Journal of Environmental Quality 30:1887-1895 (2001)
© 2001 American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America
TECHNICAL REPORT
Atmospheric Pollutants and Trace Gases
Predicted 1,3-Dichloropropene Air Concentrations Resulting from Tree and Vine Applications in California
S. A. Cryer* and
I. J. van Wesenbeeck
Dow AgroSciences, 9330 Zionsville Road, Indianapolis, IN 46268
* Corresponding author (sacryer{at}dowagro.com)
Received for publication December 4, 2000.
 |
ABSTRACT
|
|---|
The preplant soil fumigant 1,3-dichloropropene (1,3-D) is effective for nematode control and is expected to further replace methyl bromide (MeBr) as MeBr use is phased out. Acute human exposure to soil fumigants is managed in part by using buffer zones between treated fields and occupied structures. The required buffer zone for 1,3-D in California is 91.4 m (300 ft) for all uses. However, a 30.5-m (100-ft) buffer setback is desired for 1,3-D to be an important replacement for MeBr in the orchard and vineyard markets. The Industrial Source Complex Short-Term model, Version 3 (ISCST3) was used to simulate township-wide long-term average and short-term air concentration distributions of 1,3-D. The Gaussian plume model ISCST3 can be used to assess dispersion of air pollutants and pollutant concentrations on receptors from a variety of sources and in diverse airsheds. Long-term and daily-average air concentrations can be compared with the California permitted chronic or acute toxicity endpoints, respectively, to assess the potential risk for individuals living within the township at the proposed buffer setback. Modifications to ISCST3 were made for specific nonpoint-source agricultural constraints and management practices. Chronic and acute air concentration distributions of 1,3-D with a 30.5-m buffer constraint around treated fields are similar to currently permitted air concentration distributions in California. Refinement of exposure as a function of buffer distance, application rate, and field size is possible due to the resolution of the simulation and external post-processing capabilities. Simulated examples of 1,3-D acute and chronic exposure cumulative distributions are presented.
Abbreviations: CDMS, Crop Data Management System • CDPR, California Department of Pesticide Regulation • ISCST3, Industrial Source Complex Short-Term model, Version 3 • PUR, California pesticide use records • T&V, tree and vine crops
|
INTRODUCTION
|
|---|
THE soil fumigant 1,3-dichloropropene (1,3-D) is the active ingredient in Telone II (trademark of Dow AgroSciences) products. 1,3-Dichloropropene is an effective product for nematode control and is currently labeled for use on a variety of crops including preplant applications for tree and vine (T&V) crops. 1,3-Dichloropropene is expected to continue to replace methyl bromide as a nematicide in several agricultural markets as MeBr is being phased out as mandated by the Montreal Protocol of 1987 (United Nations Environment Programme, 1995). This protocol is carried out in the USA by the USEPA through Title VI of the Clean Air Act for substances that deplete the ozone layer.
Buffer zones between treated fields and occupied structures are presently used to mitigate the acute inhalation exposure potential for soil fumigants. A buffer is an area of land surrounding a treated field where chemical is not applied. The buffer zone is the distance from a treated field edge to an occupied structure. Currently, the buffer zone for 1,3-D is 91.4 m (300 ft) for all crops and uses in California. A 30.5-m (100-ft) buffer zone setback is required for 1,3-D to serve as a more viable MeBr alternative in California T&V markets. As the buffer zone between occupied structures and treated fields decreases, concentrations of soil fumigants at the buffer edge increase. Hence, air concentration predictions for 30.5-m buffer zones are required to determine the 1,3-D concentrations associated with acute or chronic exposure potentials for individuals living at or outside the proposed buffer zone.
