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 Reregistration Division (7509P) 

The analysis of the possible factors which may impact soil fumigant emissions from treated agricultural 

fields is attached. This document not only addresses these possible factors but also attempts to 

characterize how such information could be interpreted as well as the basic methods for generating both 

field- and laboratory-based emissions data. First principles modeling (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, 2008 
3

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 Kd 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...................................................................................................................... 160 
4

1.0 Introduction 

It is important for the Agency in the development of a viable risk management strategy for soil 

fumigants to understand the basic factors which may impact emission rates from treated 

agricultural fields. Figure 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 stakeholders 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 identified a list of possible factors which may primarily 

be responsible for regulating the rate at which administered fumigants volatilize from treated 

agricultural fields. The analyses of the factors which have been identified are presented in 

Section 2.0: Factors Analysis based on the use of empirical data (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 their choices of field preparation and cultural 

practices. Section 2.4 addresses factors 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. The following describes the 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 emissions above, a number of related issues are 

discussed including research needs and possible directions (Section 3.0); characterization of the 

results of this analysis (Section 4.0); and a summary of the key conclusions that can be drawn 

from this analysis (Section 5.0). Each of the factors 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 this document to describe critical elements 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 which could assist in development 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 describes the analysis and data reviews completed by the Agency 

in its efforts to evaluate how specific factors, or combinations of factors 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 the use of water seals and adjuvants such as 

potassium thiosulfate, as well as how organic matter levels can influence emissions. Application 

Methods (Section 2.3) addresses how varied application methods and equipment can alter 

emissions. Field Conditions (Section 2.4) describes many of the properties that are inherent in a 

specific field setting which may influence emissions such as soil texture. This section also 

addresses certain factors that are inherent in field conditions but that may be manipulated in 

different degrees through cultural practices (e.g., moisture levels) thereby altering emissions. 

Finally, First Principles Modeling (Section 2.5) describes the efforts to evaluate the sensitivity 

of various factors using a modeling context (i.e., CHAIN2D). 

2.1 Tarp Use 

According to Laverde (2002) [1] total sales of agricultural films in the United States in 1998 was 

180 ktonnes of which 37 percent were used for mulch film (i.e., ~67 ktonnes). This article also 

notes that most films are between 0.75 and 3.0 mils depending upon the area where it is installed 

and that most films are between 31 and 90 inches 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 consistent with many recent observations the Agency 

has had with a variety of stakeholders. These interactions 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 production practices in agricultural 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 culture. Similar systems are also 

used in ornamental production and forest seedling production. Tarps can be used for many 

purposes but cultural systems can be categorized by whether or not flat fume (broadcast or strip) 

applications are made to flat fields or if raised beds of some design are used. In broadcast 

applications, the primary function of tarp use relative to fumigant applications is to reduce 

emissions and retain more fumigant to achieve higher concentrations over time in order to 

achieve higher efficacy, perhaps using lower application rates in some 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 and reducing water vapor 

loss), control emissions by serving as a barrier, act 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 surface) with the caveat that the performance of such tarps depends 

on the material from which they are made, temperatures, and the identity of the fumigant to 

which they are subjected. Of course, the other issues described above are 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 impact 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, Chellimi, Wang, Ajwa and others have demonstrated 

that tarps reduce emissions and also that their 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 unadjusted and the recovery adjusted emission 

ratios, for broadcast applications, there are only two distinguishable groups: non-tarp and 

tarp.” 

It should also be noted that in the California Department of Pesticide 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/rulepkgs/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 standardization pertaining to how tarp products are 

described and there is also a lack of performance criteria for tarps under varied conditions that 

can be used to describe true in-field performance. For example, it is widely believed that the 

determination of mass transfer coefficients (MTCs) 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 tests 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 Outreach conference (henceforth 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 Area Wide Project. In this presentation, he 

described several categories of films 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 polyethylene (Figure 2); metalized film, which is 

essentially a layer of polyethylene with a thin layer of metal coating adhered to its surface; and 

TIF (Totally Impermeable Film), that is a sandwich of outer layers of polyethylene with 2 layers 

of ADMERTM adhesive and an inner layer of EVALTM resin (Figure 3). In this presentation, 

both high-density (HDPE) and low-density polyethylene (LDPE) are mentioned but no further 

descriptions of these materials were provided. The American Society for Testing and Materials 

(hereafter referred to as ASTM) in its Standard D4635-01 describes low density polyethylene 
8

materials (based on standard D4976) as having a density not necessarily equal to the density of a 

molded plaque from the same resin and when polyethylene 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 coefficient of friction as 

described in ASTM standard D2103-05 that describes specifications for plastic sheeting were 

provided [5]. Yates reported [6] slightly different 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 Definition, PE=polyethylene, LD=Low density) 

Figure 3: Totally Impermeable Film (Ajwa 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 possible approach for establishing a systematic 

nomenclature for films used in agriculture. These may or may not ultimately be the most 

appropriate ways to categorize tarp materials. There are alternative 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 standard 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 compared 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, there are likely many other factors that could be used to describe 

plastic films based on how the industry describes 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 linked to film performance. However, 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 with many associated professional 

organizations with many possible ways to categorize or otherwise describe plastic products 

including films that could be used in agriculture. Some of these include ASTM 

(www.ASTM.org); Society of the Plastics Industry (http://www.socplas.org); American 

Chemistry Council - Plastics Division (http://www.americanchemistry.com/plastics); and Society 

of Plastics Engineers (http://www.4spe.org). Additionally, 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 Committee D20 on Plastics was formed in 1937. This 

committee currently has jurisdiction over 480 standards published in the Annual Book of ASTM 

Standards, Section 8 on Plastics. D20 has 23 technical subcommittees that maintain these 

standards. Information on this subcommittee structure and its associated portfolio of approved 

standards and work items under development is available from their List of Subcommittees, 

Standards, and Work Items. One of the standards developed 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 Standards For Categorizing Films 

Along with providing a possible approach for describing different types of films detailed above, 

Ajwa 2007 [3] described a laboratory-based performance-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 (MTCs). [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 based on laboratory-based column tests at ~20oC using 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 embossed (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 below 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 lower 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. The 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 there is disparity in MTC trends 

between the different fumigants as seen in Table 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 conclude that the average MTC for iodomethane was 

generally small for all films except embossed high permeability films. 

Table 2: MTCs (cm/hr) High Permeability 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 10.75 
11

Table 3: MTCs (cm/hr) Low Permeability 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) Impermeable 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) Virtually 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 < 10-13
12

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 tests, for varying thicknesses of HDPE and silver 

Mylar films for 3 chemicals (Table 6). These values indicate films thickness and the material 

composition impact emissions as does the identity of the fumigant. 

Table 6: Mass transfer and sorption coefficients for fumigants 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 also reported varied measures of permeability 

defined by laboratory testing. In fact, 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 values 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 value of (R) can be calculated 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 reported mass transfer coefficients or other similar 

measures of permeability in the literature. However, linking the performance of films in a 

laboratory setting to actual field 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 conference. 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 field 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 concluded that each has attributes as illustrated in 

Figure 8. Essentially, Yates noted that laboratory studies are cheaper and more reproducible but 

they often represent idealized conditions. Conversely, large field research is more expensive but 

it is more representative yet highly variable. 
15

Figure 8: Yates Illustration of Relationships 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 later significantly reduce 

emissions. It is also clear that their performance 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 actually reduce flow by as much as 90 percent 

and in some cases even by orders of magnitude. This type of performance in the field, under real 

world conditions, is just not likely for any number of reasons, but most importantly, as Yates 

[10] indicated, that laboratory tests 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 for factors such as film stretching, atmospheric 

condition changes, and changes in field conditions relative to the performance of a film in the 

field. Instead, the results of laboratory based research 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 below, 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 LDPE 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 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). 

Scale

Increasing

0

25

50

75

100

Laboratory Field

Plot

Large

Field

Regional

Variability &

Uncertainty

Representativeness

50% ± 25 

35% ± 10 
16

2.1.3 Field Study Based Standards For Categorizing Films

Along with laboratory based research programs described above, a variety of field-based 

research has been completed which can be described 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 data. 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 variable and costly but at the same 

time they are the most representative of true emissions. In the results described above, the tarps 

considered have been essentially evaluated based on their effect solely as a boundary layer even 

though it is clear that other factors can contribute to reductions in emissions (e.g., enhancing 

longevity of soil moisture retention during and after application/irrigation 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 described where emissions were quantified 

and many factors were recorded that substantiate the conditions of the experiment but, in large 

part, these factors were not constrained. 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 quantify 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. Instead, a purposive selection of a study site and 

condition was made for any number of reasons ranging from logistics and ease of cooperation 

from a grower to an assigned representativeness of the site by the investigators. It should also be 

noted that field scale and even small plot research can be prohibitively expensive and it is 

typically not considered as representative 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 existing data were generated using standard cultural 

practices that involved the typical 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/factsheets/iodomethane_fs.htm) a series of data 

were required as a condition of the experimental use permit that was granted prior to the 

registration decision. The focus of these data was to evaluate the effectiveness 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 barrier film. In these cases reductions were not 

always observed compared to standard polyethylenes. 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

Hour

Flux (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 Emissions Using Various Tarp Materials 

In addition to the differences illustrated in Figure 9 above, the studies conducted under the 

iodomethane experimental use permit also quantified 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

Hour

Flux (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 Emissions 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 application rates for a more direct comparison of the impacts of tarp use. 
18

More typically, it would be anticipated that these values would be even lower because the rates 

would be reduced since the non-typical tarps would retain more fumigant and would likely 

achieve needed levels of pest control at lower dosage rates. In the 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/cm2/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/cm2

/hour or less) with the higher barrier 

films from the Bainbridge GA and Hart MI sites. Conversely, flux reaches levels of 

approximately 30 μg/cm2

/hour at the Dover FL site using metalized 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 anticipated 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 these 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 where the objective was to directly evaluate the 

reduction in emissions when various techniques are employed and compared against one another. 

Examples of such research were presented by Ajwa [19] at the October 2007 MBAO meeting 

where the results are summarized in Figures 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

6:00

Emission Rate (ug/m2/sec)

VIF

VIF + K-thiosulfate

Standard Tarp + K-thiosulfate

Standard Tarp

Date/Time after fumigant application 
19

Figure 12: Impacts On Cumulative Chloropicrin Emissions From Varied Surface Treatments 

Figure 13: Impacts on 1,3-Dichloropropene Emission 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/Time

1,3-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

Period

Cumulative Percentage of Chloropicrin Lost to Applied

VIF

VIF + K-thiosulfate

Standard Tarp + K-thiosulfate

Standard Tarp
20

Figure 14: Impacts On Cumulative 1,3-Dichloropropene 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 percent 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: availability, price, stretching, gluing, etc. 

Ajwa [19] also reported the following conclusions 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) cumulative 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 evaluated 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. Another 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/Time

1,3-D emission relative to amount applied (%)

Standard PE Tarp

Standard PE Tarp + KTS/Water

SIF + KTS/Water

SIF
21

samples were collected using direct in-field samplers which minimizes cross contamination, and 

applications were done in similar weather conditions). With this attribute, these data likely 

represent one of the most comparable sets of field 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 Gao [20] evaluated the impact 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 Varied Surface Seals On 1,3-D Hourly Flux Rates 
22

Figure 16: Effect of Varied Surface Seals On Chloropicrin Hourly Flux Rates 

Figure 17: Effect of Varied Surface Seals On 1,3-D and Chloropicrin Cumulative Flux Rates 
23

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 field 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 will 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 conditions 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, elasticity, or other physical characteristics of the film, 

even after 1 month's exposure to relatively high temperatures and intense solar radiation.” 

