaper which alters the pressures applied during bed forming operations or the mass of a cultipacker dragged behind injection shanks during broadcas t applications can impact comp action levels. Additionally, when shanks are used, the size a nd orientation of the closing shoe s and coulters in implements such as Yetter© rigs can also impact the leve l of compaction achieved which is critical in eliminating chimney effects.

The goal of the Agency is to begin to develop a quantitative approach for describing levels of soil compaction relative to application requirement s. No real technique has been defined for ascertaining a simple way to eval uate how chimney effects are e liminated. Conversely, there are likely several methods that can be used to m easure how compaction is achieved in a general manner. One such technique which appears to hold promise as applicable, inexpensive, and reproducible is the soil penetr ometer [46, 47, 48]. There are undoubtedly many papers in the
scientific literature which desc ribe this device but the following provides a synopsis of its design and possible use related to monitoring field condi tions relative to soil fumigant emissions.

A penetrometer consists of a 30- degree circular stainless steel c one with a driv ing shaft and a pressure gauge (Figure 51) [46]. The penetromet er usually comes with two cones, one with a base diameter of 0.798 (3/4) inches for soft so ils and the other with a base diameter of 0.505 (1/2) inches for hard soils. The tip is slightly wider than the dr iving shaft to limit friction of the shaft with the soil. The driving shaft is usually graduated every 3 inches to allow the determination of depth of compaction. The pres sure gauge indicates pressure in pounds per square inch.54 Figure 51: Schematic of Soil Penetrometer (Pressure Gauge)

The penetrometer is designed to mimic a plant root. Unfortunately, the penetrometer does not capture pores created by physical or biological forces such as freezing/ thawing, wetting/ drying, earthworm burrowing, and root channeling. Plant roots will find and grow through these spaces in the soil if they are present. The readings take n with the penetrometer are called the cone index. The readings should be taken when the whole pr ofile is at field capacity (approximately 24 hours after a soaking rain). If the so il is too wet (muddy), compaction could be underestimated because the soil acts as a liquid. If the soil is too dry, compaction could be overestimated because roots will be able to penetrate the soil when it dampens. After completing the sampling, a
recommendation can be formulat ed using table 16 where the compaction rating would be of
interest relative to reduci ng fumigant emissions [46].

Table 16. Interpretation of penetration resistance measurements.

Percentage of measuring points having cone index > 300 psi in top 15 inches Compaction rating Subsoiling recommended

Adapted from: Lloyd Murdock, Tim Gray, Freddie Higgins, and Ken Wells, 1995. Soil Compaction in Kentucky. Cooperative Extension Service, University of Kentucky, AGR-161.

<30 Little-none No
30-50 Slight No

50-75 Moderate Yes

>75 Severe Yes

To illustrate how a cone pentrometer can be used , results from Vepraskas et al [49] illustrate cone index values as a function of water content, bulk density, and partic le size distributions in loamy fine sand and loamy sand (Figure 52). Thes e could be useful for providing a quantifiable means for describing field conditions. Busscher et al [50] also provides some insights into how soil compaction measurements collected using th is device should account for water content.55 Figure 52: Cone Index Values As Functi ons of Water Content and Particle Size Distribution For A Loamy Fine Sand (A) and Loamy Sand (B)

Research is presented above, where available, ba sed on 5 critical factors that are believed to impact emission rates after applic ation of fumigants that can be controlled or adjusted to some degree by growers and those making applications. As indicated above, the in tent of this aspect of the analysis was to evaluate factors that are not related to the use of tarp s or to the specifics of the application equipment used, that could impact emissions. [Note: Thes e factors are discussed separately in this document.] The critical fact ors which were considered as the basis for this analysis (e.g., reactive adjuvants/water seals, organic matter levels, soil moisture, and field conditions) all clearly impact em ission rates but the impacts vary by chemical and field condition. For example, Gan [33] concluded that the degradation of methyl bromide and MITC was significantly accelerated in soils amended with organic matter but the enhancement was greater for MITC than methyl bromide. Gan also indicated that differences in dissipation
kinetics between non-sterile and sterile soils illustrate methyl bromide dissipation in amended soils is a chemical process while MITC dissi pation was essentially a microbial process. Ashworth [31] also shows that for 1,3-D organi c matter levels significantly impact emissions (i.e., ~factor of 10 between low and high organic matter) and irrigation also impacts emissions but to a lesser extent (i.e., ~fact or of 3). Based on this inform ation, it is clear that assigning a general credit for all fumigants based on the factors considered in this analysis is impractical unless a conservative approach is considered. F actors specific to certain conditions as examined in the research may be more practical. For ex ample, a factor of 10 fold for 1,3-D could be considered based on Ashworth if high levels of organic matter are observed at an application site (i.e., > ~3% organic matter) but no credit would be given where low organic levels are present. Also, as with the tarp use discussion in Secti on 2.1 above, it is important to consider this discussion in context with the analysis of field scale research as well.56 

### 2.2.2 Field Study Based Standards Fo r Categorizing Soil Amendments

Along with laboratory based research programs described above, a va riety of field-based research has been completed that evaluated the us e of soil amendments relative to their emission reduction potential. Much of these data have already been presented in Section 2.1 above because the research evaluated many factors includi ng high barrier tarp use. Ajwa [19] (Figures 11 and 13 above) indicates that potassium thiosulf ate (KTS) reduces peak chloropicrin emissions by approximately 50 percent and peak 1,3-D emi ssions by approximately 35 percent in studies reflecting strawberry production practices in Ca lifornia. Gao [20] (Figures 15 and 16 above) indicate that pre-irrigation of soil to achieve proper soil moisture levels prior to fumigation can substantively reduce emissions (i.e., ~40% for 1,3-D and ~8 to 10 fold for chloropicrin). Ajwa [26] and Gamliel [8] also similarly indicate (F igures 20-22) that pre- fumigation irrigation and soil moisture management are critical to in creasing CxT parameters which can decrease
emissions.

In addition to these data, Gao conducted a series of other experiments [51] [52] which evaluated emissions from field applications. In [51] the scope is similar to that presented in Gao [20] while [52] presents results after appl ication in an orchard replant sc enario. In [51] the following scenarios were evaluated for both 1,3-D and chloropicrin:

• Control;

• Manure/HDPE (composted manure 5 tons/ac);

• KTS/HDPE (2:1 KTS: fumigant);

• Pre-irrigation (4 days prior to fumigation);

• Intermittent water seals (0, 12, 24, 48 h); and

• Intermittent KTS/water app lications (0, 12, 24, 48 h)

Hourly flux estimates for each treatment and bo th chemicals are presented in Figure 53 while cumulative emissions are presented in Figure 54. Re sidual fumigant levels in soil are presented in Figure 55. Based on the result s of this study, Gao concluded:

• With the amount of water that is unlikely to reduce fumigant concentrations in soil air, pre-irrigation is probably the easiest pr actice for controlling fumigant emissions, particularly for 1,3-D;

• KTS application to soil surface is the mo st effective method to reduce fumigant emissions with unknown reactions;

• Amendment of soil with organic matter to effectively minimize emissions needs further understanding; and

• Current results indicate that organic matter application combined with HDPE tarp may
not reduce emissions.57 Figure 53: Hourly Flux Profiles Of 1,3-D and Chloro picrin In Hanford Sandy Loam After Varied Surface
Treatments

Figure 54: Cumulative Flux Profiles Of 1,3-D and Chloropicrin In Hanford Sandy Loam After Varied
Surface Treatments 1,3-D

0 5 10 15 20 25 30 35

0 48 96 144 192 240 288 336
Time (h)Cumulative emission (% applied) Control

Manure + HDPE

KTS + HDPE

Pre-irrigated

Intermittent water
seals
Intermittent KTS applications CP

0 5 10 15 20 25 30 35

0 48 96 144 192 240 288 336 Time (h)Cumulative emission (% applied) 1 - Control

0 10 20 30 40

0 48 96 144 192 240 288 336 time (h)Flux (μg m-2s-1) 1,3-D

CP 2 - Manure+HDPE

0 10 20 30 40

0 48 96 144 192 240 288 336 time (h)Flux (μg m-2s-1) 1,3-D

CP

3 - KTS+HDPE

0 10 20 30 40

0 48 96 144 192 240 288 336 time (h)Flux (μg m-2s-1) 1,3-D

-CP 4 - Pre-irrigation

0 10 20 30 40

0 48 96 144 192 240 288 336 time (h)Flux (μg m-2s-1) 1,3-D

CP

6 - Intermittent KTS applications

0 10 20 30 40

0 48 96 144 192 240 288 336 time (h)Flux (μg m-2s-1) 1,3-D

CP KTS/w ater a pp l. 5 - Intermittent w ater seals

0 10 20 30 40

0 48 96 144 192 240 288 336 time (h)Flux (μg m-2s-1) 1,3-D

CP Water appl.58 Figure 55: Residual 1,3-D and Chloropicrin Levels In Hanford Sandy Loam After Varied
Surface Treatments

In Gao [52] a field trial was conducted in a pe ach orchard replant field (coarse Hanford sandy loam) to investigate the effect s of fumigation method (shank-i njection vs. subsurface dripapplication treatments) and surface treatments (water applications a nd plastic tarps) on emissions of 1,3-dichloropropene (1,3-D) and chloropicrin (CP). Shank-injection of Telone C-35 and drip application of InLine were evaluated. Treatment s included control (no water or soil surface treatment); standard high-density polyethylene (HDPE) tarp; vi rtually impermeable film (VIF) tarp and pre-irrigation all over shank injectio n; and HDPE tarp and irrigation with microsprinklers before and after the drip application. The highest 1,3-D and CP emission losses over a 2-week period were from the control (36% 1,3 -D and 30% CP) and HDPE tarp (43% 1,3-D and 17% CP) over shank injection. The pre-irrigation 4 days before fumigation and VIF tarp over shank injection had similar total emission loss es (19% 1,3-D and 8–9% CP). The HDPE tarp and irrigations over subsurface drip -application treatments resulted in similar and the lowest emission losses (12–13% 1,3-D, and 2–3% CP). Lowe r fumigant concentrati ons in the soil-gas phase were observed with drip a pplication than in the shank-in jection treatments; however, all treatments provided 100% kill to citrus nemat odes in bags buried from 30 to 90 cm depth. Preirrigation and drip application seem to be effective to minimize emissions of 1,3-D and chloropicrin (Figures 56 and 57). 0

20

40

60

80

100 0 2 4 6 8 10 1,3-D in soil (mg/kg)depth (cm) Control

Manure + HDPE

KTS + HDPE

Pre-irrigation

Intermittent water seals

Intermittent KTS appl. 0

20

40

60

80

100 0.0 0.2 0.4 0.6 0.8 1.0 CP in soil (mg/kg)depth (cm)59 Figure 56: Hourly Flux Profiles Of 1, 3-D and Chloropicrin In Hanford Sandy
Loam After Varied Surface Treatmen ts In Orchard Replant Fumigation

Figure 57: Cumulative Flux Profiles Of 1,3-D and Chloropicrin In Hanford Sandy Loam After Varied Surface Treat ments In Orchard Replant Fumigation60 

### 2.2.3 Summary of Emission Reduction Potential of Soil Amendments

Soil amendments can be any material which re duces diffusion rates at the soil/atmospheric boundary layer compared to standard conditions that would be normally found in agriculture such as higher levels of organi c material or enhanced soil moisture from irrigation. Baseline conditions are difficult to quantify because, as desc ribed above, there is minimal standardization and typical conditions will vary from field to fiel d and even within the same field. Given these confounding factors, it is also cl ear that in the existing research of all types indicates that a variety of factors can influence emissions such as irrigation, organic matt er levels or reactive barriers such as ammonium or potassium thiosu lfate or thiourea. Research in many cases
indicates substantive reductions in emissions by at times as much as 10 fold or more. However, the results can be confounding as seen in the conclusions by Ga o [51] that indicates organic matter addition with HDPE may not reduce emissi ons. Also, as noted with tarp use above, performance of the various amendments in the fi eld varies from that obs erved in the laboratory because of many factors (see Figure 8 above) an d there are limited replications which makes comparison of like experimental conditions, which is the basis of a reduction factor determination, difficult.

Given the issues and the types of research described above, defining quantitative emission
reduction factors for individual soil amendments th at could routinely and generically be applied regardless of field conditions is uncertain. This is because reductions vary from chemical to chemical, vary for the same chemical, and vary due to other conditions such as changing soil
moisture levels or soil texture which can confound the determinati on of a single reduction factor. Complicating the situation further is the lack of si de by side studies where controls are utilized. Finally, the lack of field studies where only soil amendments are studied (i.,e, wit hout the use of tarps) is limited or nonexistent. It is recommended, however, th at no emissions credit based on soil amendments be any higher than a reduction in overall emissions attrib uted to high barrier film use. However, special consideration coul d apply to specific chemicals. For example, methyl bromide was noted to degrade chemical ly and degradation of MITC is enhanced by increased levels of organic matter so it is possi ble that additional cred it could be applied for higher organic matter when MITC is used.61 

### 2.3 Application Methods

This section addresses methods and technique s outside of tarps and soil amendments (e.g., addition of reactive adjuvants) that growers can possibly control in thei r application practices which will both reduce emissions and improve efficacy. In Section 2.1: Tarp Use above, the impact of tarps as a boundary layer as well as the impact tarps have on subsurface processes that impact concentration profiles over time were considered. The impacts of various soil
amendments are discussed in Section 2.2: Soil Amendments above. The overall goal is similar
to those sections in that instead of the impact of tarps or so il amendments the scope of this analysis is to assess the viability of other approach es that can be used by growers relative to their application methods. Specifically, the types of factors which have been identified include:

• Varied application equipment;

• Application rates;

• Application depth; and

• Competitive impacts from co-application and multiple fumigant formulations

In fact, Chellemi [27] summarized many of th e same possible factors which could impact emissions based on the application methods that are being evaluated in the USDA Area-Wide Pest Management program. This list is not intended to inclusively apply to all growers. In some cases, growers will be able to intentionally mani pulate all of these factors to some degree or another. In other cases, grower s will only be able to manipulate a single factor or none at all. There are a variety of reasons fo r this related to the availabili ty of resources (e.g., different application equipment), costs (e .g., modifications to applicati on equipment can possibly reduce emissions but they are expensive), cropping syst em impacts (e.g., lowering application rates can impact efficacy) and field conditions (e.g., extens ively rocky subsurface conditions may prohibit utilization of Noble plows). This section also in troduces much of the “art” of fumigation into the discussion because many of these factors have been identified and discussed and there is general consensus that they impact emissions (e.g., combin ations of chisels and coulters designed to reduce emissions); however, it is not clear how to quan tify them. As above, different types of
research are available as desc ribed by Yates [10] in Figure 8 above and the same type of characterization applies in that laboratory scale research is more available than field studies designed to evaluate sp ecific factors.

There is also some clear overlap in the factors addressed in this secti on and factors that were
addressed in other areas of this document. For example, soil moisture and the required level of
moisture to form a raised bed dur ing application are differentiated in this context. Soil moisture
levels in this context refers to the conditions of the soil prior to an appli cation to ensure a desired CxT is achieved but it may also refer to the proper conditions under whic h applicators need to achieve the proper formation of a raised bed cultu ral system. In some cases, irrigation water can be applied by growers. In other cases, it is dete rmined by natural phenomena such as rain or lack thereof or the depth of the wa ter table in a specific area.62 In Sections 2.1: Tarp Use and 2.2: Soil Amendments above, three distinct types of research
were evaluated that were differentiated as fo llows: laboratory based studies assessing boundary layer permeability (Section 2.1 only), field base d research that account for boundary and subsurface conditions leading to emissions, and mechanistic research focused on both boundary conditions and the subsurface processes that lead to emissions. This section is similar to 2.2 in
that there is no differentiation be tween the laboratory and mechanis tic research as in 2.1 above.



### 2.3.1 Mechanistic Based Standards For Categorizing Application Methods

The factors that the Agency believes can possibl y be manipulated by growers in varied degrees include varying the application equipment itself, per se, and also the sp ecific conditions of the application such as depth, rate and which chemi cals are co-applied (and in what amounts relative to each other). This analysis evaluates these factors. In many cases, the research was not directly designed to evaluate how emission rates are influenced by changes in specific factors as
also discussed in Sections 2.1 and 2.2 above wh ich makes developing conclusions confounding.

Above, several pieces of research were presented which illustrated the impact of tarps and soil amendments but the research also concurrently a ddressed several other fact ors. In Table 13 for example, Zheng [41] illustrated that 1,3-D emissi on rates were reduced the deeper the fumigant was injected into the soil (i.e., 20 to 40 cm). Fi gure 50 from Zheng [43] also indicated that if 1,3-D was present, the degradation rate of chlo ropicrin was accelerated but, conversely, trans 1,3-D degradation was inhibited by chloropicrin possibly due to de creased microbial activity. In Figure 20, Ajwa [26] indicates that the use of a dr ip tape with irrigation water, as opposed to a shank injection, enhanced the subsurface con centration contour of 1,3-D in both size and intensity.

In addition to the studies which were previously cited because they contained information that is also pertinent either to the prev ious discussion of tarps or soil amendments, other research was identified which evaluated how application met hods might impact emissions. For example, Guo [53] investigated the distribution and leaching of iodomethane in soil following based on the differences between shank injection and subsurfa ce drip application (Figure 58). Iodomethane was directly injected or drip-applied at a 20-cm depth into Arlington sandy loam columns (12-cm i.d., 70-cm height) tarped with virtually impermeable film. Relative to shank injection, drip application inhibited diffusi on, resulting in significantly lower concentration profiles in the soil air. Potential leaching of i odomethane was significant from the soil columns under both application methods but the leaching was greater following shank injection than drip application, with an overall potential of 33 g ha –1 for shank injection and 19 g ha –1 for drip application.63 Figure 58: Concentration profiles of gas-phase iodome thane (MeI) in soil columns under shank injection and drip application 12 h after placement. Symbols repres ent the mean of duplicate measurements and error bars
indicate the standard deviation.

Kim [54] investigated the effect of formulation on the phase partitioning, transformation rate, and volatilization of 1,3-D isomers in differing formulations (Telone II or Telone EC). Air–water partitioning coefficients ( K H ) were slightly higher for Telone II than for Telone EC, presumably due to the higher apparent water solubility of th e EC formulation. Sorption of 1,3-D isomers in two soils was not affected by formulation. Formulation had no significant effect on the rate of 1,3-D transformation in water or soil. In general, di fferences in the rate of 1,3-D transformation and phase partitioning due to formulation as Telone II or Telone EC were very small. Thus, the
effect of formulation on 1,3-D fate may be ignored in transformation and phase partition of 1,3-D
in water and soil. Packed soil columns without plastic tarp indicated that with relatively shallow subsurface (10 cm) drip applica tion of Telone EC, emission of 1,3-D isomers was more rapid and produced greater maximum instantaneous flux than deeper (30 cm) shank injection of Telone II.
Both application methods resulted in the same cumulative emissions for both isomers, 45% for ( E )-1,3-D and approximately 50% for ( Z )-1,3-D although peak emissions varied based on
formulation with Telone EC ha ving higher flux values (Figures 59 & 60). These results suggest that for drip application of fumigants to be effective in reducing emissions, the fumigant must be applied at sufficient depths to prevent rapid volatilization from the soil surface if the water application rate does not sufficiently restrict vapor diffusion.64 Figure 59: Volatilization flux of ( A ) ( Z )-1,3-dichloropropene (1,3-D) and ( B ) ( E )-1,3-D in untarped soil columns receiving shank inject ion of Telone II (30- cm depth) or drip application of Telone emulsifiable concentrate (EC) (10-cm depth).

