Inferring past pesticide exposures: a matrix of individual active ingredients in home and garden pesticides used in past decades.

BACKGROUND: In retrospective studies of the health effects of home and garden pesticides, self-reported information typically forms the basis for exposure assessment. Study participants generally find it easier to remember the types of pests treated than the specific pesticides used. However, if the goal of the study is to assess disease risk from specific chemicals, the investigator must be able to link the pest type treated with specific chemicals or products.

OBJECTIVES: Our goal was to develop a "pesticide-exposure matrix" that would list active ingredients on the market for treating different types of pests in past years, and provide an estimate of the probability that each active ingredient was used.

METHODS: We used several different methods for deriving the active ingredient lists and estimating the probabilities. These methods are described in this article, along with a sample calculation and data sources for each.

RESULTS: The pesticide-exposure matrix lists active ingredients and their probabilities of use for 96 distinct scenarios defined by year (1976, 1980, 1990, 2000), applicator type (consumer, professional), and pest type (12 categories). Calculations and data sources for all 96 scenarios are provided online.

CONCLUSIONS: Although we are confident that the active ingredient lists are reasonably accurate for most scenarios, we acknowledge possible sources of error in the probability estimates. Despite these limitations, the pesticide-exposure matrix should provide valuable information to researchers interested in the chronic health effects of residential pesticide exposure.

KEY WORDS: Exposure assessment, herbicides, insecticides, pesticides, residential. Environ Health Perspect 115:248-254 (2007). doi:10.1289/ehp.9538 available via http://dx.doi.org/ [Online 7 November 2006]

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Retrospective studies of the health effects of home and garden pesticides face challenges in exposure assessment, particularly for diseases with long latency periods where the relevant exposures may have occurred decades before diagnosis. Typically, self-reported information forms the basis for exposure assessment, sometimes supplemented with inventories of stored pesticide products or measurements of pesticide residues in environmental or biologic samples. Study participants have had difficulty recalling specific brand or chemical names of pesticides they have used (Bradman et al. 1997; Daniels et al. 2001; Pogoda and Preston-Martin 1997). Teitelbaum (2002) suggested that people can more easily remember the types of pests treated, as was observed in a study of childhood neuroblastoma (Daniels et al. 2001). If this approach is used, and if the goal is to assess disease risk from specific chemical exposures, the investigator must be able to link the type of pest treated with specific chemicals.

We have developed a "pesticide-exposure matrix" to assist in that task. The matrix is designed to be used in conjunction with self-reported information on the types of pests treated in 4 years: 1976, 1980, 1990, and 2000. For each pest-year combination, the matrix lists the active ingredients that were on the market and provides a rough estimate of the probability that a product containing each ingredient was used. For example, if a consumer treated his or her home for rodents in 1990, we estimate that there is an 87% probability that the product contained brodifacoum, a 10% probability for warfarin, and a 3% probability for other, unspecified active ingredients. The probabilities sum to > 100% when a product contains more than one active ingredient (e.g., if only one product was available and it contained three ingredients, the probability of use would be 100% for each). The probabilities are unrelated to the concentrations of the active ingredients in the product and are therefore unrelated to the intensity of exposure.

We developed the matrix for a population-based case-control study of non-Hodgkin lymphoma (NHL) conducted between July 1988 and June 2000 (Hartge et al. 2005). Participants completed a lifetime residential history calendar and were later interviewed. Starting with the current home, interviewers asked whether pesticides were used to treat each of 12 pest types: lawn insects, lawn weeds, outdoor plant/tree insects, outdoor plant/tree weeds, outdoor plant/tree diseases, crawling insects, flying insects, termites, fleas/ticks on pets, fleas/ticks in the home, insects on indoor plants, and rodents. As the interviewer asked about each pest type, he or she displayed a card with examples of specific pests. The interviewer asked who applied the pesticide (respondent, exterminator, someone else), how frequently, and in what form (e.g., spray, powder). This was repeated for each home in which the subject lived for at least two years, going back 30 years. The questionnaire and cards can be found at http://dceg.cancer.gov/modules/PesticideHist.pdf [National Cancer Institute (NCI) 2006a].

The pesticide-exposure matrix covers each of the 12 pest types in the NHL study. Probability estimates are provided for 4 years (1976, 1980, 1990, and 2000) and two types of appliers (consumers, using pesticides purchased at supermarkets and hardware stores; and professionals such as pest control operators and lawn services). Thus, 96 "scenarios" (12 pest types x 4 years x 2 appliers) are covered. The matrix does not cover synergists [chemicals added to products to increase potency, such as N-octyl bicycloheptene dicarboximide (MGK 264) and piperonyl butoxide], repellents [e.g., DEET (diethyltoluamide)], solvents, emulsifiers, spreaders, stickers, buffering agents, or other ingredients that are not considered active ingredients but must be listed on the label.

Data Sources

Reports prepared by Kline & Company, Inc. (Little Falls, NJ) (Anonymous 1982, 1991; Cyr and Dansbury 2000; Fugate and Cyr 1997; Fugate and Hall 2002; Fugate et al. 2000, 2001, 2002; Garushenko et al. 1977; Goodbread et al. 1983; Hall and Dansbury 2000; Hodge and Rafter 1991, 1992a, 1992b; Ramsey and Kollonitsch 1977) were the major information source. Since 1976, Kline has conducted proprietary analyses of product sales, market share, and active ingredient sales of home and garden pesticides and fertilizers for both the consumer and professional markets. All major pesticide manufacturers in the United States are purchasers of these reports. The data are used for market planning purposes in developing business strategies focused on the consumer and professional markets. The types of information presented in the reports vary, depending on the type of market (consumer vs. professional), the pest type, and the year (data have become more detailed over time). Data may be provided on the number of acres treated nationwide with individual products, pounds of specific active ingredients used, dollar sales of products or active ingredients, prices per pound of product, dollar sales by company, and/or main products by company.

