An Improved Screening Tool for Predicting Volatilization of Pesticides

Dec 10, 2012 - Pesticides and the conditions under which the greatest volatilization losses exist were ... Pest Management Science 2016 72 (7), 1309-1...
0 downloads 0 Views 2MB Size
Download PDF
Article pubs.acs.org/est

An Improved Screening Tool for Predicting Volatilization of Pesticides Applied to Soils Cleo L. Davie-Martin,† Kimberly J. Hageman,†,* and Yu-Ping Chin‡ †

Department of Chemistry, University of Otago, Dunedin 9016, New Zealand School of Earth Sciences, The Ohio State University, Columbus, Ohio 43210, United States

‡

S Supporting Information *

ABSTRACT: Pesticide volatilization and vapor drift can have adverse effects on nontarget, sensitive ecosystems and human health. Four approaches for pesticide volatilization screening based on Fick’s Law were investigated. In each approach, vapor pressures or environmentally relevant partition coefficients were used to describe pesticide behavior in an agricultural field system and to predict 24-h cumulative percentage volatilization (CPV24h) losses. The multiphase partitioning approach based on soil−air (Ksoil−air) and water−air (Kwater−air) partition coefficients was found to most accurately model literature-reported pesticide volatilization losses from soils. Results for this approach are displayed on chemical space diagrams for sets of hypothetical Ksoil−air and Kwater−air combinations under different temperature, relative humidity, and soil organic carbon conditions. The CPV24h increased with increasing temperature and relative humidity and with decreasing soil organic carbon content. Pesticides and the conditions under which the greatest volatilization losses exist were easily identified using this visual screening technique.

■

INTRODUCTION Pesticide vapor drift occurs during the hours to days following application, when pesticides volatilize from agricultural fields and are transported offsite by wind.1 Vapor drift results in a loss of pesticide efficacy within the application zone as well as a significant financial loss for the grower. Further, pesticides originating from vapor drift can have numerous deleterious effects on nontarget plants,2 wildlife,2,3 and humans.4 While most damage usually occurs within a localized radius of the application site, some pesticides also undergo long-range atmospheric transport and deposition to remote ecosystems, where they may cause long-term problems.5,6 Volatilization, which is the precursor to vapor drift, can result in pesticide losses over 50%;7−9 however, losses as high as 99% within 24 h have also been observed.7 Pesticide volatilization field studies are time-consuming and expensive and comparisons between studies are made difficult by the differences in environmental conditions and the limited number of pesticides that can be examined at once. Screening techniques are commonly employed by regulatory authorities to identify problematic pesticides that require more extensive testing and monitoring.10,11 Currently, the Tier I screening approaches used by the European Union (EU)10 and United States Environmental Protection Agency (USEPA)11 to assess pesticide volatilization potential use vapor pressure as a risk indicator. In several studies, the volatilization rates of selected pesticides from plant and/or soil surfaces were correlated with vapor pressure,12−15 but other studies report no such relationship.9,16 These results indicate that pesticide volatilization © 2012 American Chemical Society

screening based on vapor pressure alone is not always appropriate for agricultural systems. Pesticide volatilization from soils depends on the physical properties of the pesticide as well as environmental factors, including soil moisture content, soil mineral and organic matter fractions, temperature, wind speed, and relative humidity.17 In the environment, pesticides interact with a number of environmental matrices (e.g., soil, water, and vegetation) and these interactions influence volatilization in a manner that differs from expectations based on vapor pressure alone.18 These complex processes could explain the discrepancies in pesticide volatilization observed in the literature. Partition coefficients are often used to predict pesticide fate, transport, and distribution between different environmental matrices.5,19−21 Numerical models that simulate pesticide behavior within soil, such as the pesticide leaching model (PELMO)22 and pesticide emission assessment at the regional and local scale model (PEARL),23 predict volatilization fluxes using pesticide air−water partition coefficients (Kair−water) and soil sorption coefficients. However, these models do not use air−soil partition coefficients (Kair−soil) directly, volatilization is only one of many transport pathways considered, and the large numbers of inputs required make them too complex for implementation as volatilization screening techniques. Received: Revised: Accepted: Published: 868

May 21, 2012 September 24, 2012 December 10, 2012 December 10, 2012 dx.doi.org/10.1021/es3020277 | Environ. Sci. Technol. 2013, 47, 868−876

Environmental Science & Technology

Article

where Dair is the air diffusion constant (m2 h−1), cair(turbulent) is the concentration in the turbulent air (g m−3), and cair(boundary) is the boundary air concentration (g m−3). The cair(turbulent) was set to zero because it was assumed that ‘wind’ continually replenished the compartment with fresh air. Values of Dair were assumed to be the same for every pesticide (0.0179 m2 h−1 at 20 °C)29 and temperature dependence was accounted for using eq 4.22

The objectives of this work were to develop a visual screening technique for predicting the volatilization of pesticides from bare agricultural soils. Further, our model identifies the pesticides and conditions under which the greatest risk of volatilization occurs. Four flux-based approaches were investigated using vapor pressure and environmentally relevant partition coefficients as the primary descriptors. The 24-h volatilization losses predicted by each of the flux-based approaches were compared with one another and to volatilization losses reported in the literature. The influence of different temperatures, relative humidity, and soil organic carbon contents were examined using a series of illustrative chemical space diagrams. Sensitivity analysis was performed using a wide range of hypothetical pesticide partitioning combinations so that the relative importance of model inputs could be assessed. Although pesticide-vegetation interactions are also likely to have a significant effect on volatilization from most agricultural fields, this work used a bare soil model so that pesticide-soil interactions in the absence of vegetation could first be investigated. Additionally, degradation rates were not considered because we focused on pesticide behavior during the 24 h after spraying and the majority of pesticides investigated have atmospheric half-lives greater than one day.24