Figure 60: Cumulative emission losses of ( A ) ( Z )-1,3-dichloropropene (1,3-D) and ( B ) ( E )-1,3-D in untarped soil columns receiving shank injection of Telone II (30-cm depth) or dr ip application of Telone emulsifiable concentrate (EC) (10-cm depth).65 Ma [55] investigated the impact of altering appl ication rates, in essen ce, through comparing the degradation of methyl isothiocyanate (MITC) and 1,3-dichloropropene (1,3-D) in an Arlington sandy loam at four temperatures and four initial con centrations (Figures 61 & 62). The degradation of MITC and 1,3-D isomers at each of the four initial concentrations was adequately described by first-order and half-order kinetics, but not by second-order kinetics and the Michalies–Menten model. However, the dependence of the derived first-order and half-order degradation rate constants on initial concentration makes it invalid to use first-order and halforder kinetics to describe the degradation. Overall, none of the models tested satisfactorily described degradation of MITC and 1,3-D isomers over the range of initial concentrations. This raises a serious question as to the validity of first-order kinetics for representing fumigant degradation in soils at different initial concentrations. This pr oblem tends to be overlooked since the first-order degradation rate constant is usually obtained at one initial concentration. Thus, the
concentration effect as observed in this study is i gnored. Even worse, the derived first-order degradation rate constant at one initial concentration is often used in integrated simulation models for predicting chemical fate and dissipation. Comparisons of fumigant degradation in
sterilized and nonsterilized soils revealed that microbial de gradation was the major dissipation pathway for all fumigants, although microbial degradation was more involved in degrading MITC than in degrading 1,3-D isomers. Analyses of MITC degradat ion at high and low initial
concentrations in sterilized and nonsterilized soils demonstrated that the reduced MITC degradation rate constant at high concentration may have resulted from th e inhibitory effects of the fumigant on degrading microorganisms. The ove rall degradation rate constants of MITC and 1,3-D isomers changed exponen tially with temperature and followed the empirical Arrehnius equation between 20 and 40°C.

Figure 61: Degradation of methyl isothiocyanate (MITC) in Arlington sandy loam at different
initial concentrations at 20°C. The points are the mean s of three measurements (±standard errors) and the curves are first-order fits.66 Figure 62: Degradation of the ( E )-1,3-dichloropropene isomer in Ar lington sandy loam at different
initial concentrations at 20°C. The points are mean of three meas urements (±standard errors) and
the curves are first-order fits.

Papiernik [56] investigated the effect drip system emitter configuration and application depth on emissions using 1,3-dichloropr opene (1,3-D), propargyl br omide (PrBr), and methyl isothiocyanate (MITC) in bedded systems was (Figures 63 & 64). These studies were conducted using the sand mesocosm method and field scal e experimentation. Results indicated that changing the drip emitter spacing and using multiple drip lines in each bed had little effect on the emissions and distribution of any fumigant. Increas ing the depth of application from 15 to 30 cm reduced volatilization of MITC by ~20 to >90%; em issions were reduced due to a decrease in the flux from the bed top, and deeper injection did not change the amount of fumigant volatilized from the bed side slope and furro w. Increasing the application dept h resulted in a sl ight decrease in the rate of fumigant dissipa tion in soil, indicating the potentia l for some improvement in pestcontrol efficacy with deeper application.

Sullivan [57] summarized a laboratory-based seal ing research study that was conducted to help identify preferred methods to reduce off-gassing for both chemigation and shank injection. This study involved testing relative emission rates for simulation of both major application methods (chemigation and shank injection). Air and soil temperatures in the chambers that were used to
conduct these studies were maintained at a consta nt 90°F, i.e. diurnal temperature cycles were not simulated. Routine sealing methods for ch emigation applications in California were simulated as the reference sealing method (typical ly 0.5 inches of water applied over 2-h periods at the completion of each applic ation, followed by a second seal of 0.5 inch applied 24 h later). Alternative sealing methods were tested incl uding mechanical compaction, foam, tarp, and a
method termed “intermittent wate r sealing.” All tests were conduc ted with three replicates. The relative results are shown in Figure 65. As shown, intermittent water seali ng and tarps (virtually impermeable film (VIF)) were shown in this rese arch to be the most effective methods to reduce off-gassing rates. The intermittent water sealing for shank injection was clearly shown in this study to provide the potential to increase field retention and redu ce off-gassing rates. The results in terms of the benefits of intermittent water sealing for chemigation were mixed. The first trial showed benefits comparable to the shank inject ion trials. The subsequent two trials did not.67 Figure 63: Methyl isothiocyanate (MITC) flux in the (A ) first sand mesocosm experi ment and (B) field experiment. Emitter spacing was 30 cm. Values indicate the mean of triplicate mesocosms or duplicate rows (for the field experiment). Error bars indica te the standard error. Flux from the furrow was negligible in the field experiment.

Figure 64: Dissipation of cis- 1,3-dichloropropene in the first sand mesocosm experiment. Values are the mean of two mesocosms with drip tubing at the same depth, and error bars indicate the standard error. The volume under the
contours of soil g as concentration was used as an indicator of the mass remainin g in the monitored zone. Lines indicate the exponential decrease in mass with time.68 Figure 65: Analysis of the cumulative MITC air samples released from the soil by type of application and sealing method (average results based on three replicates each).

Zhang [58] investigated the im pact of altering application ra tes by determining degradation rates of MITC and chloropicrin (CP) in two fore st soils from Georgia and Wisconsin. Also, the impacts of nursery management on degradation of MITC and CP were evaluated. The half-life
values of MITC and CP were evaluated in the laboratory under isothermal conditions (22 ± 2°C). Three rates representing 0.5 x , 1 x , and 2 x field application rates for each fumigant were used in laboratory incubations (Figures 66 & 67). Microbial degradati on was determined by conducting
incubations with both fresh and sterilized soils. Soil moisture effects were also studied. There
were no differences in MITC or CP degradation between fumigated and nonfumigated forest

nursery soils. Soil sterilization and high soil moisture content (15% by wt.) reduced MITC and CP degradation. The degradation rates of MITC and CP varied with factors such as nursery history, fumigant application rates, a nd freshness of tested soils.69 Figure 66: Comparison of half-life ( t 1/2 ) of (a) methyl isothiocyanate (MITC) and (b) chloropicrin (CP) at three application rate s of 195, 390, and 785 kg ha –1 for MITC and 140, 280, and 560 kg ha –1

for CP in fresh soils and sterilized soils of two fumi gated nursery soils in incu bation at 22°C. Vertical
bars are standard errors.

Figure 67: Relative contribution of microbial degradation to total degradation of methyl isothiocyanate (MITC) and chloro picrin (CP) in two nursery soils at three application rates of 195, 390, and 785 kg ha –1 for MITC and 140, 280, and 560 kg ha –1 for CP.70 In Zheng [59] the effect of combined application in differing mixtures of chloropicrin and methyl isothiocyanate (MITC) on their transformations and persisten ce was again investigated [see 43]. In aqueous solution, no direct react ion between MITC and chloropicrin occurred and relatively slow rates of hydrolysis of these compounds were observed in aquatic environments free of suspended solids. The transformation of chloropicrin, however, was accelerated in aqueous
solution with MITC because of a reduction reaction with bisulfide (HS

– ), which is a by-product of MITC hydrolysis. In soil, when fumigants were applied simultaneously, the degradation of MITC was suppressed under th e bi-fumigant application due to the inhibition of soil microbial activity and a possible abiotic competition with chloropicrin for a limited number of reaction sites on the surface of soil pa rticles. However, the degradation rate of chlor opicrin was significantly
enhanced in the bi-fumigant soil system , which was primarily attributed to the reaction of chloropicrin and HS

– . Two sequential application approaches were developed to investigate the feasibility of the combined application of metam sodium (parent compound of MITC) and chloropicrin in soil and assess their potential effects on environmental fate. For both application

sequences, the degradation of chloropicrin was accelerated and that of MITC, as a major breakdown product of metam sodium, was inhibited in soil (Figures 68 & 69). Tables 17 and 18 present the derived rate constants for MITC and chloropicrin combinations Zheng investigated.

Figure 68: Influence of binary-fumigant application on degradation rates in Arlington sandy loam at three initial concentrations. The dashed line represents the degradation of methyl isothiocyanate (MITC) an d the solid line is chloropicrin71 Figure 69: Effect of fumigant sequential application on the dissipation of (a) methyl isothiocyanate (MITC) and (b) chloropicrin in soil. Metam sodium was applied 3 d followed by chloropicrin, and then incubated for 5 and 10 d at 25 ± 0.5°C.

Table 17: Pseudo-first-order rate constants ( k ) of methyl isothiocyanate (MITC) degradation in Arlington sandy loam with and without chloropicrin. MITC Chloropicrin mmol/kg k x 10 2 (d-1) r 2 Inhibition (%)

0.1 0 29.6 ± 0.8 0.991 37.6
0.1 0.1 18.5 ± 0.3* 0.996
0.6 0 10.7 ± 0.1 0.997 40.6
0.6 0.6 6.32 ± 0.21* 0.982
1.8 0 5.88 ± 0.20 0.981 41.7
1.8 1.8 3.43 ± 0.13* 0.975 0.6 (sterile soil) 0 3.91 ± 0.25 0.903 12.9 0.6 (sterile soil) 0.6 3.40 ± 0.31* 0.889

* The degradation rate constants of MITC in the presence an d absence of chloropicrin ar e significantly different at the 0.05 probability level.

Correlation coefficient of fitting.72 Table 18: Pseudo-first-order rate constants ( k ) of chloropicrin degradation in Arlington sandy loam with and without methyl isothiocyanate (MITC). Chloropicrin MITC mmol/kg k x 10 2 (d-1) r 2 Acceleration (%)

0.1 0 18.3 ± 1.5 0.978 8.5
0.1 0.1 19.9 ± 1.4 0.984
0.6 0 1.39 ± 0.08 0.951 54.7
0.6 0.6 2.15 ± 0.11 * 0.971
1.8 0 0.44 ± 0.03 0.925 84.5
1.8 1.8 0.81 ± 0.03 * 0.985 0.6 (sterile soil) 0 0.56 ± 0.07 0.886 105.4 0.6 (sterile soil) 0.6 1.15 ± 0.08 * 0.966

* The degradation rate constants of MITC in the presence an d absence of chloropicrin ar e significantly different at the 0.05 probability level.

Correlation coefficient of fitting.

Research is presented above, where available, based on various factors related to fumigant application methods that are believed to impact emission rates and concentration contours after application of fumigants. As i ndicated above, the intent of this aspect of the analysis was to
evaluate factors that are not re lated to the use of tarps or how soil amendments can impact emissions. [Note: These factor s are discussed separately in th is document.] The factors which were considered as the basis for this analysis (e .g., depth, type of equipment, application rates, formulation type, and competitive effects) can impact emission rates but the impacts vary by chemical and field condition. For example, Zh eng illustrated [41] that 1,3-D emissions were reduced by increasing injection depth and Papeinik [56] also illustrated that changes in injection depth substantively reduced MITC emissions as well. Altering application rates alters degradation rates as illustrated by Ma [55] where lower rates we re observed at higher initial concentrations. Formulation types had no real impact on 1,3-D emissions as illustrated by Kim [54]. Differences in concentra tion contours were noted with di fferent application methods and Guo [53] also described how diffusion of iodo methane was reduced with drip applications compared to shank applications that resulted in lower soil gas concentrations. Zheng [43 ] indicates that if 1,3-D was present the degrada tion rate of chloropicrin was accelerated but, conversely, trans 1,3-D degradati on was inhibited by chloropicrin possibly due to decreased microbial activity. Zheng [59] al so investigated the impacts of varied mixtures of MITC and chloropicrin. The degradation of MITC was suppressed when mixed with chloropicrin due to the inhibition of soil microbial activity and a possible abiotic competition with chloropicrin for a
limited number of reaction sites on the surface of soil particles. However, the degradation rate of chloropicrin was significantly enhanced when mixed with MITC which was primarily attributed to the reaction of chloropicrin and HS

– . The factors considered in the mechanistic research described above are specific to certain conditions as examined so determination of emission reduction factors that could be ge nerally application is not practical given the complexity of the information presented and the lack of similar in formation for all chemicals and situations of interest. However, the data could be used in a si tuation specific basis if so desired. For example, Papiernik [56] illustrates substantive reductions in MITC emissions with injection depth (i.e., 90 to 20% total emissions when depth reduced from 15 to 30 cm). However, as with the other factors presented in Sections 2.1 and 2.2 above, it is important to consider this discussion in context with the analysis of field scale research as well.73 

### 2.3.2 Field Study Based Standards For Categorizing Application Methods

Along with laboratory based research programs described above, a va riety of field-based research has been completed that evaluated th e use of application me thods relative to the
emissions associated with each. Much of these data have already been presented in Section 2.1 above, especially the limited data where high ba rrier films and/or so il adjuvants are used. Specifically, research from Arysta Life Science [ 11-16] was used to illustrate how high barrier films impact emissions of both chloropicrin and iodomethane compared to baseline studies using more typical tarp materials and application met hods (Figures 9 & 10). Additionally, Ajwa [19], Gao [20] and Wang [23] also evaluated the use of higher barrier films with different chemicals (Figures 11-18). As indicated above, in all cases, high barrier tarp use substantively reduced emissions. Gao [52] also directly compared em issions from drip and shank injections (Figure 56) which routinely indicated shank injection em issions were higher than emissions after drip applications.

In Figures 9 and 10, the emissions data for iodom ethane and chloropicrin that were generated using high barrier tarps [11-16] were developed in different locations from the other tarped raised bed emissions data to which they were co mpared. As can best be determined, the soil
conditions at the varied sites we re somewhat similar in that al l soils were basically loamy sand, sandy loam, or sand and organic matter was low (i .e., <1% in high barrier studies and generally <2% in baseline studies). Additi onally the baseline studies were generated using an application control system that esse ntially output a continuou s source of fumigant down a treated row. One major difference with the recent high barrier emi ssions data presented in Figures 9 & 10 above is that these data were not only generated with high barrier tarps but a novel programmable controller system (i.e., Symmetry TM) developed by the registrant was used in the application. The major difference in this system compared to a more typical approach is best illustrated by Figure 70 which illustrates how pul ses versus a more typical con tinual flow device can impact the amounts of material applied a nd subsurface concentration contours. It is unclear based on the study designs how much barrier f ilms or this novel application method contributes to emission
reduction.74 Figure 70: Differences In Subsurface Concentration Co ntours Achieved With Pulsed Applications Using SymmetryTM System Compared With Conventional Continuous Flow Application Equipment

In its fumigant assessments ( http://www.epa.gov/pesticides/r eregistration/soil_fumigants/ ), the Agency has basically used the same approach that the California Depa rtment of Pesticide Regulations (DPR) ( http://www.cdpr.ca.gov/doc s/legbills/rulepkgs/03- 004mbr/03004buffer_zones.pdf ) established in its use permit conditions for methyl bromide where emissions are separated by the application method as was defined by Barry’s analysis of the differing emissions field data associated with each [2]. This type of analysis has not been completed for other chemicals by DPR at this poin t due to a lack of sufficient monitoring data. In 2007, DPR issued emission adjustment factor s for calculating volatile organic chemical (VOC) emissions for fumigants [Barry, 60] which recognizes that in so me application methods some of the applied material is retained in th e soil and not emitted into the atmosphere (Tables 19-22). Originally, 100 percent of the mass a pplied was assumed to be a VOC which is unrealistic based upon what is known about emissions. The factors in the tables below are used to modify the 100 percent of mass baseline assumption for different application methods and chemicals. In essence, they reflect changes in total emissions that occu r because of application method. These factors do not appear to impl y any changes in peak emissions relative to application method.

Table 19: CA DPR VOC Adjustment Factors For Methyl Bromide75 Table 20: CA DPR VOC Adjustment Factors For 1,3-Dichloropropene

Table 21: CA DPR VOC Adjust ment Factors For Chloropicrin

Table 22: CA DPR VOC Adjustment Factors For MITC

For illustrative purposes, the Agency has combined the various flux data used in its risk assessments (Figures 71-74). The flux values have all been normalized to an application rate of 100 pounds per acre to illustrate differences due to emission profiles only and not due to an arbitrarily selected application ra te. In some cases, no tarps are used but in most cases typical HDPE or LDPE types of tarps are used. In ma ny cases, further characterization of the tarp
materials could not be achieve d. In most cases, soil types we re similar (i.e., sandy loam or loamy sand). In a few instances, there was a clay component but only in th e chloropicrin studies in Arizona was clay predominant. In most cases , organic matter levels were also very low (i.e., <2%). Limited studies used a tarp different than HDPE or LDPE but the results in all cases were similar (sometimes even higher emissions) than with a typical tarp. The iodomethane and chloropicrin results presented in Figures 9 and 10 above are not presented in these figures to allow for a more contrasting comparison with appropriate baseline studies.76 Figure 71: Normalized Fumigant Flux Rates (100 lb ai/A Effective Broadcast Rate) Untarped Broadcast (Flat Fume) Application Methods

0.01 0.10 1.00 10.00 100.00 1000.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Time (hours)Flux (ug/m2-s) Mebr Deep

MebrShallow

Pic

Dazo.DinubaSurf.

Dazo.DinubaInc.

Tel.ImpCAPlt1Day7

Tel.ImpCAPlt2Day4

Tel.SalCAPlt1Day2

Tel.SalCAPlt2Day1

Figure 72: Normalized Fumigant Flux Rates (100 lb ai/A Effective Broadcast Rate) Tarped Broadcast (Flat Fume) Application Methods

0.01 0.10 1.00 10.00 100.00 1000.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Time (hours)Flux (ug/m2-s) Mebr

MeI MantecaCA (1mil)

MeIWatsonville(1mil)

PicAz(1mil)

PicYakimah(1mil)

PicBradenton(1mil)77 Figure 73: Normalized Fumigant Flux Rates (100 lb ai/A Effective Broadcast Rate) Tarped & Untarped Raised Bed Application Methods

1.00 10.00 100.00 1000.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Time (hours)Flux (ug/m2-s) Mebr

MeIGuada(1.5 mil poly)

MeIOxn(1.5mil)

MeIPlantCity(1.25mil)

PicAZ(1.5mil)

PicAZ(no tarp)

TelCA(no tarp)

Figure 74: Normalized Fumigant Flux Rates (100 lb ai/A Effective Broadcast Rate) Tarped & Untarped Drip Irrigation Application Methods

0.01 0.10 1.00 10.00 100.00 1000.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Time (hours)Flux (ug/m2-s) Mebr

MeICam(1mil)

MeILaSelva(1mil)

MeIGuada(1mil)

PicSalinas(HytibarVIF)

PicGA(StdPoly)

PicSalinas(StdPoly)

TelSalinas(HytibarVIF)

TelGA(StdPoly)

TelTx(no tarp)

At the 2007 MBAO meeting in San Diego, Hanson et al [61] proposed a multi-year research program that will evaluate efficacy and emissi ons from multiple chemicals with approved nursery stock treatments. Specifically Noble pl ows, standard shanks, and “Buessing” shanks
will be evaluated with various other different surface treatments (e.g., 1,3-D with HDPE, water seals, or VIF). Only methyl bromide emissions will be evaluated using a Noble plow. Chellemi and a variety of others also indi cated that delivery systems includi ng factors such as the internal diameter of plumbing and the types of valve arrangements on application rigs can also78 potentially impact the quality of th e application process. These wi ll be important to include or address as part of the overal l development of a viable set of good agricultural practices.

Schneider et al [62] evaluated the impact of using either si ngle- or double-c hisel injection devices using 1,3-D in small-plot field experiments and two larg e-scale field experiments.
Emissions were reduced with a si ngle-chisel injection compared to the double-chisel. In all experiments both application methods resulted in similar concentra tion contours. This study also evaluated efficacy where lower nematode control in the inter-bed regions was observed with a single-chisel device but it was concluded that yiel ds would not suffer by the investigators. In the field studies, emissions were al so evaluated with and without a polyethylene tarp which also aided in reducing emissions. Example soil gas results are presented in Figure 75 while field emission results are presented in Figures 76 and 77.