Kline derived information for the consumer markets by analyzing sales and other data obtained primarily through telephone interviews with pesticide manufacturers or formulators. Depending on the year, interviews were held with 60-75 of 85-100 manufacturers or formulators. The accuracy of the information varies with the cooperativeness of the respondents and their knowledge of the product categories, but generally increases with the size of the market. Data on market size are believed to be within 10% of the true value for product categories with sales of $500 million or more and within 25% of the true value for smaller product categories. Data for professional markets were gathered through telephone interviews with professional pesticide applicators. This market is large, highly segmented, and diffuse. Typically, 200-300 applicator companies (or branches of major chains) were interviewed in each lawn or outdoor plant/tree segment, of a universe of 15,000-18,000, and 200-800 applicator companies were interviewed in the termite, crawling insect, and flying insect segments, of a universe of about 20,000. Data accuracy varies with the cooperativeness of the respondents, their knowledge of different product categories, the number of interviews, and end user and supplier concentration in each market segment.

Two U.S. Environmental Protection Agency (EPA) databases were used. The Pesticide Product Information System (PPIS) (U.S. EPA 2003a) contains information on pesticide products that have been registered in the United States, including registrant names and addresses, ingredients, toxicity category, product names, distributor brand names, site uses, pest uses, pesticidal type, formulation code, and registration status. A related resource is U.S. EPA's Pesticide Product Label System (PPLS) (U.S. EPA 2003b), a collection of pesticide label images. We used these databases to estimate probabilities for consumer treatment of crawling insects, flying insects, fleas/ticks on pets, and fleas/ticks in the home, and to provide information on product formulations and application rates.

Another information source, the U.S. EPA National Home and Garden Pesticide Use Survey (Whitmore et al. 1992) (the "U.S. EPA Survey"), involved home interviews with > 2,000 households in 1990. Interviewers inventoried stored pesticide products, recorded the active ingredients on the label, and asked respondents to identify the pests on which the product had been used during the preceding year. The survey covered continuous-use products (e.g., flea/tick collars, roach/ant traps) only in a general way, and did not cover professionally applied products. Because the survey accounted only for products in storage, it likely underestimated the prevalence of products that are typically discarded after a single use (e.g., foggers). We used information from this survey as input to the probability estimates for consumer treatment of crawling and flying insects.

Several other sources were used to help identify active ingredients in products and to estimate application rates: C & P Press publications (Anonymous 1994, 1995; C & P Press 2004), Meister Publishing Company manuals (Anonymous 1999a, 1999b, 2000, 2003, 2005), Crop Data Management Systems, Inc. (2004), and Hagan et al. (1993).

Methods for Estimating Probabilities and Confidence Levels

We used several different methods to estimate the probabilities. Our choice of a method for each scenario was based on the types and quality of information available. Professional judgment (M.J.C., Senior Associate, Specialty Pesticides, Kline & Company, Inc.) played a large role in many scenarios.

Wherever possible, we tied the probabilities to Kline-reported information on the number or percent of acres, nationwide, that were treated with specific products or active ingredients. That is, if Kline reported that half of all lawn acres treated for weeds by professionals was treated with active ingredient X, we assumed that if a person hired a professional to treat his or her lawn for weeds, there is a 50% chance that the applicator used a product containing X. If acreage was not provided by Kline, we attempted to derive it; otherwise, we based the probabilities on dollar sales. The probabilities were never based strictly on the pounds used, which can be a poor indicator of the probability of use; this is illustrated in Table 1, comparing two leading products used by professionals to treat for lawn insects in 2000. The pounds data erroneously suggest that Dursban (containing chlorpyrifos) was much more widely used than Talstar (containing bifenthrin), whereas the sales and acres data show that use was somewhat similar. This is because the bifenthrin molecule is more active than chlorpyrifos, and although the products sell for a similar price per acre, bifenthrin is less concentrated then chlorpyrifos in the formulated product. Overall, acreage treated probably provides the best basis for calculating probabilities because it accounts for differences in concentrations and usage rates among different classes of pesticides. We consider dollar sales to be acceptable if it is the only information available.

Our level of confidence in the probability estimates varies by scenario, depending on the method used, the extent to which judgment played a role, the quality of the data in the source materials (we occasionally judged the source data to be of poor quality and made modifications based on professional expertise), and how closely the pest type definition in the Kline reports matched that in the NHL questionnaire. Our confidence level is higher for scenarios in which one supplier or active ingredient dominated the market.

The methods used, the scenarios to which each applies, and the confidence ratings are summarized in Table 2 and discussed below. A sample calculation is provided for each (Tables 3-9). Calculations and data sources for all 96 scenarios are provided online at http://dceg.cancer.gov/pesticide (NCI 2006b).

Method 1: number of acres treated. This method was used when the Kline reports provided the number of acres nationwide treated with specific pesticide products (scenarios 51, 52, 55, 56, 59, 60, 63, 64, and 68, all of which involve professional treatment of lawns or outdoor plants/trees). We assumed that the probability that a product (and each active ingredient in it) was used is equal to the percent of acres treated with that product.

We have a medium confidence level in the estimates for outdoor plant/tree insects (1990, 2000) and a high confidence level for the others, because the Kline data for outdoor plants do not include mature trees, which are often sprayed with insecticides by professional applicators. This use might be significant but is likely smaller than the market for insecticides applied to gardens and landscaping areas, on which the Kline estimates are based. We do not believe this to be an important limitation for outdoor plant/tree pests other than insects.