Additionally, it was concluded “in the present studies, temperature and polyethylene film type 

had the largest impact on mass transfer coefficients of fumigant compounds across HDPE films.” 

This could be important to consider when fields under drip irrigation could receive multiple 

treatments between sequential crops 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 are achieved through the use of less permeable films. Wang 

concluded that Hytibar tarp is effective in containing methyl bromide, whereas the polyethylene 

tarp is more permeable to methyl bromide (Figure 18). Wang also noted that the measured 

cumulative emission loss of approximately 60 percent 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 method on field plots near Riverside California in 

1996. 

Figure 18: Effect Of Film Type On Methyl Bromide Bromide Emissions 
24

The use of field emissions data, like the use of laboratory based measurements of emissions, can 

clearly be useful when defining the general performance 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. Conversely, when more factors are constrained, such 

as in the Ajwa research, emissions can be reduced 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 for 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. Conversely, it should also be considered that purposive 

decisions were made in site selection by researchers, but the motives for those decisions are 

unclear (e.g., logistical ease or some sort of specific site characteristic), to ensure they were 

representative for the research intent. Given all of these factors, if alternative films are used by 

growers such as VIF or other high barrier film then 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 evaluated, for lack of a better term, the mechanistic 

subsurface factors that can possibly influence emissions. After a fumigant 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 influence the rate at which fumigants 

approach the boundary layer which is the interface between the soil 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 addition of organic matter can also be used. [Note: 

These additional possible boundary layers are discussed below since this discussion is focused 

on agricultural films.] The rate at which fumigants move to the boundary layer and the amounts 

relative to the starting mass applied (which can decrease from the applied via degradation and 

metabolism) are possibly important factors relative 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 specific concentration time course 

must be achieved which is governed by Haber’s law (i.e., referred to as CxT requirements). If 

sufficient concentrations are not available at the area of the target pest, less than desirable control 

will result for the pest of interest. With this in mind, the goal of mechanistic research, in general, 
25

has been ostensibly to achieve required CxT regimens at reduced application rates and for longer 

periods of time. This has been critically important 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 been a part of these efforts and results from several researchers 

are noted below. Typically, more retentive films increase the retention time in soil which 

increases the achieved CxT. They can also increase 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 water, and the use of other soil adjuvants 

such as organic matter additions. Much of the research in this area has focused on use of more 

retentive films but it also has incorporated other factors so it makes it difficult to attribute overall 

increases in CxT effects to just the use of films. Many researchers have conducted this type of 

investigation including Ajwa, Gamliel, Yates, Wang, Gao, and Papeirnik. Of few examples are 

provided below. [Note: This phenomena has also been predicted by Cryer [24] through the first 

principles soil modeling which is discussed below.] 

Papiernik [25] investigated how the tarp material 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., basically field plots) and field scale studies. Three 

fumigant materials were used including 1,3-dichloropropene, propargyl 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 different 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 retention relative to differing soil moisture levels.] 

In Salinas sandy loam, the use of VIF significantly 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). Drip application under standard polyethylene 

slightly expanded the CxT area and a similar enhanced result would be expected for VIF is used 

in conjunction with drip application. In Watsonville sandy loam, the impact of using VIF is as 

predicted where the CxT area is enhanced and concentrations at similar depths are enhanced 

(e.g., at 50 cm the air concentration is ~100 ug/L under polyethylene 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 concentration contours for 1,3-D and chloropicrin using a variety of 

surface treatments and films as illustrated in Figures 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 the other monitoring was 

completed between drip emitters (B). As expected, the VIF contour was large and the resulting 

concentrations were high. However, HDPE coupled with pre-application irrigation provided 

similar results and the HDPE only profile was similar. 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 films 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. They also expanded the efficacious concentration 

contour by increasing size and the absolute concentration values at similar depths. As part of 

this effort, Gamliel also investigated the impact of film thickness using LDPE (30 and 100 μm) 

and found, as expected concentration contours were enhanced with the thicker material because 

boundary layer diffusion rates are impacted by barrier thickness. The effect of LDPE film 

thickness in this experiment, however, was swamped 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 specific depths for different tarp 

materials evaluated. [Note: USDA [7] Table 6 above also presents results for different tarp 

thicknesses which are consistent with Gamliel – thicker tarps reduce diffusion and retain more.] 

Figure 21: Distribution Of 1,3-D In Soil After Various Surface Treatments And Drip Application 
28

Figure 22: Distribution of Chloropicrin After Various Soil Treatments And Drip Application 
29

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 higher barrier tarp materials clearly 

have an impact on the subsurface processes which occur after application prior to emission at the 

soil boundary layer. To a lesser extent, 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 Figure 23 the CxT for 100μm LDPE at 

40 cm injection depth is 1007 g-h/m3

 and it is 4650 g-h/m3 for Barromide film. If LDPE 

provides efficacy, it follows that application rates could be significantly reduced under 

Barromide to achieve similar results. Of course, the caution is that such adjustments are not 

necessarily linear in nature and will vary between field conditions. Therefore, as indicated 

above, the results based on mechanistic studies should 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 material which reduces diffusion 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 conditions 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, and 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 higher barrier films of varied composition, or even 

thicker LDPE and HDPE films to a lesser extent, can reduce emissions and increase CxT 

parameters by enhancing the size and absolute concentrations in subsurface 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 out (see Figure 8), while these types of data pose less uncertainty 

and variability than other research, they are less representative under actual field conditions. 

Similarly, field studies conducted under varied conditions such as illustrated in Figures 9 and 10 

above indicate dramatic differences when high barrier films are used for iodomethane and 

chloropicrin, respectively. However, these data were not collected at sites in proximity to each 

other. Consequently, it is difficult to directly compare what variables have the greatest impact in 

reducing emissions. When field research 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 reduction under VIF and only 25% or so under SIF). Subsurface 

mechanistic processes also impact emissions but these 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 considered in the ultimate determination of possible 

emission reduction credits related 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 variability. It is important to consider that the 

situations which have been evaluated in all types 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 objectives 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 discrepancy 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

Transfer coefficient (cm/h)

Methyl Bromide Chloropicrin 1,3-D

Lab

Field

1-mil Bromostop

0

0.1

0.2

0.3

0.4

0.5

Methyl Bromide Chloropicrin 1,3-D

Transfer coefficient (cm/h)

Figure 24: Comparison of Laboratory and Field Based Emissions Predictions Using HDPE 

Figure 25: Comparison of Laboratory and Field Based Emissions Predictions Using 1-mil Bromostop 
32

It has been shown in research ranging from laboratory studies to large scale field tests that the 

use of agricultural films can play an important role 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 that agricultural 

films may achieve. This is clearly a future research need in order to more clearly understand the 

many factors that could impact the level of reduction (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 quantitative reductions of 

emissions in varied laboratory tests, and given the uncertainties and variability with the research 

to date, the assignment of any specific credit which would apply whenever an agricultural film is 

used, is extremely difficult. While such a credit can be informed by the current science, much 

more research is needed to eliminate the uncertainties and variability that exists today. 
33

 

2.2 Soil Amendments 

This section addresses methods and techniques other than the use of tarps, application 

reductions, and limiting application to certain equipment and mthods 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 considered. 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 been 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 intentionally 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 availability of resources (e.g., 

irrigation water or organic amendments), costs (e.g., some reactive adjuvants may reduce 

emissions but they are expensive), cropping system impacts (e.g., too much water addition could 

alter planting schedules) and field 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 and discussed, and there is general consensus that they impact 

emissions (e.g., compaction and moisture,, it is not clear how to quantify them. As discussed 

above, different types of research are available as described by Yates [10] in Figure 8. The same 

type of characterization applies in that more laboratory scale research is available than field 

studies designed to evaluate specific factors. 

There is also some clear overlap in the factors addressed in this section and factors addressed in 

other areas of this document. For example, soil moisture and water seals are differentiated in this 

context. Water seals are typically done during application or after 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 ensure proper working conditions 

pertaining to soil workability exist and to ensure 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 the 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 studies assessing boundary layer permeability, field 

based research that account for boundary and sub-surface conditions leading to emissions, and 

mechanistic research focused on both boundary conditions and the subsurface processes that lead 

to emissions. In this section, there is less differentiation because the laboratory-based research 

evaluated in this section is similar in scope to the mechanistic data presented above. Based on 

this premise, this section provides an evaluation of research focused on the 5 key factors growers 

can manipulate above based on mechanistic and field study data per Figure 8. 

2.2.1 Mechanistic Study Based Standards For Categorizing Soil Amendments

The factors that the Agency believes can possibly be manipulated by growers in varied degrees 

include the use of adjuvants and water seals, altering organic matter levels, establishing good 

field preparation practices, adjusting subsurface moisture levels (or, for example, purposively 

timing applications to coincide with proper rainfall), 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 also addresses several other factors. In Figures 21 and 22, Gao 

[23] assessed not only the impact of tarps but also the impact of varied irrigation methods on 

subsurface, mechanistic processes. In this study, 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 irrigation of propargyl bromide [3BP] 

emissions. In this study, multiple emission chambers 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 moist, a single 5-h surface irrigation 24 h after fumigation (M-5), 

and a 2-h daily surface irrigation for 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 irrigation is illustrated in Figure 26 which 

indicates similar profiles are achieved for both practices. A mass balance was also calculated 

for this study which is presented in Table 8. These results indicate that either irrigation method 

significantly reduces the amount of propargyl bromide volatilized. Based evaluations of 

volatility in this study, the investigators concluded: 

• Propargyl bromide volatilization tends to behave as methyl bromide; 

• Repeated irrigation lowers volatility similarly to single prolonged irrigation event 

using less water; 

• There is spatial variability in the volatilization (i.e., different 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 HDPE increases its effectiveness; and 

• Partial coverage of fields by growers is not sufficient for controlling 

volatilization, either furrow irrigation should be applied or total fields must be 

covered. 

Figure 26: Propargyl Bromide Volatilization Rates Over Time With Irrigation Treatments 

Table 8: Mass balance of 3BP for different treatments. 

Treatment 

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

% 

Volatilization – 21 3.0 6.3 

Cg {concentration gas phase} 0.02 2.6 2.1 1.0 

Cl {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 99.5 
36

Allaire et al [30] also investigated several mechanistic elements including concentration contour 

impacts from varied irrigation practices and subsurface 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 these analyses, the investigators reached several 

conclusions: most propargyl bromide dissipates from soil in 24 hours, without irrigation the 

movement is radial and diffusive, irrigation causes convective flux, irrigation increases duration 

in soil and reduces emissions, irrigation impacts the shape and intensity of concentration contour, 

distributions in one phase can only roughly predict mass in other phases, and distribution 

patterns of degradates do not follow 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 Fumigation For Different Treatments 
37

Figure 29: Pressure Distribution In The Gas Phase and Gas Phase Concentration 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 higher levels of organic matter (HOM at 3.16%). 

It was noted that irrigation led to a limiting of upward diffusion of the fumigant and to the 

maintenance of higher soil gas concentrations (Figures 30 and 31). 