Figure 75: 1,3-D Soil Gas Concentration At Four Depths In Plant Line Over 6 Day Period (Values Are 9 Meas urments With Error Bars)79 Figure 76: 1,3-D Air Concentrations Presen ted As Hourly Flux Values After Varied Field Treatments (DM1 Field)

Figure 77: 1,3-D Air Concentrations Presented As Hourly Flux Values After Varied Field Treatments (Field DM13)80 

### 2.3.3 Summary of Emission Reduction Po tential of Application Methods

Application methods can refer to any conscious choice which fumigators make that influences the way that actual applications are made related to the actual act of de livering the fumigant to
the field. It also refers to the type of equipment used in the a pplication. Generally, the type of application equipment used by an applicator is not actively selected from fumigation event to fumigation event because they would invest in these very expensive pieces of equipment and
utilize them over their useful lifetime. Howeve r, applicators would make very active choices on seasonal intervals and even from fumigation to fumigation event pertaining to which chemicals are used based on pest pressure, application rates, injection depths, and th e relative ratios of the
selected chemicals to one another. All of these can impact the absolute amount of emissions that
occur over time as well as the emi ssion profile associated with such treatments. It is clear that the deeper applications are made in soil the more time it takes for diffusion to occur and for materials to breach the boundary layer into the a tmosphere for most chemicals. However, Barry [2] indicates that this is not necessarily the cas e for methyl bromide where tarp use is the most effective tool for reducing its emissions, proba bly due to its high relative vapor pressure. Competitive impacts from co-application as well as application rate choices are other examples of how applicator choices can possibly impact the longevity of concentration contours and thereby influence emission patterns. Zheng [43 & 59 ] indicate that chloropicrin inhibits the degradation of both MITC and 1,3-D but both of th ese enhance the degradation of chloropicrin. These papers also indicate that application rate s influence emissions wher e degradation rates are slowed at high initial soil con centrations which are a direct result of rate. Figure 70 also illustrates how specific delivery systems can reduce overall fumigant use but achieve similar
concentration contours. This cl early alters application practic es but the effect on emissions cannot be specifically eluc idated at this time.

Given the issues and the types of research described above, defining quantitative emission reduction factors for individual application met hods that could routinel y and generically be applied regardless of field conditions is difficu lt at this time because substantial uncertainty exists. Although reductions can and do occur as a result of varied application methods, the determination of a single reduction factor or credit that could account for use of one or more of these application methods is impossible at this time. The reduction to emissions from these application methods vary from chemical to chem ical, vary for the same chemical, and other conditions, such as changing applic ation rates or mixtures of chem icals relative to each other, can confound the reductions observed. Complicating the situation further is the lack of side by
side studies where controls are utilized. Finally, the lack of field studies where only specific application methods are studied (i.,e, SymmetryTM system) is limited or nonexistent.81 

### 2.4 Field Conditions

In the data summaries and analyses presente d above in Sections 2.1 through 2.3, the focus was on elements that growers could manipulate in or der to reduce emissions from fumigations yet
still retain desired leve ls of efficacy (e.g., tarps, soil amen dments, application methods). The
focus of this section differs in that it is to address factors that are in herent in fields that are to be fumigated or factors that are essentially beyond a grower’s ability to ma nipulate. The factors
which have been identified include:

• Soil categorization;

• Biological activity;

• Soil temperature; and

• Localized weather conditions

There is some clear overlap in this list of fact ors and the analyses that were included in other areas of this document. As a result, not much no vel research is presented in this section because
the factors of concern were also considered conc urrently in the sections above along with various factors that could be manipulat ed by growers to reduce emissi ons yet still achieve efficacy. Additionally, the sections above pr esented discrete types of res earch (e.g., mechanistic and field studies) in order to delineate the information based on the context illustrated by Yates [10] in Figure 8. For this reason, a similar discrete an alysis has not been completed herein. Instead,
each of the factors listed above is discussed and th e information which pertains to that factor is presented as a whole and not delineated by the type of study.



### 2.4.1 Soil Categorization

There are many possible approaches have been used in characterizing the types of soil which may exist in specific fields to be fumigated. Soil Taxonomy is the most comprehensive soil classification system in the world. In 1975, Soil Taxonomy was published by the United States Department of Agriculture's Soil Survey Staff [63]. This system for classifying soils has undergone numerous changes since that time, and the 10 th edition was published in 2006 [64]. Soil classification was once closely tied to the pres umed genesis of the soil, but this has been replaced with observable properties; however th ese often have a strong genetic link. Soils are named and classified on the basis of physical and chemical properties in th eir horizons (layers). "Soil Taxonomy" uses color, texture, structure, and other properties of the surface two meters deep to key the soil into a clas sification system. The current versi on of the system has six levels
of classification in its hierarchi cal structure. The major divisions in this classification system, which are based on general characteristics and ar e refined to specific characteristics, are as follows: Orders; Suborders; Great groups; Subgroups; Families; and Series. At the highest level, U.S. Soil Taxonomy places soils in one of 12 categories known as orders: Andisols, Alfisols

, Aridsols

, Entisols

, Gelisols, Histosols

, Inceptisols

, Mollisols

, Spodsols

, O xisols Ultisols

, and Vertisols [65]

. At its lowest level of organization, th e U.S. system of soil classification recognizes approximately 22,000 different soil series. The distributions of th e major soils orders within the country ar e shown in Figure 78.82 Figure 78: Distribution Of Major Soil Orders

The Natural Resources Conservation Service (NRCS ) also provides extensive information about characterizing Soil Orders as indicated above [65]. The kind of information which is available is illustrated in the following example. Consider the dominant Soil Order, spodosols. These are soils in which amorphous mixtures of organic ma tter and aluminum, with or without iron, have accumulated. In undisturbed soils; there is normally an overlying eluvial horizon, generally gray to light gray in color that has the color of more or less uncoated quartz (Figure 79). Most spodosols have little silicate clay. The particle-s ize class is mostly sand y, sandy-skeletal, coarseloamy, loamy, loamy- skeletal, or coarse-silty. A distribution map for spodosols is included in Figure 80.83 Figure 79: Photograph of Spodosol Soil Horizon

Figure 80: Distribution Map Of Sp odosols and Dominant Suborders84 Soil is the result of complex natural processes that are affected by climate, time, geological materials, topography, and living organisms. Identif ication and classificati on of soils during the mapping process requires accurate information about the environment that affects soil formation. The soil survey report is, therefore, an excellent inventory of local na tural resources. A soil survey is an inventory of the soil resources of a county or ar ea. It consists of a soil map, descriptions of the soils and soil map units, and pr edictions (interpretations) of soil behavior for different uses and management [66]. Soil su rveys also provide an orderly, on-the-ground, scientific inventory of soil resources that incl udes maps showing the lo cations and extent of soils, data about the physical a nd chemical properties of those soils, and information derived from that data about potentialities and problems of use on each kind of soil in sufficient detail to meet all reasonable needs for farmers, agricultura l technicians, community planners, engineers, and scientists in planning and transferring the findings of research a nd experience to specific land areas. Soil surveys provide the basic informa tion needed to manage soil sustainably. They also provide information needed to protect wa ter quality, wetlands, and wildlife habitat. Soil surveys are the basis for predicting the behavior of a soil under alterna tive uses, its potential erosion hazard, potentia l for ground water contamination, su itability and productivity for cultivated crops, trees, and gra sses. Soil surveys are important to planners, engineers, zoning commissions, tax commissioners, homeowners, deve lopers, as well as agricultural producers.

Soil texture used as unofficial soil classifications of soils use by grower s based on their ease of cultivation and management practic es. The relative proportions of the various soil separates in a soil as described by the classes of soil texture are summarized in Figure 81. The textural classes may be modified by the addition of suitable adje ctives when rock fragments are present in substantial amounts; for example, "stony silt loam ." The sand, loamy sand, and sandy loam are
further subdivided on the basis of the proportions of the various sand separates present. The
limits of the various classes a nd subclasses are as follows:

• clay - Soil material that cont ains 40% or more clay, <45% sand, and <40% silt.

• clay loam - Soil material that contains 27 to 40% clay and 20 to 45% sand.

• loam - Soil material that contains 7 to 27% clay, 28 to 50% silt, and <52% sand.

• loamy sand - Soil material that contains between 70 and 91% sand and the percentage of silt plus 1.5 times the percentage of clay is 15 or more; and the percentage of silt plus twice the percentage of clay is less than 30.

o loamy coarse sand - Soil material that contains 25% or more very coarse and coarse sand, and <50% any other one grade of sand.

o loamy sand - Soil material that contains 25% or more very coarse, coarse, and medium sand, <25% very coarse and coar se sand, and <50% fi ne or very fine
sand.

o loamy fine sand - Soil material that contains 50 % or more fine sand (or) <25% very coarse, coarse, and medium sand and <50% very fine sand.

o loamy very fine sand - Soil material that contains 50% or more very fine sand.

• sand - Soil material that contains 85% or more of sand; percentage of silt, plus 1.5 times the percentage of cla y, shall not exceed 15.

o coarse sand - Soil material that contains 25% or more very coarse and coarse sand, and <50% any othe r one grade of sand.

o sand - Soil material that contains 25% or more very coarse, coarse, and medium sand, <25% very coarse and coarse sand, and <50% fine or very fine sand.85 o fine sand - Soil material that contains 50% or more fine sand (or) <25% very coarse, coarse, and medium sa nd and <50% very fine sand.

o very fine sand - Soil material that contains 50% or more very fine sand.

• sandy clay - Soil material that contains 35% or more clay and 45% or more sand.

• sandy clay loam - Soil material that contains 20 to 35% clay, <28% silt, and >45% sand.

• sandy loam - Soil material that contains 7 to 20% clay, more than 52% sand, and the percentage of silt plus twice the percentage of clay is 30 or more; or less than 7% clay, less than 50% silt, and more than 43% sand.

o coarse sandy 1oam - Soil material that contains 25% or more very coarse and coarse sand and <50% any other one grade of sand.

o sandy loam - Soil material that contains 30% or more very coarse, coarse, and medium sand, but <25% very coarse and co arse sand, and <30% very fine or fine sand, or <15% very coarse, coarse, and medium sand and <30% either fine sand or very fine sand and 40% or le ss fine plus very fine sand.

o fine sandy loam - Soil material that contains 30% or more fine sand and <30% very fine sand (or) between 15 and 30% ve ry coarse, coarse, and medium sand, or >40% fine and very fine sa nd, at least half of which is fine sand, and <15% very coarse, coarse, and medium sand.

o very fine sandy loam - Soil material that contains 30% or more very fine sand and <15% very coarse, coarse, and medium sa nd (or) >40% fine and very fine sand, more than half of which is very fine sand and <15% very coarse, coarse, and
medium sand.

• silt - Soil material that contains 80% or more silt and <12% clay.

• silty clay - Soil material that contains 40% or more clay and 40% or more silt.

• silty clay loam - Soil material that contains 27 to 40% clay and <20% sand.

• silt loam - Soil material that contains 50% or mo re silt and 12 to 27% clay (or) 50 to 80% silt and <12% clay.

[Note: Most available fumigant emissions data were conducted in soils that include: sand, sandy loam, loamy sand, and a few studies in sandy clay loam.]86 Figure 81: Graph Showing the Percentages of Sand , Silt, and Clay In The Soil Texture Classes

Besides the soil texture and Soil Ta xonomy descriptions above, th ere are other approaches that can be considered for defining soil categories that may be usef ul. For example, NRCS has developed a series of land resour ce regions that integrates a vari ety of data based on the example description provided below [67]. The specific land use regions ar e illustrated in Figure 82 and an example of a regional desc ription is provided below.87 Figure 82: Land Resource Regions In The United States

The example description of Region U is provided below:

“U-FLORIDA SUBTROPICAL FRUIT, TRUCK CROP, AND RANGE REGION 94,550 km 2 (36,510 mi 2 ) This region consists of the sout hern two-thirds of the Florida peninsula. The average annua l precipitation is 1,275 to 1,625 mm. The average annual temperature is 21 to 25 C. The freeze-free pe riod ranges from 290 days in the north to 365 days in the south. A large part of the regi on is south of the southern limit of annual
frost. Psamments and Udults are the dominant soils at high elevations. Aquults, Aqualfs, Aquods, and Aquents are dominant at low elev ations. Locally, Saprists and Fibrists are dominant. Many of the low-lying soils require drainage if they are used for crops. Citrus fruits, other subtropical fruits, and winter vegetables are the major crops throughout the region. The acreage in pasture and range, however, is somewh at greater than that in crops. More than two-thirds of the region is in forest or other native vegetation, much of which is grazed. Beef cattle are the princi pal livestock, and dairying is an important enterprise near the larger cities. In th e south, sugarcane is a major crop locally.”88 Ascribing soil texture or other pos sible characterization nomenclature is desirable because it may
be useful to link these factors to overall emissions although it is anticipated that the overall impacts will not be as substantive as for othe r factors described above. As illustrated in many instances above, different soil types have been in vestigated in the available research but most research has been completed using sandy loam or loamy sand soils. In figures 9 and 10 above [11-16], emissions data are summarized for iodom ethane and chloropicrin, respectively. Figure
9 summarizes iodomethane emissions data for ta rped raised beds where standard films and higher barrier agricultural films were used. A ll studies were conducted using very similar soil types (e.g., sand, loamy sand, or sandy loams) so de fining specific trends in these data based on soil types. It should also be noted that there is a low number of data points, the implications of which is discussed below. The chloropicrin data were similar. The le vel of organic matter in each of these studies was around 1 percent or so. No data in this comparison had higher organic
matter levels.

Ajwa [26] in Figure 20 above also evaluated different sandy loam soils (i.e., Salinas and Watsonville) but results were confounding to compar e because in the Salinas soil, additional drip application water was applied.

Chellemi [27, 28] discussed the relationship of water holding capacity and soil types which will be investigated as part of the ongoing USDA ar ea wide research progr am (Figure 83). In addition, Chellemi [27, 28] also discussed an importa nt consideration that is illustrated in Figure 84 where it would not be uncommon to anticipate varied soil types in a single field. This confounds the possible use of soil typing as a means for assigning a credit structure based on
these factors.

Figure 83: Water Holding Capacity Of Soil89 

### Trial #4 has four soil types:

• Pineda sand

• Riviera fine sand

• Chobee loamy sand, depressional

• Winder loamy sand

Figure 84: Multiple Soil Textures Ob served In Single USDA Field Trial

Finally, Wang [68] used an efficacy- or expos ure-based concentration–time exposure index (CTEI) to serve as a continuous quantitative efficacy assessment for soil fumigation by subsurface drip irrigation using numerical model simulations. The CTEI was defined as the ratio
between the soil volume where concentration–tim e (CxT) exceeded a th reshold value for a particular pest–fumigant combination and th e total soil volume required for fumigation treatment. Applications of CTEI as a simple efficacy index were demonstrated by simulating combinations of three soil types (loam, sandy lo am, sand); three field configurations consisting of 102- and 203-cm-wide bed system s and a flat surface system; three application depths (15, 30, 45 cm); and two application rates (82 and 327 kg ha–1) for 1,3-dichloropropene against citrus nematode (Tylenchulus semipenetrans) using a threshold air-phase CT value of 12 μg h cm–3 obtained from a separate field study. For soil fu migation by subsurface drip irrigation, the order of importance in optimizing CTEI was soil type, depth of application and depth of treatment, dosage, and field configuration. Model simulation using CTEI as a numeric efficacy index can be
an effective alternative to assi st in the planning of field tria ls for making final management decisions concerning soil fumiga tion or other pesticide applica tions. The CTEI values (mean, Std. Dev, and CVs), calculated by Wang based on va ried soil types, are presented in Figure 85.90 Figure 85: Soil Type Effects On Concentr ation-Time Exposure Index Calculated Using Monte Carlo Analysis (N=1000 Iterations)



### 2.4.2 Biological Activity

Biological activity can possibly be related to organic matter levels which can possibly be manipulated by growers. However, direct quantitative manipulati on of microorganism populations in fields, even if orga nic matter levels can be adequate ly manipulated, is not likely to be a widely implementable strate gy for controlling emissions. As such, this discussion has been
included in this section whic h growers cannot easily manipulate. There are many possible approaches for addressing how the biological acti vity in soils may ultimately impact emissions but from a field scale perspective that could be used by growers it is li kely that any viable method would be too complex and cumbersome becau se of the types of testing that would be required (e.g., define micro-organisms and their leve ls as well as specific effects on a particular fumigant). However, it is important to unders tand that different micro-organisms can have differential impacts on fumigants. For example, Zh eng [59] illustrated that MITC degradation is influenced by microbial activity levels while othe r chemicals such as methyl bromide have been shown to degrade through chemical mechanisms . Dungan [69] summarized information that links the degradation of various fumigants to specific microorganisms (Table 19).91 Table 19: Microorganisms Linked To The Degradation of 1,3-D; MITC; Chloropicrin; and Methyl Bromide

Dungan [69] indicated prelimin ary evidence suggests that the microorganisms responsible for enhanced degradation of MITC speci fically target the isothiocyanate functional group. In the case of 1,3-D, a number of bacteria have been isolated that are capable of degrading 1,3-D and
also using it as a sole “C” and energy source. Of the two isomers of 1,3-D, degradation of trans

1,3-D was found to be greater than that of cis -1,3-D in enhanced soil. Methyl bromide is mainly degraded chemically in soil by hydrolysis and methylation of nucleophilic sites on soil organic matter. Both degradation reactions occur via S 2 N nucleophilic substituti on. Methanotrophic and ammonia-oxidizing bacteria can co- oxidize methyl bromide during the oxidation of methane and ammonia, respectively. The microbiological degradation of methyl bromide is apparently catalyzed by methane and ammonia monooxygenase. Chloropicrin can be dehalogenated by
Pseudomonas spp., with the major metabolic pathway occurring through three successive reductive dehalogenations to nitromethane. Dungan presented the following conclusions:

• 9% of 1,3-D and 50% of MITC degradation are biologically associated in sandy loam;

• Soil conditions and organic matter content often have largest influence on MITC generation (sandy loam soil) – at 40C 3x > than 20C;

• For MITC changes in soil moisture conten t below saturation ha d little influence on degradation rate;

• For MITC microbial degradation accounts fo r as much as 50-80% based on sterile/nonsterile results;

• For MITC adaptive microorganisms are reported which enhance degradation;

• For chloropicrin between 68 & 92 % of overa ll degradation is accounted for by microbial degradation; and

• For methyl bromide only about 20% is from microbial degradation.92 Dungan also commented on a need to manage popul ations of microorganisms because the health of these populations can impact the overall viab ility of production fields in agricu lture. Dungan [69] also concluded that the impact of indivi dual organisms on degradation and emissions is not
well understood.

Many researchers have evaluated how levels of or ganic matter influence emissions and it follows that levels of organic matter can possibly be a generic indicator of th e microorganism activity (e.g., the higher the organic matter levels the more microbial activity that w ould be anticipated). Ashworth [31] in Figures 30 through 32 above indicates that higher organic matter reduces emissions of 1,3-D which could be related to micr oorganism levels. Dungan [32] also illustrated similar results (Figure 33 and Table 9) with amended soils. Various other researchers have
conducted similar research and reached a similar conclusion that higher levels of organic matter typically lead to reduced emissions because Cx T profiles are generally enhanced in size and intensity (e.g., Gan [33]; McDona ld [39]; Gao [51,52]; Guo [35]; and Xu [36]). Of course, relating these results to specific or even genera l classes of microorganisms at this point is supposition and the question of how organic ma tter possibly coupled with microorganism activity as an indicator of emission reducti on is a prevalent one in current research.



