Simple Fugacity Models of Off-Site Exposure to Agrochemicals - ACS

Chapter 2

Simple Fugacity Models of Off-Site Exposure to Agrochemicals

Downloaded by UNIV OF GUELPH LIBRARY on August 6, 2012 | http://pubs.acs.org Publication Date: September 21, 2007 | doi: 10.1021/bk-2007-0966.ch002

Don Mackay and Eva Webster Canadian Environmental Modelling Centre, Trent University, Peterborough, Ontario, Canada

A brief review is presented of the environmental and social incentives for quantifying the fate of agrochemicals and for using mass balance models to complement empirical measurements of concentrations resulting from specific applications. The role of the fugacity concept for use in such models is described and discussed. It is suggested that four relatively simple fugacity models can play a useful role in these agricultural situations by quantifying partitioning, loss processes (especially degradation) and transport processes in bulk soil, vegetation, invertebrates, and small mammals at a screening level. In total, these models provide information not only on the fate of pesticides and other "inert" ingredients in the soil but also on the potential for contamination by off-site transport. Their use can, we suggest, contribute to more effective selection and application of these agrochemicals, to reduced risk to humans and wildlife, and ultimately to increased public acceptance of their benefits.

© 2007 American Chemical Society

In Rational Environmental Management of Agrochemicals; Kennedy, I., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2007.

15


Introduction Whereas there is widespread public acceptance that pesticides play an invaluable role in agriculture and can be safely applied, there is increasing concern that there may be public exposure "off-site". Examples include pesticides used for non-agricultural purposes such as indoors, for weed or rodent control in urban areas, as residues in and on foodstuffs, and contamination of nearby water bodies by run-off and atmospheric transport. Whereas the public generally accepts the merits of using pesticides to help control unwanted organisms and plants in the vicinity of applications, they are often intolerant of the presence of these substances "off-site" where exposure is inadvertent and non-target organisms may be affected. This intolerance translates into political pressure and ultimately can result in regulatory actions to minimize or eliminate off-site or far-field exposure. In Canada, many municipalities have responded to such concerns by introducing bans on the "cosmetic" use of pesticides. The public has shown a willingness to pay a premium for "organically grown" or pesticide-free foods. Given this growing intolerance of off-site exposure there is an increased incentive to ensure that pesticides are used safely and sustainably and with a full knowledge of their fate. The same concern applies to metabolites or degradation products and to other "inert" ingredients of the formulations such as carriers, solvents, "adjuvants, surfactants, and impurities. Environmental science can respond to these concerns in two ways. First is to enhance our ability to quantify the fraction of the pesticide applied that is not degraded at the application site and may be transported to other locations, possibly resulting in human or wildlife exposure. This information must be provided in a simple, transparent, and credible manner if it is to be accepted. It can assist in selecting pesticides that will have less off-site impact. Second, and more difficult, is to estimate the nature and magnitude of adverse effects or toxicity of the often low doses or exposures. Here we address only the first task. The primary and most convincing method of determining quantities experiencing off-site transport is monitoring programs in which samples of air, water, soils, and biota including produce are taken at various time intervals both on-site and off-site following application and analysed for residues and possibly metabolites. This is a difficult, demanding, and expensive task. It may be that the results are significantly affected by prevailing weather conditions, notably temperature, wind, and precipitation events. Given these difficulties and the need to extrapolate the results to other application conditions, there is an incentive to use mass balance models to reconcile the empirical observations and deduce how a given substance will behave under a variety of conditions. Mass balance models are widely and successfully used to describe the fate of pesticides applied for agricultural purposes. Examples include P R Z M (I), C R E A M S (2), G L E A M S (3), and B A M (4). The use of models has become



16 sufficiently accepted that government agencies such as C R E M (Council for Regulatory Environmental Modeling, under the US E P A ) have been set up to provide guidance on the selection and use of models for regulatory purposes (5). These models can be used in a predictive mode but more commonly they are used in conjunction with monitoring data to provide a complete picture of the fate of the active ingredient, and possibly of its degradation products. A n incentive for applying models is that it is not possible to make direct measurements of the rates of certain processes in the field. It is only possible to measure concentrations at various places and times and from these data infer process rates. For example, an observed decrease in concentration may be attributed to degradation, volatilization or leaching, thus the individual rates of these processes may not be determined unambiguously in a single field experiment; only their total rate can be determined. These individual rates can, however, be estimated using a reliable model that has been subjected to casespecific validation for a number of chemicals. The model thus serves the fundamentally important purpose of synthesizing the available information into a coherent statement of mass balance. A successful statement implies a sufficiently complete understanding of the system. Public perceptions and sound regulatory policies should be based on such an understanding.