The Industrial Source Complex Short-Term model, Version 3 (ISCST3) (USEPA, 1995a) is a steady-state Gaussian plume model used to assess dispersion of air pollutants and pollutant concentrations from a variety of sources. Developed by the USEPA, ISCST3 has been used as a regulatory tool for predicting concentrations of air contaminants in diverse airsheds. Examples include vehicle exhausts in urban areas (Hao et al., 1999), industrial sulfur dioxide emissions (Kumar et al., 1999), and methyl bromide concentrations resulting from soil fumigation in rural areas (Honaganahalli and Seiber, 2000). The ISCST3 model was modified and executed using a fine spatial grid within a high-use California township where 1,3-D applications are made to T&V crops. A township is a geographic-specific 9656- x 9656-m (6- x 6-mile) square parcel of land within California. The California Department of Pesticide Regulation (CDPR) has imposed a mass limit of 1,3-D, known as the township cap, that can be applied in a given year for a specific township. 1,3-Dichlorpropene can never exceed the township cap of 35962 L (9500 gallons) of Telone II product per year. The 1,3-D township cap is a measure implemented by the CDPR to manage the chronic exposure of 1,3-D within the state.
|
MATERIALS AND METHODS
|
|---|
Model
The ISCST3 model requires the specification of receptors, sources (treated fields), source strengths (flux), and meteorology. Version 99155 of ISCST3 was modified to address specific constraints associated with 1,3-D applications in T&V scenarios such as multiple and variable strength source terms and buffer setback distances. First, receptors directly over a given area source (i.e., treated field) are excluded from 1,3-D concentration predictions. This is because it is presumed that no occupied dwellings exist within the field, and worker reentry to the treated field is restricted (according to the product label) for 5 d following the 1,3-D application. Thus, excluding receptors located on treated fields more realistically predicts human exposure levels. The excluded receptors do receive 1,3-D from surrounding treated fields.
Another important modification was the ability to import nonpoint-source flux files, because more than 1200 different source terms were simulated. Changes in mass flux rates of 1,3-D from soil (g/m2/s) are specified on an hourly basis and must be supplied as input to ISCST3. More than 400000 lines of input would be required to describe 1210 source terms for a single year of simulation for a flux profile lasting 14 d. The transient mass flux of 1,3-D from the soil to the atmosphere following an application can now be specified by a user-defined hourly emission file. Only the application date needs to be specified in the modified version of ISCST3, in addition to the name of the emission file.
Treated fields were simulated as randomly distributed area source terms having a 0-m release height (Fig. 1)
. The T&V applications are generally followed by orchard planting. Vine and orchard productivity can continue for 70 yr following the initial planting, with ranges between 20 and 70 yr. A 20-yr rotation for T&V crops was assumed in this study. Over this 20-yr period, no area was treated more than once. Year-to-year variability in air concentrations across the township is further bounded as the number of years in the rotation increases. However, 20 yr of unique simulation results are adequate to capture the general intra-year variability one can expect.
View larger version (35K):
[in this window]
[in a new window]
|
Fig. 1. Example of the use of a Gaussian plume model to simulate air concentrations resulting from agriculturally treated fields (area source terms) within a township.
|
|
Long-term and daily-average air concentrations for areas having a minimum 30.5-m (100-ft) buffer can be compared with the California permitted chronic or acute toxicity endpoints, respectively, to assess risk.
Receptors
1,3-Dichloropropene concentrations were predicted at 30.5-m (100-ft) intervals within a township using a 30.5- x 30.5-m uniform grid spacing for receptors. The receptor grid size was chosen to allow for the evaluation of a 30.5-m buffer zone, while simultaneously allowing for reasonable CPU time constraints to be met. A uniform, 30.5-m receptor placement resulted in a grid of 317 x 317, or 100489 receptors in the township of interest. All receptors were placed 1.5 m above the soil surface to approximate the breathing height zone for an adult. The maximum concentrations within the township may vary slightly due to grid resolution. However, a grid spacing of 30.5 m is adequate to capture the correct magnitude for the maximum 1,3-D air concentration that most likely occurs at the buffer edge. The terrain in Fresno, CA and surrounding townships was assumed flat.