Method 2: number of acres treated (derived from pounds of active ingredients and application rates). This method was used for lawns and outdoor plants/trees when Kline reported the pounds of individual active ingredients sold (scenarios 2-4, 6-8, 14-16, 18-20). We divided the pounds of each active ingredient by an estimated application rate (pounds per acre) to derive the number of acres treated with each active ingredient, and then proceeded as in method 1. The application rates were taken from Meister Publishing Company manuals (Anonymous 1999a, 1999b, 2000, 2003, 2005), C & P Press publications (Anonymous 1994, 1995; C & P Press 2004), and the PPLS (U.S. EPA 2003b). If the rates were presented as a range, we chose the midpoint unless we had reason to believe otherwise.

Judgment was used for all of these scenarios, and we have a medium level of confidence in the probability estimates. For lawn weeds (1980, 1990, 2000), we modified the Kline-reported pounds of some active ingredients to reflect our judgment about actual product formulations. For lawn and outdoor plant/tree insects (1980, 1990, 2000), Kline provided the active ingredient pounds aggregated across three pest types (lawn insects, outdoor plant insects, and nonplant insects), requiring us to allocate the pounds to each individual pest type. A similar situation was encountered for outdoor plant/tree diseases (1980, 1990, 2000).

Method 3: number of acres treated (derived from dollar sales, unit prices, and application rates). This approach was used when Kline reported both dollar sales and unit prices (dollars per pound or gallon) for individual products (scenarios 11 and 12). We divided the dollar sales by the unit price to estimate the pounds or gallons of each product sold. We then divided the pounds or gallons sold by an estimated application rate (pounds or gallons per acre) to derive the number of outdoor plant/tree acres treated with each product, and proceeded as in method 1. We have a medium level of confidence in the estimates.

Method 4: product sales. For scenarios 45-50, 53-54, 57-58, 61-62, 65-66, 73-76, 80, and 88-96, Kline reported dollar sales for individual products or active ingredients, but not unit prices. We assumed that the probability that a product (and each active ingredient in it) was used is equal to the product's proportion of total dollar sales.

For 1976, Kline treated two of the NHL pest types as one (professional treatment of lawn insects and outdoor plant/tree insects were combined, as were lawn weeds and outdoor plant/tree weeds, and lawn diseases and outdoor plant/tree diseases). We allocated active ingredient sales to the individual pest types using judgment, guided by information from the Kline reports.

None of the Kline reports contained a category for professional treatment of "crawling insects" or "flying insects." We used the following pest types reported by Kline to represent the crawling insect category: general pests in 1976 (these consist mainly of ants, roaches, and spiders), general pests and outdoor pests in 1980 (outdoor pests are mainly ants, roaches, and spiders treated outside, but not on the lawn or garden), and ants plus cockroaches in 1990 and 2000. Kline data on professional treatment of flying insects in 2000 pertained only to bees.

Because of the uncertainty associated with using product sales as the basis for probability of use, we have a medium confidence level in the probabilities for most of these scenarios. We gave high confidence ratings to the 1990 and 2000 professional termite scenarios, the former because the market was well understood by Kline and the latter because of the large sample size used by Kline. The consumer rodent market was rated high because it has been dominated by d-Con (warfarin) during the entire period of interest, and the professional segment was high because it has used a small number of active ingredients in a well-documented market. Our confidence level is low for professional treatment of outdoor plant/tree insects in 1976 and 1980 because the Kline data excluded insecticide applications to mature trees, and because the 1976 data were aggregated across more than one pest type. We have low confidence in the probabilities for flying insects in 2000 because they were based only on bees.

Method 5: product sales (calculated from company sales). For scenarios 1, 9, 10, 22, and 23, Kline reported dollar sales by manufacturer (but not by product or active ingredient) and identified each manufacturer's main products and (typically) each product's main active ingredients. Unit prices were not given. We identified the active ingredients in each product when necessary. We apportioned each manufacturer's dollar sales to its individual products or active ingredients using judgment. We assumed that the probability that a product (and each active ingredient in it) was used is equal to the product's percent of total dollar sales.

For indoor plants in 1990, Kline reported sales by manufacturer and we used the PPIS (U.S. EPA 2003a) to identify the active ingredients that each manufacturer might have used. Probabilities for indoor plants pertain to insects only.

Our confidence level is medium for all scenarios except lawn weeds (1976) and outdoor plant/tree weeds (1976), which are rated low because our allocation of sales to active ingredients required more judgment than the other scenarios.

Method 6: active ingredient frequencies from PPIS. For consumer treatment of household insects (scenarios 25-40), the Kline reports were not sufficiently detailed for our purposes. We used data from the PPIS (U.S. EPA 2003a) and the U.S. EPA Survey (Whitmore et al. 1992) to estimate the active ingredient probabilities.

We first selected the PPIS "site" codes (places that the product was registered to be applied) and "pest" codes (pests that the product was registered to treat) to use for searching the database. For fleas/ticks on pets, we used site codes corresponding to pets, dogs, and cats, and pest codes for fleas, ticks, deer ticks, lonestar ticks, and brown dog ticks. For the remaining scenarios, we used the site codes listed under "household or domestic dwellings," excluding codes that were not relevant (e.g., hotels, military barracks). For pest codes, we used cockroaches, ants, and spiders to represent crawling insects; and flies, mosquitoes, and bees to represent flying insects. We used these site and pest codes to search PPIS to identify all products that were actively registered for each pest type/year combination, excluding products that may be applied only by certified pest control operators, and the active ingredients in those products. We divided the number of products containing each active ingredient by the total number of products registered for that pest/year to derive the percent of products containing that active ingredient.

