Environmental Health Perspectives • VOLUME 110 | NUMBER 12 | December 2002 A 787

Evaluation of Take-Home Organophosphorus Pesticide Exposure among

Agricultural Workers and Their Children 

Cynthia L. Curl,1 Richard A. Fenske,1 John C. Kissel,1 Jeffry H. Shirai,1 Thomas F. Moate,2 William Griffith,1

Gloria Coronado,3 and Beti Thompson3,4

1Department of Environmental Health, School of Public Health and Community Medicine, University of Washington, Seattle,

Washington, USA; 2SNBL USA Ltd., Everett, Washington, USA; 3Cancer Prevention Research Program, The Fred Hutchinson Cancer

Research Center, Seattle, Washington, USA; 4Department of Health Services, School of Public Health and Community Medicine,

University of Washington, Seattle, Washington, USA

Children represent a sensitive subpopulation in

terms of exposures to pesticides because they

have higher rates of metabolism, less-mature

immune systems, unique diets, and distinct

patterns of activity and behavior when compared with adults (NRC 1993). Of particular

concern are organophosphorus (OP) pesticides,

because of their acute toxicity and widespread

use both residentially and agriculturally (WHO

1986). Children in families of farmers and farm

workers may receive higher pesticide exposures

than other children (Loewenherz et al. 1997;

Lu et al. 2000; Simcox et al. 1995). Families of

farm workers have increased risks of neuroblastoma, nervous system tumors, Hodgkin disease, bone and brain cancer, and childhood

leukemia (Buckley et al. 1989; Bunin et al.

1990; Daniels et al. 1997; Kristensen et al.

1996; Lowengart et al. 1987; Wilkins and

Koutras 1988), which may stem from increased

exposures to agricultural chemicals. 

One pathway by which children may be

exposed to higher levels of agricultural chemicals is take-home exposure. This pathway

involves the transport of contaminants from

the workplace to the residence on a worker’s

clothing or person. Compounds that are likely

to cling to and are difficult to remove from

clothing, shoes, skin, or hair (e.g., beryllium,

asbestos, lead, pesticides) are potential takehome contaminants, and the take-home pathway has been well documented for several of

these compounds (Knishkowy and Baker

1986; McDiarmid and Weaver 1993;

NIOSH 1995). A recent study by Lu et al.

(2000) indicated that the take-home pathway

is a significant contributor to residential contamination in the homes of agricultural workers. The National Institute for Occupational

Safety and Health recommends educating

workers and their families about the risks of

take-home exposure and about ways that they

can minimize their risks (NIOSH 1995).

The research presented here is part of an

intervention project designed to reduce takehome pesticide exposure through behavioral

change at the community level (Thompson

et al., in press). The goals of the current

work were to investigate the take-home pathway of pesticide exposure among agricultural

families and to establish a baseline of exposure in communities participating in the

larger intervention project.

Materials and Methods

Study design. Twenty-four agricultural

communities in the Yakima Valley of

Washington State were selected to take part

in the intervention project. Thompson et al.

(in press) describe the design of the intervention project in detail. Urine and dust samples

were collected at the outset of the project

(1999) to establish baseline exposure measurements. After sample collection, the communities were randomized into either intervention

or control status. In 2002, a similar set of

measurements was collected in these communities and will be compared with the 1999

measurements to evaluate the effectiveness of

the intervention program.

Sample population. Subjects were

recruited initially from within the population

of a concurrent study conducted by the

Fred Hutchinson Cancer Research Center

(FHCRC) in the Yakima Valley. Thompson

et al. (in press) provide a complete and

detailed discussion of the sample population

recruitment. Eligible households included an

adult farm worker actively involved in fieldwork or pesticide application (warehouse

workers were excluded) and a child between

the ages of 2 and 6 years. Adult respondents

signed informed consent to participate. Study

protocol and data collection procedures were

reviewed and approved by the Human

Subjects Review Board at the University of

Washington (UW) and the Institutional

Review Board at the FHCRC.