### 2.4.3 Soil Temperature

Soil temperature, in a general sense, can possi bly be manipulated by growers through purposive choices they make related to the use of reflective agricultural films, the timing of applications, or the addition of irrigation water or lack thereof. However, direct quantitative manipulation of soil temperature at the point of inject ion or application as well as at the soil surface if applicable is not likely to be a widely implementable strate gy for controlling emissions except for defining limitations on application conditions (e.g., settin g an upper limit temperature at the point of injection above which applications could not take place). Becau se of these limitations, this
discussion has been included in this section related to factor s which growers cannot easily manipulate. It is clear, howeve r, that growers must closely mon itor soil temperatures as a means for understanding the potential for emissions dur ing and after a fumigant application and potentially terminate applicati ons if conditions are too hot which promotes more extensive
volatilization. This can be done very easily with a simple soil temperature probe which can quantify levels at the point of injection. In th e field studies used in the risk assessments for various chemicals, soil temperatures at the point of injection fluctuated at times significantly. Based upon a cursory review of the data, most temper atures at the point of injection appear to be
within 20 and 30oC or so and in a few instances temperatures decreased to single digits or at times climbed as high as 40oC or so (between 68 and 86 oF or as high as 104 oF). It should also be noted that a significant amount of research which has been completed, outside of the field studies, has been under isothermal conditions . In many laboratory-based studies the temperatures were in the 20 to 25oC range that re flects common ambient conditions in structures. [Note: Many stakeholders have suggested an in jection point temperature cut-off for terminating applications of 32 oC or 90 oF.] Diurnal temperature changes ar e also reflected in the field
studies which served as the basis for the risk assessment as illustrated in Figures 86 and 87 (Wang [22, 29] & Sullivan [70]). Note in Figure 86 where conditi ons are essentially isothermal at night followed by steep warming trends starting around 9AM. Wang also presented a mathematical relationship which describes how te mperature impacts fumigant transport and how temperature changes can impact mass transfer co efficients in agricultural films (Figure 88).93 Figure 86: Diurnal Flux Impacts Observed On HDPE Film From Methyl Bromide

Figure 87: Diurnal Temperature Pattern Observed In Field Study At 3 Heights94 Figure 88: Impact Of Temperatu re Changes On Two Agricultural Films Using Methyl Bromide

Dungan [32] also presented di fferent degradation rates in a sandy loam soil (amended and unamended with organic ma tter) at varied temperatures (Figure 34).

Ma [55] studied the degradat ion of methyl isothiocyanate (MITC) and 1,3-dichloropropene (1,3- D) in an Arlington sandy loam (coarse-loamy, mixed, thermic Haplic Durixeralf) at four temperatures and four initial concentrations. Th e applicability of first- order, half-order, and
second-order kinetics, and the Michaelis–Menten model for describing fumigant degradation as affected by temperature and initial concentration was tested. Overall, none of the models adequately described the degradation of MITC and 1,3-D isomers over the range of the initial
concentrations. First-order and half-order kinetics adequately described th e degradation of MITC and 1,3-D isomers at each initial concen tration, with the corr elation coefficients greater than 0.78 ( r

2 > 0.78). However, the derived rate constant was dependent on the initial concentration. The
first-order rate constants varied between 6 and 10 x for MITC for the concentration range of 3 to 140 mg kg -1 , and between 1.5 and 4 x for 1,3-D isomers for the concentration range of 0.6 to 60 mg kg -1 , depending on temperature. For the same initial concentration range, the variation in the
half-order rate consta nts was between 1.4 and 1.7 x for MITC and between 3.1 and 6.1 x for 1,3-D isomers, depending on temperature. Second-order kine tics and the Michaelis–Menten model did not satisfactorily describe the degradation at all initial concentrations. Th e degradation of MITC and 1,3-D was primarily biodegradation, which was affected by temperature between 20 and 40°C, following the Arrhenius equation ( r 2 > 0.74) (Tables 20 and 21).95 Table 20: The first-order ( k 1 ) and the half-order ( k 0.5 ) degradation rate co nstants of methyl isothiocyanate (MITC) at different temp eratures and initial concentrations.

Temperature (°C)

Initial
concentration 20 30 35 40

mg kg -1 _ k 1 x 10 2 (h -1 ) _

3.31 2.08 ± 0.21 (0.99) 4.54 ± 0.23 (0.99) 4.80 ± 0.35 (0.99) 5.14 ± 0.18 (0.99)

24.75 0.39 ± 0.04 (0.97) 0.90 ± 0.06 (0.99) 1.18 ± 0.04 (0.99) 1.65 ± 0.10 (0.99)

56.01 0.30 ± 0.04 (0.97) 0.70 ± 0.07 (0.97) 0.85 ± 0.04 (0.99) 1.14 ± 0.04 (0.99)

142.70 0.23 ± 0.03 (0.93) 0.45 ± 0.04 (0.97) 0.57 ± 0.04 (0.98) 0.86 ± 0.07 (0.98)

_ k 0.5 x 10 2 (h -1 ) _

3.31 1.88 ± 0.15 (0.88) 5.24 ± 0.20 (0.98) 5.72 ± 0.21 (0.98) 5.78 ± 0.28 (0.97)

24.75 1.68 ± 0.07 (0.97) 3.10 ± 0.14 (0.96) 3.74 ± 0.16 (0.96) 4.36 ± 0.26 (0.93)

56.01 1.90 ± 0.12 (0.93) 3.70 ± 0.21 (0.94) 4.48 ± 0.16 (0.98) 5.44 ± 0.20 (0.97)

142.70 2.38 ± 0.15 (0.92) 4.24 ± 0.23 (0.95) 5.36 ± 0.20 (0.97) 7.06 ± 0.32 (0.96)

The first-order ( k 1 ) and the half-order ( k 0.5 ) degradation rate constants were obtained by fitting
measured concentrations at different times to first-order and half-ord er kinetics respectively using the least-square-err or fitting procedure.

Mean ± standard error. Data in parentheses are coefficients of fitting (adjusted r 2 ).96 Table 21: The half-order degradation rate constants ( k 0.5 ) of ( E )- and ( Z )-1,3-dichloropropene (1,3-D) isomers at diffe rent temperatures and initial concentrations.

Temperature (°C)

Initial concentration 20 30 35 40

mg kg -1 _ k 0.5 x 10 2 (h -1 ) _

( E )-1,3-D

0.60 0.60 ± 0.04 (0.98) 1.18 ± 0.06 (0.99) 1.32 ± 0.08 (0.98) 192 ± 0.18 (0.97)

9.55 1.36 ± 0.10 (0.97) 2.02 ± 0.06 (0.99) 2.44 ± 0.24 (0.94) 2.80 ± 0.28 (0.95)

14.18 1.38 ± 0.12 (0.96) 2.26 ± 0.24 (0.94) 2.80 ± 0.16 (0.98) 3.10 ± 0.34 (0.94)

58.82 1.86 ± 0.18 (0.94) 4.96 ± 0.76 (0.87) 4.48 ± 0.82 (0.82) 6.40 ± 86 (0.90)

( Z )-1,3-D

0.65 0.52 ± 0.04 (0.98) 0.90 ± 0.06 (0.97) 1.30 ± 0.06 (0.99) 1.46 ± 0.12 (0.99)

9.98 1.42 ± 0.10 (0.97) 2.20 ± 0.08 (0.99) 2.64 ± 0.22 (0.96) 3.02 ± 0.36 (0.92)

15.27 1.48 ± 0.22 (0.89) 2.58 ± 0.26 (0.95) 3.18 ± 0.20 (0.98) 3.76 ± 0.46 (0.92)

60.33 2.00 ± 0.18 (0.95) 5.52 ± 0.78 (0.89) 5.32 ± 0.88 (0.85) 7.16 ± 1.04 (0.88)

The half-order degradation rate constants were obtained by fitting measured concentrations at
different times to half-order kinetics using the least-square -error fitting procedure.

Mean ± standard error. Data in parentheses are coefficients of fitting (adjusted r 2 ).97 Fumigants (or their active degradates) are highly volatile compared to conventional pesticides which are typically categorized as semi-volatile organic chemicals. Becau se of this, they are anticipated to rapidly be emitted into the atmo sphere after an application event but these emissions can be attenuated by various factors as has been discussed in this document. Because of this high volatility, the sh ape and intensity of emission profiles probably would not be extensively impacted except in ex treme cool temperatures or very high temperature conditions as is observed in the field-scale data. However, it is clear that changes in temperature can impact specific factors which affect emissions such as degradation rates and mass transfer coefficients which are measures of permeability through agricu ltural films. It also appears clear that fumigant dissipation and degrada tion pathways, as expected, are temperature dependent such as was observed in Ma [55] where degradati on of MITC and 1,3-D followed an Arrhenius temperature dependent pattern or in Wang [22, 29] where MTCs are shown to be temperature dependent. Given the observations that, at the field sc ale, temperatures fluctuate without major discernible differences in emission patterns and also the observation that standard types of kinetics models seem to apply to fumigant disp ersion and degradation, there does not seem to be a range of temperatures that is apparent where an asymptote coul d be defined in the temperature response data that could easily be used for defining operational limits for field applicators. It is clear, however, based on the available informati on that increasing temperatures also enhance dispersion and degradation rates. Of course, en hanced degradation could also mean less overall emissions but conversely enhanced dispersion could mean that la rger areas around treated fields are impacted. It is also clear that fumigant a pplications can be more of an “art” than science because the factors which impact emissions are many and inter-rela te in varied ways. Based on inputs from stakeholders a soil injection temper ature of 90oF has been mentioned as a possible temperature at which applications would not be allowed. This s eems reasonable but, as indicated above, cannot be clearly justified outside of indicating if applicatio ns occurred at higher temperatures more dissipation would occur al so accompanied by higher degradation with the converse being true at lower temperatures.



### 2.4.4 Localized Weather Conditions

Atmospheric conditions at ground level and in th e atmospheric surface boundary layer contribute to the emission and exposure fr om volatile fumigants in air. Surface atmospheric boundary conditions influence the emissions of soil incorpor ated volatile fumigants from below the surface
as described above. Air te mperature is related to soil temperat ure by the heat tran sfer process via conduction as suggested by Hamd eh and Reeder [71].

In addition, the vertical profiled atmospheric en vironment influences the potential exposure of gaseous fumigant residues in air by the routes of dispersion influenced by turbulence, buoyancy influenced by the vertical atmospheric temperat ure profile, vertical wind shear, and downwind transport influenced by the wind sp eed and direction (Emanuel [72]; Pasquill [73]; Paine [74]). However, the atmospheric surface boundary layer conditions are also a factor for potential exposure to fumigants. Specifical ly of concern are temperature in version conditions. In general, temperature inversions occur as colder, dens er air undercuts warmer , more buoyant air. Temperature inversions can occur through air mass transport via fronts and localized phenomenon such as sea breezes and mountain-va lley circulations (Dju ric [75). However, temperature inversions occur most frequently due to radiationa l forcing at night as the land98 surface cools at a higher rate than the atmosphere al oft. Within an inversion layer, the adiabatic process whereby heat is conserved between the thermal and ambient air results in negative buoyancy under inversion conditions as thermals and plumes become denser than the ambient air. As a result, vertical mixing through conv ective processes and turbulence is minimized.
Such conditions are classified as stable as the atmospheric surface boundary layer becomes stagnant and is characterized by light winds often flowing in variable dire ctions (Stull [76). The minimization of turbulence in the stable atmosp heric surface boundary laye r is the cause for poor dispersion under inversion condition s. Airborne contaminants possess Gaussian distributions in
the vertical and horizontal dire ctions. In addition, since tota l residue mass loading in the atmosphere is determined by emissions, residues in air tend to be more concentrated in narrow plumes than wider plumes as the Gaussian dist ribution is conserved under inversion conditions. In more technical term, low sigma value for plum es within inversion conditions is the cause for elevated concentrations according to Pasquill [73] . The light and variable winds characterized by inversion conditions can also create significant drifting of highly concentrated gaseous residues in air (Paine [74]).

Mixing height is a significant variable effecting gr ound-level residue concentra tions in air. Since convection is the dominant turbulent process at the surface during the daytime, the convective mixing height determines the maximum potential height of the plume axis. Since wind shear kinetic energy is the dominant turbulent process during the nighttime, the mechanical mixing height determines the approximate maximum po tential height of the plume axis during the nighttime (EPA [77]). In general, the following two scenarios are the most frequent causes of high concentrations of fumigant residues in air at ground level:

• Elevated temperature inversion layers in the atmospheric surface boundary layer can limit the convective mixing height during the daytime. This is a particular problem during the morning hours as an unstable convective layer often emerges at the surface underneath
the residual nocturnal inversion layer (Stull [76]). Therefor e, a shallow convective mixed layer at the surface underneath an inversion la yer can cause fumigant residues in air to become congested in the atmospheric su rface boundary layer and the mixing depth
associated with conduction a nd convection becomes limited. Elevated inversions are often termed capping inversions for this reason (EPA [78]).

• Another potential problem at nighttime exists w ith the lowering of the mechanical mixing height as the main level of vertical wind shear production is maintained at the top of the nocturnal surface inversion laye r (Stull [76]). Therefore, low mechanical mixing heights at nighttime may also cause high exposure concentrations of fumigant residues in air.

Although inversions occur frequent ly, no two inversions occurring at the same level in the atmosphere contribute to air quality impacts at ground level the same way. The degree of mixing that occurs is influenced by the temperature lapse rate. In general, strong inversions with highly negative temperature lapse rates wi ll generally tend to inhibit mixi ng more than weak inversions with low negative temperature la pse rates (Salby [79]).

It is the goal of the Agency for fumigant a pplications not to be conducted under temperature
inversion conditions to prevent incidents due to conditions with poor dispersion and air stagnation. Having users identify such conditions is often difficult since vertically profiled temperature and wind data are nece ssary to directly detect invers ions in specific fields where99 applications take place because they would not likely have access to the sophisticated meteorological instrumentation need ed to define such profiles. Furthermore, current real-time high resolution numerical weather analyses that are available do not have sufficient resolution with 12 km grid spacing (or 144 km 2 grid cells) to adequately predict or diagnose temperature inversion conditions representati ve over all agricultural sites (NOAA [80]). Since there is a dearth of appropriate m easured atmospheric vertical profiled data, there are limited options to restrict fumigant use based on the detection or forecast of temperature inversions. Currently, labels advise users to create a plume of smoke and observe its rising motion and dissipation. This is an adequate measure to perform an observational-based chec k to determine whether
current conditions are characteri zed by air stagnation. This tech nique may not be valuable in predicting future inversions (e .g., one that may occur at the end of an application day).

The only other national scale forecasting progra m addressing air qualit y and potential air stagnation is the EPA AIRNOW issuance of the air quality index. The Air Quality Index is insufficient at this time for the purposes of predicting inversions in specific farm fields on the day of application and subsequent days ther eafter. The EPA AIRNOW program issues Air Quality Index (AQI) values from state and loca l planning agency produced forecasts based on airshed modeling (CMAQ) and site specific empirical statistical fitting relationships. The AIRNOW program produces daily air quality for ecasts mainly for fine particulate mater (PM 2.5 ) and ozone concentrations (EPA [81]). The AQI is calculated using the maximum predicted 8- hour average ozone concentration or 24-hour average maximum predicted PM 2.5 concentration. AQI forecasts for ozone and PM 2.5 are issued according to six AQI classes indicating the anticipated health impact as shown in Table 22 below. Each AQI class is coded in colors ranging from green, indicating good air quality, to maroon, indicating hazardous air quality.

Table 22: EPA AIRNOW 8-ozone And 24-hour PM 2.5 AQI classes and Health Impact Designations

8-hour Ozone
Concentration Range (ppb) 8-hour Ozone AQI Range Color Code Air Quality Classification
0 – 59 0 - 50 Green Good
60 - 75 51 - 100 Yellow Moderate 76 - 95 101 - 150 Orange Unhealthy for Sensitive Groups 96 - 115 151 - 200 Red Unhealthy 116 – 374 201 - 300 Purple Very Unhealthy
375 - 604 301 - 500 Maroon Hazardous

24-hour PM 2.5 Concentration Range ( μ g/m 3 ) 24-hour
PM 2.5 AQI Range Color Code Air Quality Classification
0.0 – 15.4 0 - 50 Green Good
15.5 – 40.4 51 - 100 Yellow Moderate 40.5 – 65.4 101 - 150 Orange Unhealthy for Sensitive Groups 65.5 – 150.4 151 - 200 Red Unhealthy100 Table 22: EPA AIRNOW 8-ozone And 24-hour PM 2.5 AQI classes and Health Impact Designations

150.5 – 250.0 201 - 300 Purple Very Unhealthy
250.1 – 500.4 301 - 500 Maroon Hazardous

An action day for ozone or PM 2.5 is often declared when the AQI exceeds the yellow level. The significance of this critical AQI threshold is that this is the level where predicted air concentrations exceed the Clean Air Act National Ambient Air Quality Standards attainment
standards for the forecasted area (EPA [81]). Some of the possible incentives for considering
AIRNOW as the basis for dete rmining inversions include:

• EPA AIRNOW air quality forecasting program operates in real-time.

• Fumigants can be represented by the pollutant categories forecasted for the AQI (e.g., fumigants could be classified as PM 2.5 and also possess at least slight ozone photochemical ozone creation potentials in some cases). However, AQI forecasts do not include agricultural activities in the emission inventories.

• The EPA AIRNOW encompasses many large areas on a national basis but there are limitations (see below).

• Air quality forecasts produced by modeli ng and monitoring methods indicate the potential for air stagnation. Ther efore, utilization of this pro duct as guidance to restrict fumigant usage will result in diminished activity in the agricultural community that, in turn, would have contributed to deterioration of air quality on those days.

However, the EPA AIRNOW forecasts should not be considered as a basis for mandatory restrictions on fumigant applications due to the possibilit y of inversion conditions for the following reasons (EPA [81]):

• The EPA AIRNOW forecasting operations ar e not provided for areas outside of metropolitan airsheds.

• EPA AIRNOW forecast methods are not stre amlined as forecasted AQI values are produced by methods based on modeling and m onitoring that vary between those doing the analyses for different airsheds or jurisdictions.

• EPA AIRNOW forecasts produced by the CMAQ model contain a coarse resolution of 12 km (or 144 km 2 grid cells).

• EPA AIRNOW forecasts are not currently us ed to impose mandatory restrictions on
industrial activitie s or other practices which would ma ke its use for regulatory purposes related to fumigants groundbreaking where the implications would have to be further investigated.101 Historically, another possible resource for predicting inversi on conditions was Air Stagnation Advisories issued by the Nationa l Weather Service. These are no longer issued because they have been supplanted by AQI. Air Stagnation A dvisories were issued by the National Weather Service when locally established critical va lues of surface wind speeds and mixing heights persist for 36 hours. However, this product could also not have been considered for mandatory restrictions since Air Stagnation Advisories are based on coarse resolution numerical prediction models (grid spacing > 12km) (NOAA [82]).

The following summarizes the status of current and potential tools and data that could be used to assess the atmosphere for vulnerable conditions leading to elevated exposure potentials from fumigant residues in air:

• Given the present and likely future availabi lity of real-time weather prediction models and unfeasibility of implementing expensive we ather observing networks at all fumigant use sites, it is unlikely that site specific qua ntitative methods will be available in the near future that will allow for definitive prediction of inversions in a regimented enforcement type of context required for pesticide labeling.

• The current state of nationally-available real- time weather prediction model data preclude direct applicability to farm field sized so urces exposure because airshed grids are too coarse in most cases (144 km 2 grid cells).