⎛ T ⎞1.75 Dair = Dair(ref)·⎜ ⎟ ⎝ Tref ⎠

where Dair(ref) is the reference air diffusion constant (0.0179 m2 h−1) and T and Tref are the atmospheric and reference (20 °C) temperatures, respectively. Values of cair(boundary) were calculated using the following four different approaches, where each assumes that a different volatilization mechanism or combination of mechanisms dominates. Flux I: Approach Based on Vapor Pressure. In the first approach (Flux I), cair(boundary) was calculated from the pesticide’s liquid or subcooled liquid (if the analyte is solid at 25 °C) vapor pressure (p*L, Pa), according to the ideal gas law (eq 5).

■

cair(boundary) =

MATERIALS AND METHODS Standard Agricultural Field System. The bare soil model environment consisted of turbulent air, boundary air, soil water, and surface soil compartments and was used to investigate pesticide volatilization from agricultural soils. The system had an area (Afield) of 1 ha (10 000 m2) and an atmospheric height (h) of 1000 m. A surface soil depth (hsoil) of 0.001 m and air boundary layer depth (d) of 0.001 m were chosen based on the PELMO default values.25,26 Consequently, the total soil volume (Vsoil), turbulent air volume (Vair(turbulent)), and air boundary volumes (Vair(boundary)) were 1 × 104 L, 1 × 1010 L, and 1 × 104 L, respectively. The volume of soil water (Vwater) was a function of the soil moisture content (M%, w/w), soil mass (msoil), and density of water (ρwater) (eq 1). M % msoil Vwater = · 100 ρwater (1)

2

p*L(T) = p*L(298.15K) ·e[(

−ΔA U 1 1 )] )( − R T 298.15K

(6)

where ΔAU is the internal energy of vaporization (kJ mol ), R is the ideal gas constant (0.008314 kJ K−1 mol−1), and p*L (T) and p*L (298.15 K) are the liquid or subcooled liquid vapor pressures at atmospheric temperature and 298.15 K, respectively. Equation 7 was used to estimate ΔAU values, which were assumed to remain constant over the environmental temperature range examined (5−30 °C).30 ΔA U = −3.82ln p*L(298.15K, Pa) + 67.5

(7)

Equation 7 is an empirical relationship derived from a database of approximately 200 organic compounds, covering a wide variety of chemical classes.30 However, few of the calibration compounds had complex structures and/or were polar molecules and eq 7 has not been validated for pesticides. Flux II: Approach Based on Air−Water Partitioning. In the second approach (Flux II), cair(boundary) was calculated from the pesticide’s Kair−water (eq 8).

−2

(2)

cair(boundary) = c water·K air ‐ water

(8)

where cwater is the concentration of pesticide in the soil water (g m−3), which was calculated using eq 9. mapplied c water = Vwater (9)

(cair(turbulent) − cair(boundary)) d

−1

−1

where RH is the atmospheric relative humidity (%). We derived eq 2 using a polynomial regression performed on 179 RH and M % pairs reported by Schneider and Goss27 for 17 soils with different clay contents (Supporting Information (SI) Figure S1). Using eq 2, an RH of 100% corresponded to an M% of 5.8%. Where the clay content of a soil is known, the reader is referred to Schneider and Goss27 to obtain a soil-specific relationship between M% and RH. Flux Calculations. The model used Fick’s Law of Diffusion (eq 3)28 to predict pesticide volatilization flux (Jvol) (g m−2 h−1). Jvol = −Dair ·

(5) −1

where R is the ideal gas constant (8.314 Pa m K mol ) and M is the molar mass (g mol−1). Equation 5 assumes that the pesticide’s concentration in air is mainly controlled by the intermolecular interactions in the pure phase. This approach represents the Tier I volatilization screening approaches used by the EU10 and USEPA.11 Vapor pressure temperature-dependence was accounted for using eq 6.

M %(%w/w) = 7.99 × 10 ·RH − 4.20 × 10 ·RH + 1.98

p*L ·M RT 3

where ρwater was assumed to equal 1 kg L−1. The msoil was a function of Vsoil and the soil solid density (ρsoil). We chose to use soil solid density (i.e., soil porosity was ignored) instead of bulk soil density because we assumed the “pore” air was part of the boundary air volume. M% was calculated using eq 2. −4

(4)

where mapplied is the mass of pesticide applied to the field. The Flux II approach assumes that a pesticide’s air concentration is

(3) 869

dx.doi.org/10.1021/es3020277 | Environ. Sci. Technol. 2013, 47, 868−876

Environmental Science & Technology

Article

important to note that wind speed and direction could become increasingly important for larger field sizes, which were not considered in this analysis. Pesticide Data. The octanol−water partition coefficient (Koctanol−water), Kair−water, and p*L values (at 25 °C) were collated for 224 current- and historic-use pesticides and are listed in SI Table S2. Kwater−air Values. The van’t Hoff relationship was used to convert Kwater−air values at 25 °C to those at different temperatures (eq 14).28

mainly controlled by its molecular interactions with the aqueous phase as manifested by its activity coefficient (γw). Flux III: Approach Based on Air−Soil Partitioning. In the third approach (Flux III), cair(boundary) was calculated from the pesticide’s air−soil partition coefficient (Kair−soil) (eq 10). cair(boundary) = csoil·K air−soil