We considered setting the probability for each active ingredient equal to the percent of products in which it was contained, but this could overstate probabilities for active ingredients present in a large number of products with relatively low sales, and vice versa. We therefore used judgment to modify the probabilities for some active ingredients. We used information from Kline and the U.S. EPA Survey (Whitmore et al. 1992) as the basis for most of the modifications, and judgment for the others. Although the U.S. EPA Survey data correspond to only 1 year (1990), we assumed that the adjustments that we derived from the data would, in most instances, apply to all 4 years of interest. The U.S. EPA Survey did not provide relevant information for fleas/ticks in the home.

For fleas/ticks on pets in 2000, we also incorporated information from two Kline reports (Fugate and Cyr 1997; Fugate et al. 2001), which cover newer flea and tick products sold by veterinarians to consumers. Four veterinary products based on four active ingredients comprised 62% of this market in 2000, with products sold through retail channels comprising the remainder. We used PPIS to characterize the retail market, but used judgment based on Kline data to estimate the active ingredient probabilities for veterinary products.

The Kline reports do not cover professional treatment of fleas/ticks on pets (scenarios 81-84) because the users are typically pet grooming shops, kennels, or veterinarian offices. The products used are likely similar to those used by consumers, so we set the probabilities the same as for consumer products.

We have a medium level of confidence in the probability estimates.

Method 7: professional judgment based on descriptive data. For scenarios 5, 13, 17, 21, and 67, probabilities were based mostly on judgment, sometimes with a small amount of quantitative and/or descriptive data from the Kline reports, the literature, and the PPLS (U.S. EPA 2003b). For indoor plants, the estimates pertain to treatment of insects only. Our confidence level is low.

Method 8: active ingredients listed, probabilities not estimated. Kline does not maintain data on scenarios 24, 77-79, and 85-87, and there was not enough information from other sources to support probability estimates. Therefore, we developed lists of likely active ingredients but did not estimate probabilities. The lists were based on information from the Kline reports, the PPLS (U.S. EPA 2003b), the literature, and judgment.

Method 9: no active ingredients listed or probabilities estimated. Consumer treatment of termites (scenarios 41-44) is not covered in the Kline reports. There is no evidence that a significant number of consumers purchased products to self-apply termiticides until the late 1990s, and the market remains extremely small. Indoor plants (scenarios 69-72) are rarely treated by professional applicators.

Discussion

We describe here a pesticide-exposure matrix to assist in the assessment of exposure to individual active ingredients used in residential pesticides in the past. When used in conjunction with self-reported information on the types of pests treated in the home and garden over time, the matrix can be used to identify the active ingredients that were on the market for that pest type, and to provide a rough estimate of the probability that specific active ingredients were used.

Identifying which active ingredients a person likely used is a necessary step in exposure assessment. However, many factors that are not covered by the matrix are important determinants of a person's level of exposure. The probabilities of use are unrelated to the concentrations of the active ingredients in the pesticide products and therefore cannot be used to infer the intensity of exposure, an important factor in assessing risk. Many other factors may influence exposure, such as the pesticide application method and location; whether the pesticide was applied by the subject or a third party; the chemical properties of the active ingredient; the presence of synergists in the product, which could affect uptake through the skin; and the use of personal protective equipment. The matrix does not address exposures from the diet, from pesticide applications at nearby homes or farms, or from community spraying programs.

Although we are confident that the active ingredient lists are reasonably accurate for most scenarios, there are many possible sources of error in the probability estimates. First, the data presented in the Kline reports were based on interviews with pesticide manufacturers and formulators or professional applicators, and the accuracy of the information depends on the number of interviews, the representativeness of the sample, the knowledge and cooperativeness of the respondents, and the complexity of the market. Second, some types of data (e.g., acres treated) are better surrogates for probability of use than others (e.g., dollar sales). Probabilities based on the percent of registered products [from the PPIS (U.S. EPA 2003a)] likely overstate probabilities for active ingredients present in a large number of products with relatively low sales, and vice versa; we attempted to correct for this, when possible, using data from the U.S. EPA Survey (Whitmore et al. 1992), but these data cover only one point in time. Third, only qualitative information was available for some scenarios. In short, a considerable amount of professional judgment was used to derive the probabilities for many scenarios. Given the many sources of uncertainty, the probabilities should be viewed as rough estimates of the relative importance of different active ingredients in each scenario.

Although we are unable to quantify the uncertainties in the probability estimates, we do provide a relative ranking of confidence levels. Of the 96 scenarios, we have a relatively high level of confidence in 17, a medium level in 54, and low confidence in 10 (mostly for 1976). For 7 scenarios, we listed the active ingredients but could not estimate probabilities. For eight scenarios we were unable to identify the active ingredients, but these scenarios are seldom encountered (homeowner treatment of termites and professional treatment of indoor plants).

Other limitations of the matrix are that it does not cover many substances present in pesticide products, such as synergists and "inert" ingredients, which may have adverse health effects. It does not incorporate information on product form because the source materials were not sufficiently detailed. Because the source data were national in scope, the matrix does not account for regional variations in pesticide use patterns. For a small number of scenarios there is a large "other" category, reflecting the level of detail in the source materials. Finally, this overall approach for assessing past pesticide use is contingent on study participants' recall of pests treated in past homes, the accuracy of which becomes more questionable as one goes further back in time.

We know of no other source of published historical information on individual active ingredients in home and garden pesticides. Despite the noted limitations, the pesticide-exposure matrix should provide valuable information to epidemiologists and other researchers interested in the chronic health effects of residential pesticide exposure.