• Air quality forecasts of PM 2.5 and ozone provided by EPA AI RNOW may in some cases be used to at least identify possible days w ith vulnerable meteorolog ical conditions for air stagnation. Action days declar ed by local jurisdictions ba sed on these forecasts indicate days of concern. This type of approach might serve as an early warning for users to be more aware of possible upcoming inversion conditions. Similarl y, Air Stagnation Advisories issued by the local National Weat her Service Office could be considered in the same way.

• In the future, it may be plausible to im plement a mandatory restriction program for fumigant use based on present and forecaste d meteorological conditions as computer resources continue to improve allowing for higher resolution models to become operational. The anticipated 2.5 km resolutio n with the WRF model will be sufficient enough for a spatially representative dataset to be avai lable for all agricult ural sites across most of the country. Mandatory restrictions can be based on predictions depicting the probability of air stagnation based on thresholds and maximum negative atmospheric boundary temperature lapse rates and minimum mixing heights. Mandatory restrictions
would be based on the worst-case conditions within a 24-hour period of the intended application (NOAA [80]).102 

### 2.4.5 Summary of Emission Reduction Potential of Field Conditions

In the sections prior to this, the focus of the analyses has been on factors which growers could intentionally manipulate in a cost-effective mann er to reduce emission levels and enhance CxT parameters thus allowing for the use of less fu migants which also reduces exposure. In this section, the focus has been on presenting information that pertains to factors that are more or less
inherent to the conditions of th e specific fields which fumigant users treat and the atmospheric conditions during or immediately su bsequent to applications. The factors which were identified include soil categorization, biological activit y, soil temperature, and localized weather conditions. Many factors were considered. For example, there are many possible ways of categorizing soils but the most common for this pu rpose probably is either by order or using the texture type of approach as desc ribed above. It is al so clear that most fu migant use probably occurs in soils that are predominantly sand so ther e is not likely to be much change in emissions based on common soil types. This conclusion could be disputed ba sed on laboratory research but when field scale data are compared observable diffe rences tend to be minimized. It is also clear that moisture levels can be important which in many cases can be manipu lated by growers but in other cases they cannot be other than to wait for optimal conditions . Levels of biological activity can also impact degradation and emission rates. Some fumigants are more readily degraded when certain microorganisms are present (e .g., MITC) while others are not (e.g., methyl bromide) as illustrated by Dungan [69]. Resear ch relating specific microorganisms to varied degradation rates for specific fumigants is also limited and their populations in actual fields can vary and there can be several organisms present so competitive effects could be occurring which are not well understood. Generally, there is not much specific information about these factors and how microorganism levels coul d be evaluated in a meaningful quantitative manner related to field scale fumigant emission rates. Soil temperature is another interesting issue in that there are certain things growers can do to manipulate it (e.g., metalized, re flective film use; irrigation water; or altering application date s) but true impacts on soil temper ature at the point of injection (e.g., typically 8 inches deep or more) becomes more difficult on a field scale basis. It is also clear from the data that there is an impact on degradation and emissions when temperatures change. If temperatures increase, the rates of both phenomena increase and conversely decrease if temperatures go down. These trends can ge nerally be fairly well described using common kinetics approaches such as the Arrhenius e quation which makes a change in response with changing temperature not a situation that lends itself to the determination of a specific temperature where, for example, applications should cease. As discussed a bove, there is an art to fumigant application and many indi viduals involved in applications have suggested an injection point soil temperature of 90oF as appropriate. At this point there is no scient ific basis to suggest otherwise. Finally, eliminating the possibility of atmospheric i nversions during and subsequent to applications is desirable because, as indica ted above, these conditions can lead to higher air concentrations than normal conditions for a variety of reasons. What causes inversion conditions is fairly well understood but pred ictive tools for identifying them on a farm field scale are not
available at this time. There are several reasons but the key ones are that users would need sitespecific convection information and forecasts provided by institutions such as NOAA or NWS
do not have sufficient resolution in most cases for this purpose. Some information such as AQI values could possibly be used not in a quantitative way but qualitati vely such as an early warning system that would alert users to be vigilant about po ssible inversions.103 Given these issues and the type s of research described above , defining quantitative emission reduction factors for specific fiel d conditions that coul d routinely and generi cally be applied is inappropriate. Although reductions can and do occur as a result of varied field conditions, these reductions vary from chemical to chemical, vary for the same chemical, and other conditions such as changing application rates or methods. Co mplicating the situation fu rther is the lack of side by side studies where controls are utilized. Finally, the lack of field studies where only field conditions are studied (e.g., speci fic microorganisms) is anot her reason why the available information does not lead to more quantitative conclusions. It is recommended, however, that no
emissions credit based on field conditions be a ny higher than a reduction in overall emissions attributed to high barrier film use or other factors that can ea sily be manipulated by users to
achieve emissions reductions.104 

### 2.5 First principles modeling

In addition to the analysis of the empirical da ta described above in Sections 2.1 through 2.4, a modeling approach has also been utilized that evaluated how ma nipulation of first principles factors could impact overall emissions (e.g., boundary layers or moisture). The focus of this overall analysis is to evaluate how varied fa ctors impact emissions. It does not evaluate dissipation around a field after th e emission since PERFUM has us ed for that element of the analysis (see http://www.epa.gov/pesticides/r eregistration/soil_fumigants/ ). The results of this evaluation of emission factors; however, could influence the flux or emission inputs used as the basis for a PERFUM analysis.



### 2.5.1 Model Selection

In this evaluation, the choice of modeling construct has been caref ully considered as have the input values and a strategy for evaluating the impa ct of changes of specific parameters (i.e., how to structure a sensitivity analysis). There are a number of possible mode ling choices which could be used, but they all share many similar elements. These include:

• Pesticide Root Zone Model (PRZM): is a one-dimensional, finite-difference model that accounts for pesticide and nitrogen fate in the crop root zone. PRZM-3 includes modeling capabilities for such phenomena as soil temperature simulation, volatilization and vapor phase transport in soils, irrigati on simulation, microbial transformation, and a method of characteristics (MOC) algorithm to eliminate numerical dispersion. (see http://www.epa.gov/oppefed1/ models/water/index.htm#przm ).

• CHAIN-2D: is a computer program for simulati ng two-dimensional variably saturated water flow, heat transport, and the movement of solutes involved in sequential first-orderdecay reactions. The program numerically solves the Richards' equation for saturatedunsaturated water flow and the convection- dispersion equation for heat and solute transport. The flow equation incorporates a si nk term to account for water uptake by plant roots. The water flow part of the model can deal with prescribed head, gradient, and flux boundaries, as well as boundaries contro lled by atmospheric conditions (see http://www.ars.usda.gov/Services/docs.htm?docid=8914 ).

• HYDRUS-2D: is a finite element model for simulating the movement of water, heat, and multiple solutes in variably saturated media. The program numerically solves the Richards' equation for saturated-unsaturatedwater flow and Fickian-based advectiondispersion equations for heat and solute tran sport. The flow equation incorporates a sink term to account for water uptake by plant root s. The heat transport equation considers
conduction as well as convection with flowi ng water. The solute transport equations consider advective-dispersive transport in the liquid phase, and diffusion in the gaseous phase. The transport equations also incl ude provisions for nonlinear and/or nonequilibrium reactions between the solid and liquid phases, linear equilibrium reactions between the liquid and gaseous phases, zero-order production, and two firstorder degradation reactions: one which is independent of other solutes, and one which provides the coupling between solutes involved in sequential first-order decay reactions (see http://www.pc-progress.cz/Fr_Hydrus2D.htm ).105 Each of these modeling approaches is considered a valid construct for conducting the type of first principle factors sensitivity analysis that has been completed. Each of these models has been reviewed and/or used extensively in the de velopment of peer review ed literature for many types of vadose zone analyses (i.e., not just fo cused on pesticide transport issues – additional
references or documentation can be provided upon request). The underlying physics of CHAIN2D and HYDRUS-2D are practically identical. Consequently, so the re al choice was between them and the PRZM model. Ultimately, it was decided to use CHAIN-2D for the following

reasons:

• CHAIN-2D is scientifically appropriate for this effort;

• CHAIN-2D is publicly available;

• EPA received a similar analysis by the CMTF (chloropicrin manufacturers task force) that used CHAIN-2D as its basis and using CHAIN2D facilitated its review.

Even considering these issues, the decision was one of convenien ce because it made it easier to compare with the results of the CMTF submission. The other models could be used to develop comparative analyses; however, it is believed that th e results should be similar. It should also be noted that the CMTF submitted a series of documents which used a slightly modified version of
CHAIN-2D as the basis for th e analysis [83] [84] [85].



### 2.5.2 Basic Model Structure

In the document describing the implementation of CHAIN-2D for this purpose [84] Cryer and van Wesenbeek describe how they modified the original CHAIN-2D FORTRAN source code to create a more useable graphica l user interface (GUI) by creating a pre- and post-processor using Microsoft Excel and also incorporating a new air/soil boundary c ondition proposed by Wang 1998 and 1999 [86] [87]. A dditionally, an ability to simulate tarp tearing and tarp removal at user specified time intervals as well as a bounda ry condition that ties meteorological conditions
to the resistance to mass transfer at the air/so il interface in addition to the stagnant boundary layer option was also included based on Crye r 2007 [88]. The boundary condition proposed by Wang is expressed as an Arrhenius type equation:106 In this document, Cryer and vanWesenbeek also describe how they developed various inputs for existing CHAIN-2D capabilities including:

• Grid generation based on varied a pplication methods (Figures 89, 90, 91);

• Pressure head determination based on vanG enutchen soil parameters (Table 23) [89];

• Addressing shank trace geometry and undisturbed soil (Figure 92);

• Addressing pesticide pla cement (Figure 93); and

• Computational fluid dynamics analysis within the boundary layer to account for mass
transfer coefficient modifications contribu ted from the shank trace and raised bed agriculture (Figure 94).

Figure 89: Distinct Regions Where Nodal Placement Must Remain Dense To Characterize Gradients

Figure 90: Uniform (10,200 No des) & Selective (1,596 No des) Nodal Placement Options107 Figure 91: Nodal Grids Used In Analysis For Differi ng Application Depths Based On Selective Approach

Table 23: vanGenutchen Soil Parameters As A Function Of Soil Texture With Assigned Average Bulk Density

Figure 92: Example Shank Trace Ge ometries That Can Be Assumed108 Figure 93: Representation Of Line, Partial Line, and Point Source Injectio ns Into Soil Parameterized Into A Single Input Parameter

Figure 94: Determination Of Simulation Domain Fo r Raised Bed Agriculture Using Computational Fluid Dynamics

In summary, the Agency evaluated the GUI for CHAIN-2D developed by the CMTF and found that it appears to function correctly as a pre- a nd post-processor for the mode l. It also evaluated the basic parameterization of the model base d on the above information and found it to be appropriate. Additionally, the Agency consulted with Dr. van Genuchten, a principal in CHAIN2D development, to ascertain if there were sign ificant theoretical issues with the model or the modification of the boundary layer based on Wang [86] [87] and there are not based on his review. [Note: A more detailed review of the CHAIN-2D interface developed by CMTF and used by the Agency in this analysis identified minor technical issues that we re either corrected or
otherwise characterized in that document. None of these issues were deemed significant enough to alter the conclusions of this analysis.]109 

### 2.5.3 Model Performance

There are numerous uses of all of the models me ntioned above which have been cited in the open scientific literature (examples can be provided upon request) where first principles soil modeling has been completed. Applications related to th e prediction of emissions from soil fumigations, however, are limited but the basic ph ysics of these models would sti ll apply. To ensure that the CHAIN-2D could be used in an appropriate ma nner for predicting emissions based on first principles factors for fumigants, the CMTF submitted a validation report (van Wesenbeek [83]). In this report comparisons were completed betw een CHAIN-2D prediction s and field emissions data generated using mostly chloropicrin and to a lesser extent 1,3-D. In general, cumulative emissions from field studies compared well w ith the predictions based on CHAIN-2D as illustrated in Table 24 and Figure 95 which show good numerical concurrence between the two
factors based on emissions studies from 5 sites a nd varied application methods. However, it is worthy to note that errors in cumulative flux ma y arise due to an incorrect initial mass transfer boundary condition as the user-defined value base d on the tarp material in CHAIN-2D may not be representative of actual conditions (e.g., compar ison of Arizona tarped and untarped results in Table 24).

Table 24: Comparison Of Cumulative Chloropi crin Emissions In Field Compared To CHAIN-2D

Figure 95: Comparison of Cumulative Chloropi crin Emissions In Field Compared To CHAIN-2D110 Similar results were achieved when field resu lts and CHAIN-2D predictions were compared from a single field study using 1,3-D as illustrate d in Figure 96. Note that in this figure both static and dynamic boundary layer conditions we re included with no significant differences
between the two.

Figure 96: Comparison of Cumulative 1,3-D Emissions In Field Compared to CHAIN-2D

Figures 97 and 98 illustrate how altering a single factor can alter the predictions of CHAIN-2D. In these situations, data from a chloropicrin study conducted in Arizona that quantified emissions after broadcast application with no tarp was compared with CHAIN-2D predictions using no shank trace (Figure 97) or with a shank trace (Figure 98). In the pr ediction without a shank trace, the peak emission does not compare with the observed values. Converse ly, as illustrated in Figure 98, the peak emissions compare favorably with the field data, presumably because a
shank trace was a substantive contributor to emi ssions in that study. This comparison has been provided to illustrate in some cases a single factor can determin e emission profiles. In other cases, many factors are inter-related which ma kes defining how changing them relative to emission changes very confounding.

Figure 97: Comparison of Chloropicrin Emi ssion Profile In Field Compared to CHAIN2D (no shank trace simulated)111 Figure 98: Comparison of Chloropicrin Emissi on Profile In Field Compared To CHAIN-2d (shank trace simulated)

Figure 99 illustrates a critical is sue that should be considered in the overall interpretation of analyses completed using CHAIN-2D. In this example, peak emissions from a study that quantified chloropicrin flux after a broadcast tarped application in Arizona could not be reproduced. This is a consistent problem th e Agency has encountered with the use of
CHAIN-2D and it is not clear what factors impact this issue. It is believed that several soil condition and boundary layer factors in conjunction with each other cause this phenomena and it is difficult to refine the analysis because they are typically not qua ntified in field studies either because they cannot be easily measured or protoc ols didn’t specify the measurement. There is significant further discussion of th is issue below. This issue was not addressed in the CMTF submission (van Wessenbeek [83]).

Figure 99: Comparison of Chloropicrin Emission Profile in Field Compared To CHAIN-2D (no shank trace simulated)112 Cryer also used CHAIN-2D to develop subsurface concentration contours for chloropicrin to
simulate values with and without the use of a HD PE tarp in Georgia soil conditions (Figure 100). As expected, these results are consistent with what has been observed in empirical data (see Section 2.1 above) because the size of the concen tration contour, the inte nsity of the contour (i.e., red and orange are more in tense or higher concentrations),, and the longevity of the contour is enhanced when a boundary layer such as HDPE is used. This result is another indication that
CHAIN-2D can be used to predict fumigant emissions.

Figure 100: Concentration Contours For Chlorp icrin Generated Using CHAIN-2D (Cryer [24])

Cryer [90] also published other comparisons of field emissions data wi th CHAIN-2D and PRZM predictions. In a separate st udy Cryer [91] also compared me asured losses of 1,3-D over time with PRZM and CHAIN-2D outputs and in this ca se the PRZM model results were very similar to the monitored data while CHAIN-2D again sl ightly underpredicted th e monitored data. It
should be noted that in both cas es, the general shape of the prediction curves aligned with the monitoring data to which they were compared a nd the predictions were we ll within an order of magnitude, which given the complexity of the system, appears reasonable. In Figure 101 the percent of applied 1,3-D wh ich volatilized is presented based on empirical laboratory information (developed by Gan [92]). In the m odel outputs comparison with the Gan data in the Cryer study, predicted results were calculated using both PRZM and CHAIN-2D at different depths and they under predicted the percent volatili zed which is similar to the results illustrated in Figure 102.113 Figure 101: Functional Nonlinear Dependence of Cumu lative Volatilization Losses For 1,3-D As A Function Of Application Depth

Figure 102: Comparison of PRZM prediction s for FL (10 inch shank bed) and CA (18 inch shank) to shank injection field observations, (CHAIN-2D output is for FL)114 In summary, the Agency evaluated the valid ity of using CHAIN-2D for the purposes of simulating emissions from treated agricultural fields . Based on this investigation it appears that CHAIN-2D can effectively predict cumulative emi ssion profiles. It can also crudely estimate peaks and the timing of peaks but of ten does not reliably predict peaks. This issue could lead to uncertainty and variability in the predictions generated using the mode l. [Note: A more detailed
review of CHAIN-2D identified mi nor technical issues that were either corrected or otherwise
characterized in that document. None of these issues were de emed significant enough to alter the conclusions of this analysis. The Agency also, as part of this analysis, simulated field study emission profiles which serve as the basis for the sensitivity analysis presented below. ]



### 2.5.4 Sensitivity Analysis To Quantify How Changes In Conditions Impact
Emissions

In addition to the review and validation work de scribed above, a sensitivity analysis has been conducted to evaluate how changes in varying parame ters. The results of this analysis should be
considered in context with the review of the tren ds identified in the mon itoring data presented in Sections 2.1 through 2.4 above. The CMTF comple ted a sensitivity analysis based on a variety of chloropicrin data. The results of this anal ysis are presented in van Wesenbeek [84]. In general the Agency concurs with the results of this analysis gi ven the uncertainties associated
with CHAIN-2D.

The Agency conducted a sensitivity analysis sim ilar to the CMTF’s for chloropicrin, on metam sodium, and methyl bromide. The major differe nce between the CMTF’s Chain-2D sensitivity analysis and the Agency’s analysis is that the CMTF mainly focused on cumulative emissions over hundreds of hours while the Agency focused more on the first 48 hours of emissions. The Agency’s sensitivity analysis focused on the following factors, including:

 Injection depth (from study ± 10% and 25%),

 Soil moisture (from study ± 10% and 25%),

 Organic matter via K d value (from study ± 10% and 25%),

 Soil type (from study ± 10% and 25%),

 Bulk density (from study ± 10% and 25%), and

 Tarp type via mass transfer coefficient (from study ± 10% and 25%).

 [Note: Evaluating changes in soil temp erature is not applicable because soil temperatures are incorporated on an hourly basis from the empirical data and also the boundary layer mass transfer coeffi cient is temperature dependent.]

In order to perform the sensitiv ity analysis, the Agency needed to calibrate Chain-2D using actual chloropicrin, metam sodium , and methyl bromide field vol atility studies as well as a number of chemical and environmental fate pr operties. This calibra tion process includes extracting soil and meteorological parameters from the field volatility studies. In some cases, pieces of these data are not provided in the field vo latility studies. In these cases representative soil and meteorological data was selected from the California Irrigation Management Information System (CIMIS) and/or the Florid a Automated Weather Network (FAWN). Table 25 shows the specific field vola tility studies that were used to calibrate Chain-2D for chloropicrin, metam sodium, and methyl br omide and their sources. Chemical and environmental fate properties for chloropi crin, metam sodium, and methyl bromide are
summarized in Table 26.115 Table 25: Field volatility studies used to calibrate Chain-2D for chloropicrin, metam sodium, and methyl bromide.