(10) −3

where csoil is the concentration in the soil (g m ), which was calculated using eq 11. mapplied csoil = Vsoil (11)

K water − air(T) = K water − air(298.15K)·e[(

(14)

The Flux III approach assumes that the pesticide’s concentration in air is mainly controlled by its molecular interactions in the soil phase. Flux IV: Approach Based on Multiphase Partitioning. In the final approach (Flux IV), cair(boundary) was calculated using the fraction of pesticide in the air boundary layer (Fi,air) within the multiphase standard agricultural system at equilibrium (eq 12). mapplied cair(boundary) = Fi,air · Vair(boundary) (12)

where Δwater−airU is the internal energy of water to air phase transfer. Soil water temperature was assumed to equal that of the ambient atmosphere. Equation 15 was used to estimate Δwater−airU values, which were assumed to remain constant over the environmental temperature range examined.31,32 Δ water − air U = ΔW U − ΔA U

(15)

where ΔWU is the internal energy of phase transfer between the pure liquid and water. A ΔWU value of 27 kJ mol−1 was used for all pesticides, as suggested by Smit et al.; measured values of ΔAU and ΔWU are available for selected pesticides from the same reference.33 Ksoil−air Values. Soil-air partitioning is complex and depends on a number of soil-specific and meteorological parameters. Hippelein and McLachlan34 derived an equation for estimating Ksoil−air values for PCBs and chlorobenzenes under different temperature, RH, and soil organic carbon fraction ( f OC) conditions. We modified their equation to make it more suitable for estimating Ksoil−air values for pesticides. In brief, this was done by regressing measured Ksoil−air values for 17 organochlorine pesticides under different environmental conditions35−37 against Ksoil−air values calculated with the Hippelein and McLachlan equation and then adjusting the normalization and RHsensitivity constants in the equation until the best fit was found (eq 16). The approach for deriving eq 16 is described in detail in the SI. This description includes regression plots of measured versus calculated Ksoil−air values (SI Figure S3), a table listing the measured Ksoil−air values of the 17 organochlorine pesticides used in the derivation (SI Table S3), and temperature regression coefficients (SI Table S4).

Fi,air was calculated from the mass-balance distribution of pesticides between boundary air, soil water, and soil compartments and is based on the soil−air (Ksoil−air) and water−air (Kwater−air) partition coefficients (eq 13), which are the inverse of Kair−soil and Kair−water, respectively.28 Fi,air =

−Δ water − air U 1 1 )( − )] R T 298.15K

1 ⎛ ⎞ Vwater V ⎜1 + K + K soil − air · V soil ⎟ water − air · V ⎝ air(boundary) air(boundary) ⎠ (13)

The Flux IV approach assumes that the pesticide’s concentration in air is controlled by its interactions with both soil and water at equilibrium. Twenty-Four Hour Cumulative Percentage Volatilization (CPV24h). The percent mass of pesticide lost per hour due to volatilization from the model field was calculated from the flux values. These values were summed over the period of one day to obtain a CPV24h. For the first 1-h time step, the volatilization flux was based on mapplied and in subsequent hours, it was based on the mass of pesticide remaining on the field (mremaining). For pesticides with high Jvol values, the mass applied completely volatilized in less than 1 h, meaning it was possible to obtain a cumulative percentage volatilization (CPV) greater than 100% in the first 1-h time step. As such, any CPV predictions exceeding 100% were reset to 100%. Further details on the equations used to calculate the CPV24h can be found in the SI. Time Step and Wind Speed. To determine the most appropriate time step to use in this study, CPV losses calculated with a 15-min time step were compared to those with a 1-h time step using the Flux IV approach. At shorter prediction intervals (1 or 6 h), the CPV was lower for 15-min time steps than 1-h time steps (SI Figure S2). However, CPV24h predictions based on 15min and 1-h time steps were almost identical. Thus, a 1-h time step was used for all CPV24h calculations. The influence of wind speed was assumed to be negligible because for a 1-ha field (100 × 100 m), the wind speed would need to be 5.8%) in all experiments. We assumed that the system was oversaturated (i.e., RH was 100%) because soil− air equilibrium had not been established; thus, the Vwater we used was calculated from the reported M% values. In most of the experiments, M% was reported as a percentage of the soil’s mean water-holding capacity (MWC) although the actual MWC was not provided. As such, we assumed that the MWC was 20% for soils with an f OC 2%. These assumptions were based on data reported by the Food and Agriculture Organization of the United Nations39 and enabled calculation of the CPV24h for all 71 pesticides using the physical property data and the temperature and f OC conditions provided by Guth et al.13 Chemical Space Diagrams. Chemical space diagrams are useful for visually depicting how chemicals with different partitioning properties behave under different environmental scenarios and have been used in several previous studies.32,40−44 In our study, chemical space diagrams were prepared by plotting log Ksoil−air versus log Kwater−air with the magnitude of the CPV24 displayed as a contoured background shading. One of our objectives was to use a matrix of chemical space diagrams to display the CPV24h for a range of hypothetical pesticide partitioning combinations under different f OC and RH conditions. However, a complication arose in that the RH and f OC values were already accounted for in the equations used to calculate Ksoil−air. To remedy this, correction factors (CFs) were derived by subtracting pesticide log Ksoil−air values under standard conditions (i.e., T 288.15 K, f OC 0.02, RH 100%) from those under a range of different f OC and RH conditions. Under standard conditions, the CF was zero. For each RH and f OC combination, the derived CF was then applied to the corresponding hypothetical log Ksoil−air value, prior to calculation of the Fi,air, as shown in eq 18.

