Ranking Pesticides by Environmental Impact New models combine human

health, ecosystem impact,

and natural resource data to

identify the most hazardous

agricultural pesticides.

ALAN N E W M A N

Two 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 agricul­

tural 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 pres-endy 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," ad-

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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 high-ranking pesticides, argues Pease. In effect, "the strin­gency of the regulatory response should determine what quality we demand of the underlying data."

Pease and his collaborators have proposed a Cal­ifornia pesticide tax to fund state programs for en­vironmental protection and integrated pest man­agement which, for example, could be tied to a hazard ranking system. In practice, this pesticide tax would mirror current federal taxes on chlorofluorocar-bons that make environmentally safer chlorofluoro-carbon alternatives more economically attractive. However, such a pesticide tax would require some sci­entific consensus on an appropriate model, which doesn't now exist.

"In the absence of anything better, [ranking pes­ticides by hazard] is not unreasonable," says risk pol­icy expert Granger Morgan of Carnegie Mellon Uni­versity. 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.

California model Pease's group is developing its hazard ranking un­der the University of California's environmental health policy program, whose mission is to create new strat­egies to reduce the use of toxic substances. Since 1991 Pease and his collaborators have published a series

of reports ranking pesticide hazards by selected at­tributes such as reported farmworker illnesses or de­tection in groundwater.

There has been great interest among research­ers and policy experts in the rankings his group has generated, says Pease. Even pesticide manufactur­ers have called, hoping that a low hazard ranking could be used as an endorsement of their product. A ranking of pesticides using California groundwa­ter contamination data, released in May, was widely reported in the press because it identified an in­creased human health risk resulting from high re­sidual levels of the pesticide 1,2-dibromochloropro-pane in the drinking water of approximately 50 central California towns and cities. Dibromochloro-propane, a suspected carcinogen known to cause ste­rility 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 con­tain 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 analy­ses of well waters over a 20-year period, which pro­vide an extensive database on pesticide contamina­tion in groundwater. Other data used in the University of California hazard rankings include standard phys­ical and chemical data such as solubilities, human toxicity and carcinogenicity information from EPA, and aquatic toxicity values from the National Oce­anic and Atmospheric Administration.

In the simplest case the pesticide ranking relies on a single attribute, such as reported pesticide-related illness among farmworkers or 50% lethal con­centration (LC50) data for fish, as a surrogate for aquatic toxicity. The top 10 hazardous pesticides in

Hazard ranking for pesticides in California Human health impacts Natural resource impacts Multiattribute impacts

Rank Farm workers" General public, non-agricultural workers'

Groundwater0 Aquatic life EIQ field use rating8 Linear model'

1 Sulfur

2 Propargite 3 Glyphosate 4 Methomyl 5 Chlorine

10

Chlorpyrifos Parathion Methyl bromide Aluminum

phosphide Mevinphos

Sodium hypochlorite *

Chlorine Chlorpyrifos Diazinon Quaternary

ammonia Malathion Propetamphos Glyphosate Pyrethrins/piperonyl

butoxide Gluteraldehyde

Dibromochloro- Trifluralin Sulfur propane

Simazine Chlorpyrifos Copper hydroxide Diuron Propargite Chlorpyrifos Atrazine Azinphos-methyl Propagite Deethyl-atrazine Endosulfan Cryolite

Deisopropyl-atrazine Diazinon 1,2-dichloropropane Methyl bromide Bromacil Permethrin Ethylene dibromide Methomyl

Bentazon Carbofuran

Dimethoate Chlorothalonil Maneb Diazinon

Copper sulfate (basic)

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).

Sodium hypochorite Metam sodium Diclofop-methyl Propargite Methamidophos

Oxydemeton-methyl Mepiquat chloride Cyanazine Mevinphos

Difenzoquat

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California based on several of these single-attribute 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 single-attribute 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.

EIQ ranking Hazard rankings can be targeted for other social de­mands. The Environmental Impact Quotient (EIQ)

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.

Alan Newman is associate editor on the Washington staff o/ES&T.

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