Figure 30: Mean cis 1,3-D soil gas concentrations with depth over time for LOM soil 
38

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. Cumulative emissions in LOM were reduced from 33 to 17 percent with 

irrigation and from 6 to 3 percent with irrigation. The authors raised concerns about 

implementing such an irrigation scheme in commercial 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 Soils 
39

Dungan et al [32] evaluated the impact of temperature, organic amendment rate, and moisture 

content on the degradation of 1,3-dichloropropene 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 temperature 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 degradation 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 Constants 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 where the impact of organic waste application on 

the emissions of MITC and methyl bromide in an Arlington sandy loam. Similar soil 

amendments were used as described in Dungan [32]. Temperature and moisture effects were not 

evaluated to the same degree as in Dungan [32]. Degradation results are presented in Figure 35 

and impacts on emissions are presented in Table 10 and Figure 36. Gan concluded that the 

degradation of both compounds was significantly accelerated in amended soils 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 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-tarped control, emissions were 51 percent of applied. Irrigation 

reduced emissions to between 41 and 46 percent of applied. HDPE film use also reduced 

emissions to 45 percent of applied with water on top of HDPE further 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 investigators. See Table 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 Treatments (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 concentration 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 factors 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 researchers 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 and varied irrigation schemes on emissions and soil 

gas concentrations as illustrated above, there are also reactive chemical layers that can be used to 

accomplish similar effects. The most common reactive 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 control (dry soil 5% w/w), water seals (9mm water added), ATS (1:1 and 

2:1); ATS and HDPE (1:1); manure 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 manure 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 treatments 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 translated to the field as conditions didn't vary. 
44

Figure 39: Distribution of 1,3-D In Soil Gas Phase 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 Hydrolysis In Hanford Sandy Loam Soil At Different 

Moistures 

Figure 42: Chloride Release From 1,3-D Hydrolysis In Unamended And Amended Hanford Sandy 

Loam 
46

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 Experiment 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 degradation of 1,3-D and chloropicrin was evaluated. This study 

indicates that ATS can accelerate degradation and reduce emissions. In the control, the half-life 

was 86 hours for 1,3-D and 16.3 hours for chloropicrin. 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 for 1,3-D and chloropicrin (Figure 46). Qin also calculated 

concentration-time indices for the different systems which were evaluated which are presented in 

Table 12. 

Figure 46: The effect of drip application 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, duplicate 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 (Figures 47 & 48, Table 13). Also, 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 reactive barrier as well as competitive 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 and that subsurface distribution in the profile 

was not impacted in this case. It was also concluded that tarps and thiourea had a synergistic 

effect. In [42] Zheng concluded that degradation products could cause sulfur-like odors that 

result from thiosulfate use depending upon soil conditions which could be problematic for those 

who happen to be in proximity to applications. In [43] Zheng concluded 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 possibly due to decreased microbial activity. The 

degradation rate of both 1,3-D and chloropicrin were both significantly 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) instantaneous flux; (B) cumulative volatilization. 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 cisa transa 

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 different 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). The 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 below 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 factors, in addition to the use of irrigation, reactive adjuvants such as ATS 

and increasing levels of organic matter, can impact emissions and subsurface CxT contours. 

Based on discussions with many field researchers (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. [Note: 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 dockets for respective fumigant chemicals.] With regard to both of 

these factors, there has been substantive discussions around a means to categorize and quantify 

the "art" of proper field preparation and ensuring proper levels of compaction during applications 

to both reduce fumigant emissions and, where raised bed culture is practiced, 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 other 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 compaction immediately following 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 all 
52

applications. [Note: Some factors in the sensitivity analysis below in Section 2.5 using CHAIN 

2D address compaction. This issue will be further discussed quantitatively there and in the 

conclusions of this document.] The key for the purposes 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 unless 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 description of field conditions. 

Table 14: Terms For Describing Soil Consistence 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. Defining a quantitative approach or even semiquantitative approach for describing proper levels of compaction is unresolved at this time. 

There is certainly a level of intuition and experience which fumigators and researchers use to 

define these levels but it appears 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 specific 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 broadcast applications can impact compaction levels. Additionally, 

when shanks are used, the size and orientation of the closing shoes and coulters in implements 

such as Yetter© rigs can also impact the level 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 requirements. No real technique has been defined for 

ascertaining a simple way to evaluate how chimney effects are eliminated. Conversely, there are 

likely several methods that can be used to measure how compaction is achieved in a general 

manner. One such technique which appears to hold promise as applicable, inexpensive, and 

reproducible is the soil penetrometer [46, 47, 48]. There are undoubtedly many papers in the 

scientific literature which describe this device but the following provides a synopsis of its design 

and possible use related to monitoring field conditions relative to soil fumigant emissions. 

A penetrometer consists of a 30-degree circular stainless steel cone with a driving shaft and a 

pressure gauge (Figure 51) [46]. The penetrometer usually comes with two cones, one with a 

base diameter of 0.798 (3/4) inches for soft soils and the other with a base diameter of 0.505 

(1/2) inches for hard soils. The tip is slightly wider than the driving 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 pressure 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 taken with the penetrometer are called the cone index. 

The readings should be taken when the whole profile is at field capacity (approximately 24 hours 

after a soaking rain). If the soil 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 formulated using table 16 where the compaction rating would be of 

interest relative to reducing 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 particle size distributions in 

loamy fine sand and loamy sand (Figure 52). These 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 this device should account for water content. 
55

Figure 52: Cone Index Values As Functions of Water Content and Particle Size 

Distribution For A Loamy Fine Sand (A) and Loamy Sand (B) 

Research is presented above, where available, based on 5 critical factors that are believed to 

impact emission rates after application of fumigants that can be controlled or adjusted to some 

degree by growers and those making applications. As indicated above, the intent of this aspect 

of the analysis was to evaluate factors that are not related to the use of tarps or to the specifics of 

the application equipment used, that could impact emissions. [Note: These factors are discussed 

separately in this document.] The critical factors 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 emission 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 dissipation was essentially a microbial process. 

Ashworth [31] also shows that for 1,3-D organic 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., ~factor of 3). Based on this information, 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. Factors specific to certain conditions as examined 

in the research may be more practical. For example, 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 Section 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 For Categorizing Soil Amendments

Along with laboratory based research programs described above, a variety of field-based 

research has been completed that evaluated the use 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 including high barrier tarp use. Ajwa [19] (Figures 

11 and 13 above) indicates that potassium thiosulfate (KTS) reduces peak chloropicrin emissions 

by approximately 50 percent and peak 1,3-D emissions by approximately 35 percent in studies 

reflecting strawberry production practices in California. 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 (Figures 20-22) that pre-fumigation irrigation and 

soil moisture management are critical to increasing 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 application in an orchard replant scenario. 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 applications (0, 12, 24, 48 h) 

Hourly flux estimates for each treatment and both chemicals are presented in Figure 53 while 

cumulative emissions are presented in Figure 54. Residual fumigant levels in soil are presented 

in Figure 55. Based on the results 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 practice for controlling fumigant emissions, 

particularly for 1,3-D; 

• KTS application to soil surface is the most 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 Chloropicrin 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-2 s-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-2 s-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-2 s-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-2 s-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-2 s-1) 

1,3-D

CP

KTS/w ater appl.

5 - Intermittent w ater seals

0

10

20

30

40

0 48 96 144 192 240 288 336

time (h)

Flux (μg m-2 s-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 peach orchard replant field (coarse Hanford sandy 

loam) to investigate the effects of fumigation method (shank-injection vs. subsurface dripapplication treatments) and surface treatments (water applications and 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. Treatments included control (no water or soil surface 

treatment); standard high-density polyethylene (HDPE) tarp; virtually impermeable film (VIF) 

tarp and pre-irrigation all over shank injection; 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 losses (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). Lower fumigant concentrations in the soil-gas 

phase were observed with drip application than in the shank-injection treatments; however, all 

treatments provided 100% kill to citrus nematodes 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 Treatments In Orchard Replant Fumigation 

Figure 57: Cumulative Flux Profiles Of 1,3-D and Chloropicrin In Hanford 

Sandy Loam After Varied Surface Treatments In Orchard Replant Fumigation 
60

2.2.3 Summary of Emission Reduction Potential of Soil Amendments

Soil amendments can be any material which reduces diffusion rates at the soil/atmospheric 

boundary layer compared to standard conditions that would be normally found in agriculture 

such as higher levels of organic material or enhanced soil moisture from irrigation. Baseline 

conditions are difficult to quantify because, as described above, there is minimal standardization 

and typical conditions will vary from field to field and even within the same field. Given these 

confounding factors, it is also clear that in the existing research of all types indicates that a 

variety of factors can influence emissions such as irrigation, organic matter levels or reactive 

barriers such as ammonium or potassium thiosulfate 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 Gao [51] that indicates organic 

matter addition with HDPE may not reduce emissions. Also, as noted with tarp use above, 

performance of the various amendments in the field varies from that observed in the laboratory 

because of many factors (see Figure 8 above) and 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 that 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 determination of a single reduction factor. 

Complicating the situation further is the lack of side by side studies where controls are utilized. 

Finally, the lack of field studies where only soil amendments are studied (i.,e, without the use of 

tarps) is limited or nonexistent. It is recommended, however, that no emissions credit based on 

soil amendments be any higher than a reduction in overall emissions attributed to high barrier 

film use. However, special consideration could apply to specific chemicals. For example, 

methyl bromide was noted to degrade chemically and degradation of MITC is enhanced by 

increased levels of organic matter so it is possible that additional credit could be applied for 

higher organic matter when MITC is used. 
61

2.3 Application Methods 

This section addresses methods and techniques outside of tarps and soil amendments (e.g., 

addition of reactive adjuvants) that growers can possibly control in their 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 soil amendments the scope of this 

analysis is to assess the viability of other approaches 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 the 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 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 availability of resources (e.g., different 

application equipment), costs (e.g., modifications to application equipment can possibly reduce 

emissions but they are expensive), cropping system impacts (e.g., lowering application rates can 

impact efficacy) and field conditions (e.g., extensively rocky subsurface conditions may prohibit 

utilization of Noble plows). This section also introduces 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., combinations of chisels and coulters designed to 

reduce emissions); however, it is not clear how to quantify them. As above, different types of 

research are available as described 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 specific factors. 

There is also some clear overlap in the factors addressed in this section 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 during application are differentiated in this context. Soil moisture 

levels in this context refers to the conditions of the soil prior to an application to ensure a desired 

CxT is achieved but it may also refer to the proper conditions under which applicators need to 

achieve the proper formation of a raised bed cultural system. 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 the depth of the water 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 follows: laboratory based studies assessing boundary 

layer permeability (Section 2.1 only), field based 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 between the laboratory and mechanistic research as in 2.1 above. 