Far-field or Off-site Transport and Exposure Far-field exposure can result from the substance being present in a variety of environmental media. •

Volatile substances can be transported in air flow from the application site becoming dispersed over large areas. The chemical may degrade, be inhaled or be deposited by wet and dry deposition to soils, vegetation and water bodies.

•

Water soluble substances may leach into groundwater or run-off to surface waters. There may then be contamination of aquifers and wells and there may be impacts on organisms in ditches, rivers and lakes.

•

Soil particles may be transported from the site, along with associated chemical in air-borne or water-borne solids.

•

Biota ranging from insects to amphibians, birds and mammals may become contaminated in the near-field and migrate to the far-field. As prey, they may introduce the pesticide into the food webs. Some fish- and insecteating birds appear to be particularly vulnerable.

•

Finally, agricultural produce may convey quantities of the chemical to the far-field for ultimate consumption by humans or other animals.



17 A process that is not considered here is off-site transport during application by spray drift. Transported quantities may be appreciable, but they depend on the application process. We suggest that it is useful to have available a set of relatively simple but comprehensive models that can be used to assess which, if any, of these processes may be significant. If a specific transport process is judged to be significant, a more detailed model can be applied. Examples are models that address specific aspects of pesticide fate such as migration through the soil column, run-off, evaporation followed by atmospheric dispersion, uptake in growing vegetation, or bioaccumulation in birds. In this paper we describe simple models and present illustrative results for a number of compounds. It is hoped that the models may be of value for assigning order-of-magnitude estimates to fate processes and thus direct priorities for subsequent model applications and monitoring programs. Far-field exposure assessment is thus addressed as a two-stage process: simple or screening-level followed by detailed process-specific models. We further suggest that fugacity modeling is particularly suitable for screening level assessments. It is also suitable for certain detailed assessments, especially in defined environments such as indoors and when the pesticide is relatively persistent. When degradation rates are fast, many of the equilibrium assumptions inherent in fugacity models break down and conventional concentration-kinetic models may be preferred. However, it is the more persistent substances that have a potential for far-field effects and are generally of greatest concern.

The Fugacity Concept Although most models are written in concentration format, in many respects fugacity models are advantageous for simple models of the type described here. In principle, both concentration and fugacity models will give identical results when provided with identical input parameters and fate equations. They are thus fully equivalent and inter-convertible. The concept of fugacity was introduced oyer a century ago by G.N. Lewis (6) as a criterion of equilibrium. It can be viewed as a partial pressure or escaping tendency and has units of pressure (Pa). When a chemical achieves equilibrium between different phases such as pore air, pore water, organic matter and a plant root, the fugacities in each phase are equal. The concentrations differ, however, often by many orders of magnitude. They are related by partition coefficients such as K the air-water partition coefficient. In the fugacity formalism each phase is assigned a capacity term Z, the proportionality constant between concentration and fugacity, f , namely C (mol/m ) = Z A W

3


18 3

(mol/m Pa). f (Pa). Clearly, the air-water partition coefficient K is simply the ratio of the Z values in air and water, i.e., K w C / C = Z f / Z f = Z / Z . Essentially, a Z value is " h a l f a partition coefficient. Figure 1 gives an example of an equilibrium distribution calculation of a defined quantity of chemical between four soil phases. The mass balance equation states that the total amount of chemical, M (mol) must equal the sum of the amounts in each phase nij (mol) which in turn must be V j Q or VjZjf where Vj is the phase volume (m ), Q the concentration (mol/m ), Z\ is the phase specific capacity term, and f is the common fugacity. Specifically, M = Snij = E V j Q = EVjZj f = fEVjZj thus f = M/SVjZj. From f, all the concentrations can be calculated as fZj and the masses as fVj Zj. It is noteworthy that all concentrations are expressed on a volumetric basis rather than on a mass basis as is usual for solid phases. The phase density is required for conversion between these concentrations. A

W

=

A

A

W

A

w

A

W

3

3


t

Figure 1. Equilibrium distribution of atrazine between the four soil phases as calculated by Model 1. The partition coefficient for any pair ofphases is the ratio of their Z values, e.g., K = Z /Z = 4 x 10' /1650 = 2.4 x 10' . The concentration in any phase is the the fZproduct, e.g., 218 x (1.48 x l(f )or 0.03 mol/m for organic matter. 4

