Pest Management Science
Pest Manag Sci 63:404–411 (2007)
A screening tool for vulnerability assessment of pesticide leaching to groundwater for the islands of Hawaii, USA Fredrik Stenemo,1∗ Chittaranjan Ray,1 Russell Yost2 and Steven Matsuda3 1 Department
of Civil and Environmental Engineering and Water Resources Research Center, University of Hawaii at Manoa, Honolulu, HI 96822, USA 2 Department of Tropical Plant and Soil Science, University of Hawaii at Manoa, Honolulu, HI 96822, USA 3 Pesticides Branch, Hawaii Department of Agriculture, Honolulu, HI 96814, USA
Abstract: This paper describes an updated version of a screening tool for groundwater vulnerability assessment to evaluate pesticide leaching to groundwater, based on a revised version of the attenuation factor. The tool has been implemented in a geographical information system (GIS) covering the major islands of the state of Hawaii, USA. The Hawaii Department of Agriculture currently uses the tool in their pesticide evaluation process as a first-tier screening tool. The basic soil properties and pesticide properties necessary to compute the index, and estimates of their uncertainty, are included in the GIS. Uncertainties in soil and pesticide properties are accounted for using first-order uncertainty analysis. Classifications of pesticides as ‘likely’, ‘uncertain’ or ‘unlikely’ to leach are made on the basis of the uncertainty and a comparison of the revised attenuation factor with values and uncertainties of two reference chemicals. The reference chemicals represent what are considered to be a ‘leachable’ and a ‘non-leachable’ pesticide under Hawaii conditions. It is concluded that the tool is suitable for screening new and already used pesticides for the islands of Hawaii. However, the tool is associated with uncertainties that are not accounted for, so a conservative approach with respect to interpretation of the results and selection of pesticide parameters used in the tool is recommended. 2007 Society of Chemical Industry
Keywords: groundwater; vulnerability assessment; pesticide leaching; modeling
1 INTRODUCTION Pesticide fate and transport models are used for vulnerability assessments of pesticide leaching to groundwater. These include simple index models1 – 6 and transient and deterministic simulation models.7 – 10 Models of the latter type have been used in several research studies11 – 15 as well as in regulatory contexts16,17 to evaluate pesticide leaching to groundwater. Index models have been used to assess the leaching potential of pesticides18 – 20 and as part of decision support systems,21 and they are often considered suitable for relative vulnerability assessments. The simple index models require less data than full simulation models and are fast and easy to use. However, these index models exclude important processes and factors affecting the potential leaching of pesticides to groundwater and are associated with large uncertainties.22,23 Previous groundwater vulnerability assessments of pesticides for Hawaii used an attenuation factor4 approach in combination with a geographical information system.19 Subsequent studies, with focus on uncertainties in the predictions, summarized by Loague et al.,23 concluded that there are large variabilities associated with the attenuation factor,
originating from uncertainties in climate, soil and pesticide properties22 as well as land use.24 Other studies evaluated the uncertainty of the attenuation factor with respect to the temporal resolution of rainfall25 and the temperature.26 Pesticide fate and transport modeling is associated with several sources of uncertainty.27 It is important to account for this uncertainty and clearly state what sources of uncertainty are neglected when the model results are used as decision support for regulatory agencies. In order to account for the uncertainties related to soil and pesticide properties, Li et al.28 revised the attenuation factor and introduced the concept of reference chemicals for the purpose of conducting relative vulnerability assessments for the islands of Hawaii. Reference chemicals are pesticides with known leaching behavior under local conditions, and serve as reference points to determine the classification of other pesticides as ‘leachers’ or ‘nonleachers’, eliminating the need for absolute boundaries for the classification of a pesticide’s potential leaching. To estimate the uncertainty in the values of the revised attenuation factor, Li et al.28 used first-order uncertainty analysis29 to define uncertainty bands for the reference chemicals and the pesticides they were evaluating. The use of reference chemicals with
∗ Correspondence to: Fredrik Stenemo, Department of Soil Sciences, Swedish University of Agricultural Sciences, PO Box 7014, 750 07 Uppsala, Sweden E-mail:
[email protected] (Received 1 November 2005; revised version received 21 August 2006; accepted 14 October 2006) Published online 22 February 2007; DOI: 10.1002/ps.1345
2007 Society of Chemical Industry. Pest Manag Sci 1526–498X/2007/$30.00
Assessment of pesticide leaching to groundwater in Hawaii
observed leaching potential in Hawaii removes some of the arbitrariness in classification, but not all, given the uncertainties in the calculations. The purpose of this paper is to describe and demonstrate an updated screening tool, based on a previous version of the tool (Yost R, unpublished data), that is currently used by the Hawaii Department of Agriculture for vulnerability assessment of pesticide leaching to groundwater for the islands of Hawaii, USA. The tool, both the updated and previous versions, extends what was done by Li et al 28 by incorporating the revised attenuation factor in a geographical information system for the islands of Hawaii with a linked pesticide properties database, by defining a classification scheme used in practical vulnerability assessments and by updating the reference chemicals.