Briefly, each of the input parameters listed in Table 1 were increased by 10% (with the exception of RH, which was decreased by 10%) and the change in CPV24h output recorded and converted to an S(xi). Table 1. Flux IV Model Input Parameters Used in Sensitivity Analysis acronym

active air boundary layer thickness air diffusion coefficient

Δsoil−airU

internal energy of soil−air phase transfer internal energy of water−air phase transfer soil organic carbon fraction depth of soil surface layer mass of applied pesticide soil moisture content (%, w/w) soil solid density atmospheric relative humidity atmospheric temperature air boundary layer volume soil volume soil water volume

Δwater−airU f OC hsoil mapplied M% ρsoil RH T Vair(boundary) Vsoil Vwater

default value 0.001 m 0.0179 m2 h−1 (20 °C)a 102 kJ mol−1a −70 kJ mol−1a 0.02 0.001 m 1000 g 5.8%a 2.4 kg L−1 100% 288.15 K 1 × 104 La 1 × 104 La 1.39 × 103 La

a

Input parameters were indirectly modified by changes in other input parameters.

In cases where increasing the input parameter of interest caused changes in other input parameters (e.g., Dair values increased when the temperature was increased), these effects were included in the sensitivity analysis.

■

RESULTS AND DISCUSSION Comparison of CPV24h Values Calculated with Different Flux Approaches. The four flux approaches gave different CPV24h predictions and orders of volatility. Flux I predictions were equal to or higher than Flux III and IV predictions for 201 of the 224 pesticides in our database. For Flux II and III, lower pesticide Kwater−air and Ksoil−air values resulted in greater CPV24h predictions. However, Flux II (Kwater−air) predictions were generally higher than Flux III (Ksoil−air) predictions, with the exception of the highly water-soluble pesticides (e.g., amitrole), because the soil volume in the agricultural field system exceeds that of water. In all cases, Flux IV predictions were lower than those of Flux II and III because partitioning into both soil and water phases were considered. Interestingly, our model demonstrated that a number of high vapor pressure pesticides were found to exhibit low partitioning into the air phase based on the Flux IV approach (Figure 1a). These pesticides tended to be insoluble in water but exhibited favorable partitioning into the soil phase (log Ksoil−air > 6.5). In addition, a number of low vapor pressure pesticides exhibited relatively high partitioning into the air phase based on the Flux IV approach (Figure 1). These pesticides had relatively low affinities for the soil phase (log Ksoil−air < 6.5). These results indicate that multiphase pesticide interactions in the environment exert significant influences on volatilization flux. Comparison of Calculated CPV24h Losses and Literature Values. CPV24h values were calculated with each of the four flux approaches and compared to measured volatilization losses in the literature. Figure 2 shows the correlation plots

Fi,air = 1 ⎛ ⎞ Vwater V ⎜1 + K + (10[log K soil −air + CF ]) · V soil ⎟ water − air · V ⎝ air(boundary) air(boundary) ⎠ (18)

With this approach, Ksoil−air values of real pesticides need only be known for standard conditions in order to evaluate their behavior under a variety of RH and f OC conditions. The SI contains further details about CF derivation and values (SI Table S5). Sensitivity Analysis. Sensitivity analysis was conducted to determine which input parameters had the largest effects on the CPV24h values calculated with the multiphase partitioning approach (Flux IV). The analysis was performed for a set of 1369 hypothetical compounds covering a range of Kwater−air and Ksoil−air values. Sensitivity analysis was conducted according to the method described by Meyer et al. in which the sensitivity (S(xi)) was defined as the relative deviation of the output value resulting from the variation in an input parameter (Xi).45 In our case, the equation was S(x i) = ∂CPV24h /CPV24h· X i /∂X i

input parameter (X)

d Dair

(19) 871

dx.doi.org/10.1021/es3020277 | Environ. Sci. Technol. 2013, 47, 868−876

Environmental Science & Technology

Article

Figure 1. Comparison of Flux I−IV CPV24h predictions for selected (a) high vapor pressure pesticides and (b) low vapor pressure pesticides.

Figure 2. Correlation plots for 123 measured pesticide CPV24h values from Guth et al. versus calculated CPV24h values based on (a) vapor pressure, (b) Kwater−air, (c) Ksoil−air, and (d) multiphase partitioning (Kwater−air and Ksoil−air). The solid line shows the fitted linear regression for the data and the dotted line shows a 1:1 relationship.

coefficients of determination (R2) and had intercepts farthest from zero. In contrast, the Flux IV (multiphase) method had the highest R2 value, with an intercept closest to zero. Flux III predictions were almost as accurate as Flux IV predictions and thus, if one was to use a single screening parameter, Ksoil−air would

obtained for measured versus calculated CPV24h for the Guth et al.13 pesticide data set based on each of our flux approaches. All were significant at the 99.5% level. The ideal regression would exhibit a slope of one and an intercept of zero. The flux methods based on vapor pressure and Kair−water alone exhibited the lowest 872

dx.doi.org/10.1021/es3020277 | Environ. Sci. Technol. 2013, 47, 868−876

Environmental Science & Technology

Article

Figure 3. Chemical space diagrams showing Flux IV CPV24h values for hypothetical pesticides under different environmental conditions. All diagrams are for 288.15 K. CFs for the different combinations of RH and f OC are shown in orange. Pesticides are plotted according to their Ksoil−air and Kwater−air values under standard conditions (T 288.15 K, f OC 0.02, RH 100%).