REFERENCES

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[Anonymous.] 1991. Professional Markets for Pesticides and Fertilizers Vol I, 1990. Fairfield, NJ:Kline & Company, Inc.

[Anonymous.] 1994. Crop Protection Chemicals Reference, 10th ed. New York:C & P Press.

[Anonymous.] 1995. Turf & Ornamental Reference for Plant Protection Products, 4th ed. New York:C & P Press.

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[Anonymous.] 2005. Crop Protection Handbook 2005. Willoughby, OH:Meister Publishing Company.

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Daniels JL, Olshan AF, Teschke K, Hertz-Picciotto I, Savitz DA, Blatt J, et al. 2001. Residential pesticide exposure and neuroblastoma. Epidemiology 12:20-27.

Fugate D, Cyr M. 1997. The U.S. Market for Flea and Tick Control Products in Companion Animal Protection, 1996-1997. Fairfield, NJ:Kline & Company, Inc.

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Fugate D, Frister S, Cyr M. 2002. The U.S. Professional Pest Management Market, 2001. Little Falls, NJ:Kline & Company, Inc.

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Goodbread AR, Cuono LC, Tarantola JV, Kollonitsch V. 1983. Professional Markets for Pesticides and Fertilizers, 1982. Fairfield, NJ:Kline & Company, Inc.

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Joanne S. Colt, (1) Mancer J. Cyr, (2) Shelia H. Zahm, (1) Geoffrey S. Tobias, (1) and Patricia Hartge (1)

(1) Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland, USA; (2) Kline & Company, Inc., Little Falls, New Jersey, USA

Address correspondence to J.S. Colt, National Cancer Institute, Occupational and Environmental Epidemiology Branch, 6120 Executive Blvd., Room 8112, Rockville, MD 20852 USA. Telephone: [phone]. Fax: [phone]. E-mail: [e-mail]

This research was supported by the National Institutes of Health Intramural Research Program and National Cancer Institute contracts MQ207208, MQ219223, MQ318225, MQ414366, MQ513892, and MQ609394 with Kline and Company, Inc.

Mancer J. Cyr is a consultant associated with Kline & Company, Inc., a market research consulting firm whose clients include major pesticide manufacturers. The other authors declare they have no competing financial interests.

Received 20 July 2006; accepted 7 November 2006.

Table 1. Comparison of two leading products used by professional
applicators to treat lawns for insects in 2000.

         Active
Brand    ingredient    Chemical family  Sales (US$)  Acres treated

Talstar  Bifenthrin    Pyrethroid       10 million   276,000
Dursban  Chlorpyrifos  Organophosphate   8 million   289,000

         Active
Brand    ingredient (lb)

Talstar   31,000
Dursban  309,000

Data from Fugate et al. (2001).

Table 2. Methods used to estimate probabilities of use of specific
pesticide active ingredients, and level of confidence in probability
estimates.