Questionnaire. A questionnaire consisting

of 73 items relating to agricultural job tasks,

protective practices, and sociodemographic

characteristics was administered to all adult

participants (Thompson et al., in press).

Information from two questions relevant to

pesticide exposure pathways has been incorporated into this analysis. First, farm workers

were asked to estimate the distance between

Address correspondence to C.L. Curl, University of

Washington, DEH Box 357234, PNASH Center,

Health Sciences Building, F-233, 1959 NE Pacific

St., Seattle, WA 98195-7234 USA. Telephone:

(206) 616-1958. Fax: (206) 616-2687. E-mail:

ccurl@u.washington.edu 

This work was supported by the National Institute

of Environmental Health Sciences, National

Institutes of Health, and the U.S. Environmental

Protection Agency (R826886/PO1 ES09601), with

additional support from the National Institute for

Occupational Safety and Health Agricultural

Centers program (U07/CCU012926). This work

does not necessarily reflect the views of the agencies,

and no official endorsement should be inferred. 

Received 25 April 2002; accepted 12 August 2002.

Children’s Health Articles

We analyzed organophosphorus pesticide exposure in 218 farm worker households in agricultural

communities in Washington State to investigate the take-home pathway of pesticide exposure and

to establish baseline exposure levels for a community intervention project. House dust samples (n =

156) were collected from within the homes, and vehicle dust samples (n = 190) were collected from

the vehicles used by the farm workers to commute to and from work. Urine samples were obtained

from a farm worker (n = 213) and a young child (n = 211) in each household. Dust samples were

analyzed for six pesticides, and urine samples were analyzed for five dialkylphosphate (DAP)

metabolites. Azinphosmethyl was detected in higher concentrations (p < 0.0001) than the other

pesticides: geometric mean concentrations of azinphosmethyl were 0.53 μg/g in house dust and

0.75 μg/g in vehicle dust. Dimethyl DAP metabolite concentrations were higher than diethyl DAP

metabolite concentrations in both child and adult urine (p < 0.0001). Geometric mean dimethyl

DAP concentrations were 0.13 μmol/L in adult urine and 0.09 μmol/L in child urine. Creatinineadjusted geometric mean dimethyl DAP concentrations were 0.09 μmol/g in adult urine and 0.14

μmol/g in child urine. Azinphosmethyl concentrations in house dust and vehicle dust from the

same household were significantly associated (r 2= 0.41, p < 0.0001). Dimethyl DAP levels in child

and adult urine from the same household were also significantly associated (r 2 = 0.18, p < 0.0001),

and this association remained when the values were creatinine adjusted. The results of this work

support the hypothesis that the take-home exposure pathway contributes to residential pesticide

contamination in agricultural homes where young children are present. Key words: biologic monitoring, children, dialkylphosphate metabolites, dust, exposure, organophosphorus pesticides, takehome. Environ Health Perspect 110:A787–A792 (2002). [Online 12 November 2002]

http://ehpnet1.niehs.nih.gov/docs/2002/110pA787-A792curl/abstract.html
their homes and the nearest field or orchard

to which pesticides or farm chemicals were

applied. Response categories consisted of < 1

block, 1–2 blocks, 2–4 blocks, 4–8 blocks, 8

blocks–1 mile, > 1 mile, don’t know, and

refused. Second, farm workers were asked if

they had worked on each of the following

crops in the past three months: apples, hops,

pears, peaches, cherries, and grapes. Response

categories for each crop consisted of yes, no,

don’t know, and refused. Workers were also

asked to specify any other crops on which

they had worked.

Dust sampling and analysis. Dust samples

were collected from July through October

1999 with a Nilfisk vacuum cleaner unit (GS80; Nilfisk of America, Malvern, PA) by

trained field staff from the Yakima Valley.

House dust was collected in each household in

the area where the parent or adult participant

said the child played most frequently.