Sources
In the 20-yr simulation, area sources, represented by treated fields, were randomly distributed within each township. Simulated applications were made at the maximum-labeled T&V use rate of 327.4 L/ha (35 gallons/acre). This results in a total of 110 ha (271 acres) maximum treated area per township annually (given the township cap constraint imposed by the CDPR). The area of square sources with edges on a 30.5-m grid spacing is:
 | [1] |
where n = 1, 2..., and the discrete field size was defined based upon the choice of n. By aligning field edges with this grid, all fields, regardless of size, have the closest receptor 30.5 m (100 ft) away (as long as Eq. [1] is satisfied).
Actual field sizes for Telone T&V applications were obtained from the California pesticide use records (PUR) for 1996 through 1998, and the Crop Data Management System (CDMS) information for 1999. Data was queried to select only those records where a 1,3-D application rate exceeded 28 gallons/acre in order to reflect T&V applications. The software package Crystal Ball PRO (Decisioneering, 1998) was used to fit a discrete, cumulative distribution of field size to the PUR and CDMS data. The best fit distribution was log-normal with a mean of 11.5 ha (28.4 acres) and a standard deviation of 16.4 ha (40.6 acres). The larger field sizes in the distribution reflect the practical limit of the number of hectares that can be treated by a fumigation crew in a single day. The field sizes were discretized using Eq. [1] to approximate the cumulative distribution of actual field sizes (Table 1, Fig. 2)
. The optimal field size distribution was made via integer linear programming from the list of discrete choices (Eq. [1]) such that the residual between the PUR and CDMS distribution and the calculated discrete field size distribution was minimized. A constraint for this optimization procedure was that the 1,3-D township cap was approached, but not exceeded. Results from this optimization procedure are seen in Fig. 2, where the comparison between the optimized selection of field sizes and the continuous PUR distribution is presented. Table 1 summarizes the variable field size results for a township (e.g., 10 sources [treated fields] per year) resulting from the optimization procedure. Each simulation year and township used this set of field sizes for the area source terms.
View this table:
[in this window]
[in a new window]
|
Table 1. Discrete field area based upon California pesticide use records (1996–1998) and Crop Data Management System information (1999).
|
|
View larger version (20K):
[in this window]
[in a new window]
|
Fig. 2. Cumulative distributions for field size for representative Fresno County tree and vine (T&V) applications.
|
|
With the field size distribution represented in Table 1, a total of 35890 L (9481 gallons) of 1,3-D product was applied per township per year. The 1,3-D product volume of 35890 L is within 1% of the township cap and was the total mass used for each township. Any future reference to the simulated township cap represents a product volume of 35890 L. Figure 3
represents the source location and size distribution for the township of interest for all years of the 20-yr rotation cycle T&V simulation.
View larger version (38K):
[in this window]
[in a new window]
|
Fig. 3. Location of randomly distributed source terms in the township of interest for the 20-yr simulation.
|
|
Temporal Distribution of 1,3-Dichloropropene Applications
Based on PUR and CDMS data, T&V applications are seasonally highest in October and November (Fig. 4)
. To obtain an application date, the distribution in Fig. 4 was randomly sampled for an application month and the day within the month was randomly sampled based upon a uniform distribution.
1,3-Dichloropropene Flux
Measured 1,3-D soil mass flux from a field study located near Salinas, CA was used as the basis for the hourly flux profile required in the simulation (Knuteson and Petty, 1995). Total measured 1,3-D mass loss in the Salinas study was approximately 25% of applied. A cumulative total mass loss for 1,3-D of 25% of applied was assumed for applications made throughout the fall to spring. Summer emission rates (22 June through 21 September) used the measured flux pattern for the Salinas field trial but were scaled to 40% mass loss (Fig. 5)
for consistency with previous simulation studies at the request of the CDPR (B. Johnson, California Department of Pesticide Regulation, personal communication, 2000). The original basis for this upward scaling was to account for potentially higher emissions during warmer summer conditions, because the Salinas flux study was conducted in late September. The measured flux was based upon an application rate of 132 kg/ha (1,3-D active).
