Ranking Pesticides by Environmental Impact New models combine human health, ecosystem impact, and natural resource data to identify the most hazardous agricultural pesticides. ALAN
NEWMAN
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T
wo research groups recently have proposed mathematical models for determining the most environmentally hazardous agricultural pesticides. Although the models have different goals—influencing policy decisions versus influencing agricultural practices—they both rank pesticides by combining different types of data on human health, ecosystem impact, and groundwater and soil contamination. The models fall short of a formal quantitative risk assessment, but they can be used to rate the relative hazard of many pesticides using data that are available today. These models come as national leaders are pushing for comparative risk assessments as the basis for environmental regulation and action. In fact, the 1988 Federal Insecticide, Fungicide and Rodenticide Act, which regulates pesticides nationally, directs EPA to ban any pesticide whose use "poses unreasonable risks of adverse effects on human health or the environment." However, in practice, say many experts, broad risk assessments for pesticides are difficult or impossible to calculate because of data limitations. Thus, despite the political clamor for full-blown risk assessments, a more limited hazard ranking system is being promoted as a more viable approach. "These analyses are complex, but you can do it," says ecologist Joe Kovach of Cornell University's New York State Agricultural Experiment Station in Geneva, NY, who has developed one of the models. "It can come down to some number that helps make sense of the world." What separates hazard ranking from a quantitative risk assessment is, by definition, information about how much humans and wildlife are exposed to these chemicals. According to toxicologist William Pease at the School of Public Health at the University of California-Berkeley, those exposure data generally don't exist. "Historically, the risk assessment exercise has been fairly narrow," says Pease. "As a result, we exclude a lot of the kinds of policy options we might want to explore as a society." For example, we don't presendy identify highly hazardous pesticides that should be targeted for replacement by integrated pest management practices, says Pease. To explore more of these policy options, Pease's group is developing a hazard ranking system that can quickly identify the most hazardous agricultural pesticides of the hundreds in use. This list can then be used to target pesticides for further research, or it can lead to policy decisions such as use restrictions or safety warnings. "The most intelligent criticism [of this system] is that there is no measure of pesticide exposures, and therefore it doesn't truly predict the real risks," ad0013-936X/95/0929-324A$09.00/0© 1995 American Chemical Society
mits Pease. "A completely correct criticism, but if we try to address it, [what we are doing] would break down for lack of data. We probably could rank fewer than 20 compounds." Recognizing that limit leads Pease to be careful about what policy decisions can be based on these rankings. "I would be nervous if [these rankings] were used to ban a pesticide." However, they might be more appropriate for levying a "hazards tax" on highranking pesticides, argues Pease. In effect, "the strin gency of the regulatory response should determine what quality we demand of the underlying data." Pease and his collaborators have proposed a Cal ifornia pesticide tax to fund state programs for en vironmental protection and integrated pest man agement which, for example, could be tied to a hazard ranking system. In practice, this pesticide tax would mirror current federal taxes on chlorofluorocarbons that make environmentally safer chlorofluorocarbon alternatives more economically attractive. However, such a pesticide tax would require some sci entific consensus on an appropriate model, which doesn't now exist. "In the absence of anything better, [ranking pes ticides by hazard] is not unreasonable," says risk pol icy expert Granger Morgan of Carnegie Mellon Uni versity. The key, he argues, is to identify the important parameters that influence the ordering and to be clear about the uncertainties in the values. "You may have to do the best you can with poor data and then set up research priorities [to improve the data]," says Morgan.