be more appropriate than Kwater−air or vapor pressure. In saying that, the Flux III and IV approaches generally gave higher CPV24h values than were measured. We made a number of assumptions in order to compare our modeled results with the Guth et al. data set and these may have contributed to the overestimation. Also a limitation of our current analysis is that all of the Guth et al. measurements were for saturated soils (i.e., RH 100%) meaning that the RH term in the Ksoil−air equation was not fully activated. Regarding possible comparisons of physical properties for pesticides where Flux IV predicts CPV24h values of 100%, no similarities in physicochemical properties were observed. The pesticides themselves were not identified in the Guth et al. paper, thus, we were unable to look for similarities in structure or functional groups. Regardless, pesticide interactions with soil solids become increasingly important under drying conditions and interactions with soil water are important for saturated soils. Therefore, we propose that the Flux IV is the most appropriate approach for describing pesticide volatilization from a variety of agricultural soils because interactions with both soil solids and soil water are considered. As such, we only utilized the Flux IV approach when investigating the effects of different environmental conditions (e.g., temperature and RH) on pesticide volatilization in the following sections. Screening Technique Comparisons. Current Tier I screening techniques used by the EU10 have a pesticide vapor pressure trigger value of 10−4 Pa, above which further screening and measurements are required before registration approval. Therefore, we ranked the pesticides in our database in two ways (SI Table S6). In the first case, they were ranked in order from highest to lowest vapor pressure. In the second case, they were ranked from highest to lowest CPV24h (Flux IV). The objectives of this exercise were to determine whether the flux approach

produced comparable screening outcomes to those based on vapor pressure. We observed major discrepancies between the outcomes of the Flux IV approach and screening techniques that use vapor pressure; only 10 pesticides ranked in the top 20 most volatile pesticides on both lists. Further investigation into these discrepancies revealed that 21 pesticides with vapor pressures above the EU trigger value had Flux IV CPV24h values < 0.005% under standard conditions (SI Table S7). These compounds, with five exceptions (cisdicrotophos, trans-monocrotophos, glyphosate, asulam, and oxycarboxin) have low aqueous solubility (defined as < 1000 mg L−1), which suggests that they should predominantly partition into air. However, they all exhibited strong sorption to soil (log Ksoil−air > 7.5) as well, which decreased their volatilization potential and overall exposure to the atmosphere. These results suggest that current volatilization screening and modeling techniques based on vapor pressure and Kwater−air may overestimate the volatilization potential of high vapor pressure, insoluble pesticides because soil interactions are ignored. This means that a greater number of pesticides than necessary may undergo screening and testing. In contrast, five pesticides with vapor pressures below the trigger value of 10−4 Pa had relatively high Flux IV CPV24h values, that is, they were > 10% under standard conditions (SI Table S7). These pesticides are relatively water soluble, with the exception of tolylfluanid, so they were expected to favorably partition into the soil water. However, their low affinity for the soil phase (log Ksoil−air < 6.5) coupled with small Vwater values as defined by the agricultural field, resulted in a relatively high flux into the overlying air. At higher Vwater, the CPV24h for these compounds would decrease. Thus, these pesticides are very susceptible to small changes in environmental conditions and this will be 873

dx.doi.org/10.1021/es3020277 | Environ. Sci. Technol. 2013, 47, 868−876

Environmental Science & Technology

Article

Kwater−air < 6.5) completely volatilized within the 24-h period, and as such, a change in temperature, unless drastic, would not influence the CPV24h. For pesticides positioned near the horizontal (6.5 < log Kwater−air < 7.5) and vertical (5.5 < log Ksoil−air < 6.5) boundaries (i.e., 2,4-D EHE and 2-hydroxybenzoic acid (2-HBA)), the increase in CPV24h with increasing temperature was substantial. The CPV24h of pesticides falling within the purple region on the chemical space diagram (e.g., simazine and metolachlor) was also not strongly affected by temperature. Chemical space diagrams provide a way to view and interpret the combined effects of different RH and f OC combinations without requiring extensive knowledge of the equations used to calculate the CPV24h. Pesticide volatilization under a range of f OC and RH conditions can be assessed if only the Ksoil−air and Kwater−air values under standard conditions (i.e., T 288.15 K, f OC 0.02, RH 100%) are known. SI Table S2 contains Ksoil−air and Kwater−air values under standard conditions for 224 current- and historic-use pesticides. Sensitivity Analysis. Sensitivity maps are displayed in Figure 5, where yellow to red coloring signifies that an increase in the specified input parameter resulted in an increase in the CPV24h. Blue to purple coloring indicates that an increase in the specified input parameter resulted in a decrease in the CPV24h and green indicates that the CPV24h was insensitive to changes in the input parameter. The sensitivity of CPV24h to different environmental input parameters varied among regions of the chemical space diagrams. Highly volatile pesticides, situated in the bottom left corner of the diagrams (log Ksoil−air < 5 and log Kwater−air < 6), were insensitive to changes in any input parameter because they completely volatilized within 24 h. The influence of changes in f OC on the CPV24h were pronounced for soil-sorbed pesticides in the bottom right corner (log Ksoil−air > 5.5 and the soil−water partition coefficient, log Ksoil−water < −1) but had no effect on the highly volatile or water-soluble pesticides, situated in the top left corner (log Kwater−air > 6.5 and log Ksoil−water < −2). Similar patterns were observed for hsoil and Vsoil, which both increase the soil’s absorptive capacity. An increase in RH led to a decrease in CPV24h for water-soluble pesticides and an increase in CPV24h for soil-sorbed pesticides due to increased sorbate dissolution in the soil water phase and preferential displacement of soil-sorbed pesticides by water molecules, respectively. Similar results could also be expected for an increase in M%; however, because the Ksoil−air calculation only accounted for the influence of RH and not M%, only water-soluble pesticides were sensitive to changes in M%. Similar sensitivity patterns were observed for ρsoil and Vwater, which are directly related to M% (eq 1). As expected, an increase in temperature, and subsequently Dair, caused an increase in CPV24h for all but the most volatile pesticides, whereas an increase in d caused a decrease in CPV24h (due to the greater diffusion distance). The sensitivity analysis illustrates that the majority of pesticides are particularly susceptible to changes in most input parameters. As such, slight changes or combinations of changes in any of these input parameters could significantly alter a pesticide’s volatilization potential. On the other hand, all regions of the chemical space diagram were insensitive to changes in mapplied because values were given as percentage (not relative) losses. CPV24h was also insensitive to changes in Δwater−airU and Δsoil−airU because changes that occur independently of a change in temperature have no effect on the K values. Interestingly, an increase in Vair(boundary) also had no effect