Sampling was avoided within 3 feet of the

home entryway. The area vacuumed

depended on the floor surface type, and a

square half-meter by half-meter template was

used as a guide. In general, four template areas

were vacuumed if the floor was plush carpet

(e.g., thick or shag carpet), six if it was

thin/flat carpet (e.g., thin carpets, area rugs),

and eight if it was hard/smooth floor (e.g.,

linoleum, wood). 

Vehicle dust collection was added to the

study protocol based on the recommendation

of the project’s community advisory board. A

vehicle dust sample was collected from each

of the households in which the adult farm

worker regularly used a vehicle to get to and

from work. Both the front and back footwells

were vacuumed, except in the case of trucks

without rear footwells, and mats were not

removed before vacuuming. All dust samples

were stored at –10°C in the field laboratory

until shipped on ice to the UW laboratory,

where they were again stored at –10°C until

analysis. 

Dust was analyzed for six OP pesticide

residues—four dimethyl pesticides (azinphosmethyl, malathion, methyl parathion, and

phosmet) and two diethyl pesticides (chlorpyrifos and diazinon)—following the extraction and gas chromatographic procedures

described by Moate et al. (2002). These six

pesticides represent the major organophosphates applied in the lower Yakima Valley.

Samples were transferred from the vacuum

cleaner bags to 150-μm metal sieves (VWR,

West Chester, PA) and were sieved for 10

min in a sieve shaker (Model RX-24; WS

Tyler Inc, Mentor, OH). At least 0.7 g fine

(< 150 μm) dust was necessary for analysis. 

Urine sampling and analysis. Urine was

collected from one adult farm worker and one

child 2–6 years of age in each household concurrent with dust sampling. Child samples

were collected using commode specimen collection pans (Sage Products, Inc., Crystal

Lake, IL) and were subsequently transferred

into 100-mL polypropylene containers with

screw-cap lids. Samples from adults and some

older children were collected directly into the

polypropylene containers. A complete urine

sample consisted of a composite of either two

or three independent voids, each separated by

a minimum of 3 days, and all collected within

a 2-week period. Each void was collected by

field staff on the day that it was provided and

stored in the field lab at –10°C until all voids

were available. Composites consisted of equal

volumes of the independent voids. Ideally,

each contributed 15 mL; however, if one void

was less than 15 mL, that volume was

matched by the other void(s). All urine samples were stored at –10°C in the field lab until

shipped on ice to the UW laboratory, where

they were stored at –10°C until analysis.

Urine was analyzed for five of the six

dialkylphosphate (DAP) compounds that

are produced by metabolism of most OP

pesticides, following the extraction and gas

chromatographic procedures described by

Moate et al. (1999). The five DAPs included

dimethylphosphate (DMP), dimethylthiophosphate (DMTP), dimethyldithiophosphate (DMDTP), diethylphosphate

(DEP), and diethylthiophosphate (DETP).

Diethyldithiophosphate (DEDTP) was not

analyzed because none of the pesticides targeted in this study metabolize into DEDTP.

Creatinine concentrations were ascertained

using a colorimetric procedure based on the

Jaffé reaction (Creatinine Procedure No. 555;

Sigma Diagnostics, Dorset, UK).

Quality control and quality assurance.

Urine surrogate solutions (5 mL) consisted of

double-deionized water and the 10 most significant components of urine by weight. Dust

surrogates (1 g) were subsamples of house

dust or vehicle dust previously determined to

be free of OP pesticides. Fortified and blank

samples were taken into the field intermittently with actual samples. Transport into the

field neither contaminated blank samples nor

degraded fortified samples.

Data management. Urinary metabolite

and dust residue data sets included samples

containing concentrations less than the limit

of quantitation (LOQ), which yielded analyte

peaks with signal-to-noise ratios either greater

than 3:1 (termed “< LOQ ”) or less than 3:1

[termed nondetectable (ND)]. Percentile–percentile plots demonstrated that the concentrations of metabolites and pesticides above the

Children’s Health • Curl et al.