2 METHODS 2.1 Tool description The purpose of developing the tool was to provide decision-makers at the Hawaii Department of Agriculture with a first-tier screening method in their pesticide evaluation procedure. The tool is used as the first step in order to determine the need for further evaluation of pesticides that are likely to leach to groundwater. The tool has been implemented in the GIS software ArcGIS30 and is available for the major islands of Hawaii. Vulnerability classification of pesticides is done using a revised version of the attenuation factor together with reference pesticides. The uncertainties in model parameters are accounted for by using first-order uncertainty analysis. A soil and pesticide database is linked to the tool. 2.2 The attenuation factor The attenuation factor,4 based on a conceptual model of water flow and chemical transport in soils, is defined as − ln 2 · d · RF · θFC (1) AF = exp q · t1/2 where d is the compliance or groundwater depth (m), θFC is the water content at field capacity, q is the average water flowrate through the soil (often referred to as the recharge rate) (m/d), t1/2 is the pesticide half-life (d) and RF is a retardation factor defined as RF = 1 +
ρb · foc · Koc θFC
(2)
where ρb is the soil bulk density (kg/m3 ), foc is the fractional organic carbon content and Koc is the soil organic carbon sorption coefficient (m3 /kg). The attenuation factor takes on values between 0 and 1, and can be thought of as the fraction of the pesticide applied at the soil surface that will pass the compliance depth d. Therefore, if the attenuation factor equals 1, all of the applied pesticide will leach pass the compliance depth d. Pest Manag Sci 63:404–411 (2007) DOI: 10.1002/ps
2.3 The revised attenuation factor Li et al.28 modified the attenuation factor by taking the logarithm twice, resulting in d · RF · θFC AFR = ln +k (3) q · t1/2 where the constant k ensures that the AFR has a value greater than unity. Li et al.28 gave two reasons for this transformation: 1. AF is highly skewed with respect to values computed for one pesticide for different soils, and requires transformation for proper variance estimation and probability assessment. 2. The discriminating power of the AF analysis, with respect to the reference chemicals, is increased when AFR is used (see below). For the tool presented in this paper, only the second reason is of relevance. 2.4 Uncertainty Using first-order uncertainty analysis,29 the total uncertainty in AFR, δAFR , is 2 δAFR = CVRF + CVd2 + CVq2 + CVθ2FC + CVt21/2 (4) where CVx is the coefficient of variation for the respective parameter in Eqn (1). Note that the coefficient of variation for the retardation factor, RF, is determined by a first-order uncertainty analysis of the terms in Eqn (2). An uncertainty band is defined as the average AFR value ± δAFR . Since the underlying data in the database, i.e. soil and pesticide properties, are associated with different degrees of uncertainty for different soils and pesticides, the uncertainty for the calculated AFR values will differ from soil to soil and from pesticide to pesticide. 2.5 Soil and pesticide properties The tool’s soil database is based on Yost et al.,31 which in turn is partly based on Soil Survey Investigation Report No. 2932 and additional soil samples from the Pearl Harbor area collected in 1985. The database of Yost et al.31 contains bulk and particle density, water content at field capacity and organic carbon content for profiles sampled to different depths. For some profiles only the organic carbon content was sampled. Average soil parameter values for the upper 20 cm of the soil were calculated and included in the tool, together with estimated standard deviations. The mean value and standard deviation were calculated for different categories of soil taxonomy, depending on the available data. The parameter values calculated for the upper 20 cm of the soil were assumed to be constant down to the compliance depth. Agricultural areas for which measured data were missing were identified, and soil properties from the taxonomically most analogous soil data were assigned to these areas (Yost R, unpublished). 405