2.3.1 Mechanistic Based Standards For Categorizing Application Methods

The factors that the Agency believes can possibly be manipulated by growers in varied degrees 

include varying the application equipment itself, per se, and also the specific conditions of the 

application such as depth, rate and which chemicals 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 which 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 addressed several other factors. In Table 13 for 

example, Zheng [41] illustrated that 1,3-D emission rates were reduced the deeper the fumigant 

was injected into the soil (i.e., 20 to 40 cm). Figure 50 from Zheng [43] also indicated 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 possibly due to decreased microbial activity. In 

Figure 20, Ajwa [26] indicates that the use of a drip tape with irrigation water, as opposed to a 

shank injection, enhanced the subsurface concentration 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 previous discussion of tarps or soil amendments, other research was 

identified which evaluated how application methods 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 subsurface 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 diffusion, resulting in significantly lower concentration profiles in the soil 

air. Potential leaching of iodomethane was significant fromthe 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 iodomethane (MeI) in soil columns under shank injection and 

drip application 12 h after placement. Symbols represent 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 (KH) were slightly higher for Telone II than for Telone EC, presumably 

due to the higher apparent water solubility of the EC formulation. Sorption of 1,3-D isomersin 

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, differences 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 offormulation 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 application of Telone EC, emission of 1,3-D isomers was more rapid and 

produced greater maximum instantaneousflux than deeper (30 cm) shank injection of Telone II. 

Both application methods resulted in the same cumulative emissionsfor 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 having 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 notsufficiently 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 injection 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 drip application of 

Telone emulsifiable concentrate (EC) (10-cm depth). 
65

Ma [55] investigated the impact of altering application rates, in essence, through comparing the 

degradation of methyl isothiocyanate (MITC) and 1,3-dichloropropene (1,3-D) in an Arlington 

sandy loam at fourtemperatures and four initial concentrations (Figures 61 & 62). The 

degradation of MITC and 1,3-D isomers at each of the fourinitial 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 ofthe 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 problem 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 ignored. 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 degradation 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 degradation at high and low initial 

concentrationsin sterilized and nonsterilized soils demonstrated that the reduced MITC 

degradation rate constant at high concentration may have resulted from the inhibitory effects of 

the fumigant on degrading microorganisms. The overall degradation rate constants of MITC and 

1,3-D isomers changed exponentially 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 means of three measurements (±standard 

errors) and the curves are first-order fits. 
66

Figure 62: Degradation of the (E)-1,3-dichloropropene isomer in Arlington sandy loam at different 

initial concentrations at 20°C. The points are mean of three measurements (±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-dichloropropene (1,3-D), propargyl bromide (PrBr), and methyl 

isothiocyanate (MITC) in bedded systems was (Figures 63 & 64). These studies were conducted 

using the sand mesocosm method and field scale 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. Increasing the depth of application from 15 to 30 cm 

reduced volatilization of MITC by ~20 to >90%; emissions 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 furrow. Increasing the application depth resulted in a slight decrease 

in the rate of fumigant dissipation in soil, indicating the potential for some improvement in pestcontrol efficacy with deeper application. 

Sullivan [57] summarized a laboratory-based sealing 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 constant 90°F, i.e. diurnal temperature cycles were 

not simulated. Routine sealing methods for chemigation applications in California were 

simulated as the reference sealing method (typically 0.5 inches of water applied over 2-h periods 

at the completion of each application, followed by a second seal of 0.5 inch applied 24 h later). 

Alternative sealing methods were tested including mechanical compaction, foam, tarp, and a 

method termed “intermittent water sealing.” All tests were conducted with three replicates. The 

relative results are shown in Figure 65. As shown, intermittent water sealing and tarps (virtually 

impermeable film (VIF)) were shown in this research 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 reduce 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 injection trials. The subsequent two trials did not. 
67

Figure 63: Methyl isothiocyanate (MITC) flux in the (A) first sand mesocosm experiment 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 indicate 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 gas concentration was used as an indicator of the mass remaining 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 impact of altering application rates by determining degradation rates 

of MITC and chloropicrin (CP) in two forest 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 0.5x, 1x, and 2x field application rates for each fumigant were used in 

laboratory incubations (Figures 66 & 67). Microbial degradation 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, and freshness of tested soils. 
69

Figure 66: Comparison of half-life (t1/2) of (a) methyl isothiocyanate (MITC) and (b) chloropicrin 

(CP) at three application rates 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 fumigated nursery soils in incubation at 22°C. Vertical 

bars are standard errors. 

Figure 67: Relative contribution of microbial degradation to total degradation of methyl 

isothiocyanate (MITC) and chloropicrin (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 persistence was again investigated [see 43]. 

In aqueous solution, no direct reaction 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 the bi-fumigant application due to the inhibition of soil microbial 

activity and a possible abiotic competition with chloropicrin for a limited number ofreaction sites 

on the surface of soil particles. However, the degradation rate of chloropicrin 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) and the solid line is chloropicrin 
71

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 102 

(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 and absence of chloropicrin are 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 102 

(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 and absence of chloropicrin are 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 indicated above, the intent of this aspect of the analysis was to 

evaluate factors that are not related to the use of tarps or how soil amendments can impact 

emissions. [Note: These factors are discussed separately in this 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, Zheng 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 were observed at higher initial 

concentrations. Formulation types had no real impact on 1,3-D emissions as illustrated by Kim 

[54]. Differences in concentration contours were noted with different application methods and 

Guo [53] also described how diffusion of iodomethane 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 degradation rate of chloropicrin was accelerated but, 

conversely, trans 1,3-D degradation was inhibited by chloropicrin possibly due to decreased 

microbial activity. Zheng [59] also 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 ofreaction 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 generally application is not practical given the complexity of the 

information presented and the lack of similar information for all chemicals and situations of 

interest. However, the data could be used in a situation 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 variety of field-based 

research has been completed that evaluated the use of application methods 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 barrier films and/or soil 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 methods (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 emissions from drip and shank injections (Figure 

56) which routinely indicated shank injection emissions were higher than emissions after drip 

applications. 

In Figures 9 and 10, the emissions data for iodomethane 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 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). Additionally the baseline studies were generated using an application 

control system that essentially output a continuous source of fumigant down a treated row. One 

major difference with the recent high barrier emissions 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 pulses versus a more typical continual flow device can impact 

the amounts of material applied and subsurface concentration contours. It is unclear based on the 

study designs how much barrier films or this novel application method contributes to emission 

reduction. 
74

Figure 70: Differences In Subsurface Concentration Contours Achieved With Pulsed Applications Using 

SymmetryTM System Compared With Conventional Continuous Flow Application Equipment 

In its fumigant assessments (http://www.epa.gov/pesticides/reregistration/soil_fumigants/), the 

Agency has basically used the same approach that the California Department of Pesticide 

Regulations (DPR) (http://www.cdpr.ca.gov/docs/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 point due to a lack of sufficient monitoring data. 

In 2007, DPR issued emission adjustment factors for calculating volatile organic chemical 

(VOC) emissions for fumigants [Barry, 60] which recognizes that in some application methods 

some of the applied material is retained in the soil and not emitted into the atmosphere (Tables 

19-22). Originally, 100 percent of the mass applied 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 occur because of application 

method. These factors do not appear to imply any changes in peak emissions relative to 

application method. 

Table 19: CA DPR VOC Adjustment Factors For Methyl Bromide 
75

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

Table 21: CA DPR VOC Adjustment 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 rate. In some cases, no tarps are used but in most cases typical 

HDPE or LDPE types of tarps are used. In many cases, further characterization of the tarp 

materials could not be achieved. In most cases, soil types were similar (i.e., sandy loam or 

loamy sand). In a few instances, there was a clay component but only in the 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 emissions from multiple chemicals with approved 

nursery stock treatments. Specifically Noble plows, 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 indicated that delivery systems including factors such as the internal 

diameter of plumbing and the types of valve arrangements on application rigs can also 
78

potentially impact the quality of the application process. These will be important to include or 

address as part of the overall development of a viable set of good agricultural practices. 

Schneider et al [62] evaluated the impact of using either single- or double-chisel injection 

devices using 1,3-D in small-plot field experiments and two large-scale field experiments. 

Emissions were reduced with a single-chisel injection compared to the double-chisel. In all 

experiments both application methods resulted in similar concentration 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 yields would not suffer by the investigators. In the 

field studies, emissions were also 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 Measurments With Error Bars) 
79

Figure 76: 1,3-D Air Concentrations Presented 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 Potential 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 delivering the fumigant to 

the field. It also refers to the type of equipment used in the application. 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. However, 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 the 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 emission 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 atmosphere for most chemicals. However, Barry 

[2] indicates that this is not necessarily the case for methyl bromide where tarp use is the most 

effective tool for reducing its emissions, probably 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 these enhance the degradation of chloropicrin. 

These papers also indicate that application rates influence emissions where degradation rates are 

slowed at high initial soil concentrations 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 clearly alters application practices but the effect on emissions 

cannot be specifically elucidated at this time. 

Given the issues and the types of research described above, defining quantitative emission 

reduction factors for individual application methods that could routinely and generically be 

applied regardless of field conditions is difficult 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 chemical, vary for the same chemical, and other 

conditions, such as changing application rates or mixtures of chemicals 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 presented above in Sections 2.1 through 2.3, the focus was 

on elements that growers could manipulate in order to reduce emissions from fumigations yet 

still retain desired levels of efficacy (e.g., tarps, soil amendments, application methods). The 

focus of this section differs in that it is to address factors that are inherent in fields that are to be 

fumigated or factors that are essentially beyond a grower’s ability to manipulate. 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 factors and the analyses that were included in other 

areas of this document. As a result, not much novel research is presented in this section because 

the factors of concern were also considered concurrently in the sections above along with various 

factors that could be manipulated by growers to reduce emissions yet still achieve efficacy. 

Additionally, the sections above presented discrete types of research (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 analysis has not been completed herein. Instead, 

each of the factors listed above is discussed and the 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 10th edition was published in 2006 [64]. 

Soil classification was once closely tied to the presumed genesis of the soil, but this has been 

replaced with observable properties; however these often have a strong genetic link. Soils are 

named and classified on the basis of physical and chemical properties in their horizons (layers). 

"Soil Taxonomy" uses color, texture, structure, and other properties of the surface two meters 

deep to key the soil into a classification system. The current version of the system has six levels 

of classification in its hierarchical structure. The major divisions in this classification system, 

which are based on general characteristics and are 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, Oxisols Ultisols, and

Vertisols [65]. At its lowest level of organization, the U.S. system of soil classification 

recognizes approximately 22,000 different soil series. The distributions of the major soils orders 

within the country are 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 matter 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-size class is mostly sandy, 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 Spodosols and Dominant Suborders 

 
84

Soil is the result of complex natural processes that are affected by climate, time, geological 

materials, topography, and living organisms. Identification and classification 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 natural resources. A soil 

survey is an inventory of the soil resources of a county or area. It consists of a soil map, 

descriptions of the soils and soil map units, and predictions (interpretations) of soil behavior for 

different uses and management [66]. Soil surveys also provide an orderly, on-the-ground, 

scientific inventory of soil resources that includes maps showing the locations and extent of 

soils, data about the physical and 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, agricultural technicians, community planners, engineers, 

and scientists in planning and transferring the findings of research and experience to specific 

land areas. Soil surveys provide the basic information needed to manage soil sustainably. They 

also provide information needed to protect water quality, wetlands, and wildlife habitat. Soil 

surveys are the basis for predicting the behavior of a soil under alternative uses, its potential 

erosion hazard, potential for ground water contamination, suitability and productivity for 

cultivated crops, trees, and grasses. Soil surveys are important to planners, engineers, zoning 

commissions, tax commissioners, homeowners, developers, as well as agricultural producers. 