View larger version (24K):
[in this window]
[in a new window]
|
Fig. 5. Experimentally measured soil flux rates for 1,3-dichloropropene (25% release) for an application rate of 132 kg/ha (active ingredient) and an injection depth between 30.5 and 45.7 cm (12 and 18 in).
|
|
Meteorological Data
Meteorological data inputs required by ISCST3 include hourly air stability class, wind speed, air temperature, wind direction, and mixing and ceiling height for the airshed. Meteorological data for 9 yr in Fresno County (1984–1992) were obtained from the USEPA Support Center for Regulatory Air Models (SCRAM) (USEPA, 2001), which contains weather files specifically for the purposes of regulatory air quality modeling. To obtain 20 yr of meteorological data, all weather years were used twice and 1984 and 1985 were used three times.
Stability class was estimated using the USEPA meteorological program PCRAMMET (USEPA, 1999). The PCRAMMET program determines hourly stability classes based on Turner's method using time of day, surface wind speed, and observations of cloud cover and cloud ceiling height (Turner, 1964). The PCRAMMET program also interpolates the hourly mixing height from twice-daily upper air data. However, the closest available source for upper air data was for the coastal community of Oakland, CA. The mixing height was set to 320 m (USEPA, 1995b; Holzworth, 1972) because coastal weather information is not appropriate for the Central Valley of California.
Surrounding Township Contributions
Air concentrations within a given township may be influenced by 1,3-D treated fields in neighboring townships. The impact of treated fields in neighboring townships was evaluated using single-year simulations and varying the number of bordering townships. Locations and application timings for fields surrounding the township of interest were determined by the same random selection procedure described earlier. The township of interest was centrally located with 0, 1, 2, 3, 4, or 5 surrounding townships on either side (1–121 townships simulated). Figure 6
represents the two-township scenario on either side (encompassing 25 townships), showing both area source size and location.
View larger version (26K):
[in this window]
[in a new window]
|
Fig. 6. Location of source terms for the simulation domain (single year) having two-township buffers on all sides of the centrally located township of interest.
|
|
All townships were treated at 35890 L of 1,3-D product based upon the discrete field size analysis and maximum-labeled application rate. All flux was scaled up to 40% volatilization of applied 1,3-D mass, regardless of the time of the year when the application was made. A cumulative loss of 40% of applied is valid for summertime applications, but more than 96% of the applications made in T&V occur outside of summer, where a 25% mass loss is appropriate. The 40% mass loss was used for illustrative purposes to attenuate the effect of neighboring townships, especially during the late fall when the majority of the 1,3-D applications are made.
Spatial and Temporal Resolution
A fundamental approach to understand air quality issues is to have a mechanism that can successfully separate the net air concentration at a specific location into the contributions from individual components. For example, are the concentrations at a specific location resulting from nearby treated fields or background concentrations arising from treated fields in neighboring townships? Once the magnitude of each component representing the composite air concentration is resolved, then appropriate management practices can be put into place to keep air quality below threshold values. Temporal and spatial air concentrations are the sum of a nearby treated field and background concentrations associated with surrounding treated fields in the township of interest and surrounding townships.
Concentration predictions at various buffer setbacks and times for various field sizes can indicate which conditions lead to the upper percentiles of 1,3-D air concentrations. Setbacks up to 305 m in 30.5-m increments were investigated. Stand-alone post-processing programs were written to parse through each of the 20 yr of output and summarize appropriate information found in these large data files.
|
RESULTS
|
|---|
Figure 7
represents the cumulative concentration distribution for acute (24-h) maximum and chronic (annual average) time periods as a function of surrounding townships. Concentrations have been scaled by the maximum 24-h receptor concentration observed in the township (447.5 µg/m3) for this year. Although both the acute and chronic air concentrations increase as the number of neighboring townships increases (and thus, source terms), the largest jump occurs between zero and one township. This difference is approximately an order of magnitude at the lower percentiles.