explored in the next section. In any case, our results suggest that current EU10 and USEPA11 volatilization screening techniques based on vapor pressure are likely to miss a number of pesticides that may volatilize because their approach does not account for the multiphase interactions that occur in the real environment. Environmental Influences. The influences of f OC, RH, and temperature on CPV24h values calculated with the multiphase partitioning approach (Flux IV) were investigated with a series of chemical space diagrams. Figure 3 shows the combined effects of f OC and RH on the CPV24h for a set of hypothetical chemicals covering a range of environmental partitioning behaviors. Three pesticides are plotted on the chemical space diagrams using their Kwater−air and Ksoil−air values at standard conditions for specific scenarios. Results showed that the CPV24h decreased with increasing f OC, which increases the sorptive capacity of the soil phase.46 As f OC increased from 0.001 to 0.02% (at 100% RH), the CPV24h of 2,4-dichlorophenoxyacetic acid ethylhexyl ester (2,4-D EHE) decreased from 100% to 38.8% (Figure 2). In contrast, CPV24h increased with increasing RH because pesticides can adsorb to mineral surfaces (in addition to soil organic matter) under dry soil conditions (RH < 30%).47 At higher RH, the surface M% increases and water molecules adsorbed to mineral surfaces can displace adsorbed pesticides.47 Under saturated conditions (100% RH), all mineral adsorption sites are occupied by water molecules and as such, these conditions produce the highest CPV24h. Figure 3 shows that as RH increased from 50 to 90%, the CPV24h of dichlobenil increased from 0.13% to 100% (for an f OC of 0.008%). For pesticides such as trichlorfon, with high Kwater−air or Ksoil−air values, the CPV24h did not exceed 5% under any RH and f OC combination, as dissolution in water or soil sorption processes predominate. The chemical space diagrams in Figure 3 were calculated using an average daily temperature of 288.15 K. While it is possible to account for hourly temperature fluctuations with this model, we found that doing so gave CPV24h predictions on the same order of magnitude as those based on daily averages. Figure 4 shows that the CPV24h increased with increasing temperature. Highly volatile pesticides (log Ksoil−air < 5.5 and log

Figure 4. Chemical space diagram showing Flux IV CPV24h values for hypothetical pesticides under standard conditions (T 288.15 K, f OC 0.02, RH 100%). Diagonal lines show the temperature dependence of CPV24h values from 5 to 30 °C for a set of selected current-use pesticides. 874

dx.doi.org/10.1021/es3020277 | Environ. Sci. Technol. 2013, 47, 868−876

Environmental Science & Technology

Article

Figure 5. Chemical space diagrams displaying the sensitivity of calculated Flux IV CPV24h values to changes in environmental input parameters as a function of Ksoil−air, Kwater−air, and Ksoil−water.

on the CPV24h. This occurred because although the mass of pesticide partitioning into the air phase increased with an increase in Vair(boundary) (according to the Fi,air), volatilization flux was based on concentration and thus had a diluting effect on the cair(boundary), which was too small to observe in the sensitivity analysis. In summary, the multiphase partitioning approach (Flux IV) could be used to assess the volatilization potential of any organic compound (not only pesticides) and could be extended to include other environmental media (e.g., vegetation). Future work should focus on the evaluation, improvement, and extension of equations for predicting Ksoil−air values for currentuse pesticides. Clearly, more measurements are needed for pesticide Ksoil−air values in controlled environments under different temperature, f OC, and RH conditions. To obtain results more applicable to real-world situations, the behavior of pesticides applied in formulations should also be investigated so that the effects of adjuvants, safeners, and surfactants on pesticide volatilization can be assessed. Directed field studies are also required to further evaluate the validity of the model and prediction equations used herein.

■

ACKNOWLEDGMENTS

■

REFERENCES

This project was funded by the New Zealand Ministry of Science and Innovation, Project No. LVLX0901. Additionally, we thank Andrew Hewitt (Lincoln Ventures, New Zealand) for his involvement and Martin Scheringer (Safety and Environmental Technology Group, ETH Zürich) for useful advice at the project’s onset.