A 788 VOLUME 110 | NUMBER 12 | December 2002 • Environmental Health Perspectives

Table 1. Percentage of house dust and vehicle dust samples containing six target OP pesticides at levels

above the LOQ, at levels < LOQ, and at ND levels.

Azinphosmethyl Malathion m-Parathion Phosmet Chlorpyrifos Diazinon

House dust (n = 156)

Above the LOQ (%)a 133 (85) 24 (15) 20 (13) 22 (14) 41 (26) 6 (3.8)

< LOQ (%)b 6 (3.8) 26 (17) 26 (17) 74 (47) 54 (35) 22 (14)

ND (%) 17 (11) 106 (68) 110 (71) 60 (38) 61 (39) 128 (82)

Vehicle dust (n = 190)

Above the LOQ (%) 165 (87) 30 (16) 23 (12) 41 (22) 34 (18) 4 (2.1)

< LOQ (%)b 14 (7.4) 71 (37) 42 (22) 81 (43) 17 (9.0) 12 (6.3)

ND (%) 11 (5.8) 89 (47) 125 (66) 68 (36) 139 (73) 174 (92)

ND, nondetectable. 

aPercentage of total samples. b< LOQ; LOQ (μg/g) are as follows: house dust: azinphosmethyl = 0.09, malathion = 0.16,

m-parathion = 0.12, phosmet = 0.13, chlorpyrifos = 0.15, diazinon = 0.17; vehicle dust: azinphosmethyl = 0.11, malathion =

0.08, m-parathion = 0.12, phosmet = 0.09, chlorpyrifos = 0.11, diazinon = 0.11.

Table 2. GM, GSD, maximum values, and selected percentiles of six OP pesticide residue levelsa in house

dust and vehicle dust (μg/g).

Percentiles

GM GSD 25th 50th 75th 95th Maximum

House dust (n = 156)

Azinphosmethyl 0.53 4.3 0.22 0.53 1.42 5.31 14.9

Malathion 0.05 3.0 0.02 0.04 0.14 0.29 1.03

m-Parathion 0.03 4.8 0.01 0.04 0.10 0.28 1.71

Phosmet 0.02 11 0.00 0.02 0.09 1.30 16.9

Chlorpyrifos 0.05 4.6 0.02 0.05 0.14 0.68 2.56

Diazinon 0.01 5.1 0.00 0.01 0.03 0.12 0.77

Vehicle dust (n = 190)

Azinphosmethyl 0.75 5.3 0.21 0.85 2.81 9.56 38.3

Malathion 0.02 5.3 0.01 0.02 0.05 0.31 2.12

m-Parathion 0.01 8.1 0.00 0.01 0.03 0.38 3.13

Phosmet 0.01 20 0.00 0.02 0.07 1.19 34.9

Chlorpyrifos 0.03 4.8 0.01 0.03 0.09 0.42 1.97

Diazinon 0.00 7.6 0.00 0.00 0.01 0.05 0.55

GM, geometric mean.

aIncludes generated values for NDs and < LOQ s. 
LOQ were normally distributed following a

log transformation, a trend reported previously for biologic and environmental samples

from residential locations (Gordon et al.

1999; U.S. EPA 1998). We therefore elected

to treat the censored values as log-normally

distributed. This approach has been used in

previous work with left-censored data (Lyles et

al. 2001; Lynn 2001). Quartile–quartile plots

were created, and random values ranging from

0 to the LOQ and falling along the log-normal distribution of the points above the LOQ

were assigned to the ND and < LOQ samples.