AW

A

W

3


7

19 The Z values depend on the phase and on chemical properties such as vapor pressure, solubility in water, the octanol-water partition coefficient, K w , and the organic carbon-water partition coefficient, K c - They are thus temperature dependent. If sorption is linear, Z values are constant, but under non-linear conditions they vary with concentration and Freundlich or Langmuir expressions must be included. Under the dilute conditions normally found in the environment Z values are constant at an "infinite dilution" value. Thus Z is first defined for the air phase, using the Ideal Gas Law, as 1/RT where R is the gas constant and T is absolute temperature. For other phases Z can be calculated from measured or estimated partition coefficients. Definitions are summarised in Table I, full details and justification are given by Mackay (7). 0


0

Table I. Definitions for fugacity capacities, Z; as described in Mackay (7) except Z which from Arnot et al (16). Q

Air

Vapor

Z =1/(RT)

Aerosols

ZQ =

Bulk

^Air

A

Z

Water Soil

0.1

=

K A Z

Z A (|>Vapor + Z Q (J)Q

= WS-MW/VP

W

Pore Air

ZporeAir "~ Z A

Pore Water

ZporeWater

Organic Matter

ZOM

Mineral Matter

ZMM

Bulk

=

ZE

=

=

Z\y

KOM Z

=

w

KMW ZW

Zp

o r e

A i r ^ Pore Air ~*~ Zp Water ore

+ ZQM ^ O M

Flora

Fauna

Roots

ZR

Above-ground vegetation

Zsht

Invertebrates

where

=

x

t

=

Zworm

Small terrestrial animals

A

0

Zshw

=

x

Rt

+

Pore Water

ZMM ^ M M

KQW Z w

sht KQW ZW

=

Xworm ^ O W Z w x

s h w Kow

ZW

R = gas constant T = air temperature, K c|)i = volume fraction of phase i in the bulk medium 3

WS = water solubility, g/m MW = molar mass, g/mol VP = vapor pressure, Pa

Xj = octanol-equivalent fraction


20 and subscripts are

A = air vapor W = water Q = aerosols OM = organic matter in soil M M = mineral matter in soil E = soil, or "earth" Rt = roots


Sht = above-ground vegetation, or "shoots" Shw = small terrestrial animals, or "shrews"

Equilibrium distributions are thus easily deduced, but usually conditions are non-equilibrium and it is necessary to estimate transport and transformation rates. When mass balance equations are written to describe these nonequilibrium conditions in terms of fugacity the rate, N (mol/h), is given by D f where D is a fugacity-based rate constant with units of mol/Pa.h. D values can be defined for a variety of processes including degrading reactions, evaporation, leaching, uptake by roots from soil, and feeding by organisms. Table II summarises the definitions of D values used in this context as a function of kinetic terms including degradation rate constants, mass transfer coefficients and flow rates. D values are essentially conductivities, thus a large D value indicates a fast process. When loss processes from a phase occur in parallel the D values add. For series resistance processes the resistances, or reciprocal D values, add. This commonality of transport and transformation rate coefficients is very convenient in that they can be compared and added. This is not possible with concentrationbased parameters and is therefore one of the great strengths of the fugacity concept.

Chemical Properties Five chemicals, including pesticides and solvents, namely Atrazine (Atr), Benzene (Ben), Diazinon (Dia), D D T , Pendimethalin (Pen), and Pyrene (Pyr), were selected to cover a wide range of partitioning properties. Table III lists the properties of these chemicals estimated from the literature. These have been chosen. This range is conveniently displayed by locating the chemicals on a "chemical space" diagram as a plot of log K vs log K w , illustrated in Figure 2. A

W

0


21

Table II. D values as functions of kinetic terms including degradation rate constants, mass transfer coefficients and flow rates as discussed in Mackay (7) except where noted. Diffusion is based on the Millington-Quirk equation. Parameter values and common symbols are given in Table I V .