(1) Bedos, C.; Cellier, P.; Calvet, R.; Barriuso, E.; Gabrielle, B. Mass transfer of pesticides into the atmosphere by volatilization from soils and plants: Overview. Agronomie. 2002, 22 (1), 21−33. (2) Freemark, K.; Boutin, C. Impacts of agricultural herbicide use on terrestrial wildlife in temperate landscapes: A review with special reference to North America. Agric. Ecosyst. Environ. 1995, 52 (2−3), 67− 91. (3) Vos, J. G.; Dybing, E.; Greim, H. A.; Ladefoged, O.; Lambré, C.; Tarazona, J. V.; Brandt, I.; Vethaak, A. D. Health effects of endocrinedisrupting chemicals on wildlife, with special reference to the European situation. Crit. Rev. Toxicol. 2000, 30 (1), 71−133. (4) Summary of Results from the Californian Pesticide Illness Surveillance Program - 2009; Californian Environmental Protection Agency, Department of Pesticide Regulation: Sacramento, CA, 2012; http:// www.cdpr.ca.gov/docs/whs/pisp/sum_rpts/2009_sumdata.pdf (accessed March 23, 2012). (5) Bidleman, T. F. Atmospheric transport and air-surface exchange of pesticides. Water Air Soil Poll. 1999, 115 (1−4), 115−166. (6) Fernández, P.; Grimalt, J. O. On the global distribution of persistent organic pollutants. Chimia. 2003, 57 (9), 514−521. (7) Rüdel, H. Volatilisation of pesticides from soil and plant surfaces. Chemosphere 1997, 35 (1−2), 143−152. (8) Glotfelty, D. E.; Taylor, A. W.; Turner, B. C.; Zoller, W. H. Volatilization of surface-applied pesticides from fallow soil. J. Agric. Food Chem. 1984, 32 (3), 638−643. (9) Carlsen, S. C. K.; Spliid, N. H.; Svensmark, B. Drift of 10 herbicides after tractor spray application. 1. Secondary drift (evaporation). Chemosphere 2006, 64 (5), 787−794.

ASSOCIATED CONTENT

S Supporting Information *

Additional information and a complete list of abbreviations can be found in the Supporting Information. This material is available free of charge via the Internet at http://pubs.acs.org.

■

■

AUTHOR INFORMATION

Corresponding Author

*Phone: +64-3-479-5214; fax: +64-3-479-7906; e-mail: [email protected] Notes

The authors declare no competing financial interest. 875

dx.doi.org/10.1021/es3020277 | Environ. Sci. Technol. 2013, 47, 868−876

Environmental Science & Technology

Article

(10) FOCUS, 2008. Pesticides in Air: Considerations for Exposure Assessment. In Report of the FOCUS Working Group on Pesticides in Air, EC Document Reference SANCO/10553/2006 Rev 2 June 2008; http://focus.jrc.ec.europa.eu/ai/docs/FOCUS_AIR_GROUP_ REPORT-FINAL.pdf (accessed May 15, 2011). (11) Scientific Issues Associated with Volatilization of Conventional Pesticides; United States Environmental Protection Agency: Office of Pesticide Programs, Health Effects Division and Environmental Fate and Effects Division, 2009; http://www.regulations.gov/ #!documentDetail;D=EPA-HQ-OPP-2009-0687-0006 (accessed March 1, 2011). (12) van Wesenbeeck, I.; Driver, J.; Ross, J. Relationship between the evaporation rate and vapor pressure of moderately and highly volatile chemicals. Bull. Environ. Contam. Toxicol. 2008, 80 (4), 315−318. (13) Guth, J. A.; Reischmann, F. J.; Allen, R.; Arnold, D.; Hassink, J.; Leake, C. R.; Skidmore, M. W.; Reeves, G. L. Volatilisation of crop protection chemicals from crop and soil surfaces under controlled conditions-prediction of volatile losses from physico-chemical properties. Chemosphere 2004, 57 (8), 871−887. (14) Woodrow, J. E.; Seiber, J. N.; Dary, C. Predicting pesticide emissions and downwind concentrations using correlations with estimated vapor pressures. J. Agric. Food Chem. 2001, 49 (8), 3841− 3846. (15) Woodrow, J. E.; Seiber, J. N.; Baker, L. W. Correlation techniques for estimating pesticide volatilization flux and downwind concentrations. Environ. Sci. Technol. 1997, 31 (2), 523−529. (16) Butler Ellis, M.; Lane, A.; O’Sullivan, C.; Miller, P. The determination of volatilisation rate of fungicides in a field experiment. Aspects Appl. Biol. 2010, 99, 317−324. (17) van den Berg, F.; Kubiak, R.; Benjey, W. G.; Majewski, M. S.; Yates, S. R.; Reeves, G. L.; Smelt, J. H.; van der Linden, A. M. A. Emission of pesticides into the air. Water, Air, Soil Pollut. 1999, 115 (1− 4), 195−218. (18) Goss, K. U.; Schwarzenbach, R. P. Rules of thumb for assessing equilibrium partitioning of organic compounds: Successes and pitfalls. J. Chem. Educ. 2003, 80 (4), 450−455. (19) Mackay, D.; Di Guardo, A.; Paterson, S.; Cowan, C. E. Evaluating the environmental fate of a variety of types of chemicals using the EQC model. Environ. Toxicol. Chem. 1996, 15 (9), 1627−1637. (20) Shoeib, M.; Harner, T. Using measured octanol-air partition coefficients to explain environmental partitioning of organochlorine pesticides. Environ. Toxicol. Chem. 2002, 21 (5), 984−990. (21) Jury, W. A.; Spencer, W. F.; Farmer, W. J. Behavior assessment model for trace organics in soil: I. Model description. J. Environ. Qual. 1983, 12 (4), 558−564. (22) Klein, M. PELMO: Pesticide Leaching Model Version 4.0 User Manual; Fraunhofer Institute for Molecular and Applied Ecology: Germany, 2011. (23) PEARL Model for Pesticide Behaviour and Emmissions in Soil-Plant Systems; Descriptions of the Processes in FOCUS PEARL v 1.1.1; Alterra: Wageningen, 2001; http://www.pearl.pesticidemodels.eu/pdf/ pearlthe.pdf (accessed May 15, 2011). (24) Mackay, D.; Shu, W.; Ma, K.; Lee, S. Physical-Chemical Properties and Environmental Fate Handbook; CRC Press LLC: Boca Raton, FL, 2006. (25) Ferrari, F.; Klein, M.; Capri, E.; Trevisan, M. Prediction of pesticide volatilization with PELMO 3.31. Chemosphere 2005, 60 (5), 705−713. (26) Wolters, A.; Klein, M.; Vereecken, H. An improved description of pesticide volatilization: Refinement of the Pesticide Leaching Model (PELMO). J. Environ. Qual. 2004, 33 (5), 1629−1637. (27) Schneider, M.; Goss, K. U. Prediction of the water sorption isotherm in air dry soils. Geoderma 2012, 170, 64−69. (28) Schwarzenbach, R. P.; Gschwend, P. M.; Imboden, D. M., Eds.; Environmental Organic Chemistry, 2nd, ed.; John Wiley & Sons Inc.: Hoboken, NJ, 2003. (29) Wania, F.; Mackay, D. A global distribution model for persistent organic chemicals. Sci. Total Environ. 1995, 160−161, 211−232.