Application Scenario 1 Study ID EPA Bibliography
Source Soil Data
Available Meteorological
Data Available Chloropicrin Studies Phoenix, AZ Shank Injection - Broadcast/Tarped 44149201 Beard, et. Al, 1996 Yes Yes

Phoenix, AZ Shank Injection - Broadcast/Untarped 44149201 Beard, et. Al, 1996 Yes Yes

Phoenix, AZ Shank Injection - Bedded/Tarped 44149201 Beard, et. Al, 1996 Yes Yes

Dover, FL Shank Injection - Bedded/Metalized Tarped 47295202 Baker and Arndt, 2007 Yes Yes

Bainbridge, GA Shank Injection - Bedded/VIF Tarped 47295203 Baker and Arndt, 2007 Yes Yes

Hart, MI Shank Injection - Bedded/VIF Tarped 47295204 Baker and Arndt, 2007 Yes Yes

Phoenix, AZ Shank Injection - Bedded/Untarped 44149201 Beard, et. Al, 1996 Yes Yes

Bradenton, FL Shank Injection - Broadcast/Tarped 44149201 Beard, et. Al, 1996 Yes Yes

Yakima, WA Shank Injection - Broadcast/Tarped 44149201 Beard, et. Al, 1996 Yes Yes

Douglas, GA Drip Irrigation - Poly Tarped 45112902 Wesenbeeck and Philips, 2000 Yes Yes

Salinas, CA Drip Irrigation - Poly Tarped 46420201 Rotondaro, 2004 Yes Yes

Salinas, CA Drip Irrigation – VIF Tarped 45112901 Knuteson and Dolder,
2000 Yes Yes

Metam Sodium Studies Bakersfield, CA Sprinkler Irrigation -
Standard Water Seal 45703701 Merricks, 2002 CIMIS CIMIS

Bakersfield, CA Sprinkler Irrigation -
Intermittent Water Seal USDA CSREES Project # 74 Sullivan and Holdsworth, 2003 CIMIS Yes

San Joaquin, CA Sprinkler Irrigation -
Intermittent Water Seal 45703702 Merricks, 2002 Yes CIMIS

Mettler, CA Sprinkler Irrigation - Double Water Seal 46969601 Sullivan, 2005 Yes Yes

Bakersfield, CA Shank Injection - Standard Water Seal 45703701 Merricks, 2002 CIMIS CIMIS

Bakersfield, CA Shank Injection -
Intermittent Water Seal USDA CSREES Project # 74 Sullivan and Holdsworth, 2003 CIMIS Yes

Lost Hills, CA Shank Injection -
Intermittent Water Seal 45703704 Merricks, 2001 Yes CIMIS

Mount Vernon, WA Shank Injection - Roozen Rig No ID Sullivan and Holdsworth, 2004 Yes Yes

Helms, CA Shank Injection – Compacted Soil 47131601 Sullivan, 2006 Yes Yes

Citra, FL Shank Injection – Tarped USDA CSREES Project # 74 Sullivan and Holdsworth, 2004 FAWN Yes

Citra, FL Drip Irrigation – Tarped USDA CSREES Project # 74 Sullivan and Holdsworth, 2004 FAWN Yes

Irvine, CA Drip Irrigation – Tarped 45703708 Sullivan and Holdsworth, 2001 CIMIS CIMIS116 Irvine, CA Drip Irrigation – Untarped 45703708 Sullivan and Holdsworth, 2001 CIMIS CIMIS

Brawley, CA – Flood 47314301 Chart, 2007 Yes CIMIS Methyl Bromide Studies 2 Monterey County, CA Shank Injection - Bedded/Untarped SE1.3 Hicks, 1992b CIMIS CIMIS

Santa Barbara County, CA Shank Injection - Bedded/Tarped BR787.2B Johnson, 1999 CIMIS CIMIS

Madera County, CA Deep Shank Injection - Broadcast/Untarped EH164-7 Wofford & Segawa,
1998a CIMIS CIMIS

Kern County, CA Shank Injection - Broadcast/Tarped TC199 Siemer, 1992c CIMIS CIMIS

Imperial County, CA Drip Hot Gas EH150-4 Sanders, 1997 CIMIS CIMIS 1 Not all application scenarios ar e presented below for each factor. 2 Representative methyl bromide studies were selected from the studies used to setup the CDPR’s methyl bromide permit conditions as described in the risk assessment.

Table 26: Chloropicrin, metam sodium, a nd methyl bromide physical, chemical, and environmental properties.
Factor Chloropicrin
Values Metam Sodium (MITC) Values Methyl Bromide
Values Solubility (mg/L) 0.2 0.842 17500 Henry’s Law Constant 0.131 1.05 x 10 -2 0.255 Vapor Pressure (mm Hg) 19.8 16 1640 Boiling Point (oC) 112 119 3 Octanol/water partition coefficient (Log K OW ) 2.5 0.94 1.92

Adsorption Co-efficient, Koc (cm 3 /g) 36 30.27 20.25

Diffusion Co-efficient in Gas
Phase (cm2 day-1) 7.31 x 10 3 9.34 x 10 3 9.75 x10 3

Diffusion Co-efficient in
Water (cm2 day-1) 0.847 1.07 1.20

The three figures below (Figures 103 – 105) show example Chain-2D calibrations for chloropicrin, metam sodium, and methyl bromid e. The calibration figures compare the flux estimated from the actual field study as well as the flux calculated by Chain-2D for the first 48 hours after application. [Note: In Figures 106 and above the te rm “initial” reflects the study conditions.] Although there are some discrepanc ies between the flux from the actual field studies and the flux calculated by Chain-2D (see example in Figur e 99) the Agency believes that the calibration results generally s upport the use of Chain-2D for predicting cumulative fumigant emissions. Calibrations were completed using in formation from the specific field studies (e.g., temperature, soil type, etc.) and populating the CHAIN2D interface with the specific
information. Some factors such as mass transfer coefficients and other in puts which are hard to quantify were obtained from repres entative outside sources. All specific inputs are included in the files and can be provided. The objective of calibration was to attempt to reproduce study emission profiles which could then be evaluate d in the subsequent sensitivity analysis.117 Phoenix, AZ Shank Broadcast Tarped

0 50 100 150 200 250

0 6 12 18 24 30 36 42 48

HourFlux (ug/m2-s)

EPA Estimated Flux Chain2D

Figure 103: Chloropicrin Phoenix, AZ Shank Injection - Broadcast/Tarped Calibration Example

Mettler, CA Sprinkler Irrigation Double Water Seal

0 10 20 30 40 50 60 70 80 90

0 4 8 12 16 20 24 28 32 36 40 44 48

PeriodFlux (ug/m2-s)

Field Study Chain 2D

Figure 104: Metam Sodium Mettler, CA Sprinkler Irrigation - Double Water Seal Example118 Monterey County, CA Methyl Bromide Shallow Shank Untarped

0 50 100 150 200 250 300 350

0 4 8 12 16 20 24 28 32 36 40 44 48

HourFlux (ug/m2-s)

EPA Estimated Flux Chain2D

Figure 105: Methyl Bromide Monterey County, CA Shallow Shank Injection – Bedded/Untarped Example

The following sections discuss the results of th e Agency’s Chain-2D sensitivity analysis. The analysis focuses on the factors considered in the analysis, including: injection depth, soil moisture, organic matter via K d value, soil type, bulk density, and tarp type via mass transfer coefficient. The results of the sensitivity anal ysis should be considered in context with the
review of the trends id entified in the scie ntific literature presented in Sections 2.1 through 2.4
above.

For each factor below, a series of graphs are pres ented that illustrate the impacts the factor has on chloropicrin, metam-sodium, and methyl bromide emission profiles. A sensitivity analysis has been completed for all emission profiles for all ch emicals. If desired, a CD of each sensitivity analysis for chloropicrin, metam sodium, and methyl bromide can be provided by the Agency. A graphical presentation of the results has been us ed because it readily illustrates how changes in key factors impact overall emission results.



### 2.5.4.1 Injection depth

Figures 106-120 suggest that genera lly across all three fumigants, shank injection depth can have a significant impact on emissions. Increasi ng injection depth by 10 or 25 percent reduced emissions up to 25 or 30 percent. However, decreasing injection dept h by 10 or 25 percent increased emissions, sometimes by 100 percent or greater.119 Sensitivity Analysis - Injection Depth

Phoenix Shank Broadcast Tarped

0 20 40 60 80 100 120 140 160 180

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (26.5 cm) 19.9 cm 23.9 cm 29.1 cm 33.1 cm

Figure 106: Chloropicrin Sensitivity To Change s In Injection Depth (Shank Broadcast Tarped)

Sensitivity Analysis - Injection Depth Phoenix Shank Broadcast Untarped

0 10 20 30 40 50 60 70 80 90 100

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (31.8 cm) 23.8 cm 28.6 cm 34.9 cm 39.7 cm

Figure 107: Chloropicrin Sensitivity To Changes In Injection Depth (Shank Broadcast Untarped)

Sensitivity Analysis - Injection Depth Phoenix Shank Bedded Tarped

0 50 100 150 200 250

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (25.4 cm) 19.1 cm 22.9 cm

Figure 108: Chloropicrin Sensitivity To Chan ges In Injection Depth (Shank Bedded Tarped)120 Sensitivity Analysis - Injection Depth

Phoenix Shank Bedded Untarped

0 50 100 150 200 250 300

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (31.8 cm) 23.8 cm 28.6 cm 34.9 cm 39.7 cm

Figure 109: Chloropicrin Sensitivity To Change s In Injection Depth (Shank Bedded Untarped)

Sensitivity Analysis - Injection Depth Dover Shank Metalized Tarped

0 50 100 150 200 250 300

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (20.3 cm) 15.2 cm 18.3 cm 22.4 cm 25.4 cm

Figure 110: Chloropicrin Sensitivity To Changes In Injection Depth (Shank Bedded Metalized Tarped)

Sensitivity Analysis - Injection Depth Hart Shank Bedded VIF Tarped

0 2 4 6 8 10 12 14 16 18 20

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (20.3 cm) 15.2 cm 18.3 cm 22.4 cm 25.4 cm

Figure 111: Chloropicrin Sensitivity To Change s In Injection Depth (Shank Bedded VIF Tarped)121 Sensitivity Analysis - Injection Depth Bainbridge Shank Bedded VIF Tarped

0 2 4 6 8 10 12 14 16

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (20.3 cm) 15.2 cm 18.3 cm 22.4 cm 25.4 cm

Figure 112: Chloropicrin Sensitivity To Change s In Injection Depth (Shank Bedded VIF Tarped)

Sensitivity Analysis - Injection Depth
Bakersfield Shank Standard Water Seal

0 20 40 60 80 100 120 140 160

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (15.2 cm) 11.4 cm 13.7 cm 16.8 cm 19.1 cm

Figure 113: Metam Sodium Sensitivity To Change s In Injection Depth (Shank Standard Water Seal)

Sensitivity Analysis - Injection Depth
Bakersfield Shank Intermittent Water Seal

0 20 40 60 80 100 120 140 160 180

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (15.2 cm) 11.4 cm 13.7 cm 16.8 cm 19.1 cm

Figure 114: Metam Sodium Sensitivity To Changes In Injection Depth (Shank Intermittent Water Seal)122 Sensitivity Analysis - Injection Depth
Lost Hills Shank Intermittent Water Seal

0 20 40 60 80 100 120 140

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (16.5 cm) 12.4 cm 14.9 cm 18.2 cm 20.6 cm

Figure 115: Metam Sodium Sensitivity To Changes In Injection Depth (Shank Intermittent Water Seal)

Sensitivity Analysis - Injection Depth Citra Shank Bedded Tarped

0 50 100 150 200 250

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (8.4 cm) 6.3 cm 7.5 cm 9.2 cm 10.5 cm

Figure 116: Metam Sodium Sensitivity To Ch anges In Injection Depth (Shank Bedded Tarped)

Sensitivity Analysis - Injection Depth Shallow Shank Bedded Untarped

0 100 200 300 400 500 600

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (30.5 cm) 22.9 cm 27.4 cm 33.5 cm 38.1 cm

Figure 117: Methyl Bromide Sensitivity To Injection Depth (Shallow Shank Application)123 Sensitivity Analysis - Injection Depth Deep Shank Broadcast Untarped

0 50 100 150 200 250 300 350

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (%)

Initial (48.3 cm) 36.2 cm 43.4 cm 53.1 cm 60.3 cm

Figure 118: Methyl Bromide Sensitivity To Injection Depth (Deep Shank Broadcast Untarped)

Sensitivity Analysis - Injection Depth Shank Bedded Tarped

0 200 400 600 800 1000 1200

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (%)

Initial (15.24cm) 11.4 cm 13.7 cm 16.8 cm 19.1 cm

Figure 119: Methyl Bromide Sensitivity To Injection Depth (Shank Bedded Tarped)

Sensitivity Analysis - Injection Depth Shank Broadcast Tarped

0 50 100 150 200 250 300

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (%)

Initial (25.4cm) 19.1 cm 22.9 cm

Figure 120: Methyl Bromide Sensitivity To Injection Depth (Shank Broadcast Tarped)124 

### 2.5.4.2 Soil Moisture

Figures 121–140 suggest that generally across all three fumigants, soil moisture can have a significant impact on emissions. In the following analyses, the percent soil moisture represents the percent of the
available water content which is a measure of the ra nge of moisture levels between the wilting point of the soil and its field capacity. As values increase, moisture levels get closer to the field capacity. Increasing soil moisture levels by 10 or 25 percen t reduced emissions by 10 percent and sometimes up to 100 percent depending on the specific study examin ed. Decreasing soil moisture by 10 or 25 percent seemed to generally increase emissions but not significantly.

Sensitivity Analysis - Soil Moisture Phoenix Shank Broadcast Tarped

0 20 40 60 80 100 120 140

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (24.4%) 5% 14.40% 34.40% 49.40%

Figure 121: Chloropicrin Sensitivity To Soil Moisture (Shank Broadcast Tarped)

Sensitivity Analysis - Soil Moisture Phoenix Shank Broadcast Untarped

0 10 20 30 40 50 60 70 80

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (12.2%) 2.2% 5% 22.2% 37.2%

Figure 122: Chloropicrin Sensitivity To Soil Moisture (Shank Broadcast Untarped)125 Sensitivity Analysis - Soil Moisture

Phoenix Shank Bedded Tarped

0 20 40 60 80 100 120 140 160 180

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (13.3%) 3.3% 5% 23.3% 38.3%

Figure 123: Chloropicrin Sensitivity To Soil Moisture (Shank Bedded Tarped)

Sensitivity Analysis - Soil Moisture Phoenix Shank Bedded Untarped

0 20 40 60 80 100 120 140 160

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (7.6%) 5% 17.6% 32.6%

Figure 124: Chloropicrin Sensitivity To Soil Moisture (Shank Bedded Untarped)

Sensitivity Analysis - Soil Moisture

Dover Shank Metalized Tarped

0 20 40 60 80 100 120 140 160 180 200

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (11.2%) 1.2% 5% 21.2% 36.2%

Figure 125: Chloropicrin Sensitivity To Changes In Injection Depth (Shank Bedded Metalized Tarped)126 Sensitivity Analysis - Soil Moisture

Hart Shank Bedded VIF Tarped

0 2 4 6 8 10 12 14

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (11.6%) 1.6% 5% 21.6% 36.6%

Figure 126: Chloropicrin Sensitivity To Change s In Injection Depth (Shank Bedded VIF Tarped)

Sensitivity Analysis - Soil Moisture Bainbridge Shank Bedded VIF Tarped

0 2 4 6 8 10 12

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (25.4%) 0.4% 15.4% 35.40% 50.40%

Figure 127: Chloropicrin Sensitivity To Change s In Injection Depth (Shank Bedded VIF Tarped)

Sensitivity Analysis - Soil Moisture
Bakersfield Shank Standard Water Seal

0 10 20 30 40 50 60 70 80 90

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (9.4%) 5% 19.4% 34.4%

Figure 128: Metam-Sodium Sensitivity To Soil Moisture (Shank Standard Water Seal)127 Sensitivity Analysis - Soil Moisture
Lost Hills Shank Intermittent Water Seal

0 10 20 30 40 50 60 70

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (15.9%) 5% 5.9% 25.9% 40.90%

Figure 129: Metam-Sodium Sensitivity To Soil Moisture (Shank Intermittent Water Seal)

Sensitivity Analysis - Soil Moisture Bakersfield Sprinkler Irrigation Standard Water Seal

0 20 40 60 80 100 120 140 160 180

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (3%) 5% 13% 28%

Figure 130: Metam-Sodium Sensitivity To Soil Mo isture (Standard Water Seal Sprinkler Irrigation)

Sensitivity Analysis - Soil Moisture San Joaquin Sprinkler Irrigation Intermittent Water Seal

0 10 20 30 40 50 60 70 80 90

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (6%) 5% 16% 31%

Figure 131: Metam-Sodium Sensit ivity To Soil Moisture (Intermittent Water Seal Spri nkler Irrigation)128 Sensitivity Analysis - Soil Moisture Mettler Sprinkler Irrigation Double Water Seal

0 20 40 60 80 100 120 140 160

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (%)

Initial (5.4%) 5% 11.80% 26.80%

Figure 132: Metam-Sodium Sensitivity To Soil Moisture (Double Water Seal Sprinkler Irrigation)

Sensitivity Analysis - Soil Moisture Citra Shank Bedded Tarped

0 20 40 60 80 100 120 140 160

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (1.8%) 5% 11.80% 26.80%

Figure 133: Metam-Sodium Sensitivit y To Soil Moisture (Shank Bedded Tarped)

Sensitivity Analysis - Soil Moisture Irvine Drip Irrigation Tarped

0 5 10 15 20 25 30 35

0 4 8 12 16 20 24

Time (hr)Flux (ug/m2-s)

Initial (0.5%) 5% 10.5% 25.5%

Figure 134: Metam-Sodium Sensitivity To Soil Moisture (Drip Irrigation Tarped)129 Sensitivity Analysis - Soil Moisture Irvine Drip Irrigation Untarped

0 20 40 60 80 100 120 140 160 180 200

0 4 8 12 16 20 24

Time (hr)Flux (ug/m2-s)

Initial (24%) 5% 14% 34% 49%

Figure 135: Metam-Sodium Sensitivity To Soil Moisture (Drip I rrigation Untarped)

Sensitivity Analysis - Soil Moisture Shallow Shank Bedded Untarped

0 50 100 150 200 250 300 350

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (5%) 15% 30%

Figure 136: Methyl Bromide Sensitivity To Soil Moisture (Shallow Shank Untarped)

Sensitivity Analysis - Soil Moisture Shank Bedded Tarped

0 100 200 300 400 500 600 700 800 900 1000

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (%)

Initial (6.5%) 5% 16.5% 31.5%

Figure 137: Methyl Bromide Sensitiv ity To Soil Moisture (Bedded Tarped)130 Sensitivity Analysis - Soil Moisture

Deep Shank Broadcast Untarped

0 20 40 60 80 100 120 140 160 180 200

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (%)

Initial (7.5%) 5% 17.5% 32.50%

Figure 138: Methyl Bromide Sensitivit y To Soil Moisture (Deep Shank Untarped)

Sensitivity Analysis - Soil Moisture Shank Broadcast Tarped

0 20 40 60 80 100 120 140 160 180 200

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (%)

Initial (5%) 15% 30%

Figure 139: Methyl Bromide Sensitivity To Soil Moisture (Shallow Broadcast Untarped)

Sensitivity Analysis - Soil Moisture Drip Hot Gas

0 100 200 300 400 500 600 700 800

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (%)

Initial (5%) 15% 30%

Figure 140: Methyl Bromide Sensitiv ity To Soil Moisture (Drip Hot Gas)131 

### 2.5.4.3 Organic Matter via K d 

### Value

Figures 141 – 160 suggest that across all three fumi gants, organic matter can have a small impact on emissions. Increasing the K d value by 10 or 25 percent generally re duced emissions by 10 or 20 percent. Decreasing the K d value by 10 or 25 percent increased emissi ons by 10 or 20 percent. There may be some uncertainty because K d has been used for modeli ng purpose rath er than K
oc which is K d normalized by organic content but based on CMTF analysis the results appear to be similar, at least for chloropicrin. Although, organic matter appears to ha ve a small impact on emissions, it may not be a very practical parameter to alter on farm except in limited circumstances.