                                                         Confidence
Scenario  Pest/applier/year                  Method (a)  level

 1        Lawn weeds, consumer, 1976         5           Low
 2        Lawn weeds, consumer, 1980         2           Medium
 3        Lawn weeds, consumer, 1990         2           Medium
 4        Lawn weeds, consumer, 2000         2           Medium
 5        Lawn insects, consumer, 1976       7           Low
 6        Lawn insects, consumer, 1980       2           Medium
 7        Lawn insects, consumer, 1990       2           Medium
 8        Lawn insects, consumer, 2000       2           Medium
 9        Outdoor plant and tree weeds,      5           Low
          consumer, 1976
10        Outdoor plant and tree weeds,      5           Medium
          consumer, 1980
11        Outdoor plant and tree weeds,      3           Medium
          consumer, 1990
12        Outdoor plant and tree weeds,      3           Medium
          consumer, 2000
13        Outdoor plant and tree             7           Low
          insects, consumer, 1976
14        Outdoor plant and tree             2           Medium
          insects, consumer, 1980
15        Outdoor plant and tree             2           Medium
          insects, consumer, 1990
16        Outdoor plant and tree             2           Medium
          insects, consumer, 2000
17        Outdoor plant and tree             7           Low
          diseases, consumer, 1976
18        Outdoor plant and tree             2           Medium
          diseases, consumer, 1980
19        Outdoor plant and tree             2           Medium
          diseases, consumer, 1990
20        Outdoor plant and tree             2           Medium
          diseases, consumer, 2000
21        Indoor plants, consumer, 1976      7           Low
22        Indoor plants, consumer, 1980      5           Medium
23        Indoor plants, consumer, 1990      5           Medium
24        Indoor plants, consumer, 2000      8           --
25        Crawling insects, consumer, 1976   6           Medium
26        Crawling insects, consumer, 1980   6           Medium
27        Crawling insects, consumer, 1990   6           Medium
28        Crawling insects, consumer, 2000   6           Medium
29        Flying insects, consumer, 1976     6           Medium
30        Flying insects, consumer, 1980     6           Medium
31        Flying insects, consumer, 1990     6           Medium
32        Flying insects, consumer, 2000     6           Medium
33        Fleas/ticks on pets, consumer,     6           Medium
          1976
34        Fleas/ticks on pets, consumer,     6           Medium
          1980
35        Fleas/ticks on pets, consumer,     6           Medium
          1990
36        Fleas/ticks on pets, consumer,     6           Medium
          2000
37        Fleas/ticks in home, consumer,     6           Medium
          1976
38        Fleas/ticks in home, consumer,     6           Medium
          1980
39        Fleas/ticks in home, consumer,     6           Medium
          1990
40        Fleas/ticks in home, consumer,     6           Medium
          2000
41        Termites, consumer, 1976           9           --
42        Termites, consumer, 1980           9           --
43        Termites, consumer, 1990           9           --
44        Termites, consumer, 2000           9           --
45        Rodents, consumer, 1976            4           High
46        Rodents, consumer, 1980            4           High
47        Rodents, consumer, 1990            4           High
48        Rodents, consumer, 2000            4           High
49        Lawn weeds, professional, 1976     4           Medium
50        Lawn weeds, professional, 1980     4           Medium
51        Lawn weeds, professional, 1990     1           High
52        Lawn weeds, professional, 2000     1           High
53        Lawn insects, professional, 1976   4           Medium
54        Lawn insects, professional, 1980   4           Medium
55        Lawn insects, professional, 1990   1           High
56        Lawn insects, professional, 2000   1           High
57        Outdoor plant and tree weeds,      4           Medium
          professional, 1976
58        Outdoor plant and tree weeds,      4           Medium
          professional, 1980
59        Outdoor plant and tree weeds,      1           High
          professional, 1990
60        Outdoor plant and tree weeds,      1           High
          professional, 2000
61        Outdoor plant and tree insects,    4           Low
          professional, 1976
62        Outdoor plant and tree insects,    4           Low
          professional, 1980
63        Outdoor plant and tree insects,    1           Medium
          professional, 1990
64        Outdoor plant and tree insects,    1           Medium
          professional, 2000
65        Outdoor plant and tree diseases,   4           Medium
          professional, 1976
66        Outdoor plant and tree diseases,   4           Medium
          professional, 1980
67        Outdoor plant and tree diseases,   7           Low
          professional, 1990
68        Outdoor plant and tree diseases,   1           High
          professional, 2000
69        Indoor plants, professional, 1976  9           --
          1976
70        Indoor plants, professional, 1980  9           --
71        Indoor plants, professional, 1990  9           --
72        Indoor plants, professional, 2000  9           --
73        Crawling insects, professional,    4           Medium
          1976
74        Crawling insects, professional,    4           Medium
          1980
75        Crawling insects, professional,    4           Medium
          1990
76        Crawling insects, professional,    4           Medium
          2000
77        Flying insects, professional,      8           --
          1976
78        Flying insects, professional,      8           --
          1980
79        Flying insects, professional,      8           --
          1990
80        Flying insects, professional,      4           Low
          2000
81        Fleas/ticks on pets,               6           Medium
          professional, 1976
82        Fleas/ticks on pets,               6           Medium
          professional, 1980
83        Fleas/ticks on pets,               6           Medium
          professional, 1990
84        Fleas/ticks on pets,               6           Medium
          professional, 2000
85        Fleas/ticks in home,               8           --
          professional, 1976
86        Fleas/ticks in home,               8           --
          professional, 1980
87        Fleas/ticks in home,               8           --
          professional, 1990
88        Fleas/ticks in home,               4           Medium
          professional, 2000
89        Termites, professional, 1976       4           Medium
90        Termites, professional, 1980       4           Medium
91        Termites, professional, 1990       4           High
92        Termites, professional, 2000       4           High
93        Rodents, professional, 1976        4           High
94        Rodents, professional, 1980        4           High
95        Rodents, professional, 1990        4           High
96        Rodents, professional, 2000        4           High

--, Probabilities were not estimated for these scenarios.
(a) 1 = number of acres treated; 2 = number of acres treated, derived
from pounds of active ingredients and application rates; 3 = number of
acres treated, derived from dollar sales, unit prices, and application
rates; 4 = product sales; 5 = product sales, calculated from company
sales; 6 = active ingredient frequencies from PPIS (U.S. EPA 2003a);
7 = professional judgment based on descriptive data; 8 = active
ingredients listed, probabilities not estimated; 9 = no active
ingredients listed or probabilities estimated.

Table 3. Example of method 1: professional treatment of outdoor
plant/tree insects, 1990 (scenario 63).

Product (a)  Active ingredient  Acres treated (a)

Malathion    Malathion           90,000
Dursban      Chlorpyrifos        47,000
Diazinon     Diazinon            32,000
Sevin        Carbaryl            11,000
Orthene      Acephate            10,000
Oftenol      Isofenphos           9,000
Other        Other               96,000
Total                           295,000

Product (a)  Probability of use [% (calculated)] (b)

Malathion    31
Dursban      16
Diazinon     11
Sevin         4
Orthene       3
Oftenol       3
Other        33
Total

(a) Anonymous (1991). (b) Acres treated with each active ingredient
divided by total acres treated.

Table 4. Example of method 2: consumer treatment of lawn insects,
1980 (scenario 6).

                   Pounds applied to lawns,
                   outdoor plants, and nonplants (a)
Active          Fertilizer/insecticide    Insecticide-only
ingredient (a)  combination products (a)  products (a)

Diazinon        1,200,000                   800,000
Chlorpyrifos      140,000                   110,000
Carbaryl            0,000                   650,000
Malathion           0,000                   400,000
Other             230,000                   590,000
Total           1,570,000                 2,550,000

                   Pounds applied to lawns,
Active             outdoor plants, and nonplants (b)
ingredient (a)  Lawn           Outdoor plants  Nonplants

Diazinon        1,400,000        480,000       120
Chlorpyrifos      190,000         44,000        17
Carbaryl           33,000        520,000        98
Malathion          20,000        320,000        60
Other             319,000        413,000        89
Total           1,961,000      1,777,000       383

                Application     Lawn              Probability of
Active          rate for lawns  acres treated     use for lawns
ingredient (a)  (lbs/acre) (c)  (calculated) (d)  [% (calculated)] (e)

