Department of Pesticide Regulation
Environmental Monitoring Branch1001 I Street, P.O. Box 4015
Sacramento, CA 95812-4015

A comparison between field-estimated and HYDRUS-simulated emission of
1,3-Dichloropropene from agricultural fields
Maziar Kandelous, Ph.D.
Senior Environmental Scientist (Specialist)
10/17/2019
1. Introduction
1,3-Dichloropropene (1,3-D) is a fumigant used to control nematodes, insects and diseaseorganisms in the soil. It is commonly used as a pre-plant treatment that is injected into soil. It
may also be applied through drip irrigation. Regardless of the application method, thepossibility of offsite transport of this fumigant due to volatilization may subsequently result inhuman exposure through inhalation. To mitigate its potential cancer risk, the Department of
Pesticide Regulation (DPR) limits the use of 1,3-D on a regional basis (township cap). The
current township cap is 136,000 “adjusted” pounds during a calendar year in any township (sixby six-mile area). Adjusted pounds refer to the amount of 1,3-D active ingredient multiplied by
an application factor (AF) to account for differences in air concentrations due to application
method, region, and season of application.
Application factors are multipliers that DPR originally intended to use to account for variation in
cumulative flux density between different application methods (e.g., deep versus shallow shankinjection). The basis for determining an AF is the emission ratio (ER, also referred to in somedocuments as an 'emission rating'), an estimate of the emitted fraction of total applied mass at
a given time post-application (ER = cumulative flux / mass applied). Department of Pesticide
Regulation derived ER values currently used in AF calculations from a small selection of fieldestimated ER values obtained from field flux studies. However, these initial field studies are
small in number due to various reasons including financial challenges. Therefore, DPR combinescomputer modeling with field studies to expand its ability to estimate ER for different
application methods and under different environmental conditions.
HYDRUS (Simunek et. al., 2008) is the primary model used by DPR to simulate the emission of
fumigant from soil to the atmosphere. HYDRUS is a numerical model that solves the Richards
equation for saturated and unsaturated water flow and convection-dispersion type equations
for heat and solute transport. For the purpose of simulating processes involved in fumigation,
the flow equation simulates dynamics of water within the soil profile, leaching, andevaporation. The heat transport equation accounts for heat transfer caused by conduction as

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well as by convection with flow of water. The convection-dispersion solute transport equations
consider reactions between the solid, liquid, and gas phases that are required for solutes thatexist in both adsorbed and volatile phases such as pesticides. The solute transport equations
also simulate zero-order production, first-order degradation, and first-order decay/production
reactions. In addition, it simulates solute transport simultaneously in both the liquid andgaseous phases.
The HYDRUS model has been successfully employed to simulate the fate of fumigants including
1,3-D for different application methods and under different conditions ranging from bareground broadcast to partially totally impermeable film (TIF) covered chemigated bed
application (Spurlock et al. 2013). In this report we further evaluate the ability of the HYDRUS
model to estimate 1,3-D flux under different application methods. The key difference between
this evaluation and that of Spurlock et al. (2013) is the availability of detailed site-specific input
data for HYDRUS. Therefore, the objective of this report is to evaluate how well HYDRUS model
results compare to field-estimated flux values in conditions when a limited amount of sitespecific input data is available and the HYDRUS model relies on input parameters frompreviously peer-reviewed publications.
2. Materials and methods
2.1. Simulation of field experiments
A total of 23 experiments was available for the purpose of modeling from the 1,3-D emission
database compiled by Kandelous (2018) (See appendix A for detail). The HYDRUS model
requires specific information about the experiment, such as the extent of modeling domain;soil physical, hydraulic, thermal, and chemical properties; solute properties such as diffusion,
reaction, degradation, absorption, and their temperature dependency; initial status of water,
solute, and temperature in the soil profile (so-called initial condition); boundary conditions in
respect to water (e.g., irrigation, precipitation, evaporation), heat (temperature at soil surface
and temperature of irrigation water); and solute (concentration of applied solute in irrigationwater in case of chemigation, or tarp permeability for gas diffusion) and their temporalvariation.
There were no geographical restrictions and all experiments (from four states of California,
Texas, Georgia, and Florida) with minimum required input data of domain properties, soil
properties, initial, and boundary conditions were considered for HYDRUS modeling. A total of
13 experiments from the dataset of 23 experiments available, met the minimum input
requirements and were used for HYDRUS modeling. Minimum input data from the 13
experiments was extracted and the remaining required input data was assumed based on datain published literature and from previous HYDRUS simulations conducted by Brown (2018) (Seeappendix B for detail). Table 1 shows a brief description of experiments used for HYDRUS
modeling that cover a broad range of field fumigation methods (FFM) including FFM codes
1201, 1202, 1206, 1209, 1242, and 1259.