Degradation in Soil

^React ~~ V E Z E k R

e a c t

Leaching

D = G Z where G = LR A / 1000 / 24

Volatilization from soil

Dv=l/(1/D +1/(D

L

L

w

L

E

E

+ Dsw))

S A

Air boundary layer

D = Area K v Z

Diffusion

in air in water

D D

Mass transfer coefficient

K

E

Effective diffusivity Porosity

S W

= A DIFEA Z / Y D = A DIFEW Z / Y D

E V

= D I F M A / THICK

S A

E

A

E

W

A

W

4>IPoreAir

QA =

(10/3)

2

/ (^PoreAir + 4> PoreWater.•)

Qw ~ ^>PoreWater

DAA = ( V Z

Air Advection

A

DIFEA = Q M D A / 2 4 DIFEW = Q M D W / 2 4

air water

air water

E

A

A

/ (^PoreAir + ^PoreWater)

+V

Z ) / ResTime

Q

q

ResTime = (v/A )/(3600 Wind) E

Net volatilization

D O=1/(1/DV+1/DAA)

Deposition from air

DAirE

V

Rain (based on (16))

D ,n Ra

D Aerosol deposition Exchange

Air-shoot Soil-root Root-shoot Soil-worm

R a i n

D

DRtSht

A W

w

A

A W

D = A $Q DryDep Z D wet A (J) Rain Z S QD r y

E

Q

=

Q

S

= t

Dy

We

E

=

E

+

+ DQ t

D R Y

E

D A i r S h t V ht D R

+ DQ

R a i n

= A * Rain * Z for K >1/S = A * Rain * S * Z for K < 1/S

dry wet

Animal uptake from invertebrates Animal loss to soil

=

V =

R T

k

Rt

E

Q

k ht Z ^ r and D ZporeWater &nd D S

D

S r i

tRt

Vworm kwonn D rmShw = V Wo

S H W

E

= D j sht = D A

S H T A I R

DRIE and

D

Q

=

=

Z S

h

r

E R t

DRtSht

E

w

D rmShw Wo

R t E

and k

S h w

D

W o

Z

n n E - DEWO™

W o

rm

/ BMF



22

Figure 2. Selected substances are related to the range of chemical space of a log K vs log K w plot. The diagonal lines represent constant values of log KOA (KQA K /K ) and the curves represent constant partitioning to the phase; air, water, or octanol (or octanol-equivalent fraction). The intersection of the thickest curves represents the case of a substance partitioning equally to all three phases. The thickness of these curves is reduced as partitioning to the phase is reduced. A W

0

=

0W

AW


23 Table III. Physical chemical properties of selected substances. Data are taken from Mackay and Stiver (17), Mackay et al (18), Hornsby et al (19), Worthing and Walker (20), and the EPI Suite software (21) and assuming K = 0.1 for all substances. M M

Molar Mass g/mol Atr

215.7

Water Solubility g/m 32

Ben Dia DDT Pen Pyr

78.1 304.4 354.5 281.3 202.3

1780 60 0.0031 0.59 0.132


3

Vapour Pressure Pa 9.2 X 105

Koc L/kg

log Kow

Half-life in Soil h

160

2.75

1700

12700 0.008 2 X 10 0.004 0.0006

83 820 240000 65000 62000

2.13 3.3 6.19 5.2 5.18

1200 1700 92000 1440 17000

-5

Nature and Scope of the Models In the following sections we describe and apply a series of four simple fugacity models of increasing scope, complexity, and detail. It is suggested that they be applied in sequence. The first model describes the static equilibrium partitioning of a defined quantity of chemical in the soil. It thus addresses the question of how this chemical will partition between the four soil phases and what are the likely concentrations. The fugacity in the air directly above the soil (referred to as "canopy air") is calculated as a fraction of the fugacity in soil determined by the ratio of the net volatilization D value to the D value for advection in the air. The vegetation and soil fauna are assumed to be at equilibrium with the soil. This simple assumption is likely to over-predict the concentfations in the biota. The second model includes the canopy air, and vegetation and soil fauna, but allowance is made for the likely failure of the biotic media to achieve equilibrium because of growth dilution and possible biotransformation in the biota. It thus addresses the question of how partitioning may be affected by the presence of biota and the approximate expected biotic concentrations. The third model includes estimates of the initial rates of loss by reaction and advection processes immediately after application of a defined quantity of chemical. These rates can be used to estimate half-lives. This shows which loss processes are most important and it provides a first estimate of the pesticide's persistence. The fourth model is a dynamic description of the chemical masses in the system as the substance reacts and advects over a period of time.