of reports ranking pesticide hazards by selected at tributes such as reported farmworker illnesses or de tection in groundwater. There has been great interest among research ers and policy experts in the rankings his group has generated, says Pease. Even pesticide manufactur ers have called, hoping that a low hazard ranking could be used as an endorsement of their product. A ranking of pesticides using California groundwa ter contamination data, released in May, was widely reported in the press because it identified an in creased human health risk resulting from high re sidual levels of the pesticide 1,2-dibromochloropropane in the drinking water of approximately 50 central California towns and cities. Dibromochloropropane, a suspected carcinogen known to cause ste rility in humans, was banned 15 years ago. In their reports, Pease and his co-workers have ranked as many as 150 pesticides, which represent more than 90%, by weight, of the pesticides used in the state. The data for the rankings come primarily from California state records, which generally con tain the most extensive information on agricultural pesticides in the United States. The state mandates the reporting of pesticide use and pesticide-related illnesses and has conducted about 300,000 analy ses of well waters over a 20-year period, which pro vide an extensive database on pesticide contamina tion in groundwater. Other data used in the University of California hazard rankings include standard phys ical and chemical data such as solubilities, human toxicity and carcinogenicity information from EPA, and aquatic toxicity values from the National Oce anic and Atmospheric Administration. In the simplest case the pesticide ranking relies on a single attribute, such as reported pesticiderelated illness among farmworkers or 50% lethal con centration (LC50) data for fish, as a surrogate for aquatic toxicity. The top 10 hazardous pesticides in
California model Pease's group is developing its hazard ranking un der the University of California's environmental health policy program, whose mission is to create new strat egies to reduce the use of toxic substances. Since 1991 Pease and his collaborators have published a series
Hazard ranking for pesticides in California Human health impacts
Rank Farm workers"
1 2 3 4 5
10
Sulfur Propargite Glyphosate Methomyl Chlorine Chlorpyrifos Parathion Methyl bromide Aluminum phosphide Mevinphos
General public, nonagricultural w o r k e r s '
Sodium hypochlorite * Chlorine Chlorpyrifos Diazinon Quaternary ammonia Malathion Propetamphos Glyphosate Pyrethrins/piperonyl butoxide Gluteraldehyde
Natural resource impacts
Multiattribute impacts
Groundwater 0
Aquatic life
EIQ field use rating 8
Linear model'
Dibromochloropropane Simazine Diuron Atrazine Deethyl-atrazine
Trifluralin
Sulfur
Chlorpyrifos Propargite Azinphos-methyl Endosulfan
Copper hydroxide Chlorpyrifos Propagite Cryolite
Sodium hypochorite Metam sodium Diclofop-methyl Propargite Methamidophos
Deisopropyl-atrazine 1,2-dichloropropane Bromacil Ethylene dibromide
Diazinon Methyl bromide Permethrin Methomyl
Dimethoate Chlorothalonil Maneb Diazinon
Oxydemeton-methyl Mepiquat chloride Cyanazine Mevinphos
Bentazon
Carbofuran
Copper sulfate (basic)
Difenzoquat
3
Reported illnesses, 1984-90 ( 1). " Reported illnesses due to nonagricultural uses, 1984-90 (2|. c Frequency of detection in groundwater wells (3). ύ Estimated hazard using National Océanographie and Atmospheric Administration's ranking system multiplied by pounds applied in 1991 (4). * Estimated hazard using Environmental Impact Quotient ranking system (5) multiplied by pounds applied in 1992. 'Multiattribute model, simple linear rankings (Dan Landy, master's thesis, University of California-Berkeley, 1995).
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California b a s e d on several of t h e s e singleattribute rankings are listed in Table 1. Pease points out that the clustering in the ranking, such as the top 10 most hazardous, is more important than a pesticide's ordinal ranking. Given that limited resources are available for determining which pesticides are most problematic, the fast ranking can help target research and regulatory attention to the highest risk pesticides.