 

Soil texture used as unofficial soil classifications of soils use by growers based on their ease of 

cultivation and management practices. 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 adjectives 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 and subclasses are as follows: 

• clay - Soil material that contains 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 coarse sand, and <50% fine 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 clay, shall not exceed 15. 

o coarse sand - Soil material that contains 25% or more very coarse and coarse 

sand, and <50% any other 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 sand 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 coarse 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 less 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% very coarse, coarse, and medium sand, or 

>40% fine and very fine sand, 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 sand (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 more 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 Taxonomy descriptions above, there are other approaches that 

can be considered for defining soil categories that may be useful. For example, NRCS has 

developed a series of land resource regions that integrates a variety of data based on the example 

description provided below [67]. The specific land use regions are illustrated in Figure 82 and 

an example of a regional description 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 km2

 (36,510 mi2) This region consists of the southern two-thirds of the Florida 

peninsula. The average annual precipitation is 1,275 to 1,625 mm. The average annual 

temperature is 21 to 25 C. The freeze-free period ranges from 290 days in the north to 

365 days in the south. A large part of the region 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 elevations. 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 somewhat 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 principal livestock, and dairying is an important 

enterprise near the larger cities. In the south, sugarcane is a major crop locally.” 
88

Ascribing soil texture or other possible 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 other factors described above. As illustrated in many 

instances above, different soil types have been investigated 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 iodomethane and chloropicrin, respectively. Figure 

9 summarizes iodomethane emissions data for tarped raised beds where standard films and 

higher barrier agricultural films were used. All studies were conducted using very similar soil 

types (e.g., sand, loamy sand, or sandy loams) so defining 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 level 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 compare 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 area wide research program (Figure 83). In 

addition, Chellemi [27, 28] also discussed an important 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 Soil 
89

Trial #4 has four soil types:

• Pineda sand

• Riviera fine sand

• Chobee loamy sand, 

depressional

• Winder loamy sand

Figure 84: Multiple Soil Textures Observed In Single USDA Field Trial 

Finally, Wang [68] used an efficacy- or exposure-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–time (CxT) exceeded a threshold value for a 

particular pest–fumigant combination and the 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 loam, sand); three field configurations consisting 

of 102- and 203-cm-wide bed systems 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 fumigation 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 assist in the planning of field trials for making final management 

decisions concerning soil fumigation or other pesticide applications. The CTEI values (mean, 

Std. Dev, and CVs), calculated by Wang based on varied soil types, are presented in Figure 85. 
90

Figure 85: Soil Type Effects On Concentration-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 manipulation of microorganism 

populations in fields, even if organic matter levels can be adequately manipulated, is not likely to 

be a widely implementable strategy for controlling emissions. As such, this discussion has been 

included in this section which growers cannot easily manipulate. There are many possible 

approaches for addressing how the biological activity in soils may ultimately impact emissions 

but from a field scale perspective that could be used by growers it is likely that any viable 

method would be too complex and cumbersome because of the types of testing that would be 

required (e.g., define micro-organisms and their levels as well as specific effects on a particular 

fumigant). However, it is important to understand that different micro-organisms can have 

differential impacts on fumigants. For example, Zheng [59] illustrated that MITC degradation is 

influenced by microbial activity levels while other 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 preliminary evidence suggests that the microorganisms responsible for 

enhanced degradation of MITC specifically 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 trans1,3-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 S2N nucleophilic substitution. 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 

Pseudomonasspp., 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 content below saturation had little influence on 

degradation rate; 

• For MITC microbial degradation accounts for 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 overall 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 populations of microorganisms because the health 

of these populations can impact the overall viability of production fields in agriculture. Dungan 

[69] also concluded that the impact of individual organisms on degradation and emissions is not 

well understood. 

Many researchers have evaluated how levels of organic matter influence emissions and it follows 

that levels of organic matter can possibly be a generic indicator of the microorganism activity 

(e.g., the higher the organic matter levels the more microbial activity that would 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 microorganism 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 CxT profiles are generally enhanced in size and 

intensity (e.g., Gan [33]; McDonald [39]; Gao [51,52]; Guo [35]; and Xu [36]). Of course, 

relating these results to specific or even general classes of microorganisms at this point is 

supposition and the question of how organic matter possibly coupled with microorganism 

activity as an indicator of emission reduction is a prevalent one in current research. 

2.4.3 Soil Temperature

Soil temperature, in a general sense, can possibly 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 injection or application as well as at the soil surface if applicable is 

not likely to be a widely implementable strategy for controlling emissions except for defining 

limitations on application conditions (e.g., setting an upper limit temperature at the point of 

injection above which applications could not take place). Because of these limitations, this 

discussion has been included in this section related to factors which growers cannot easily 

manipulate. It is clear, however, that growers must closely monitor soil temperatures as a means 

for understanding the potential for emissions during and after a fumigant application and 

potentially terminate applications 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 the 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 temperatures 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 reflects common ambient conditions in structures. 

[Note: Many stakeholders have suggested an injection point temperature cut-off for terminating 

applications of 32 oC or 90 oF.] Diurnal temperature changes are 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 conditions are essentially isothermal 

at night followed by steep warming trends starting around 9AM. Wang also presented a 

mathematical relationship which describes how temperature impacts fumigant transport and how 

temperature changes can impact mass transfer coefficients 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 Heights 
94

Figure 88: Impact Of Temperature Changes On Two Agricultural Films Using Methyl Bromide 

Dungan [32] also presented different degradation rates in a sandy loam soil (amended and 

unamended with organic matter) at varied temperatures (Figure 34). 

Ma [55] studied the degradation 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. The 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 ofthe initial 

concentrations. First-order and half-order kinetics adequately described the degradation of MITC 

and 1,3-D isomers at each initial concentration, with the correlation 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 10x for MITC for the concentration range of 3 to 

140 mg kg-1, and between 1.5 and 4x 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 constants was between 1.4 and 1.7x for MITC and between 3.1 and 6.1x for 1,3-D 

isomers, depending on temperature. Second-order kinetics and the Michaelis–Menten model did 

not satisfactorily describe the degradation at all initial concentrations. The 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 (k1) and the half-order (k0.5) degradation rate constants of methyl 

isothiocyanate (MITC) at different temperatures and initial concentrations. 

Temperature (°C) 

Initial 

concentration 20 30 35 40 

mg kg-1

_ k1 x 102 (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) 

_ k0.5 x 102 (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 (k1) and the half-order (k0.5) degradation rate constants were obtained by fitting 

measured concentrations at different times to first-order and half-order kinetics respectively 

using the least-square-error fitting procedure. 

Mean ± standard error. Data in parentheses are coefficients of fitting (adjusted r

2

). 
96

 Table 21: The half-order degradation rate constants (k0.5) of (E)- and (Z)-1,3-dichloropropene 

(1,3-D) isomers at different temperatures and initial concentrations. 

Temperature (°C) 

Initial concentration 20 30 35 40 

mg kg-1

_ k0.5 x 102 (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. Because of this, they are 

anticipated to rapidly be emitted into the atmosphere 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 shape and intensity of emission profiles probably would not be 

extensively impacted except in extreme 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 agricultural films. It also appears clear that 

fumigant dissipation and degradation pathways, as expected, are temperature dependent such as 

was observed in Ma [55] where degradation 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 scale, temperatures fluctuate without major 

discernible differences in emission patterns and also the observation that standard types of 

kinetics models seem to apply to fumigant dispersion and degradation, there does not seem to be 

a range of temperatures that is apparent where an asymptote could 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 information that increasing temperatures also enhance 

dispersion and degradation rates. Of course, enhanced degradation could also mean less overall 

emissions but conversely enhanced dispersion could mean that larger areas around treated fields 

are impacted. It is also clear that fumigant applications can be more of an “art” than science 

because the factors which impact emissions are many and inter-relate in varied ways. Based on 

inputs from stakeholders a soil injection temperature of 90oF has been mentioned as a possible 

temperature at which applications would not be allowed. This seems reasonable but, as indicated 

above, cannot be clearly justified outside of indicating if applications occurred at higher 

temperatures more dissipation would occur also 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 the atmospheric surface boundary layer contribute 

to the emission and exposure from volatile fumigants in air. Surface atmospheric boundary 

conditions influence the emissions of soil incorporated volatile fumigants from below the surface 

as described above. Air temperature is related to soil temperature by the heat transfer process via 

conduction as suggested by Hamdeh and Reeder [71]. 

In addition, the vertical profiled atmospheric environment influences the potential exposure of 

gaseous fumigant residues in air by the routes of dispersion influenced by turbulence, buoyancy 

influenced by the vertical atmospheric temperature profile, vertical wind shear, and downwind 

transport influenced by the wind speed and direction (Emanuel [72]; Pasquill [73]; Paine [74]). 

However, the atmospheric surface boundary layer conditions are also a factor for potential 

exposure to fumigants. Specifically of concern are temperature inversion conditions. In general, 

temperature inversions occur as colder, denser 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-valley circulations (Djuric [75). However, 

temperature inversions occur most frequently due to radiational forcing at night as the land 
98

surface cools at a higher rate than the atmosphere aloft. 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 convective 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 directions (Stull [76). The 

minimization of turbulence in the stable atmospheric surface boundary layer is the cause for poor 

dispersion under inversion conditions. Airborne contaminants possess Gaussian distributions in 

the vertical and horizontal directions. In addition, since total 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 distribution is conserved under inversion conditions. 

In more technical term, low sigma value for plumes 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 ground-level residue concentrations 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 potential 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]). Therefore, a shallow convective mixed 

layer at the surface underneath an inversion layer can cause fumigant residues in air to 

become congested in the atmospheric surface boundary layer and the mixing depth 

associated with conduction and convection becomes limited. Elevated inversions are 

often termed capping inversions for this reason (EPA [78]). 

• Another potential problem at nighttime exists with 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 layer (Stull [76]). Therefore, low mechanical mixing heights 

at nighttime may also cause high exposure concentrations of fumigant residues in air. 

Although inversions occur frequently, 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 will generally tend to inhibit mixing more than weak inversions 

with low negative temperature lapse rates (Salby [79]). 

It is the goal of the Agency for fumigant applications 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 necessary to directly detect inversions in specific fields where 
99

applications take place because they would not likely have access to the sophisticated 

meteorological instrumentation needed 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 km2

 grid cells) to adequately predict or diagnose temperature 

inversion conditions representative over all agricultural sites (NOAA [80]). Since there is a 

dearth of appropriate measured 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 check to determine whether 

current conditions are characterized by air stagnation. This technique 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 program addressing air quality 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 thereafter. The EPA AIRNOW program issues Air 

Quality Index (AQI) values from state and local planning agency produced forecasts based on 

airshed modeling (CMAQ) and site specific empirical statistical fitting relationships. The 

AIRNOW program produces daily air quality forecasts mainly for fine particulate mater (PM2.5) 

and ozone concentrations (EPA [81]). The AQI is calculated using the maximum predicted 8-

hour average ozone concentration or 24-hour average maximum predicted PM2.5 concentration. 

AQI forecasts for ozone and PM2.5 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 PM2.5 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 PM2.5

Concentration 

Range 

(μg/m3

) 

24-hour 

PM2.5 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 Unhealthy 
100

Table 22: EPA AIRNOW 8-ozone And 24-hour PM2.5 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 PM2.5 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 determining 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 PM2.5 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 modeling and monitoring methods indicate the 

potential for air stagnation. Therefore, utilization of this product 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 possibility of inversion conditions for the 

following reasons (EPA [81]): 

• The EPA AIRNOW forecasting operations are not provided for areas outside of 

metropolitan airsheds. 