Sensitivity Analysis - K d Phoenix Shank Broadcast Tarped

0 20 40 60 80 100 120 140

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (0.08) 0.06 0.07 0.09 0.1

Figure 141: Chloropicrin Sensitivity To Organic Matter Levels (Broadcast Tarped)

Sensitivity Analysis - K d Phoenix Shank Broadcast Untarped

0 10 20 30 40 50 60 70

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (0.12) 0.09 0.1 0.13 0.41

Figure 142: Chloropicrin Sensitivity To Or ganic Matter Levels (Broadcast Untarped)132 Sensitivity Analysis - K d Phoenix Shank Bedded Tarped

0 20 40 60 80 100 120 140 160 180

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (0.07) 0.05 0.06 0.08 0.09

Figure 143: Chloropicrin Sensitivity To Organic Matter Levels (Bedded Tarped)

Sensitivity Analysis - K d Phoenix Shank Bedded Untarped

0 20 40 60 80 100 120 140 160 180

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (0.05) 0.04 0.05 0.06 0.07

Figure 144: Chloropicrin Sensitivity To Organic Matter Levels (Bedded Untarped)

Sensitivity Analysis - K d Dover Shank Metalized Tarped

0 50 100 150 200 250

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (0.26) 0.20 0.24 0.29 0.33

Figure 145: Chloropicrin Sensitivity To Orga nic Matter Levels (Bedded Metalized Tarped)133 Sensitivity Analysis - K d Hart Shank Bedded VIF Tarped

0 2 4 6 8 10 12 14 16 18

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (0.14) 0.11 0.13 0.16 0.18

Figure 146: Chloropicrin Sensitivity To Organic Matter Levels (Bedded VIF Tarped)

Sensitivity Analysis - K d Bainbridge Shank Bedded VIF Tarped

0 2 4 6 8 10 12

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (0.12) 0.09 0.11 0.13 0.15

Figure 147: Chloropicrin Sensitivity To Organic Matter Levels (Bedded VIF Tarped)

Sensitivity Analysis - K d Bakersfield Shank Standard Water Seal

0 10 20 30 40 50 60 70 80 90 100

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (0.15) 0.11 0.13 0.16 0.18

Figure 148: Metam-Sodium Sensitivity To Orga nic Matter Levels (Shank Standard Water Seal)134 Sensitivity Analysis - K d Lost Hills Shank Intermittent Water Seal

0 10 20 30 40 50 60 70 80

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (0.15) 0.11 0.13 0.16 0.18

Figure 149: Metam-Sodium Sensitivity To Organi c Matter Levels (Shank Intermittent Water Seal)

Sensitivity Analysis - K d Bakersfield Sprinkler Irrigation Standard Water Seal

0 20 40 60 80 100 120 140 160 180 200

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (0.15) 0.11 0.13 0.16 0.18

Figure 150: Metam-Sodium Sensitivity To Organic Ma tter Levels (Standard Water Seal Sprinkler Irrigation)

Sensitivity Analysis - K d San Joaquin Sprinkler Irrigation Intermittent Water Seal

0 20 40 60 80 100 120

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (0.22) 0.17 0.20 0.24 0.28

Figure 151: Metam-Sodium Sensitivit y To Organic Matter Levels (Intermitt ent Water Seal Sprinkler Irrigation)135 Sensitivity Analysis - K d Mettler Sprinkler Irrigation Double Water Seal

0 20 40 60 80 100 120 140 160 180 200

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (%)

Initial (0.11) 0.08 0.1 0.12 0.13

Figure 152: Metam-Sodium Sensitivity To Organic Ma tter Levels (Double Water Seal Sprinkler Irrigation)

Sensitivity Analysis - K d Citra Shank Bedded Tarped

0 20 40 60 80 100 120 140 160 180 200

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (0.15) 0.11 0.13 0.16 0.18

Figure 153: Metam-Sodium Sensitivity To Organic Matter Levels (Shank Bedded Tarped)

Sensitivity Analysis - K d Irvine Drip Irrigation Tarped

0 10 20 30 40 50 60

0 4 8 12 16 20 24

Time (hr)Flux (ug/m2-s)

Initial (0.15) 0.11 0.13 0.16 0.18

Figure 154: Metam-Sodium Sensitivity To Or ganic Matter Levels (Drip Irrigation Tarped)136 Sensitivity Analysis - K d Irvine Drip Irrigation Untarped

0 5 10 15 20 25 30

0 4 8 12 16 20 24

Time (hr)Flux (ug/m2-s)

Initial (0.25) 0.19 0.22 0.27 0.31

Figure 155: Metam-Sodium Sensitivity To Orga nic Matter Levels (Drip Irrigation Untarped)

Sensitivity Analysis - K d Shallow Shank Bedded Untarped

0 50 100 150 200 250 300 350 400

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (0.47) 0.35 0.42 0.51 0.58

Figure 156: Methyl Bromide Sensitivity To Organic Matter Levels ( Shallow Shank Untarped)

Sensitivity Analysis - K d

Shank Bedded Tarped

0 100 200 300 400 500 600 700 800 900 1000

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (%)

Initial (0.034) 0.026 0.031 0.038 0.043

Figure 157: Methyl Bromide Sensitivity To Organic Matter Levels (Bedded Tarped)137 Sensitivity Analysis - K d Deep Shank Broadcast Untarped

0 50 100 150 200 250

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (0.15) 0.11 0.13 0.16 0.18

Figure 158: Methyl Bromide Sensitivity to Or ganic Matter Levels (Deep Broadcast Untarped)

Sensitivity Analysis - K d Shank Broadcast Tarped

0 50 100 150 200 250

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (0.165) 0.124 0.148 0.181 0.206

Figure 159: Methyl Bromide Sensitivity to Organic Matter Levels (Broadcast Tarped)

Sensitivity Analysis - K d Drip Hot Gas

0 100 200 300 400 500 600 700 800 900

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (%)

Initial (0.098) 0.073 0.088 0.108 0.122

Figure 160: Methyl Bromide Sensitivity to Organic Matter Levels (Drip Hot Gas)138 

### 2.5.4.4 Soil Type

Figures 161 – 180 suggests that across all three fumigants, soil type can have a significant impact on emissions. Generally, clay loam and sandy clay loam soils tended to show significantly lower emissions than other soil types; sometimes showing as much as a 50 percent reduction. Conversely, loamy sand and loam soils tended to show higher emissions than other soil types. As indicated above, most studies were completed in sand, loamy sand or sandy loam w ith a few in sandy clay loam. The range of soil types were selected to essentially reflect this range for the analysis.

Sensitivity Analysis - Soil Type Phoenix Shank Broadcast Tarped

0 20 40 60 80 100 120 140

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (Loam) Loamy sand Clay loam

Figure 161: Chloropicrin Sensitivit y To Soil Type (Broadcast Tarped)

Sensitivity Analysis - Soil Type Bainbridge Shank Bedded VIF Tarped

0 2 4 6 8 10 12

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (Sandy clay loam) Loamy sand Loam Clay loam

Figure 162: Chloropicrin Sensitivity To Soil Type (Broadcast Untarped)139 Sensitivity Analysis - Soil Type Phoenix Shank Bedded Tarped

0 20 40 60 80 100 120 140 160

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (Sandy Loam) Loamy sand Loam Clay loam

Figure 163: Chloropicrin Sensitiv ity To Soil Type (Bedded Tarped)

Sensitivity Analysis - Soil Type Phoenix Shank Bedded Untarped

0 20 40 60 80 100 120 140 160

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (Sandy Loam) Loamy sand Loam Clay loam

Figure 164: Chloropicrin Sensitivit y To Soil Type (Bedded Untarped)

Sensitivity Analysis - Soil Type Dover Shank Metalized Tarped

0 20 40 60 80 100 120 140 160 180 200

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (Sand) Loamy sand Loam Clay loam

Figure 165: Chloropicrin Sensitivity To Soil Type (Bedded Metalized Tarped)140 Sensitivity Analysis - Soil Type Hart Shank Bedded VIF Tarped

0 2 4 6 8 10 12 14

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (Sand) Loamy sand Loam Clay loam

Figure 166: Chloropicrin Sensitivity To Soil Type (Bedded VIF Tarped)

Sensitivity Analysis - Soil Type Bainbridge Shank Bedded VIF Tarped

0 2 4 6 8 10 12

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (Sandy clay loam) Loamy sand Loam Clay loam

Figure 167: Chloropicrin Sensitivity To Soil Type (Bedded VIF Tarped)

Sensitivity Analysis - Soil Type
Bakersfield Shank Standard Water Seal

0 10 20 30 40 50 60 70 80

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (Loamy sand) Loam Clay loam

Figure 168: Metam-Sodium Sensitivity To Soil Type (Shank Standard Water Seal)141 Sensitivity Analysis - Soil Type
Lost Hills Shank Intermittent Water Seal

0 5 10 15 20 25 30 35 40 45 50

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (Sandy loam) Loamy sand Loam Clay loam

Figure 169: Metam-Sodium Sensitivity To Soil Type (Shank Intermittent Water Seal)

Sensitivity Analysis - Soil Type Bakersfield Sprinkler Irrigation Standard Water Seal

0 20 40 60 80 100 120 140 160 180 200

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (Sandy loam) Loamy sand Loam Clay loam

Figure 170: Metam-Sodium Sensitivity To Soil Type (Standard Water Seal Sprinkler Irrigation)

Sensitivity Analysis - Soil Type San Joaquin Sprinkler Irrigation Intermittent Water Seal

0 10 20 30 40 50 60 70 80 90

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (Silty loam) Loamy sand Loam Clay loam

Figure 171: Metam-Sodium Sensit ivity To Soil Type (Intermittent Water Seal Sprinkler Irrigation)142 Sensitivity Analysis - Soil Type Mettler Sprinkler Irrigation Double Water Seal

0 20 40 60 80 100 120 140

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (%)

Initial (Loamy sand) Loam Note: Unable to model clay loam soil; run took longer than 2 days.

Figure 172: Metam-Sodium Sensitivity To Soil Type (Double Water Seal Sprinkler Irrigation)

Sensitivity Analysis - Soil Type

Citra Shank Bedded Tarped

0 20 40 60 80 100 120 140 160

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (Sand) Loamy sand Loam Clay loam

Figure 173: Metam-Sodium Sensitivit y To Soil Type (Shank Bedded Tarped)

Sensitivity Analysis - Soil Type

Irvine Drip Irrigation Tarped

0 5 10 15 20 25 30 35

0 4 8 12 16 20 24

Time (hr)Flux (ug/m2-s)

Initial (Sandy loam) Loamy sand Loam Clay loam

Figure 174: Metam-Sodium Sensitivity To Soil Type (Drip Irrigation Tarped)143 Sensitivity Analysis - Soil Type Irvine Drip Irrigation Untarped

0 10 20 30 40 50 60 70

0 4 8 12 16 20 24

Time (hr)Flux (ug/m2-s)

Initial (Loam) Loamy sand Clay loam

Figure 175: Metam-Sodium Sensitivity To Soil Type (Drip Irrigation Untarped)

Sensitivity Analysis - Soil Type Shallow Shank Bedded Untarped

0 50 100 150 200 250 300 350 400 450 500

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (Clay loam) Loamy sand Loam

Figure 176: Methyl Bromide Sensitivit y To Soil Type (Shallow Shank Untarped)

Sensitivity Analysis - Soil Type Shank Bedded Tarped

0 100 200 300 400 500 600 700 800 900 1000

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (%)

Initial (Loamy Sand) Loam Sandy Clay Loam

Figure 177: Methyl Bromide Sensitivity To Soil Type (Bedded Tarped)144 Sensitivity Analysis - Soil Type Deep Shank Broadcast Untarped

0 20 40 60 80 100 120 140 160 180 200

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (Loam) Loamy sand Clay loam

Figure 178: Methyl Bromide Sensitivit y To Soil Type (Deep Shank Untarped)

Sensitivity Analysis - Soil Type Shank Broadcast Tarped

0 50 100 150 200 250

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (Sandy clay loam) Loamy sand Loam Clay loam

Figure 179: Methyl Bromide Sensitivity To Soil Type (Tarped Broadcast)

Sensitivity Analysis - Soil Type Drip Hot Gas

0 100 200 300 400 500 600 700 800

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (Loamy sand) Loam Clay loam

Figure 180: Methyl Bromide Sensitivity To Soil Type (Hot Gas)145 

### 2.5.4.5 Bulk Density

Figures 181 – 193 suggests that across all three fumigants, change in bulk density from soil disturbance has a limited impact on emissions except when soils are heavily compacted. If the amount of soil disturbance (e.g., from chisel or sh ank being drug through soil) is in creased the associated emissions also increase, but in a limited way. However, d ecreasing the amount of soil disturbance through heavy compaction generally seemed to significantly reduce emissi ons. It is unclear at th is point if soils can be compacted to this extent it is clear that modern be d shapers and compaction devi ces are routinely used in fumigation events. Based on this analysis, however, it is difficult to assess their performance without empirical measures of soil compaction.

Sensitivity Analysis - Bulk Density Phoenix Shank Broadcast Tarped

0 20 40 60 80 100 120

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (10%) -10% 25%

Figure 181: Chloropicrin Sensitivity To Soil Distur bances Altering Bulk Density (Broadcast Tarped)

Sensitivity Analysis - Bulk Density Phoenix Shank Broadcast Untarped

0 10 20 30 40 50 60 70 80 90

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (10%) -25% -10% 25% 10%

Figure 182: Chloropicrin Sensitivity To Soil Disturba nces Altering Bulk Density (Broadcast Untarped)146 Sensitivity Analysis - Bulk Density

Phoenix Shank Bedded Tarped

0 20 40 60 80 100 120 140 160 180

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (2.5%) -25% -10% 10% 25%

Figure 183: Chloropicrin Sensitivity To Soil Dist urbances Altering Bulk Density (Bedded Tarped)

Sensitivity Analysis - Bulk Density Phoenix Shank Bedded Untarped

0 20 40 60 80 100 120 140 160

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (5%) -25% -10% 25% 10%

Figure 184: Chloropicrin Sensitivity To Soil Distur bances Altering Bulk Density (Bedded Untarped)

Sensitivity Analysis - Bulk Density

Dover Shank Metalized Tarped

0 20 40 60 80 100 120 140 160 180 200

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (5%) -25% -10% 10%

Figure 185: Chloropicrin Sensitivity To Soil Disturbanc es Altering Bulk Density (Bedded Metalized Tarped)147 Sensitivity Analysis - Bulk Density

Hart Shank Bedded VIF Tarped

0 2 4 6 8 10 12 14

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (7.5%) -25% -10% 10% 25%

Figure 186: Chloropicrin Sensitivity To Soil Distur bances Altering Bulk Density (Bedded VIF Tarped)

Sensitivity Analysis - Bulk Density Bainbridge Shank Bedded VIF Tarped

0 1 2 3 4 5 6 7 8 9 10

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (7.5%) -25% -10% 10% 25%

Figure 187: Chloropicrin Sensitivity To Soil Distur bances Altering Bulk Density (Bedded VIF Tarped)

Sensitivity Analysis - % Bulk Density
Bakersfield Shank Standard Water Seal

0 10 20 30 40 50 60 70 80 90 100

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (5%) -25% -10% 10%

Figure 188: Metam-Sodium Sensitivity To Soil Disturban ces Altering Bulk Density (Shank Standard Water Seal)148 Sensitivity Analysis - % Bulk Density
Lost Hills Shank Intermittent Water Seal

0 5 10 15 20 25 30 35 40 45 50

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (5%) -10% 10% 25%

Figure 189: Metam-Sodium Sensitivity To Soil Disturban ces Altering Bulk Density (Shank Intermittent Water Seal)

Sensitivity Analysis - Bulk Density Shallow Shank Bedded Untarped

0 50 100 150 200 250 300 350

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (25%) -10% 10%

Figure 190: Methyl Bromide Sensitivity To Soil Dist urbances Altering Bulk Density (Shallow Untarped)

Sensitivity Analysis - % Bulk Density Shank Bedded Tarped

0 100 200 300 400 500 600 700 800 900 1000

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (%)

Initial (0%) -10% 10% 25%

Figure 191: Methyl Bromide Sensitivity To Soil Dist urbances Altering Bulk Density (Bedded Tarped)149 Sensitivity Analysis - Bulk Density Deep Shank Broadcast Untarped

0 20 40 60 80 100 120 140 160 180 200

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (5%) -10% 10% 25%

Figure 192: Methyl Bromide Sensitivity To Soil Di sturbances Altering Bulk Density (Deep Untarped)

Sensitivity Analysis - % Bulk Density Shank Broadcast Tarped

0 20 40 60 80 100 120 140 160 180 200

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (0%) -25% -10% 10% 25%

Figure 193: Methyl Bromide Sensitivity To Soil Dist urbance Altering Bulk Density (Broadcast Tarped)



### 2.5.4.6 Mass Transfer Coefficie nt (via tarp type)

Figures 194 – 198 suggest that across all three fumiga nts, change in tarp type has a limited impact on emissions within the constraints of this analysis because in this analysis the MTC (mass transfer coefficient) was only varied by ± 25 percent from the observed study va lues. In the calibration step, MTC values from Ajwa and Wang were used that, as clos ely as possible, reflect the type of tarp used in the study. It is well known that tarp MTCs can be reduced by orders of magnitude and that they can reduce emissions significantly as we ll under field use. However, high ba rrier tarps with very low MTCs also impact concentration contours wh ich form after an application beca use, as illustrated in Section 2.1 above, they extend the effec tive area of the contour and they also increase the concen tration levels in contours. They also enhance moisture retention as noted in Section 2.2 above which can also impact subsurface processes that lead to emissions. Fo r these reasons, radically lower MTCs representing higher barrier films were not included in the current analysis. All of the factors that would need to be altered to attempt to do this type of an alysis are currently being evaluated.150 Sensitivity Analysis - MTC Phoenix Shank Broadcast Tarped

0 20 40 60 80 100 120

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (1.8) 1.4 1.6 2 2.3

Figure 194: Chloropicrin Sensitivity To Tarp Type (Shank Broadcast Tarped)

Sensitivity Analysis - MTC Phoenix Shank Bedded Tarped

0 20 40 60 80 100 120 140 160 180

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (8.9) 6.7 8.0 9.8 11.1

Figure 195: Chloropicrin Sensitivity To Tarp Type (Shank Bedded Tarped)

Sensitivity Analysis - MTC Citra Shank Bedded Tarped

0 20 40 60 80 100 120 140 160 180

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (45.0) 33.8 40.5 49.5 56.3

Figure 196: Metam Sodium Sensitivity To Tarp Type (Shank Bedded Tarped)151 Sensitivity Analysis - MTC Shank Bedded Tarped

0 200 400 600 800 1000 1200

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (%)

Initial (20.4) 15.3 18.3 22.4 25.5

Figure 197: Methyl Bromide Sensitivity To Tarp Type (Bedded Tarped)

Sensitivity Analysis - MTC Shank Broadcast Tarped

0 50 100 150 200 250

0 4 8 12 16 20 24 28 32 36 40 44 48

Time (hr)Flux (ug/m2-s)

Initial (6.6) 4.9 5.9 7.2 8.2

Figure 198: Methyl Bromide Sensitivit y To Tarp Type (Broadcast Tarped)



### 2.5.4.7 Agency Sensitivity Analysis Conclusions

The analysis focused on the factors: inject ion depth, soil moisture, organic matter via K d value, soil type, bulk density, and tarp type via ma ss transfer coefficient. Generall y the trends in the results were consistent with the CMTF analysis . Additionally, the resu lts appear supportive of the factors which were believed to impact emission le vels. It is also clear that soil type and the level of compaction can significantly impact results a nd in a manner anticipated prior to this analysis. For example, more clay in the soil leads to lower emissions likely because it is harder for fumigants to be mobile in clay soils. Additionally, higher levels of compaction reduce emissions probably for similar reasons. A couple of factors (i.e., soil temperature and MT C changes) which intuitively have impacts were hard to evaluate in a systematic manner. Soil temperature changes can be evaluated by considering hour by hour model outputs where soil temperatures change but it should also be realized that since this type of review would be empirical that other field factors would be changing as well. For MTC changes, it is likely that other subsurface phenomena would also be impact ed which were difficult to quantify. Techniques for further assessment of these factors are being considered.152 

### 3.0 Characterization

Fumigant emissions that ultimate ly result in some level of e xposure to individuals who live or work near treated agricultural fields can and do likely occur on a routine basis. In fact, the Agency has predicted this type of phenomena in its risk assessme nts through the use of predictive air models as well as monitoring data and by considering analyses which show residences an d other structures where people spend time are located in areas where exposures can occur. In this current analysis the Agency has attempted to develop a better understanding of the processes which govern fumigant emissions which directly relates to possible exposure levels. Empirical information was evaluated that focused on factors which can be readily manipulated by growers and applicators such as the types of agricultural films used, the application equipment used, and the a ddition of soil amendments su ch as organic matter. Additionally, several f actors were considered that are essentially inherent to specific field conditions which growers cannot easily manipulate such as soil texture or temperature at the injection depth. Along with these analyses of available empirical data , a sensitivity analysis using a first principles modeling approach based on CHAIN2D has been comp leted. Generally, the impacts defined using the modeling approach was consistent with what has been observed in the available empirical data.