Multi-attribute ranking However, says Pease, the single-attribute model leads to rankings where "there is little overlap [in the listings] between hazards to workers, ecosystems, avian populations, etc." Therefore, if the policy question is which pesticides are most hazardous to die environment, single-attribute analyses fail to provide a clear answer. Recently, Pease's group has combined the singleattribute data into a "multi-attribute ranking system" for pesticides. This new model uses data for 12 attributes that define risks to human health, wildlife, and natural resources. The last category includes groundwater contaminant detections and field half-life. Different mathematical approaches are used to combine the various attributes and calculate the final rankings, although Pease warns that none should be considered truly objective. One of the mathematical approaches in fact allows a "decision maker" to choose weights for the various hazards to the environment. "There is no consensus on how we weigh qualitatively different risks such as ecosystem health versus human health," says Pease. The weights therefore reflect what decision makers consider important and what they are willing to trade off. Basically, the expert puts more weight on certain attributes in the equation, such as farmworker illness or aquatic toxicity. Pease says that his group's first attempts at using the multi-attribute model with decision weights led to the tentative finding that only large changes in these value weights significantly affect the final rankings. A more important factor is the mathematical approach used to assign hazard scores. (For example, the linear multi-attribute model used in Table 1 assumes that a change from 0 to 1 report of worker illness represents the same specific risk attribute as a change from 99 to 100 reports.) According to Pease, the key to reaching some sort of consistent ranking may be general agreement on what model to use rather than finding the "right" weights based on societal values. However, Pease warns, these conclusions need to be further tested. "We need a more diverse group of rankers." He plans to elicit weighting choices from decision makers ranging from agricultural representatives to environmentalists to further test the consequences of changing weights. Pease points out that these hazard rankings work to fulfill what he describes as a "social demand," in this case, policies that reduce toxic pesticide use.
model was developed by Kovach and his colleagues at Cornell University to build on the integrated pest management paradigm (5). This hazard ranking equation offers farmers a framework for evaluating environmentally friendlier agricultural practices. "The model is skewed to ecological effects," says Kovach. A number of "organic" farmers are exploring this model as a way to quantify and justify their practices, says Kovach. The EIQ is a multi-attribute model that uses 13 criteria. Toxicity to beneficial arthropods is heavily weighted in the model along with acute toxicity to farmworkers (those most exposed to pesticide applications). Bees and birds also receive increased weights. Toxicity to consumers and groundwater contamination receive lower weights. "A lot of safeguards are already built into EPA's pesticide registration process for human health [of consumers]," says Kovach. The questions the EIQ addresses are whether one pesticide is less toxic than another and less toxic to whom, says Kovach. Using databases such as the Extension Toxicology Network, a collaborative effort of several major U.S. universities, the Cornell researchers have assigned more than 120 pesticides an EIQ value. To generate a ranking or "field use rating," the EIQs are multiplied by the percent active ingredient and rate used per acre. Thus, a high-EIQ pesticide that requires fewer or lighter applications may be preferable to a lower EIQ pesticide used in high volumes (see values in Table 1). Unfortunately for organic farmers, EIQ field use ratings rank some "natural" pesticides as more problematic than synthetic ones. "Sulfur is the ultimate in problems, because of the number of pounds used and toxicity to beneficial arthropods," says Kovach. In the final analysis, says Kovach, the values from this model, like those from the University of California approach, should be considered as low, medium, or high risk. "It gives me decision points and helps me sort out dangerous chemicals from ones that aren't. It is like triage." Where do these models go from here? Given that there are extensive data gaps that hamper more sophisticated analyses, the key to refining these models could be how policy makers or farmers respond, argues Pease. "If me default decision [by these groups] is no action, then the science [needed to fill the gaps] will be delayed. If the default decision is some action, then it will be a stimulus to generate the needed information."
References (1) Robinson, J. C. et al. Preventing Pesticide-Related Illness in California Agriculture; California Policy Seminar: Berkeley, CA, 1993. (2) Robinson, J. C. et al. Pesticides in the Home and Community: Health RisL· and Policy Alternatives; California Policy Seminar: Berkeley, CA, 1994. (3) Pease, W. S. et al. Pesticide Contamination of Ground Water in California; California Policy Seminar: Berkeley, CA, 1995. (4) Pease, W. S. et al. Pesticide impacts on California Ecosystems; California Policy Seminar: Berkeley, CA, 1995. (5) Kovach, J. et al. NY Food Life Sci. Bull. 1992, 139, 2-8.
EIQ ranking Hazard rankings can be targeted for other social demands. The Environmental Impact Quotient (EIQ) 3 2 6 A • VOL. 29, NO. 7, 199S / ENVIRONMENTAL SCIENCE & TECHNOLOGY
Alan Newman o/ES&T.
is associate editor on the Washington
staff