• EPA AIRNOW forecast methods are not streamlined as forecasted AQI values are 

produced by methods based on modeling and monitoring 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 km2

 grid cells). 

• EPA AIRNOW forecasts are not currently used to impose mandatory restrictions on 

industrial activities or other practices which would make 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 inversion conditions was Air Stagnation 

Advisories issued by the National Weather Service. These are no longer issued because they 

have been supplanted by AQI. Air Stagnation Advisories were issued by the National Weather 

Service when locally established critical values 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 availability of real-time weather prediction models 

and unfeasibility of implementing expensive weather observing networks at all fumigant 

use sites, it is unlikely that site specific quantitative 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 sources exposure because airshed grids are too 

coarse in most cases (144 km2

 grid cells). 

• Air quality forecasts of PM2.5 and ozone provided by EPA AIRNOW may in some cases 

be used to at least identify possible days with vulnerable meteorological conditions for air 

stagnation. Action days declared by local jurisdictions based 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. Similarly, Air Stagnation 

Advisories issued by the local National Weather Service Office could be considered in 

the same way. 

• In the future, it may be plausible to implement a mandatory restriction program for 

fumigant use based on present and forecasted meteorological conditions as computer 

resources continue to improve allowing for higher resolution models to become 

operational. The anticipated 2.5 km resolution with the WRF model will be sufficient 

enough for a spatially representative dataset to be available for all agricultural 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 manner to reduce emission levels and enhance CxT 

parameters thus allowing for the use of less fumigants 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 the specific fields which fumigant users treat and the atmospheric 

conditions during or immediately subsequent to applications. The factors which were identified 

include soil categorization, biological activity, 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 purpose probably is either by order or using the 

texture type of approach as described above. It is also clear that most fumigant use probably 

occurs in soils that are predominantly sand so there is not likely to be much change in emissions 

based on common soil types. This conclusion could be disputed based on laboratory research but 

when field scale data are compared observable differences tend to be minimized. It is also clear 

that moisture levels can be important which in many cases can be manipulated 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]. Research 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 could 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, reflective film use; irrigation 

water; or altering application dates) but true impacts on soil temperature 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 generally be fairly well described using common 

kinetics approaches such as the Arrhenius equation 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 above, there is an art to 

fumigant application and many individuals involved in applications have suggested an injection 

point soil temperature of 90oF as appropriate. At this point there is no scientific basis to suggest 

otherwise. Finally, eliminating the possibility of atmospheric inversions during and subsequent 

to applications is desirable because, as indicated 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 predictive 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 qualitatively such as an early warning 

system that would alert users to be vigilant about possible inversions. 
103

Given these issues and the types of research described above, defining quantitative emission 

reduction factors for specific field conditions that could routinely and generically 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. Complicating the situation further 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., specific microorganisms) is another 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 any higher than a reduction in overall emissions 

attributed to high barrier film use or other factors that can easily be manipulated by users to 

achieve emissions reductions. 
104

2.5 First principles modeling 

In addition to the analysis of the empirical data described above in Sections 2.1 through 2.4, a 

modeling approach has also been utilized that evaluated how manipulation 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 factors impact emissions. It does not evaluate 

dissipation around a field after the emission since PERFUM has used for that element of the 

analysis (see http://www.epa.gov/pesticides/reregistration/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 carefully considered as have the 

input values and a strategy for evaluating the impact of changes of specific parameters (i.e., how 

to structure a sensitivity analysis). There are a number of possible modeling 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, irrigation 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 simulating two-dimensional variably saturated 

water flow, heat transport, and the movement of solutes involved in sequential first-order 

decay 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 sink 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 controlled 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-unsaturated water flow and Fickian-based advectiondispersion equations for heat and solute transport. The flow equation incorporates a sink 

term to account for water uptake by plant roots. The heat transport equation considers 

conduction as well as convection with flowing water. The solute transport equations 

consider advective-dispersive transport in the liquid phase, and diffusion in the gaseous 

phase. The transport equations also include 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 development of peer reviewed literature for many 

types of vadose zone analyses (i.e., not just focused 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 real 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 convenience 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 the 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 the 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 graphical user interface (GUI) by creating a pre- and post-processor using 

Microsoft Excel and also incorporating a new air/soil boundary condition proposed by Wang 

1998 and 1999 [86] [87]. Additionally, an ability to simulate tarp tearing and tarp removal at 

user specified time intervals as well as a boundary condition that ties meteorological conditions 

to the resistance to mass transfer at the air/soil interface in addition to the stagnant boundary 

layer option was also included based on Cryer 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 application methods (Figures 89, 90, 91); 

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

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

• Addressing pesticide placement (Figure 93); and 

• Computational fluid dynamics analysis within the boundary layer to account for mass 

transfer coefficient modifications contributed 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 Nodes) & Selective (1,596 Nodes) Nodal Placement Options 
107

Figure 91: Nodal Grids Used In Analysis For Differing 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 Geometries That Can Be Assumed 
108

Figure 93: Representation Of Line, Partial Line, and Point Source Injections Into Soil Parameterized Into A 

Single Input Parameter 

Figure 94: Determination Of Simulation Domain For 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- and post-processor for the model. It also evaluated 

the basic parameterization of the model based 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 significant 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 were 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 mentioned 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 the prediction of emissions from soil fumigations, 

however, are limited but the basic physics of these models would still apply. To ensure that the 

CHAIN-2D could be used in an appropriate manner 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 between CHAIN-2D predictions 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 with 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 and varied application methods. However, it is 

worthy to note that errors in cumulative flux may arise due to an incorrect initial mass transfer 

boundary condition as the user-defined value based on the tarp material in CHAIN-2D may not 

be representative of actual conditions (e.g., comparison of Arizona tarped and untarped results in 

Table 24). 

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

Figure 95: Comparison of Cumulative Chloropicrin Emissions In Field Compared To CHAIN-2D 
110

Similar results were achieved when field results and CHAIN-2D predictions were compared 

from a single field study using 1,3-D as illustrated in Figure 96. Note that in this figure both 

static and dynamic boundary layer conditions were 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 prediction without a shank 

trace, the peak emission does not compare with the observed values. Conversely, as illustrated in 

Figure 98, the peak emissions compare favorably with the field data, presumably because a 

shank trace was a substantive contributor to emissions in that study. This comparison has been 

provided to illustrate in some cases a single factor can determine emission profiles. In other 

cases, many factors are inter-related which makes defining how changing them relative to 

emission changes very confounding. 

Figure 97: Comparison of Chloropicrin Emission Profile In Field Compared to CHAIN2D (no shank trace simulated) 
111

Figure 98: Comparison of Chloropicrin Emission Profile In Field Compared To CHAIN-2d 

(shank trace simulated) 

Figure 99 illustrates a critical issue 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 the 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 quantified in field studies either 

because they cannot be easily measured or protocols didn’t specify the measurement. There is 

significant further discussion of this 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 HDPE 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 concentration contour, the intensity of the contour 

(i.e., red and orange are more intense 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 Chlorpicrin Generated Using CHAIN-2D (Cryer [24]) 

Cryer [90] also published other comparisons of field emissions data with CHAIN-2D and PRZM 

predictions. In a separate study Cryer [91] also compared measured losses of 1,3-D over time 

with PRZM and CHAIN-2D outputs and in this case the PRZM model results were very similar 

to the monitored data while CHAIN-2D again slightly underpredicted the monitored data. It 

should be noted that in both cases, the general shape of the prediction curves aligned with the 

monitoring data to which they were compared and the predictions were well within an order of 

magnitude, which given the complexity of the system, appears reasonable. In Figure 101 the 

percent of applied 1,3-D which volatilized is presented based on empirical laboratory 

information (developed by Gan [92]). In the model 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 volatilized which is similar to the results illustrated 

in Figure 102. 
113

Figure 101: Functional Nonlinear Dependence of Cumulative Volatilization Losses For 1,3-D As A Function 

Of Application Depth 

Figure 102: Comparison of PRZM predictions 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 validity 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 emission profiles. It can also crudely estimate 

peaks and the timing of peaks but often does not reliably predict peaks. This issue could lead to 

uncertainty and variability in the predictions generated using the model. [Note: A more detailed 

review of CHAIN-2D identified minor technical issues that were either corrected or otherwise 

characterized in that document. None of these issues were deemed 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 described above, a sensitivity analysis has been 

conducted to evaluate how changes in varying parameters. The results of this analysis should be 

considered in context with the review of the trends identified in the monitoring data presented in 

Sections 2.1 through 2.4 above. The CMTF completed a sensitivity analysis based on a variety 

of chloropicrin data. The results of this analysis are presented in van Wesenbeek [84]. In 

general the Agency concurs with the results of this analysis given the uncertainties associated 

with CHAIN-2D. 

The Agency conducted a sensitivity analysis similar to the CMTF’s for chloropicrin, on metam 

sodium, and methyl bromide. The major difference 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 Kd 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 temperature is not applicable because soil 

temperatures are incorporated on an hourly basis from the empirical data and also 

the boundary layer mass transfer coefficient is temperature dependent.] 

In order to perform the sensitivity analysis, the Agency needed to calibrate Chain-2D using 

actual chloropicrin, metam sodium, and methyl bromide field volatility studies as well as a 

number of chemical and environmental fate properties. This calibration 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 volatility studies. In these cases representative 

soil and meteorological data was selected from the California Irrigation Management 

Information System (CIMIS) and/or the Florida Automated Weather Network (FAWN). Table 

25 shows the specific field volatility studies that were used to calibrate Chain-2D for 

chloropicrin, metam sodium, and methyl bromide and their sources. Chemical and 

environmental fate properties for chloropicrin, 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 CIMIS 
116

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 are 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, and 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 KOW) 2.5 0.94 1.92 

Adsorption Co-efficient, Koc 

(cm3/g) 36 30.27 20.25 

Diffusion Co-efficient in Gas 

Phase 

(cm2 day-1) 

7.31 x 103 9.34 x 103 9.75 x103

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 bromide. 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 term “initial” reflects the study 

conditions.] Although there are some discrepancies between the flux from the actual field 

studies and the flux calculated by Chain-2D (see example in Figure 99) the Agency believes that 

the calibration results generally support the use of Chain-2D for predicting cumulative fumigant 

emissions. Calibrations were completed using information 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 inputs which are hard to 

quantify were obtained from representative 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 evaluated 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

Hour

Flux (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

Period

Flux (ug/m2-s)

Field Study Chain 2D

Figure 104: Metam Sodium Mettler, CA Sprinkler Irrigation - Double Water 

Seal Example 
118

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

Hour

Flux (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 the Agency’s Chain-2D sensitivity analysis. The 

analysis focuses on the factors considered in the analysis, including: injection depth, soil 

moisture, organic matter via Kd value, soil type, bulk density, and tarp type via mass transfer 

coefficient. The results of the sensitivity analysis should be considered in context with the 

review of the trends identified in the scientific literature presented in Sections 2.1 through 2.4 

above. 

For each factor below, a series of graphs are presented 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 chemicals. 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 used because it readily illustrates how changes in 

key factors impact overall emission results. 