Diazinon        3               467,000           63
Chlorpyrifos    2                95,000           13
Carbaryl        3                11,000            1
Malathion       2                10,000            1
Other           2               159,000           21
Total                           742,000

(a) Anonymous (1982). (b) Allocation of active ingredient pounds
separately to lawns, outdoor plants, and nonplants was done as
follows: fertilizer/insecticide combination products: 100% is
applied to lawns (judgment). Insecticide-only products: 15% of the total
is applied to lawns, 70% to outdoor plants, and 15% to nonplants
(Anonymous 1982). The split of each active ingredient to lawns vs.
outdoor plants vs. nonplants is based on judgment, using the following
assumptions: diazinon: 25%-60%-15%, carbaryl and malathion: 5%-80%-15%,
chlorpyrifos: 45%-40%-15%, other: 15%-70%-15%. (c) Meister Publishing
Company manuals (Anonymous 1999a, 1999b, 2003). (d) Pounds applied to
lawns divided by application rate for lawns. (e) Lawn acres treated with
each active ingredient divided by total lawn acres treated.

Table 5. Example of method 3: consumer treatment of outdoor
plant/tree weeds, 1990 (scenario 11).

                                                Gal used
Manufacturer/   Sales              Unit price   [million
product (a)     (US$ million) (a)  ($/gal) (a)  (calculated)] (b)

Monsanto        90.0               48           1.9
Chevron Ortho
  Kleenup        7.0               60           0.1
  Weed-b-Gone   20.0               32           0.6
  Triox          4.3               24           0.2
Lebanon          3.5               18           0.2
VPG Fertilome    2.1               24           0.1
Kmart            2.0               24           0.1
Spectracide      2.0               24           0.1
Other products  16.5               20           0.8
Total

                Application     Acres treated
Manufacturer/   rate            [million           Active
product (a)     (gal/acre) (c)  (calculated)] (d)  ingredient

Monsanto        0.5             3.8                Glyphosate
Chevron Ortho
  Kleenup       0.5             0.2                Glyphosate
  Weed-b-Gone   0.4             1.6                2,4-D
                                                   MCPP
  Triox         0.5             0.4                Prometon
Lebanon         0.2             1.0                Trifluralin
VPG Fertilome   0.4             0.2                2,4-D
                                                   MCPP
Kmart           0.4             0.2                2,4-D
                                                   MCPP
Spectracide     0.4             0.2                2,4-D
                                                   MCPP
Other products  0.4             2.1                Dacthal
                                                   Diquat
Total                           9.6

                Acres treated                      Acres treated
Manufacturer/   [million           Active          [million
product (a)     (calculated)] (e)  ingredient (f)  (calculated)] (g)

Monsanto        3.8                Glyphosate      4.0
Chevron Ortho                      2,4-D           2.2
  Kleenup       0.2                MCPP            2.2
  Weed-b-Gone   1.6                Diquat          1.0
                1.6                Dacthal         1.0
  Triox         0.4                Trifluralin     1.0
Lebanon         1.0                Prometon        0.4
VPG Fertilome   0.2
                0.2
Kmart           0.2
Spectracide     0.2
Other products  1.0
                1.0
Total

                Probability
Manufacturer/   of use [%
product (a)     (calculated)] (h)

Monsanto        42
Chevron Ortho   23
  Kleenup       23
  Weed-b-Gone   11
                11
  Triox         10
Lebanon          4
VPG Fertilome
Kmart
Spectracide
Other products
Total

Abbreviations: 2,4-D, 2,4-dichlorophenoxyacetic acid; MCPP, mecoprop.
(a) Hodge and Rafter (1992a). (b) Sales divided by unit price.
(c) Meister Publishing Company manuals (Anonymous 1999a, 1999b, 2003),
C & P Press publications (Anonymous 1994, 1995; C & P Press 2004).
(d) Gallons used divided by application rate in gallons per acre.
(e) Assigning each product's acres treated to all of the active
ingredients it contains. (f) Eliminating duplicates.
(g) Combining active ingredient acres treated across all products in
which it appears. (h) Dividing each active ingredient's acres treated by
the total number of acres treated.

Table 6. Example of method 4: professional treatment of fleas/ticks
in the home, 2000 (scenario 88).

             Active              Sales       Active
Product (a)  ingredient          (US$) (a)   ingredient (b)

Archer IGR   Pyridine               355,000  Methoprene
Catalyst     Propetamphos         2,528,000  Propetamphos
Demand CS    Lambda-cyhalothrin     393,000  Permethrin
Demon        Cypermethrin           163,000  Chlorpyrifos
Diazinon     Diazinon               166,000  Deltamethrin
Dragnet SFR  Permethrin             475,000  Pyriproxifen
Dursban 50W  Chlorpyrifos           425,000  Bendiocarb
Dursban Pro  Chlorpyrifos           658,000  Lambda-Cyhalothrin
Ficam W      Bendiocarb             486,000  Diazinon
Flee         Permethrin             660,000  Pyridine
Lindane      Lindane                118,000  Tralomethrin
Nylar IGR    Pyriproxifen           134,000  Cypermethrin
Nylar        Linalool               118,000  Cyfluthrin
Precor 2000  Methoprene             817,000  Lindane
Precor IGR   Methoprene           2,189,000  Linalool
Precor IGR   Methoprene             365,000  Other
Prelude      Permethrin             161,000
Saga         Tralomethrin           244,000
Suspend      Deltamethrin           794,000
Tempo        Cyfluthrin             122,000
Ultracide    Pyriproxifen           416,000
  aerosol
Diazinon 4E  Diazinon               205,000
Other        Other                1,094,000
Total                            13,086,000

             Sales      Probability of use
Product (a)  (US$) (c)  [% (calculated)] (d)