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The compliance depth, d, is arbitrarily set constant at 0.5 m since the actual groundwater depth does not influence the vulnerability assessment and classification of pesticides in the tool. Changing the compliance depth will result in different absolute values of the AFR, but the effect is the same for all pesticides and will thus not change the relative classification [Eqn (3)]. The uncertainty in the compliance depth is set to zero. Annual average recharge rates were obtained from digitized recharge maps from previous studies in combination with data from a recent program of water assessment.33 – 42 The uncertainty in recharge estimates was evaluated by Giambelluca et al.25 for agricultural soils for the island of Oahu, Hawaii, under past or present cultivation of pineapple or sugarcane. Recharge component uncertainties were found to be 49 and 58% of the mean for sugarcane and pineapple respectively. The area studied by Giambelluca et al.25 is fairly well characterized. With respect to the data needed for recharge estimations, the uncertainty in recharge estimates for other areas might be expected to be larger. Based on this, the coefficient of variation for the recharge component is assumed to be 50%. The pesticide database in the tool contains values of pesticide half-life, t1/2 , and the soil organic carbon sorption coefficient, Koc , for 42 pesticides, together with the estimated standard deviation for the respective parameters (Table 1).43 2.6 Classification and vulnerability assessment Pesticide classification is based on a comparison of the AFR values [Eqn (3)] and associated uncertainty bands for the pesticide in question with those of two reference pesticides. Reference pesticides are pesticides with known leaching behavior under local conditions,28 where the local knowledge of the leaching behavior consists of the detection or nondetection of the pesticides in groundwater. When the revised attenuation factor is used instead of the original attenuation factor, the reference pesticides are separable, i.e. their uncertainty bands do not overlap and it is possible to use them as reference points for the classification. If the uncertainty bands of the reference pesticides overlap, as is the case if the original attenuation factor is used, it would be difficult to use the concept of reference chemicals for the classification since the distinction between a ‘leacher’ and a ‘non-leacher’ would not be clear. The pesticides atrazine and endosulfan are used as the ‘leacher’ and ‘non-leacher’ respectively. They are different reference pesticides to the original ‘leacher’ and ‘non-leacher’ identified by Li et al.28 Figure 1 shows the classification scheme used in the tool, together with the AFR and associated uncertainty bands for four example pesticides. A pesticide is classified as ‘likely’, ‘uncertain’ or ‘unlikely’ with respect to its potential to leach to groundwater. Pesticides that have an AFR value lower than that of the leacher are always classified as ‘likely’ (‘a’ 406
Table 1. Pesticide properties and standard deviations (SD) for some pesticides used in the screening tool. Pesticides in bold type are used in the example application
Active ingredient
Koc (m3 /kg)
SD Koc
Half-life (days)
SD half-life
Aldicarb Aldicarb sulfone Aldicarb sulfoxide Ametryn Anilazine Atrazine Bromacil Captafol Carbofuran Chlordane
0.0215 0.00584 0.139 0.334 3 0.147 0.069 1.34 0.0569 0.674
0.0128 0.0044 0.206 0.122 0 0.0565 0.0424 1.5 0.0357 0.899
25.1 46.2 24.7 40.8 0.75 75 280 5.6 107 1350
26.3 33.4 14.8 22.4 0.354 49.3 288 0 143 1150
Chlorpyrifos
Cyanazine 2,4-D Dalapon DBCP Dicamba Diuron EDB Endosulfan Fenamiphos Heptachlor Hexazinone
Lindane Methomyl Metribuzin Oxamyl Paraquat Prometon Prometryn Propazine Simazine 2,4,5-T Toxaphene
7.42
3.471
0.168 0.0558 0.0065 0.0938 0.003 0.477 0.0565 5.15 0.327 14.2
0.0375 0.0454 0.00778 0.0401 0.00113 0.06 0.0342 4.06 0.249 13.8
0.0328
0.0184
1.47 0.661 0.0732 0.0633 0.0757 0.0246 0.00733 0.0063 41.8 40.5 0.34 0.239 0.472 0.167 0.151 0.0206 0.135 0.0743 0.0455 0.0244 60.5 55.9
51.8
39.2 18.8 15.9 143 98.2 27.5 111 64.3 29.3 1050
30.7
46 16 9.9 63.5 132 43.8 72.7 27.2 14.8 1020
145
57
587 34.3 74 20.8 2470 341 156 108 76.2 28.5 1890
514 15.1 63.7 12.3 2060 225 198 40 47.7 18.7 3610
in Fig. 1). In general, pesticides are classified as ‘uncertain’ if their AFR value is between those of the ‘leacher’ and ‘non-leacher’ (‘c’ in Fig. 1). However, if the AFR value of the pesticide is within the uncertainty band of the leacher or has an uncertainty band that overlaps with that of the leacher (‘b’ in Fig. 1), it is classified as ‘likely’. If the pesticide has an AFR value that is greater than that of the ‘non-leacher’ it is classified as ‘unlikely’ (‘d’ in Fig. 1), unless its uncertainty band overlaps with that of the ‘leacher’, in which case it is classified as ‘uncertain’.