2.5.4.1 Injection depth 

Figures 106-120 suggest that generally across all three fumigants, shank injection depth can have 

a significant impact on emissions. Increasing injection depth by 10 or 25 percent reduced 

emissions up to 25 or 30 percent. However, decreasing injection depth 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 Changes 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 Changes 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 Changes 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 Changes 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 Changes 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 Changes 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 Changes 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 range 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 percent reduced emissions by 10 percent and sometimes up 

to 100 percent depending on the specific study examined. 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 Changes 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 Changes 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 Moisture (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 Sensitivity To Soil Moisture (Intermittent Water Seal Sprinkler 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 Sensitivity 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 Irrigation 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 Sensitivity 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 Sensitivity 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 Sensitivity To Soil Moisture (Drip Hot Gas) 
131

2.5.4.3 Organic Matter via Kd Value 

Figures 141 – 160 suggest that across all three fumigants, organic matter can have a small impact on 

emissions. Increasing the Kd value by 10 or 25 percent generally reduced emissions by 10 or 20 percent. 

Decreasing the Kd value by 10 or 25 percent increased emissions by 10 or 20 percent. There may be 

some uncertainty because Kd has been used for modeling purpose rather than Koc which is Kd

normalized by organic content but based on CMTF analysis the results appear to be similar, at least for 

chloropicrin. Although, organic matter appears to have a small impact on emissions, it may not be a 

very practical parameter to alter on farm except in limited circumstances. 

Sensitivity Analysis - Kd

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 - Kd

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 Organic Matter Levels (Broadcast Untarped) 
132

Sensitivity Analysis - Kd

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 - Kd

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 - Kd

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 Organic Matter Levels (Bedded Metalized Tarped) 
133

Sensitivity Analysis - Kd

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 - Kd

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 - Kd

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 Organic Matter Levels (Shank Standard Water Seal) 
134

Sensitivity Analysis - Kd

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 Organic Matter Levels (Shank Intermittent Water Seal) 

Sensitivity Analysis - Kd

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 Matter Levels (Standard Water Seal Sprinkler Irrigation) 

Sensitivity Analysis - Kd

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 Sensitivity To Organic Matter Levels (Intermittent Water Seal Sprinkler Irrigation) 
135

Sensitivity Analysis - Kd

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 Matter Levels (Double Water Seal Sprinkler Irrigation) 

Sensitivity Analysis - Kd

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 - Kd

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 Organic Matter Levels (Drip Irrigation Tarped) 
136

Sensitivity Analysis - Kd

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 Organic Matter Levels (Drip Irrigation Untarped) 

Sensitivity Analysis - Kd

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 - Kd

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 - Kd

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 Organic Matter Levels (Deep Broadcast Untarped) 

Sensitivity Analysis - Kd

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 - Kd

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 with 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 Sensitivity 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 Sensitivity 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 Sensitivity 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 Sensitivity 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 Sensitivity 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 Sensitivity 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 Sensitivity 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 shank being drug through soil) is increased the associated emissions 

also increase, but in a limited way. However, decreasing the amount of soil disturbance through heavy 

compaction generally seemed to significantly reduce emissions. It is unclear at this point if soils can be 

compacted to this extent it is clear that modern bed shapers and compaction devices 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 Disturbances 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 Disturbances 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 Disturbances 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 Disturbances 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 Disturbances 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 Disturbances 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 Disturbances 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 Disturbances 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 Disturbances 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 Disturbances 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 Disturbances 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 Disturbances 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 Disturbance Altering Bulk Density (Broadcast Tarped) 

2.5.4.6 Mass Transfer Coefficient (via tarp type)

Figures 194 – 198 suggest that across all three fumigants, 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 values. In the calibration step, 

MTC values from Ajwa and Wang were used that, as closely 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 well under field use. However, high barrier tarps with very low MTCs 

also impact concentration contours which form after an application because, as illustrated in Section 2.1 

above, they extend the effective area of the contour and they also increase the concentration 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. For 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 analysis 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 Sensitivity To Tarp Type (Broadcast Tarped) 

2.5.4.7 Agency Sensitivity Analysis Conclusions 

The analysis focused on the factors: injection depth, soil moisture, organic matter via Kd value, soil type, 

bulk density, and tarp type via mass transfer coefficient. Generally the trends in the results were 

consistent with the CMTF analysis. Additionally, the results appear supportive of the factors which 

were believed to impact emission levels. It is also clear that soil type and the level of compaction can 

significantly impact results and 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 MTC 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 impacted which were difficult to quantify. Techniques 

for further assessment of these factors are being considered. 
152

3.0 Characterization

 Fumigant emissions that ultimately result in some level of exposure 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 assessments through the use of predictive air models as well 

as monitoring data and by considering analyses which show residences and 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 addition of soil amendments such as organic matter. 

Additionally, several factors 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 completed. 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 variability 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 described in order to provide guidance for evaluating the trends and results 

of the analysis as well as for developing a conceptual 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. Simply put, uncertainty could be 

described as an indication of how accurate a measurement 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 some cases, where small numbers of replications in a laboratory study are 

conducted or in a field study where essentially a single emission profile was monitored, there can be 

very little analysis of either factor. In other cases, investigators included a series of experimental 

replication so that error bounds and other univariate or even non-parametric statistical approaches have 

been used to summarize and present the results. 

As indicated above, there is too much detailed information in this document to discuss the specifics for 

every factor. The SAP recommended that as much emissions data as possible should be used in the risk 

assessment process to reduce levels of uncertainty and variability. Consequently, all emissions studies 

of appropriate quality were used. However, in many cases only a single study for a specific application 

method in a growing region was available. This makes interpretation of the 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 representative distribution of such results. Using this 

same example, it is also not possible to assess any of the variability 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 completed 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 data quality objectives. In these types of studies, they might represent 

developing sufficient numbers of data points to adequately describe a temperature-based emissions 

function that can relate changes in emissions to changes in temperature. Determining whether or not the 

objective was achieved would be predicated on how any predictive function based on the subsequent 

research performed, or other factors such as a measurement of variability, was defined within some preset limits. For field scale research, other examples could also refer to situations where a high barrier tarp 

or the activity of ammonium thiosulfate (ATS) as a reactive boundary layer was evaluated compared to a 

more typical situation of polyethylene 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 difficult because so many factors can impact 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 individual factors (or selective combinations thereof) in a system would 

impact results. In many studies, multiple factors were evaluated in varied combinations with each other 

(e.g., different papers by Gao and others that evaluated different combinations of tarps, reactive barriers 

and organic amendments). Zheng, in another study, (as well as other researchers) evaluated competitive 

degradation between various fumigants because it appears that chloropicrin can impede degradation of 

MITC due to impacts on microorganisms. These types of factors would be difficult for fumigant users 

to manipulate in any meaningful way. Ajwa [19] attempted to constrain changes in possible co-critical 
154

factors through a concurrent evaluation of field scale emissions as illustrated in Figure 199 below where 

emissions from co-located fields were evaluated with 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., soil texture) as illustrated by Chellemi [27, 28] in 

Figure 84. 

Figure 199: Multiple Concurrent Field Emission Study Design By Ajwa

Another example in field scale research where co-critical factors could have a significant influence is 

illustrated in Figures 9 and 10 that present the emissions data for iodomethane and chloropicrin that 

were generated using high barrier tarps [11-16]. These data were developed in different locations from 

the other tarped raised bed emissions data for those 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). Additionally, the baseline studies were generated using more 

typical application control systems that essentially output a continuous source of fumigant down a 

treated row. However, the high barrier tarp data were not only generated 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 compared 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 concentration contours. It is unclear based on the study 

designs how much barrier films or this novel application method contribute to emission reduction. 

There are likely other examples of the impacts of co-critical factors upon the research results. These 

examples probably represent some of the most illustrative situations where conclusions 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 country that involve the use of a variety of single 

use materials (e.g., agricultural films and reactive adjuvants) as well as equipment 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 broader scale, that nomenclatures have developed to 

1 acres

ISCST Monitoring Station

Aerodynamic Monitoring Station

Flux Chambers

ISCST Weather Station

Aerodynamic Weather Station

`

83 ft

83 ft

83 ft

83 ft

40 ft

40 ft

40 ft

40 ft

Field 1- VIF Field 2- VIF

Field 3- Std

Field 4- Std

Large scale studies on using VIF & SIF 

for fumigant emission reduction

Large scale studies on using VIF

for fumigant emission reduction
155

describe specific pieces of equipment, tarps, and so forth. This is a natural result of the variety of 

possible fumigant use situations that are likely encountered across the country related to varied pests and 

pest pressures, soil conditions, growing seasons, crops 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 including that it would: reduce or eliminate any 

misconceptions that may occur in technology transfer such as describing a certain application practice; 

allow for a more systematic description of materials such as tarps, injection methods, and sealing 

methods (e.g., bed shapers and tarp laying equipment) and how well they perform relative to varied field 

situations; and begin to provide a framework for the development of a more regimented approach for 

refining current standard cultural practices (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 Purposively 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 scale emissions studies used to develop risk estimates 

are limited (i.e., ~70 for all chemicals in total). This means that only specific situations could be 

addressed in the current assessments and that these situations are defined and consequently limited by 

the conditions of the field studies. The modeling approaches used in the fumigant risk assessments were 

evaluated by the FIFRA Scientific Advisory Panel (SAP), one of the key points was there is uncertainty 

associated with generalizing limited data to regions of the country or the country as a whole [See 

http://www.epa.gov/scipoly/sap/meetings/2004/august1/august2425minutes.pdf.] To alleviate some of 

these concerns, all field scale data were individually used to calculate risks for decision makers to 

consider. The SAP also indicated that it would be useful to explore the basic 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 useful in eliminating some of the uncertainty which 

they discussed. In this paper, many sources of research have been presented 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 constrained basic elements to be representative of major 

agricultural patterns or typical field conditions (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. Yates [10] in Figure 8 above illustrated 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 designing a multi-year complex field research program 

designed to develop of series of exposure estimates for those involved in pesticide applications (See 

http://www.exposuretf.com/). As part of the evolution of this project, there have been significant 

discussions related to the ethical and statistical issues pertaining to how individuals and exposure 

scenarios are selected for the research and how monitoring events from those individuals can be used to 

represent the populations as a whole. Clearly, exposure monitoring of individuals per se does not 
156

directly apply to the fumigant issues herein but the concepts of representativeness in the research should 

be considered because it can present sources of non-quantifiable uncertainties. For example, field 

emissions data are generated in a region of high fumigant use but within that region there will be a 

distribution of emission profiles that are impacted by any number of factors. It is truly not clear how 

single measurements of emissions relate to that overall distribution. What must be acknowledged is that 

there are several approaches for selecting research situations that can range 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 discussed in this paper are clearly 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/apr-18-20-2007-public-meeting.htm 