The results of this analysis should be considered in context with several factors that illustrate some of the possible uncertainty and va riability in the results as well as other issues (e.g., lack of critical data and the objectives of the research). Some of the key elements which should be used to provide
context for these results are:

• Uncertainty and Variability;

• Data quality objectives;

• Co-critical factors;

• Standardization (e.g., description of tarp construction and materials);

• Representativeness and purposively designed research plan;

• Lack of critical database elements;

• Methodology; and

• Summary of Considerations.

Below, a general overview of how each of these elements relates to the overall interpretation of the results of the analysis is presented. In section 2, a broad array of topics was presented along with specific information related to the interpretation of the data so this section will not focus on very specific
technical issues which are addressed above. Instead, key elements that should be considered with regard to the individual analyses are descri bed in order to provide guidance fo r evaluating the trends and results of the analysis as well as for developing a concep tual framework related to how much accuracy and precision should be attributed to such results.153 

### 3.1 Uncertainty and Variability

Uncertainty and variability are two distinct, yet related factors. Si mply put, uncertainty could be
described as an indication of how accurate a measurem ent is compared to the actual value and variability could be described as how precise a measurement is when taken over multiple replications. In the studies and analyses that have been considered in this assessment, investigators have evaluated these factors in varying degrees. In so me cases, where small numbers of repl ications in a laboratory study are conducted or in a field study where essentially a si ngle emission profile was monitored, there can be very little analysis of e ither factor. In other case s, investigators included a series of experimental replication so that error bounds and other univariate or even non-parame tric statistical approaches have been used to summarize and present the results.

As indicated above, there is too much detailed inform ation in this document to discuss the specifics for every factor. The SAP recommended that as much emissi ons data as possible shou ld be used in the risk assessment process to reduce levels of uncertainty and variability. C onsequently, all emissions studies of appropriate quality were used. However, in ma ny cases only a single study for a specific application method in a growing region was available. This makes interpretation of th e results difficult. For example, evaluation of the performance of a high barrier tarp in the field is more uncertain because it is not clear where a particular result may fall on a repres entative distribution of su ch results. Using this same example, it is also not possi ble to assess any of the variabil ity associated with such systems because only a single measurement is available. There are many other such examples, but Yates [10] summarized this issue succinctly in Figure 8 above. In very few cases were techniques such as 2- dimensional Monte Carlo analyses co mpleted to evaluate these factors.



### 3.2 Data Quality Objectives

In basic and applied research such as presented above, investigators establish performance goals which
could otherwise be referred to as da ta quality objectives. In these t ypes of studies, they might represent developing sufficient numbers of da ta points to adequately describe a temperature-based emissions function that can relate changes in emissions to cha nges in temperature. Dete rmining whether or not the objective was achieved would be pred icated on how any predictive f unction based on the subsequentresearch performed, or other factors such as a measurement of variability, was defined within some preset limits. For field scale research, other examples c ould also refer to situations where a high barrier tarp or the activity of ammonium thiosu lfate (ATS) as a reactiv e boundary layer was evaluated compared to a more typical situation of polyethylen e tarp use or water sealing without ATS. This type of experiment bears replication but it is generally not done because of expense and the fact that replication of field scale research is logistically diffi cult because so many factors can imp act emissions. It is not apparent that such performance goals were established in much of the research which has been reviewed.



### 3.3 Co-critical Factors

It would be preferable to be able to manipulate specific factors within an established working range to see how such perturbations of indi vidual factors (or selective combina tions thereof) in a system would impact results. In many studies, multiple factors were evaluated in varied comb inations with each other (e.g., different papers by Gao and others that evaluate d different combinations of tarps, reactive barriers and organic amendments). Zheng, in another study, (as well as other re searchers) evaluated competitive degradation between various fumigant s because it appears that chloropicrin can impede degradation of MITC due to impacts on microorganisms. These type s of factors would be difficult for fumigant users to manipulate in any meaningful way. Ajwa [19] attempted to constrain change s in possible co-critical154 factors through a concurrent evaluation of field scal e emissions as illustrated in Figure 199 below where
emissions from co-located fields were evaluated w ith 4 different boundary layer treatments (i.e., higher barrier films and ATS applications in different combinations). Even this is difficult because of the possibility of varied subsurface conditions (e.g., so il texture) as illustrated by Chellemi [27, 28] in Figure 84.

Figure 199: Multiple Concurrent Fi eld Emission Study Design By Ajwa

Another example in field scale rese arch where co-critical factors coul d have a signific ant influence is illustrated in Figures 9 and 10 that present the emi ssions data for iodomethane and chloropicrin that were generated using high barrier ta rps [11-16]. These data were deve loped in different locations from the other tarped raised bed emissions data for thos e chemicals to which they were compared. As can best be determined, the soil conditions at the varied sites were somewhat similar in that all soils were basically loamy sand, sandy loam, or sand and organic matter was low (i.e., <1% in high barrier studies and generally <2% in baseline studies). Additionall y, the baseline studies were generated using more typical application control system s that essentially output a con tinuous source of fumigant down a treated row. However, the high barr ier tarp data were not only generate d with high barrier tarps but also using a novel programmable controller system (i.e., SymmetryTM) developed by the registrant in the application. The major difference in this system compar ed to a more typical approach is best illustrated by Figure 70 which shows how pulses versus a more typical continual flow device can impact the amounts of material applied and subsurface concentra tion contours. It is unclear based on the study designs how much barrier films or this novel appl ication method contribute to emission reduction.

There are likely other exampl es of the impacts of co-critical fact ors upon the research results. These examples probably represent some of the most illustr ative situations where c onclusions could be made
that were errant unless all of the factors of the specific research were considered in context.



### 3.4 Standardization

There are many elements of fumigant use across the c ountry that involve the use of a variety of single use materials (e.g., agricultural films and reactive adj uvants) as well as equipm ent that has an active working lifetime of several years (e. g., bed shapers, injection rigs, and tarping rigs). It appears that within regions of the country, and sometimes on a broa der scale, that nomenclatu res have developed to 1 acres

ISCST Monitoring Station

Aerodynamic Monitoring Station

Flux Chambers ISCST Weather Station

Aerodynamic Weather Station ` 8 3 f t

8 3 f t 8 3 f t

8 3 f t 40 ft 40 ft 40 ft

40 ftField 1- VIFField 2- VIFField 3- StdField 4- Std Large scale studies on using VIF & SIF for fumigant emission reduction Large scale studies on using VIF for fumigant emission reduction155 describe specific pieces of equipm ent, tarps, and so forth. This is a natural result of the variety of possible fumigant use situations that are likely encountered across the c ountry related to varied pests and pest pressures, soil conditions, growing seasons, cr ops produced, economics, and availability of water
and other needed resources.

As illustrated in the discussion about tarps above (See Figures 1-3 and Table 1), it is clear that there is a need to attempt to begin to more systematically characterize the equipment and materials used in fumigations. This would serve many purposes incl uding that it would: reduce or eliminate any misconceptions that may occur in te chnology transfer such as describi ng a certain application practice; allow for a more systematic description of materi als such as tarps, inje ction methods, and sealing methods (e.g., bed shapers and tarp la ying equipment) and how well they perform relative to varied field situations; and begin to provide a framework for th e development of a more regimented approach for refining current standard cultural prac tices (or at least describing them in a more quantitative manner).

Professional organizations such as ASTM and the ASABE have developed many standards and criteria that could potentially serve as the basis for a more systematic approach for describing the materials and methods used in fumigations. It is important to consider this issue when interpreting results from the research and modeling analysis presented above because it is important to alleviate as many misconceptions as possible in any process based on this information.



### 3.5 Representativeness and Purpos ively Designed Research Plan

Fumigant use occurs in many areas of the country under a wide range of conditions. Current risk
assessments are limited in that the number of field s cale emissions studies used to develop risk estimates are limited (i.e., ~70 for all chemicals in total). This means that only specif ic situations could be
addressed in the current assessments and that thes e situations are defined and consequently limited by the conditions of the field studies. The modeling appro aches used in the fumigant risk assessments were evaluated by the FIFRA Scientific Advisory Panel (S AP), one of the key points was there is uncertainty associated with generalizing limited data to region s of the country or the country as a whole [See http://www.epa.gov/scipoly/sap/mee tings/2004/august1/august2425minutes.pdf.] To alleviate some of these concerns, all field scale data were individuall y used to calculate risk s for decision makers to
consider. The SAP also indicated that it would be useful to explore the basi c factors which influence emissions through modeling approaches and other means, which is one of the purposes of this paper, in order to examine whether such approaches could be us eful in eliminating some of the uncertainty which they discussed. In this paper, many sources of re search have been presente d and, as indicated above, many (~70) field studies were used as the basis for the risk assessments. In essentially all of these data, it appears that researchers selected fields or constr ained basic elements to be representative of major agricultural patterns or typical field conditi ons (e.g., using a specific soil type at ambient temperature for a laboratory test or a tomato production field in the middle of a large tomato production area). This is certainly an appropriate approach. However, what is generally lacking in much of this research is a definitive characterization of how representative the results were, based on the choices made, of the
intended cultural situations. Ya tes [10] in Figure 8 above illu strated some of this issue.

The Agency has been involved for the last several years in a project with the Agricultural Handlers Exposure Task Force (AHETF) that entails design ing a multi-year complex field research program designed to develop of series of exposure estimates for those involve d in pesticide applications (See http://www.exposuretf.com/). As part of the evolu tion of this project, th ere have been significant
discussions related to the ethical and statistical issues pertaini ng to how individuals and exposure
scenarios are selected for the research and how monito ring events from those individuals can be used to represent the populations as a whole. Clearly, exposure monitoring of individuals per se does not156 directly apply to the fumigant issu es herein but the concepts of repr esentativeness in th e research should be considered because it can present sources of n on-quantifiable uncertainties. For example, field emissions data are generated in a region of high fumigant use but with in that region there will be a distribution of emission profiles that are impacted by any number of fact ors. It is tr uly not clear how single measurements of emissions relate to that over all distribution. What must be acknowledged is that there are several approaches for selecting research situ ations that can rang e from a truly random probability sample to a more directed approach where purposive decisions are made to constrain
elements of the research. Most of the data discusse d in this paper are clearl y based on the latter. For more information please refer to the following:

http://www.epa.gov/scipoly/sap/meetings/2007 /january/january2007finalmeetingminutes.pdf

http://www.epa.gov/OSA/hsrb/jun-27-29-2007-public-meeting.htm

http://www.epa.gov/OSA/hsrb/a pr-18-20-2007-pub lic-meeting.htm

This issue is not just ap plicable to fumigant issues or human exposure monitoring but it is a general uncertainty that could pertain to many areas of research. It has been hi ghlighted here so that it will be
considered in the context related to the results of this analysis and that it also be considered relative to any fumigant related research in the future.



### 3.6 Lack of Critical Database Elements

This analysis is one of the first attempts at develo ping an integrated approach for evaluating how factors impact emissions using laboratory- and field-based research as well as first principl es modeling. A critical part of this analysis ha s been to obtain research informati on from many sources which have not been generated as part of an overall unified research plan. As a result, there are many inconsistencies in the available information. For example, if one created a matrix and attempted to summarize the data presented herein and the associated results, there would be many open spaces in that matrix. This limitation also limits the kinds of generalities that can be drawn from the available information which
should be considered in context wh en evaluating the analyses presente d herein. In many cases, it may not even be readily apparent because there may be basic factors which define the scope of the matrix
which still remain unidentified.

Additionally, a significant level of information gathering was atte mpted during the course of the development of this document. Th is included searching a number of electronic information services to obtain information and soliciting data from affected st akeholders and researchers in this area at the 2006 and 2007 Methyl Bromide Alternatives Outreach conf erences (http://www.mbao.org) . To that end, it is
believed that the trends in the current research are appropriately represented inthis document but it is possible that key research has not be en included because it was either not available or identified in the process. Clearly, this document addresses a wide range of topics that invol ves many fields of basic research. As more and updated information become ava ilable, it will be included herein as appropriate. This issue has been raised because it is important to acknowledge that: this effort is believed to represent the state of the science; this area of scientific re search is rapidly evolving; and a mechanism will exist for incorporating such advances into the regulatory aspects of fumigant use in the future.157 

### 3.7 Methodology

Figure 8 presented in Yates [10] provi des an excellent illustra tion of the issues that should be considered when evaluating the results of th is analysis in the context of likely occurrences under actual use conditions. As illustrated in this figure, it is clea r that laboratory-based data can be reproduced easily, compared to field studies, and that this eliminates possible uncertainties rela tive to the factors being measured. However, it also indicates that the re presentativeness of such research to actual field performance is questionable. Convers ely, field research result s are likely to be more representative of actual use occurrences but they have higher degrees of uncertainty associ ated with them. Much of these factors which are key to appropriate interpretation of these data are not quantifiable at this time and are a
focus of much research curren tly being conducted by Yates, Chellemi, Ajwa, Gao and others.

There are also basic monitoring protocol issues that should be considered. For example, Ajwa used a permeability cell shown in Figure 4 to generate mass transfer coefficients where films are sandwiched between a treated cell and an empty cell to measure diffusion. This pr ovides good information related to film performance as a boundary layer. However, fu migant emissions are dictated by factors in an integrated system specific to each field application so researchers have been attempting to more broadly simulate a systems approach with protocols like th e sand mesocosm that attempts to take a more comprehensive approach to quantifying factors that impact emissions. Additionally, in field research experiments there are different approaches for quan tifying flux as described in the Agency's risk assessments (e.g., integrated vertical flux or indirect methods). Generally, it should be noted, the field emission methods tend to provide similar results.

Issues pertaining to methodology are important to cons ider when interpreting th e results presented in this document. For example, dramatic reductions in emissions from the use of a high barrier film could be predicted based on mass transfer coefficient values. However, field performance differs due to other
factors that can or cannot be considered in different types of resear ch solely because of the methods used
to determine the result.



### 3.8 Summary of Considerations

There are clearly many factors that can influence fumigant emissions as well as several underlying
factors which need to be considered in the interpretation of this analysis . The critical elements are noted above. It should be noted that the information presente d herein is believed to re present that state of the
science related to the field emissions of fumigant chemicals and that much research is currently ongoing to further refine any conclusions wh ich could be drawn based on this an alysis. Also, an important fact to consider is that any determin ation of possible emission reduction factors should account for the fact that fumigant emissions are determined by a series of integrated physical-chemical processes as well as purposive decisions that are made by fumigant users. With this in mind, it would not be appropriate to apply a series of factors to pred ict large theoretical emission reductio ns without consideration of how those applied factors may be inter-related. For example, it is clea r that high barrier tarps perform well as a boundary layer. However, temperature can impact a tarp’s permeability and can also impact the subsurface processes that lead to diffusion throug h such tarps or possibly diffusion around such tarps and through furrows in raised bed cultural systems.158 

### 4.0 Conclusions

Many factors can influence the rate of emissions fr om treated fields after the application of soil fumigants. They include factors that can be manipulated by users to re tain efficacy and reduce emissions and also factors that cannot be easily be manipulated by growers yet still have a major impact on fumigant emission rates. Fumigant emissions ar e clearly governed by inte grated systems that are comprised of several mechanisms based on physical-chemical properties, populations of microorganisms, and conditions which have been ma nipulated by growers such as the use of reactive adjuvants and/or high barrier tarps as a boundary layer. These mechanis ms can be categorized into three basic phases including subsurface degradation an d diffusion away from the application point; permeability through a soil surface boundary layer; and impacts on emissions once the boundary layer has been penetrated. Any discussion of fumigant emissions ultimately has to be linked to how changes impact in the basic cond itions relate to changing the potential for exposure to those who live and work around fumigant applications and each of these processes can impact exposures.

This analysis has clearly demonstrated that an inte grated systems approach has to be considered when estimating the impacts of altering field conditions eith er through purposive choices of users. It is also important in establishing what coul d be determined to be a minimum st andard of care for users in all situations to ensure that speci fic field conditions ar e well understood and what, if any, actions are needed to be completed by users to ensure proper app lication procedures are followed. This analysis has also clearly demonstrated that th e degree on integration be tween factors is highly complex and has not definitively been quantified in a general sense, and certainly not in a site-specific sense, because all application events differ slightly due to many factors.

The factors which were considered in this analysis are represented by the list below:

• Tarp Use

o High barrier films;

o Film thickness;

• Soil Ammendments

o Use of reactive adjuvants & water seals;

o Organic matter levels;

o Soil moisture;

o Field Preparation;

o Soil compaction;

• Application Methods

o Application methods

o Application implements

• Field Conditions

o Soil texture;

o Biological activity;

o Soil temperature;

o Localized weather conditions159 This analysis indicates that these factors represent the critical measur es or conditions which ultimately
have the most influence on the amount of emissions from fumigant treated fields. For many purposes, it would be desirable at this point to create a matrix of the critical f actors and to independently ascribe a series of relative impacts to each factor. Neither the current data nor the available methodologies support such an approach at this ti me from a purely scientific basis. Although there are clear cut trends in responses relative to changing conditions, there are many inter-related f actors which are not well
understood because of a lack of information and a ssociated high levels of potential uncertainty.

Fumigant applications and treated fields represent highly complex systems. Limited data demonstrate
that there are clear trends associated with reductions in emissions with measures that are within, as well as outside of, the control of users. While it is difficult to assign qua ntitative factors to account for these reductions, it is not inappropriate to recognize that emissions will not be as great if users take certain actions. Care must be given, recognizing the limitations in the current science a nd data available, that the predicted reductions in emissions are not overstate d. It is clear that the following techniques, which can be manipulated by fumigant users, hold the mo st promise for reducing soil fumigant emissions:

• Use of high barrier films in most cases where mass transfer coefficients are used as the basis for a crude performance ranking system by chemical active ingredient;

• Use of reactive boundary layers such as ATS fo r certain chemicals and application methods;

• Manipulation of soil moisture where applicable;

• Manipulation of soil compaction wh ere applicable and feasible;

• Altering application equipment to ensure better control over applications; and

• Enhancing organic matter levels where feasible (this is expected to be very limited).160 

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