Archer IGR   3,371,000  26
Catalyst     2,528,000  19
Demand CS    1,296,000  10
Demon        1,083,000   8
Diazinon       794,000   6
Dragnet SFR    550,000   4
Dursban 50W    486,000   4
Dursban Pro    393,000   3
Ficam W        371,000   3
Flee           355,000   3
Lindane        244,000   2
Nylar IGR      163,000   1
Nylar          122,000   1
Precor 2000    118,000   1
Precor IGR     118,000   1
Precor IGR   1,094,000   8
Prelude
Saga
Suspend
Tempo
Ultracide
  aerosol
Diazinon 4E
Other
Total

(a) Fugate et al. (2000). (b) Eliminating duplicates. (c) Combining
active ingredient sales across all products in which it appears.
(d) Sales for each active ingredient divided by total sales.

Table 7. Example of method 5: consumer treatment of indoor plants
(insects only), 1990 (scenario 23).

                  Sales      Active          Sales Active
Manufacturer (a)  (US$) (a)  ingredient (b)  (US$) (c)

Safer             1,344,000  Fatty acids     1,344,000
Ortho (Chevron)   1,000,000  Acephate          500,000
                             Resmethrin        500,000
Hyponex (Scotts)    800,000  Pyrethrins        800,000
                             Resmethrin        800,000
Dexol               641,000  Dysiston          641,000
SC Johnson RAID   1,500,000  Pyrethrins        750,000
                             Allethrin         750,000
                             Tetramethrin      375,000
                             Resmethrin        750,000
                             Phenothrin        375,000
United            1,183,000  Pyrethrins        394,000
                             Allethrin         394,000
                             Tetramethrin      394,000
                             Phenothrin        789,000
Other               746,000  Fatty acids       362,000
                             Pyrethrins        362,000
                             Acephate          145,000
                             Dysiston          145,000
                             Permethrin        145,000
                             Other             290,000
Total             7,214,000

                  Active          Sales      Probability [%
Manufacturer (a)  ingredient (d)  (US$) (e)  (calculated)] (f)

Safer             Pyrethrins      2,306,000  32
Ortho (Chevron)   Resmethrin      2,050,000  28
                  Fatty acids     1,706,000  24
Hyponex (Scotts)  Phenothrin      1,164,000  16
                  Allethrin       1,144,000  16
Dexol             Dysiston          786,000  11
SC Johnson RAID   Tetramethrin      769,000  11
                  Acephate          645,000   9
                  Permethrin        145,000   2
                  Other             290,000   4
United
Other
Total

(a) Hodge and Rafter (1992a). (b) PPLS (U.S. EPA 2003b). (c) Assignment
of dollar sales to individual active ingredients was based
on the PPLS (U.S. EPA 2003b) and judgment. (d) Eliminating duplicates.
(e) Combining active ingredient sales across all manufacturers
that produce it. (f) Sales of each active ingredient divided by total
sales.

Table 8. Example of method 6: consumer treatment of crawling insects,
2000 (scenario 28).

Active          No. of        Probability          Probability
ingredient (a)  products (a)  [%(calculated)] (b)  [%(adjusted)]

Permethrin        436         17.1                 17
Pyrethrins        746         29.3                 15 (c)
Chlorpyrifos      321         12.6                 13
Allethrin         250          9.8                 10
Propoxur           80          3.1                  9 (c)
Diazinon          213          8.4                  8
Tetramethrin      190          7.5                  7
Hydramethylnon     15          0.6                  8 (d)
Fipronil           12          0.5                  8 (d)
Dichlorvos         52          2.0                  6 (c)
Sulfluramid         8          0.3                  6 (e)
Phenothrin        136          5.3                  5
Resmethrin        213          8.4                  4 (c)
Boric acid         89          3.5                  3
Carbaryl           80          3.1                  3
Pyriproxifen       70          2.7                  3
Esfenvalerate      68          2.7                  3
Cyfluthrin         56          2.2                  2
Deltamethrin       41          1.6                  2
Fenvalerate        40          1.6                  2
Malathion          39          1.5                  2
Methoprene          9          0.4                  2 (e)
Hydroprene          8          0.3                  2 (c)
Prallethrin        29          1.1                  1
Cypermethrin       28          1.1                  1
Eugenol            10          0.4                  1 (e)
Other             196          7.7                  8
Total           2,546

(a) From analysis of PPIS (U.S. EPA 2003a) data. The numbers do not sum
to the total number of products because many products contain more than
one active ingredient. (b) Number of products containing each active
ingredient divided by the total number of products. (c) We modified the
probability based on information on treatment of cockroaches, ants, and
spiders from the U.S. EPA Survey (Whitmore et al. 1992). (d) Based on
information from Kline (Hall and Dansbury 2000; Fugate and Hall 2002)
and judgment. (e) We modified the probability based on judgment.

Table 9. Example of method 7: consumer treatment of outdoor plant/tree
diseases, 1976 (scenario 17).

Active ingredient  Probability (%) (a)

Captan             20
Folpet             20
Sulfur             20
Chlorothalonil     15
Maneb              10
Zineb               5
Thiram              5
Ferbam              5

(a) According to Kline (Ramsey and Kollonitsch 1977), Ortho was the
largest manufacturer in this segment, with a 33% market share.
Ortho's main active ingredients were captan, folpet, and sulfur,
which were used by other manufacturers as well. Other active ingredients
listed by Ramsey and Kollonitsch (1977), and most likely used by both
Ortho and other manufacturers, were chlorothalonil, maneb, zineb,
thiram, and ferbam. Based on this information, we used judgment to
derive the probabilities.