This issue is not just applicable to fumigant issues or human exposure monitoring but it is a general 

uncertainty that could pertain to many areas of research. It has been highlighted 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 developing an integrated approach for evaluating how factors 

impact emissions using laboratory- and field-based research as well as first principles modeling. A 

critical part of this analysis has been to obtain research information 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 when evaluating the analyses presented 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 attempted during the course of the 

development of this document. This included searching a number of electronic information services to 

obtain information and soliciting data from affected stakeholders and researchers in this area at the 2006 

and 2007 Methyl Bromide Alternatives Outreach conferences (http://www.mbao.org). To that end, it is 

believed that the trends in the current research are appropriately represented in this document but it is 

possible that key research has not been included because it was either not available or identified in the 

process. Clearly, this document addresses a wide range of topics that involves many fields of basic 

research. As more and updated information become available, 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 research 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] provides an excellent illustration of the issues that should be considered 

when evaluating the results of this analysis in the context of likely occurrences under actual use 

conditions. As illustrated in this figure, it is clear that laboratory-based data can be reproduced easily, 

compared to field studies, and that this eliminates possible uncertainties relative to the factors being 

measured. However, it also indicates that the representativeness of such research to actual field 

performance is questionable. Conversely, field research results are likely to be more representative of 

actual use occurrences but they have higher degrees of uncertainty associated 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 currently 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 provides good information related to 

film performance as a boundary layer. However, fumigant 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 the 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 quantifying 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 consider when interpreting the 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 research 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 presented herein is believed to represent 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 which could be drawn based on this analysis. Also, an important fact 

to consider is that any determination 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 predict large theoretical emission reductions without consideration of how 

those applied factors may be inter-related. For example, it is clear 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 through 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 from treated fields after the application of soil 

fumigants. They include factors that can be manipulated by users to retain 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 are clearly governed by integrated systems that are 

comprised of several mechanisms based on physical-chemical properties, populations of 

microorganisms, and conditions which have been manipulated by growers such as the use of reactive 

adjuvants and/or high barrier tarps as a boundary layer. These mechanisms can be categorized into three 

basic phases including subsurface degradation and 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 conditions 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 integrated systems approach has to be considered when 

estimating the impacts of altering field conditions either through purposive choices of users. It is also 

important in establishing what could be determined to be a minimum standard of care for users in all 

situations to ensure that specific field conditions are well understood and what, if any, actions are 

needed to be completed by users to ensure proper application procedures are followed. This analysis has 

also clearly demonstrated that the degree on integration between 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 conditions 
159

This analysis indicates that these factors represent the critical measures 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 factors 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 time from a purely scientific basis. Although there are clear cut trends 

in responses relative to changing conditions, there are many inter-related factors which are not well 

understood because of a lack of information and associated 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 quantitative 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 and data available, that 

the predicted reductions in emissions are not overstated. It is clear that the following techniques, which 

can be manipulated by fumigant users, hold the most 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 for certain chemicals and application methods; 

• Manipulation of soil moisture where applicable; 

• Manipulation of soil compaction where 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

5.0 References 

[1] Laverde, G. 2002. Agricultural Films: Types and Applications, Journal of Plastic Film and Sheeting 

(Vol. 18, October 2002) pp. 269-277. 

[2] Barry (1999) Methyl Bromide Emission Ratio Groupings available at: 

http://www.cdpr.ca.gov/docs/emon/pubs/ehapreps/analysis_memos/mthbrmemissgroup.pdf 

[3] Ajwa, H., A. 2007. Testing Film Permeability to Fumigants Under Laboratory and Field Conditions. 

2007 Methyl Bromide Alternatives Outreach Conference.

http://mbao.org/2007/PDF/Preplant/PP3/Ajwa(16).pdf

[4] ASTM Standard D4635-01 Standard Specification for Polyethylene Films Made From Low-Density 

Polyethylene for General Use and Packaging Applications, ASTM 100 Barr Harbor Drive, West 

Conshohocken PA 19428-2959. 

[5] ASTM Standard D2103-05 Standard Specification for Polyethylene Films and Sheeting, ASTM 100 

Barr Harbor Drive, West Conshohocken PA 19428-2959. 

[6] Yates, S., Film Permeability Measurements in support of USDA-ARS Area-Wide Research. 2007 

Methyl Bromide Alternatives Outreach Conference. 

http://mbao.org/2007/PDF/Preplant/PP3/Yates(17).pdf

[7] USDA. Film Permeability Table. 2005. 

http://www.ars.usda.gov/Research/docs.htm?docid=10474&page=2

[8] Gamliel, A., 1998. “Permeability of Plastic Films to Methyl Bromide”. Crop Protection. 17 pp.241-

248 

[9] Ashworth, Daniel J. 2007. Laboratory Measurements of Fumigant Emissions from Agricultural 

Soils. 2007 Methyl Bromide Alternatives Outreach Conference. 

http://mbao.org/2007/PDF/Preplant/PP1/AshworthD(2).pdf

[10] Yates, S., 2007. Preplant Session VI: Emission Determination of Soil Fumigation Emission 

Reduction Factors. 2007 Methyl Bromide Alternatives Outreach Conference. 

http://mbao.org/2007/PDF/Preplant/PP6/Yates(PPSVI).pdf

[11] EPA MRID 46385202; Baker, F.; Estigoy, L.; Belcher, T. (2004) Environmental (Off-Site) 

Monitoring and Indirect Flux Determination of Iodomethane (TM-425) Under Field Conditions 

Following Tarped/Raised Bed/Shallow Shank Injection Application. Project Number: 1254W, 1254W/1, 

AR24004. Unpublished study prepared by PTRL West, Inc., Access Research and Consulting, Inc. and 

Pacific Agricultural Research Corp. 591 p. 

[12] EPA MRID 458791-01; Baker, F.; Estigoy, L.; Belcher, T. (2003) Environmental (Off-Site) 

Monitoring and Indirect Flux Determination of Iodomethane (TM-425) Under Field Conditions: Lab 

Project Number: 1142W: 1142W-1: ERS22087. Unpublished study prepared by PTRL West, Inc., Excel 

Research Services, Inc., and Pacific Ag Group. 538 p. 

[13] EPA MRID 455937-10; Baker, F.; Estigoy, L.; Reiss, R. et al. (2002) Volatility of (Carbon 14) 

Iodomethane (TM-425) Under Field Conditions in California and Florida: Lab Project Number: 893W: 

893W-1: 893W-FL. Unpublished study prepared by Plant Sciences, Inc. 929 p. 
161

[14] EPA MRID 472952-02; Baker, F.; Arndt, T. (2007) Direct and Indirect Flux Determination of 

Iodomethane and Chloropicrin Under Field Conditions Following Tarped/Raised Bed/Shallow Shank 

Injection of Midas 50:50 in Dover, FL. Project Number: 1595W, 1595W/1. Unpublished study prepared 

by PTRL West, Inc, Paragon Research Services and Pacific Ag Group. 707 p. [White metalized tarps] 

[15] EPA MRID 472952-03; Baker, F.; Arndt, T. (2007) Direct and Indirect Flux Determination of 

Iodomethane and Chloropicrin Under Field Conditions Following Tarped/Raised Bed/Shallow Shank 

Injection of Midas 50:50 in Bainbridge, GA. Project Number: 1619W, 1619W/1. Unpublished study 

prepared by PTRL West, Inc, Paragon Research Services, Pacific Ag Group. 590 p. [Black Hytiblock] 

[16] EPA MRID 472952-04; Baker, F.; Arndt, T. (2007) Direct and Indirect Flux Determination of 

Iodomethane and Chloropicrin Under Field Conditions Following Tarped/Raised Bed/Shallow Shank 

Injection of Midas 50:50 in Hart Michigan. Project Number: 1646W, 1646W/1. Unpublished study 

prepared by PTRL West, Inc, Paragon Research Services, Pacific Ag Group. 590 p. [Black Blockade] 

[17] EPA MRID 441492-01; Beard, Murphy, Fontaine, Weinberg (1996) Monitoring of Potential 

Worker Exposure, Field Flux, and Off-Site Air Concentration During Chloropicrin Field ApplicationUntarped Arizona Emissions Data; Sponsor CMTF (Chloropicrin Manufacturers Task Force). 

[18] EPA MRID 441492-01; Beard, Murphy, Fontaine, Weinberg (1996) Monitoring of Potential 

Worker Exposure, Field Flux, and Off-Site Air Concentration During Chloropicrin Field ApplicationChloropicrin Tarped Arizona Emissions Data – 1.5 mil mulch. 

[19] Ajwa, H.,. 2007. Fumigant Emission Reductions by Using Low Permeability Film and Thiosulfate 

Water Seal. 2007 Methyl Bromide Alternatives Outreach Conference. 

http://mbao.org/2007/PDF/Preplant/PP1/Ajwa(6).pdf

[20] Gao, S., 2007. Surface Seals Reduce 1,3-Dichloropropene and Chloropicrin Emissions in Field 

Tests. J. Environ. Qual. 2007 36: 110-119. [Abstract] [Full Text] [PDF]

[21] Papiernik, S., 2002. Effect of environmental conditions on the permeability of high density 

polyethylene film to fumigant vapors. Environmental Science and Technology 36:1833-1838. 

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[22] Wang, D., 2007. Estimating Plastic Film Permeability Under Field Conditions. 2007 Methyl 

Bromide Alternatives Outreach Conference. http://mbao.org/2007/PDF/Preplant/PP1/Wang(3).pdf

[23] Wang, D., 1997., Reducing Methyl Bromide Emission with a High Barrier Plastic Film and 

Reduced Dosage. Environ. Sci. Technol.; (Article); 1997; 31(12); 3686-3691. Abstract Full: HTML / 

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[24] Cryer, S., 2007. Preplant Session VI: Emission Determination of Soil Fumigation Emission 

Reduction Factors. 2007 Methyl Bromide Alternatives Outreach Conference. 

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[25] Papiernik, S., 2001. Effect of Surface Tarp on Emissions and Distribution of Drip-Applied 

Fumigants. Environ. Sci. Technol.; (Article); 2004; 38(16); 4254-4262. Abstract Full: HTML / PDF

[26] Ajwa, H., 2002. Application of alternative fumigants through drip irrigation systems. 

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[27] Chellemi, Dan O. 2007 Factors Affecting the Performance of MB Alternatives – A South Atlantic 

Perspective. 2007 Methyl Bromide Alternatives Outreach Conference. 

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[28] Chellemi, Dan O. 2007. Preplant Session VI: Emission Determination of Soil Fumigation Emission 

Reduction Factors. 2007 Methyl Bromide Alternatives Outreach Conference. 

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[29] Wang, D., 2007. Preplant Session VI: Emission Determination of Soil Fumigation Emission 

Reduction Factors. 2007 Methyl Bromide Alternatives Outreach Conference. 

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Movement in Soil. Vadose Zone J. 2004 3: 656-667. [Abstract] [Full Text] [PDF]

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from Agricultural Soils. Environ. Sci. Technol.; (Article); 2007; 41(7); 2231-2236. Abstract Full: 

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degradation of 1,3-dichloropropene in soil. Pest Manag. Sci. 57:1107-1113. 

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[33] Gan, J., 1998. Application of Organic Amendments To Reduce Volatile Pesticide Emissions from 

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[34] Gao, S., 2006. Using Surface Water Application to Reduce 1,3-Dichloropropene Emission from 

Soil Fumigation. J. Environ. Qual. 2006 35: 1040-1048. [Abstract] [Full Text] [PDF]

[35] Guo, M., 2004. Effects of Environmental Factors on 1,3-Dichloropropene Hydrolysis in Water and Soil. J. 

Environ. Qual. 2004 33: 612-618. [Abstract] [Full Text] [PDF]

[36] Xu, J.M.; Gan, J.; Papiernik, S.K.; Becker, J.O.; and Yates (2003) Incorporation of Fumigants Into 

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