View larger version (26K):
[in this window]
[in a new window]
|
Fig. 7. Effect of neighboring township sources on 24-h maximum and annual receptor concentrations (C/Cmax-acute [five township] = 447.5 µg/m3).
|
|
At the higher percentiles, chronic or acute concentration distributions converged to the same value (Fig. 8)
. This suggests that the largest 24-h or annual receptor concentrations were due to source terms from nearby fields that were likely to be found in the township of interest. Based upon this analysis, a one-township buffer on each side of the township of interest was assumed for the 20-yr rotation simulation. This reduces CPU requirements while still providing reasonable estimates for air concentrations. A simulation with a one-township buffer on all sides (i.e., 3 x 3 = 9 townships) consists of an 838.7-km2 (324-mi2) area.
View larger version (22K):
[in this window]
[in a new window]
|
Fig. 8. Enlargement of Fig. 7 illustrating upper percentiles arising from the effect of neighboring township sources on 24-h maximum and annual receptor concentrations (C/Cmax-acute [five township] = 447.5 µg/m3).
|
|
Results from a single-year simulation illustrate a two–order of magnitude difference between acute and chronic concentrations (Fig. 7). More quantitative investigations were made to see where the upper percentile exposure values for the cumulative exposure distribution were generated (i.e., near treated fields, slightly downwind, etc.). Figure 9
represents results for a single township simulation having one township on every side of the township of interest and employing a 91.4-m (300-ft) receptor spacing. It is evident that the geographic locations for the source terms (fields) found within the boundaries of the township of interest were responsible for receptors having the highest predicted 24-h maximum air concentration. Thus, for this simulation year, the 24-h maximum concentration was largely responsible for the magnitude of the annual receptor concentration, although full townships surrounding the township of interest all had contributing source terms at the 1,3-D township cap.
View larger version (33K):
[in this window]
[in a new window]
|
Fig. 9. Overlay of source terms with 24-h maximum and annual receptor concentrations for the single-year simulation (one-township border on each side).
|
|
This trend of high exposures near treated fields is further illustrated in Fig. 10
. The location for the top 1000 receptors having the highest air concentrations for both the 24-h maximum and annual average values for one of the 20-yr township simulations (30.5-m grid) is presented. This simulation had 100489 receptors within the township boundary. Therefore, these 1000 receptors represent the top 1% in terms of 1,3-D concentrations for all receptors in the township of interest.
View larger version (18K):
[in this window]
[in a new window]
|
Fig. 10. Location of the top 1000 township receptors for both 24-h maximum and annual average air concentrations of 1,3-D for 1 yr in the tree and vine (T&V) rotation cycle.
|
|
Analysis of results for individual years of simulation indicates that both acute and chronic air concentrations were at their highest near the boundaries of treated fields. This observation suggests that upper percentile concentrations can be approximated from single-field simulations. However, if one is interested in median air concentrations or the distribution of air concentrations for the entire township range, then a full township simulation with neighboring townships should be performed.
Township 20-Year Rotation
Figure 11
represents the predicted long-term (20-yr) annual-average air concentration distribution for every receptor within the township of interest. In addition, the 24-h maximum average air concentration (average receptor concentration over all years of simulation) and the 24-h maximum (no averaging, all receptor concentrations for all years of simulation) are included. The 24-h maximum average and the highest 24-h maximum concentration for 1,3-D observed in the simulation were 56.8 and 655 µg/m3, respectively. The median 24-h acute concentration was 10.5 µg/m3. The prior acute distribution represents the maximum acute exposure air concentration an individual living within the township may receive sometime within the 20-yr rotation cycle.