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Table 1. List of field experiments used for evaluation of HYDRUS model to simulate 1,3-D flux from fumigated fields.

1
2

Study

FFM
code

Application
type

Application depth(in)Application rate
(lb/ac)Duration
(day)

Tarp
type

Tarp cut(day)

Gillis and Dowling, 1999

1201

Broadcast14

121.51

14

NA

NA

NA

NA

Gillis and Dowling, 1999

1201

Bed

12

67.41

14

3

Ajwa and Sullivan, 2012b1202

Broadcast12

122.80

14

PE

6

4

Ajwa, et al., 20128

153.5010

HDPE

No cut5

1202*

BroadcastAjwa, et al., 2012

Broadcast8

146.2510HDPE

No cut6

Knuteson, et al. 1992b

1202*
1206

Broadcast18

121.21

14

NA

NA

7

1206

Bed

21

119.24

21

NA

NA

Drip

1

67.23

14

PE

No cut9

Knuteson, et al. 1995
van Wesenbeeck and Philips,
2000
Ajwa and Sullivan, 2012a1242

Broadcast12

230.80

18

TIF

16

10

Ajwa and Sullivan, 2012a1242

Broadcast12

221.00

12

TIF

10

11

Ajwa and Sullivan, 2012a1242

Broadcast12

239.80

7

TIF

5

Ajwa and Sullivan, 2012b

1242

Broadcast12

126.39

14

TIF

6

Ajwa, 2015

1259

Drip

1

173.11

13

TIF

No cut

8

12
13

1209

*These two studies had an application depth shallower than required minimum of 12 inches.

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It should be noted that although the minimum depth for FFM 1202 is 12 inches, two
experiments included in this study were conducted at a depth of only 8 inches. However, forthe purpose of evaluating HYDRUS’ ability to produce results comparable to the field-estimatedflux values, these two FFM 1202 experiments were included in this study. These are noted inTable 1.
DomainInformation about application method and associated field preparation, application equipment
specifications, injection depth, and soil layering were extracted for each individual experimentand a representative simulation domain was created in HYDRUS. The simulation domain
represents a vertical cross-section of soil profile perpendicular to the injection line (i.e., chisels
or drip-lines). The width of the domain was set to a distance half-way between adjacentinjection lines. While the width of simulation domain (B+C+D) varied between different studies,
the length of the domain (A+E) was set to 125 cm to eliminate any potential effect of bottom
boundary condition on simulation results. Figure 1 shows a general shape of the HYDRUS
domain with associated dimensions for the individual studies.
Initial condition
Soil moisture and temperature measured at the beginning of experiment were used togenerate soil moisture and temperature profiles for initial conditions in the HYDRUS domain.
Fumigant concentration of entire profile, except the injection area, was assumed to be zero.
The concentration of fumigant (C) within the injection area was determined as C = M / V, where
M is the amount of fumigant applied to the soil surface area of HYDRUS domain, and V is thechisel’s volume of influence (i.e. volume of soil that was covered by fumigant at injection time).
An illustrative example is presented in appendix C.