3 EXAMPLE APPLICATION For illustrative purposes, the tool was used to classify the leaching potential for two pesticides (Table 1) for the island of Oahu. Chlorpyrifos is classified as ‘unlikely’ to leach for all map units for the island of Oahu (Fig. 2), whereas the vulnerability classification map for hexazinone shows classifications of ‘likely’ for all map units (Fig. 3). If a pesticide is classified as ‘uncertain’, this is indicated with a different color. In Pest Manag Sci 63:404–411 (2007) DOI: 10.1002/ps
Assessment of pesticide leaching to groundwater in Hawaii uncertainty band
d) ‘unlikely’
c) ‘uncertain’
b) ‘likely’ a) ‘likely’
leacher more likely to leach
non-leacher AFR
less likely to leach
Figure 1. The classification in the tool described schematically with a set of example pesticides (a–d) and their classification.
Figure 2. Vulnerability classification map for chlorpyrifos for the island of Oahu (black = ‘unlikely , dotted = ‘no data’).
Figure 3. Vulnerability classification map for hexazinone for the island of Oahu (gray = ‘likely’, dotted = ‘no data’).
Pest Manag Sci 63:404–411 (2007) DOI: 10.1002/ps
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these two example cases, all map units show the same classification, but it is possible for a pesticide to be classified differently for different soils. In general, a pesticide is not likely to be classified as both ‘unlikely’ and ‘likely’ for the same island owing to differences in soil properties. Classifications of both ‘unlikely’ and ‘uncertain’ or both ‘likely and ‘uncertain’ are more common. The classification maps created by the tool should be seen as general qualitative information. They should not be used to determine the potential leaching of a pesticide for individual map units. The value of the classification maps lies in the overall judgment of a pesticide’s leaching risk that is provided. If a pesticide is classified as ‘likely’ for all map units, this might lead to a different course of action than if, for example, 5% of the area is classified as ‘likely’ and 95% as ‘uncertain’. This is also true for pesticides classified as ‘unlikely’ and ‘uncertain’ for different portions of an island.
4 DISCUSSION AND CONCLUSIONS Rao et al.4 mentioned that a number of simplifying assumptions are made when using the attenuation factor. In the attenuation factor the vadose zone properties are assumed to be constant with depth, and an average groundwater recharge is used. Furthermore, the soil organic sorption coefficient is assumed to be suitable to describe sorption of the pesticide, and an average pesticide half-life is used. These assumptions are not valid for most sites. However, preliminary investigations of the tool have shown that using model parameters that are constant with depth has little or no influence on the pesticide classifications in the tool. Giambelluca et al.25 investigated the effect of natural recharge variability on uncertainty in the attenuation factor and found recharge variabilities of 10, 44 and 176% relative to the mean for the annual, monthly and daily timescales respectively for sugarcane plantation, and recharge variabilities of 31, 112 and 344% for pineapple. Therefore, the assumption of a constant average groundwater recharge is clearly an additional source of uncertainty that is not accounted for in the tool. Only a few of the pesticides included in the tool database do not sorb mainly to organic carbon. The attenuation factor does not account for preferential flow, which occurs for a wide range of soils44 and influences the relative leaching potential of pesticides to groundwater. The difference in transport of pesticides with varying hydrophobicity and degradation rates is reduced in the presence of preferential flow.45,46 In a simulation study, the effect of macropore flow was found to be especially important for compounds that would be considered ‘non-leachable’ by most simple index approaches, i.e. strongly sorbed and quickly degrading compounds.47 Increases in simulated leaching by more than four orders of magnitude for moderately to 408
strongly sorbing pesticides with rather short halflives were demonstrated by Larsson and Jarvis.48 The above complicates the use of the tool for relative vulnerability assessments for areas where preferential flow is important. The vadose zone in Hawaii consists of a sequence of aggregated soils, followed by weathered basalt (saprolite) and fractured basalt. Preferential flow has been suggested as an explanation for the appearance of some pesticides in Hawaii groundwater.49 Future development of the tool, or a second-step evaluation, needs to consider the effect of neglecting preferential flow, and approaches to include it in the vulnerability assessment procedure. On the other hand, the tool does not include a description of pesticide retardation below the compliance depth, which for some areas might be an overly conservative approach. In addition to the above, future development of the tool ought to include a more realistic description of the groundwater depth and recharge conditions. With respect to uncertainty in the pesticide properties used by the screening tool, data might be sparse for new pesticides. However, it is important to provide reasonable estimates of the uncertainty of these parameters in terms of standard deviations. Variability of the half-life50 – 52 and the sorption53 – 55 of pesticides