View larger version (29K):
[in this window]
[in a new window]
|
Fig. 11. Cumulative distribution for 24-h maximum 1,3-dichloropropene air concentrations for the entire 20-yr simulation interval and the 20-yr annual average.
|
|
The 1,3-D exposure predictions from this assessment can be put into perspective when considering current practices. For example, the 1,3-D township cap was never reached in any California township in 1999. Eight townships reached >90% of the township cap. Thirty-six Fresno County townships were treated with 1,3-D, with 19 townships receiving less than 10% of their annual cap. Only 242 townships were treated statewide over the entire year. Therefore, the likelihood for multiple bordering townships all to be treated at the township cap is minimal under today's scenarios. All townships were treated at the 1,3-D township cap to address the impact, if any, that source terms from neighboring townships may have on the central township of interest. However, predictions presented in this paper are representative of potential future 1,3-D exposure values when and if all townships within the T&V agricultural regions of California apply the maximum allowable 1,3-D in a given year.
The highest observed annual 20-yr-average air concentration at any of the 100489 receptors in the township of interest was 0.831 µg/m3. Only at the upper percentiles (>95) does the acute exposure increase dramatically as seen by the steep slope on the log scale in Fig. 11. Table 2 compares the annual average township concentrations predicted by this present analysis with the CDPR-predicted air concentrations for 91.4-m (300-ft) buffers (California Department of Pesticide Regulation, 1997). The CDPR extrapolations are higher at the upper percentiles when compared with the more refined ISCST3 township simulation, although the overall magnitudes are similar. Predictions of chronic 1,3-D air concentrations using 30.5-m buffers were similar to current California acceptable levels for T&V crops based upon 91.4-m buffers.
View this table:
[in this window]
[in a new window]
|
Table 2. Comparisons of California Department of Pesticide Regulation (CDPR) empirically extrapolated annual-average 1,3-dichloropropene air concentrations (µg/m3) with the present analysis.
|
|
Subsets of the simulation results can be used to further refine upper percentile distributions surrounding treated fields when both spatial and temporal constraints are imposed. Because 1,3-D volatility flux rates were at their highest 3 d post-application, all receptor concentrations surrounding a treated field 3 d post-application were parsed and summarized as functions of buffer distances. Both background levels (from neighboring townships) and primary levels from fields treated in the township of interest are addressed because the simulation contained both.
Figure 12
represents a cumulative exposure distribution (exceedence percentile) for the 24-h period immediately following when the highest air concentrations occur (3 d post-application) for the largest field. All 20 yr of simulation results are included in this analysis. The largest field was selected for illustration purposes because larger fields typically yield the highest neighboring receptor concentrations. All of the distributions seen in Fig. 12 have a percentage of receptors where the 1,3-D concentrations were zero. Most daily receptor concentrations within the township will be zero unless an application has been made near the temporal value being investigated (because only the 24-h air concentrations at a specific day are summarized). Likewise, wind direction dictates which receptors' surrounding fields will have the largest magnitude. A large percentage of receptors have zero concentration as the buffer distance increases, because there are now more receptors that can be "upwind" of the field. For the 305-m buffer, approximately 22% of the surrounding receptors do not experience the effect of the field 3 d post-application. When the buffer was reduced to 30.5 m, approximately 10% of the nodes surrounding a 30.2-ha (74.4-acre) field had zero concentrations for the 20 yr of township simulations. Zero concentration nodes surrounding treated fields may give some indication of the buffer zone perimeter receiving no acute dose of 1,3-D during a time when the nearby field is emitting the largest mass of 1,3-D.
View larger version (36K):
[in this window]
[in a new window]
|
Fig. 12. Acute 24-h maximum concentrations surrounding each treated field 3 d following an application for the largest field (30.2 ha) in the simulation.
|
|
|
CONCLUSIONS
|
|---|
This work provides realistic air concentration profiles of 1,3-D (acute and chronic) for 30.5-m (100-ft) buffer zones. Exposure results are coupled with spatial and temporal constraints to determine where the highest township concentrations occur. Nonuniform field sizes, experimentally determined 1,3-D soil flux rates, variable application timings, meteorological data for Fresno from the USEPA Support Center for Regulatory Air Models, and current township cap limits for 1,3-D were all considered in an attempt to obtain realistic predictions. The annual average air concentrations predicted for 30.5-m (100-ft) buffer setbacks were similar in magnitude to previous analysis for a Fresno T&V scenario (91.4-m [300-ft] buffer), which is the basis for the CDPR's exposure assessment (California Department of Pesticide Regulation, 1997).