Figure 1. General shape of the HYDRUS domain with associated dimensions for individual
studies. Parameters A-E are in cm.

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Boundary condition
The bottom boundary for water flow was set as “free drainage” and the “third-type” boundary
condition for both solute and heat transport. For non-tarpaulin soil surface, the top boundary
was set as atmospheric boundary condition for water flow, “volatile type” for solute transport,
and “first type” for heat transport. Similar boundary conditions were used for tarpaulin soil
surface in broadcast application in which the entire soil surface was covered.
In bed applications including drip (bed chemigation), “atmospheric boundary” was used for thepart of soil surface covered with tarpaulin and the remaining area was assigned the “variable
flux 1” boundary condition. The top boundary for solute transport was set as “volatile type” andfor heat transport as “first type.” However, a different set of boundary temperatures wereassigned for soil surface covered by tarpaulin in compare with the bare soil. For drip
application, the boundary around dripline was set as “variable flux 2” for water flow, and the“third-type” boundary condition for both solute and heat transport.
Evaporation for tarpaulin soil surface was set to zero, while evapotranspiration data fromnearby California Irrigation Management Information System (CIMIS) stations were used for
non-tarpaulin soil surface. A sine-wave shape temperature curve was assumed to represent
diurnal variation of temperature. The magnitude of average and peaks as well as timing ofpeaks’ occurrence of soil surface (depth < 5cm) measured in the field were considered forgenerating sine-wave shape soil surface temperature for both bare soil and under tarpaulinconditions.
Soil properties
Soil profile and layering information provided by field studies was used as the basis tocharacterize the HYDRUS domain profile for each simulation. Soil textural properties and bulk
density were used as inputs for ROSETTA to estimate soil hydraulic properties required by
HYDRUS.
Unless specified in this report, soil, solute, and heat specific parameters were the same as those
employed by Brown (2018).
2.2. Determination of Emission Ratio and Peak Flux Values
Emission ratio
Emission ratios for both field study estimates and HYDRUS simulations were determined by
dividing the total amount of 1,3-D emitted from the application area within the monitoring
period by the total amount of applied 1,3-D.
Peak flux
HYDRUS simulations were constructed such that emission values were simulated over the sametime intervals as in the corresponding field experiment, thereby allowing for pair-wisecomparisons of fluxes. For example, a field experiment collecting air samples in 6-hour intervals
would be modeled here with flux output simulated over the corresponding 6-hour intervals.
The sampling intervals varied within and between experiments ranging from 2-hour to 24-hour
periods. For both field study and HYDRUS simulation, 24- and 72-hour rolling averages of fluxes
for the entire monitoring period were calculated. It should be noted that for those instances inPage | 5