24 These models will be available from the authors through the C E M C website (http://www.trentu.ca/cemc). The parameters describing the environment, the chemical properties and application quantities can all be selected by the user. A series of illustrative applications follows for the environment specified in Table IV. This illustrative environment consists of an area of 1 ha of soil consisting of soil solids (mineral and organic matter), pore water and air. In addition there is an overlying air phase of height 1 m, vegetation and fauna. Z values of these media are defined from the chemical properties in Table III and using the expressions in Table I. If empirical data on soil sorption or air-water partitioning are available they can be readily and directly used to deduce the Z values. It may also prove to be useful to calculate Z for aerosol particles to which evaporated chemical may sorb.

Model 1. Static Equilibrium in Soil This model performs a simple equilibrium distribution calculation. Using the environmental data from Table IV, the chemical property data in Table III, and assuming a mass of chemical, the equilibrium distributions of the chemicals can be estimated for the four soil phases as shown Table V . This simple equilibrium calculation shows how a given mass of chemical of say 1 kg, will partition at equilibrium between the soil phases, air above the soil ("canopy air"), vegetation, and soil fauna. No degradation kinetics are considered. O f interest are the relative concentrations and masses in each compartment. It is recognized that instantaneous equilibrium will not necessarily be reached, thus the masses and concentrations should be viewed as only indicative of maximum potential values. Although the percentages in the soil pore water and air are low for all of the chemicals considered here, they display considerable variability reflecting the chemical properties. Table III suggests that a "volatile" chemical is one which has a percentage in air exceeding 0.5 to 1%. Likewise a "water-soluble" chemical has a percentage in water exceeding 0.5 to 1%. These chemicals with low binding to organic or mineral matter are viewed as being particularly susceptible to loss by evaporation or leaching respectively. Chemical such as DDT that tend to bioaccumulate can also be identified by their log K w exceeding approximately 5. The primary advantage of this calculation is its simplicity and transparency. There is now a quantitative appreciation of the likely partitioning tendency of the substance. 0

Non-attainment of Equilibrium In screening-level models it is often convenient to assume that phases that are in close contact achieve the same fugacity as was done in Model 1. This


25 Table IV. Properties of near-field environment as used in this illustration. Values are based on the Soil Model described by Mackay (7) except where noted. Symbols used in Table II are given here. Soil Properties Area

A

1 ha or 10000 m

E

0.25 m

Soil depth Diffusion path length

YD

0.1 m 2 xlO'

Volume fraction of aerosols in Downloaded by UNIV OF GUELPH LIBRARY on August 6, 2012 | http://pubs.acs.org Publication Date: September 21, 2007 | doi: 10.1021/bk-2007-0966.ch002

2

11

Water leaching rate

LR

2 mm/d

A i r boundary layer thickness

THICK

0.00475 m

Molecular diffusivity in air

MDA

0.43 m /d or 4.98x10" cm /s

2

Mass fraction of OC in dry soil Volume

fraction

Densities

2

2

0.03

Pore air

0.2

^PoreAir

1200 kg/m 2500 k 2 / m

Organic Matter Mineral Matter

3

3

Canopy Air Properties 1m

A i r height

1 m/s

Wind

Wind speed

200000

Aerosol scavenging ratio Rain rate

Rain

0.0001 m/h"

Aerosol dry deposition rate

DryDep

lOm/h*

Biota Properties Number of Biota per Unit Area m

2

Volume of Single Organism cm 100

Octanolequivalent fractions x 0.05

n/a

0.01

2

0.012 ^

10

0.07'

3

Roots

2

Shoots

n/a c

Invertebrates

100

Small animals

0.01

Shoot-root volume ratio

1

Density

1200 kg/m

t

3

1

Uptake rate constants, h" Air-to-shoots^ Soil-to-roots

g

ksht

5

5xl0 /24 38434 Continued on next page.


26


Table IV. Continued. Soil-to-worm

h

Worm-to-mammal' Shrew biomagnification factor

k

W o r m

k h S

W

BMF

ln(2)/120 38375 5

Growth rate as a fraction of body volume per day (for all biota: roots, shoots, worms, and shrews)

3

3

0.01 m /m d

a based on (24); b based on (25); c based on (22) for worms; d based on (23) for worms; e based on (23) for shrews;/ based on (11); g based on (12); h based on (14); i based on (15)

Table V. The relative amounts (%) of the selected substances in the four phases in soil as calculated by Model 1. Pore Air Atr Ben Dia DDT Pen Pyr

< 0.01 1.54

Simple Fugacity Models of Off-Site Exposure to Agrochemicals - ACS

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