The 95th percentile chronic exposure value predicted by ISCST3 for a 30.5-m (100-ft) setback buffer was below the currently permitted levels for California (which were based upon a much coarser resolution than this analysis). Thus, the proposed setback reduction from 91.4 to 30.5 m (300 to 100 ft) should not pose any increase in chronic risk because predicted chronic air concentrations, using current state-of-art tools and input characterization, are similar to the currently permitted California levels.
|
REFERENCES
|
|---|
- California Department of Pesticide Regulation. 1997. Risk assessment of 1,3-dichloropropene. CDPR, Sacramento.
- Decisioneering. 1998. Crystal Ball PRO. Windows Version 4.0. Decisioneering, Denver, CO.
- Hao, J., D. He, Y. Wu, L. Fu, and K. He. 1999. A study of the emission and concentration distribution of vehicular pollutants in the urban area of Beijing. Atmos. Environ. 34:453–465.
- Holzworth, G.C. 1972. Mixing heights, wind speeds, and potential for urban air pollution throughout the contiguous United States. Publ. no. AP-101. USEPA Office of Air Programs, Washington, DC.
- Honaganahalli, P.S., and J.N. Seiber. 2000. Measured and predicted airshed concentrations of methyl bromide in an agricultural valley and applications to exposure assessment. Atmos. Environ. 34:3511–3523.
- Knuteson, J.A., and D.G. Petty. 1995. Field volatility experiments for 1,3-dichloropropene. In Abstracts of technical papers. 210th ACS Annual Meeting, Chicago, IL. 20–24 Aug. 1995. Am. Chem. Soc., Washington, DC.
- Kumar, A., N.K. Bellam, and A. Sud. 1999. Performance of an industrial source complex model: Predicting long-term concentrations in an urban area. Environ. Prog. 18:93–100.
- Turner, D.B. 1964. A diffusion model for an urban area. J. Appl. Meteorol. 3:83–91.
- United Nations Environment Programme. 1995. The 1987 Montreal Protocol on Substances that Deplete the Ozone Layer [Online]. Available at http://www.unep.org/ozone/mp-text.shtml (verified 25 June 2001). UNEP, Nairobi, Kenya.
- USEPA. 1995a. Users guide for the industrial source complex (ISC3) dispersion models. Vol. I—Users instructions. EPA-454/B-95-003a, Sept. 1995. USEPA Office of Air Quality Planning and Standards Emissions, Monitoring, and Analysis Division, Research Triangle Park, NC.
- USEPA. 1995b. SCREEN3 model user's guide. EPA-454/B-95-004, Sept. 1995. USEPA Office of Air Quality Planning and Standards Emissions, Monitoring, and Analysis Division, Research Triangle Park, NC.
- USEPA. 1999. PCRAMMET user's guide. EPA-454/B-96-001, June 1999. USEPA Office of Air Quality Planning and Standards Emissions, Monitoring, and Analysis Division, Research Triangle Park, NC.
- USEPA. 2001. Support Center for Regulatory Air Models [Online]. Available at http://www.epa.gov/scram001/ (verified 25 June 2001). USEPA, Washington, DC.
This article has been cited by other articles:
|
|
|
|
 
S. A. Cryer
Predicting Soil Fumigant Air Concentrations under Regional and Diverse Agronomic Conditions
J. Environ. Qual.,
November 7, 2005;
34(6):
2197 - 2207.
[Abstract]
[Full Text]
[PDF]
|
|
|
TOP
ABSTRACT
INTRODUCTION