which the rolling periods of either 24 or 72 hours were not available, the closest rolling period
was chosen such that it did not exceed 25 and 73 hours for 24- and 72-hour rolling averages,
respectively (see Kandelous 2018, Appendix C). In the next step, maximum 24- and 72-hourrolling averages fluxes were determined.
2.3. Statistical indicators
The HYDRUS model’s performance for simulating the flux of 1,3-D from the soil surface was
evaluated by comparing field-estimated fluxes to HYDRUS-simulated fluxes and calculating a
range of error estimates, dimensionless efficiency tests, and a test of significance in linear
relation.
The error estimates included mean error (ME), mean absolute error (MAE), and root meansquare error (RMSE). Linear regression analysis was used to calculate the coefficient ofdetermination (R2) and to test the level of linear relationship between field-estimated and
HYDRUS-simulated fluxes. Linear model assumptions were evaluated using a global validation
criteria introduced by Peña and Slate (2006). Lastly, the HYDRUS model’s performance insimulating 1,3-D fumigant flux was evaluated by two indicators of model efficiency: one
proposed by Nash and Sutcliffe (1970) (Nash and Sutcliffe model efficiency or NSE) and theother introduced by Willmot (1981), the index of agreement (IA). The NSE ranges between -∞and 1 (perfect fit), where a value between 0 and 1 is considered to indicate acceptableperformance. The IA ranges from 0 to 1, where a value of 1 means a perfect agreement
between model and observation. See appendix D for equations used to calculate thesestatistical indicators.
3. Results and discussion
Figure 2 shows the comparison between simulated and field-estimated emission ratio and peak
24-hour and 72-hour rolling average fluxes. The regression analysis for the slope resulted in rsquare values of 0.76, 0.77, and 0.83 for emission ratio and peak 24-hour and 72-hour rolling
average fluxes, respectively (See appendix E for detailed information of regression analysis andevaluation of linear model assumptions). These relatively high r-square values are indicationsof the strength in linear relationship between simulated and field-estimated values. It is also
shown that the 95% confidence interval around the regression line includes the slope of one.
The close proximity of slope values to one and relatively high values for the coefficient ofdetermination show that simulated values are close to those calculated from field data. The
error estimate indicators of ME, MAE, and RMSE show relatively small values for all three
indices indicating small differences between simulated and field values. In order to provide acomprehensive evaluation of model performance, NSE and IA were calculated for all three ofthe emission ratios and peak 24-hour and 72-hour rolling average fluxes (Fig.2). Nash andSutcliffe model efficiency values were all greater than 0.75, which shows that the HYDRUS
model is highly efficient in recreating field-estimated data. In addition, IA was calculated for all
three indices, and all IA values were above 0.92. These values show that there are strong
agreements between simulated and field-estimated emission ratios and peak 24-hour and 72hour rolling average fluxes.

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Figure 2. Comparison between simulated and field-estimated emission ratios, peak 24-hr rolling
average fluxes, and peak 72-hr rolling average fluxes. The red dashed line shows the linear
regression line, the gray shaded area shows the 95% confidence interval, and the black line
represents the 1:1 ratio line. RMSE is root mean square error, MAE is mean absolute error, ME
is mean error, NSE is Nash and Sutcliffe model efficiency, and IA is index of agreement.
Figure 3 shows a paired comparison of simulated and field-estimated emission ratios and peak
24-hour and 72-hour rolling average fluxes. In general, it shows that the simulated values areslightly lower for fumigation methods with shallow injection depths (i.e., FFM 1201 and 1202)
and are slightly higher than those estimated from field studies for fumigation methods withdeep injection depths (i.e., FFM 1206 and 1209). It also shows generally good agreement
between simulated and field-estimated values when soil surface is covered with TIF (i.e., FFM
1242 and 1259).
4. Conclusion
As stated in the objective, the goal of this report was to evaluate the applicability of the
HYDRUS model to estimate 1,3-D emissions using the conventionally collected data. The final
comparison is not between simulated data and observed data, as the field scale fluxes were
estimated using Aerodynamic, Integrated Horizontal Flux, or Back-Calculation approaches.
Therefore, the analysis is a comparison between two approaches to estimate 1,3-D emission
rate (flux).

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Given the statistical comparisons conducted in this report, we conclude that simulated fluxes by
the HYDRUS model are comparable to those estimated using submitted field study data.
Therefore, HYDRUS can provide an alternative or assistive tool to estimate the emission rate of
1,3-D from the application area.
Field variability in application rate, soil type, soil moisture, tarp properties and possible tarp
perforation provide considerable spatiotemporal variation in emission rate at field scale. While
the variability in most of these parameters cannot be realistically quantified, a comprehensive
HYDRUS modeling study should consider those variations whenever possible.
The advantage of using HYDRUS is that it can demonstrate the uncertainty in fumigant
emissions on a field scale while other approaches report a single emission value despite theexistence of considerable uncertainty in the estimated value.