(30) Goss, K. U.; Schwarzenbach, R. P. Empirical prediction of heats of vaporization and heats of adsorption of organic compounds. Environ. Sci. Technol. 1999, 33 (19), 3390−3393. (31) Goss, K. U.; Buschmann, J.; Schwarzenbach, R. P. Adsorption of organic vapors to air-dry soils: Model predictors and experimental validation. Environ. Sci. Technol. 2004, 38 (13), 3667−3673. (32) MacLeod, M.; Scheringer, M.; Hungerbühler, K. Estimating enthalpy of vaporization from vapor pressure using Trouton’s rule. Environ. Sci. Technol. 2007, 41 (8), 2827−2832. (33) Estimation Method for the Volatilization of Pesticides from Fallow Soil, 1997. http://www.pesticidemodels.eu/pdf/MPB_Report_ 2.pdf (accessed October 7, 2012). (34) Hippelein, M.; McLachlan, M. S. Soil/air partitioning of semivolatile organic compounds. 2. Influence of temperature and relative humidity. Environ. Sci. Technol. 2000, 34 (16), 3521−3526. (35) Meijer, S. N.; Shoeib, M.; Jantunen, L. M. M.; Jones, K. C.; Harner, T. Air-soil exchange of organochlorine pesticides in agricultural soils. 1. Field measurements using a novel in situ sampling device. Environ. Sci. Technol. 2003, 37 (7), 1292−1299. (36) Meijer, S. N.; Shoeib, M.; Jones, K. C.; Harner, T. Air-soil exchange of organochlorine pesticides in agricultural soils. 2. Laboratory measurements of the soil-air partition coefficient. Environ. Sci. Technol. 2003, 37 (7), 1300−1305. (37) He, X.; Chen, S.; Quan, X.; Zhao, Y.; Zhao, H. Temperaturedependence of soil/air partition coefficients for selected polycyclic aromatic hydrocarbons and organochlorine pesticides over a temperature range of −30 to +30 °C. Chemosphere 2009, 76 (4), 465−471. (38) Hippelein, M.; McLachlan, M. S. Soil/air partitioning of semivolatile organic compounds. 1. Method development and influence of physical-chemical properties. Environ. Sci. Technol. 1998, 32 (2), 310−316. (39) Pesticide Residues in Food - 1996. Evaluations; Part 1 - Residues; Food and Agriculture Organisation of the United Nations (FAO) and World Health Organisation (WHO): Italy, 1997; http://www.fao.org/ docrep/W5897E/W5897E00.htm (accessed November 17, 2011). (40) Wania, F. Assessing the potential of persistent organic chemicals for long-range transport and accumulation in polar regions. Environ. Sci. Technol. 2003, 37 (7), 1344−1351. (41) Wania, F. Potential of degradable organic chemicals for absolute and relative enrichment in the Arctic. Environ. Sci. Technol. 2006, 40 (2), 569−577. (42) Wania, F.; Westgate, J. N. On the mechanism of mountain coldtrapping of organic chemicals. Environ. Sci. Technol. 2008, 42 (24), 9092−9098. (43) Gouin, T.; Mackay, D.; Webster, E.; Wania, F. Screening chemicals for persistence in the environment. Environ. Sci. Technol. 2000, 34 (5), 881−884. (44) Wong, F.; Wania, F. Visualising the equilibrium distribution and mobility of organic contaminants in soil using the chemical partitioning space. J. Environ. Monit. 2011, 13 (6), 1569−1578. (45) Meyer, T.; Wania, F.; Breivik, K. Illustrating sensitivity and uncertainty in environmental fate models using partitioning maps. Environ. Sci. Technol. 2005, 39 (9), 3186−3196. (46) Scholtz, M. T.; Bidleman, T. F. Modelling of the long-term fate of pesticide residues in agricultural soils and their surface exchange with the atmosphere: Part II. Projected long-term fate of pesticide residues. Sci. Total Environ. 2007, 377 (1), 61−80. (47) Goss, K. U. The air/surface adsorption equilibrium of organic compounds under ambient conditions. Crit. Rev. Environ. Sci. Technol. 2004, 34 (4), 339−389.

876

dx.doi.org/10.1021/es3020277 | Environ. Sci. Technol. 2013, 47, 868−876