within a field may be used from previous studies when the uncertainty of pesticide parameters needs to be estimated for new pesticides. The aforementioned and similar studies do not give exact values for the new pesticides, but such data may be used to give an idea of the magnitude of the uncertainties. The use of reference chemicals in the tool for classification of pesticides as ‘leachers’ and ‘nonleachers’ is useful, since management decisions can be based on current knowledge and observations of pesticide leaching.28 However, updating the reference chemicals on the basis of new findings constitutes an after-the-fact update. This means that the choice of reference chemicals needs to be made carefully, and perhaps also needs to be more conservative than current findings suggest. This is also motivated by the fact that the screening tool should only be used to identify pesticides that are highly unlikely to pose a threat to groundwater, i.e. it should only be used as a first-step screening tool. On the other hand, it is important to recognize the critical role of pesticides in Hawaii agriculture, and not select a ‘non-leacher’ reference chemical that would lead to exaggerated estimates of leachability. The selection of reference chemicals is indeed both a difficult and critical part of the development of the screening tool. Furthermore, the use of reference chemicals assumes that their leaching behavior is indeed both known and valid for all conditions. With respect to validity, it might be argued that the ‘non-leacher’ reference chemical might very well be a ‘leacher’ under certain conditions, e.g. at sites with a shallow groundwater table, occurrence of preferential flow or Pest Manag Sci 63:404–411 (2007) DOI: 10.1002/ps
Assessment of pesticide leaching to groundwater in Hawaii
under intensive irrigation regimes. This means that the classification tool should preferably be used together with additional information (e.g. actual groundwater depth, flow processes and irrigation regimes) to avoid using the classification for areas where the definition of the ‘non-leacher’ is unsuitable. However, in the Hawaii setting, this may pose problems because of the deep vadose zone without accurate characterization of organic carbon content, bulk density, hydraulic parameters, water content, etc. Another complication with the reference chemicals is the certainty with which a pesticide is defined as a ‘non-leacher’. The tool does not include a time component, and it may be that the pesticide just has not yet leached to the groundwater. The choice of the ‘non-leacher’ reference pesticide should preferably be further supported with field or simulation studies resembling vulnerable conditions under which the pesticide might be applied. Furthermore, the historical use of pesticides and results from long-term monitoring should be considered, especially in areas where the groundwater is deep. The tool uses three classes for the classification of pesticides. In practice, the decision based on the classification is whether further study of the pesticide is necessary before a regulatory decision is made. Two classes are sufficient for this, and it is the authors’ opinion that both the ‘uncertain’ and ‘likely’ classifications should prompt further, possibly different, actions and that a classification of ‘unlikely’ should be required for a pesticide to be approved directly for usage. This conservative approach is motivated both by the uncertainties associated with the tool, some of which are accounted for and some of which are neglected, and by the fact that the tool is not, in all aspects, strictly based on worst-case assumptions. In spite of the associated uncertainties, the tool is judged suitable as a screening tool for the islands of Hawaii. However, given the uncertainties that are not accounted for, a conservative approach with respect to interpretation of the results and selection of model input of pesticide parameters is recommended. The tool could be used as a decision support tool, together with other sources of information, in order to make the final decision concerning a pesticide’s potential leaching to groundwater. To complement the vulnerability assessments for the islands of Hawaii, the use of deterministic simulation models as a screening tool or second-step evaluation (often referred to as tier II models) ought to be examined and methods to include dosage, and perhaps toxicity, data studied. Furthermore, field studies to evaluate the classifications obtained by the tool would be valuable, and perhaps even necessary. In line with the European Union initiatives,16 the USEPA is currently contemplating the adaptation of tier II models for pesticide registration. Pest Manag Sci 63:404–411 (2007) DOI: 10.1002/ps
ACKNOWLEDGEMENTS The project was funded by the Hawaii Department of Health (DOH) with federal pass-through money from the US Environmental Protection Agency. The authors thank Daniel Chang, DOH project manager, for his interest in this work. They also thank Robert Whittier of the Water Resources Research Center at the University of Hawaii and Milton Martinez of the Natural Resources Conservation Service of the US Department of Agriculture for providing recharge and soil property information for various islands of Hawaii. This is Water Resources Research Center contributed paper CP-2006-00.
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