Figure 3. Bar plots of comparison between simulated and field-estimated emission ratio, peak
24-hr rolling average fluxes, and peak 72-hr rolling average fluxes. The X-Axis show FFM code
associated with each bar and in the same order as in Table 1. FFM 1201 represents non-tarped
shallow broadcast, FFM 1202 represents tarped shallow broadcast, FFM 1206 represents non-Page | 8

tarped deep broadcast, FFM 1209 represents chemigated tarped bed, FFM 1242 represents TIF
shallow broadcast, and FFM 1259 represents chemigated TIF bed.

5. References
Ajwa, H., 2015. Monitoring of 1,3-Dichloropropene Emissions from Drip Application Under EVAL
Barrier Resin Film (Vaporsafe) (No. 50046-0198). University of California Davis.
Ajwa, H., Sullivan, D., 2012a. Soil Fumigant Emissions Reduction Using Eval Barrier Resin Film
(Vaporsafe) and Evaluation of Tarping Duration Needed to Minimize Fumigant Total Mass
Loss (No. 50046-0198). University of California Davis.
Ajwa, H., Sullivan, D., 2012b. Monitoring of Chloropicrin, 1,3-Dichloropropene and Methyl
Bromide Field Emissions from Shank Applications and Drip Fumigation Under EVAL-Resin
Barrier Films (No. 0199-0142). DOWELANCO.
Ajwa, H., Sullivan, D., Chellemi, D., 2012. Monitoring 1,3-Dichloropropene and Chloropicrin
Emissions from Solid-Tarp Shank Injections at Two Sites Near Fort Pierce, Florida (No.
0199–0143). DOWELANCO.
Brown, C., 2018. Hydrus-simulated Flux Estimates of 1,3-Dichloropropene Maximum PeriodAveraged Flux and Emission Ratio for Approved Application Methods. Department of
Pesticide Regulation.
Gillis, M.J., Dowling, K.C., 1999. Effect of Broadcast and Row Application Methods On 1,3Dichloropropene Emissions (No. 50046-0127). DOWELANCO.
Kandelous, M., 2018. Creation of a 1,3-Dichloropropene Flux Database from Emission Studies
Submitted to the Department of Pesticide Regulation from 1992 - 2015. Department of
Pesticide Regulation.
Knuteson, J.A., Dixon-White, H.E., Petty, D.G., 1995. Field Volatility of 1,3-Dichloropropene in
San Joaquin Valley, California (No. 50046-0088). DOWELANCO.
Knuteson, J.A., Petty, D.G., Shurdut, B.A., 1992b. Field Volatility of 1,3-Dichloropropene in the
Salinas Valley, California (No. 50046-0067). DOWELANCO.
Nash, J.E., Sutcliffe, J.V., 1970. River Flow Forecasting through Conceptual Models Part I: A
Discussion of Principles. Journal of Hydrology 10, 282–290. https://doi.org/10.1016/00221694(70)90255-6
Peña, E.A., Slate, E.H., 2006. Global Validation of Linear Model Assumptions. J Am Stat Assoc
101, 341. https://doi.org/10.1198/016214505000000637
Šimůnek, J., van Genuchten, M.T., Šejna, M., 2008. Development and Applications of the
HYDRUS and STANMOD Software Packages and Related Codes.Vadose Zone Journal 7, 587–
600. https://doi.org/10.2136/vzj2007.0077
Spurlock, F., Johnson, B., Tuli, A., Gao, S., Tao, J., Sartori, F., Qin, R., Sullivan, D., Stanghellini, M.,
Ajwa, H., 2013. Simulation of Fumigant Transport and Volatilization from Tarped Broadcast
Applications. Vadose Zone Journal 12. https://doi.org/10.2136/vzj2013.03.0056

Page | 9van Wesenbeeck, I., Phillips, A.M., 2000. Field Volatility of 1,3-Dichloropropene andChloropicrin from Surface Drip Irrigation Application of In-line® to Vegetable Beds Under
Polyethylene Tarp (No. 50046-0152). DOWELANCO.
Willmott, C.J., 1981. On the Validation of Models. Physical Geography 2, 184–194.
https://doi.org/10.1080/02723646.1981.10642213

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