Geometric means and SDs were calculated for

the overall data sets, including the assigned

values, and new log-normal distributions with

the same characteristics and size were

generated for each compound, as described by

Cohen (1991). The NDs and < LOQs in the

original data set were replaced with the data

from the generated distribution, with NDs

considered smaller than < LOQs, and these

values were used in subsequent analyses. All

concentrations above the LOQ were retained

from the original data set. No adult urine

samples contained concentrations of DEP

above the LOQ, and therefore the distributional characteristics of DEP in child urine

were used.

Total molar quantities (μmol/L) were

calculated by combining individual metabolites according to their chemical structures as

follows:

[DAPdimethyl] = [DMP]/125 + [DMTP]/141 

+ [DMDTP]/157 [1]

[DAPdiethyl] = [DEP]/153 + [DETP]/169, [2]

where the metabolite concentrations are in

units of micrograms per liter and the molecular weights by which they are divided are in

units of grams per mole. Results were also

adjusted by urinary creatinine concentration

by dividing the individual metabolite concentrations (micrograms per liter) by urinary

creatinine concentration (milligrams per

deciliter) to yield creatinine-adjusted metabolite concentrations (micrograms metabolite

per grams creatinine). Adjusted metabolite

concentrations (micrograms per gram) were

then summed to their molar equivalents

(micromoles metabolite per gram creatinine)

as described by Equations 1 and 2. All analyses were conducted using both the adjusted

and nonadjusted values.

The analytic method for determining

DEP concentration consistently overestimated

by 37%, and therefore DEP values were

adjusted for recovery. All other metabolites

had mean recovery efficiencies within one SD

of the mean. Dust concentrations were not

corrected for recovery because variations were

inconsistent. The data were log-transformed,

and linear regression and analysis of variance

(ANOVA) calculations were performed in

STATA (STATA 6, College Station, TX).

Results

Study participants. A total of 218 households were enrolled in this study. Ninetyseven percent of the participants were

Hispanic and 3% were Caucasian. Seven

children and five adults who were enrolled in

the study declined to provide urine samples

at the time of collection. Therefore, urine

samples were provided by 211 children and

213 adults. House dust samples were collected in 210 homes, but 54 of these samples

did not contain sufficient mass for analysis.

Commuter vehicles were available and were

sampled at 205 households, but 15 of these

samples did not contain sufficient mass for

analysis. Thus, pesticide residue analysis was

conducted for 156 house dust samples and

190 vehicle dust samples. 

Environmental and biologic samples.

Azinphosmethyl was the most commonly

detected pesticide in both house dust and vehicle dust (Table 1): 85% of house dust samples

and 87% of vehicle dust samples contained

concentrations of azinphosmethyl above the

limit of quantitation. The geometric mean

concentration of azinphosmethyl was 0.53

μg/g [geometric standard deviation (GSD) =

4.3] in house dust and 0.75 μg/g (GSD = 5.3)

in vehicle dust. Residue concentrations for all

six target pesticides in house dust and vehicle

dust are presented in Table 2. Concentrations

of azinphosmethyl were more than an order of

magnitude higher than concentrations of any

of the other pesticides in both house dust and

vehicle dust (p < 0.0001).

DMTP was the most commonly detected

metabolite (Table 3): 88% of child urine

samples and 92% of adult samples contained

concentrations of DMTP above the limit of

quantitation. Table 4 presents the urinary

DAP results, both as urine concentrations

and creatinine-adjusted concentrations. The

geometric mean concentration of dimethyl

DAP metabolites was 0.09 μmol/L (GSD =

2.9) in child urine and 0.13 μmol/L (GSD =

6.9) in adult urine. When the levels were

adjusted for creatinine concentration, the

geometric mean concentration of dimethyl

DAP metabolites was 0.14 μmol/g (GSD =

3.2) in child urine and 0.09 μmol/g (GSD =

7.2) in adult urine. Dimethyl DAP levels

were higher than diethyl DAP levels in both

child and adult urine (p < 0.0001), when the

data both were and were not adjusted for urinary creatinine concentration. Adult urine

samples contained significantly higher

concentrations of dimethyl DAP metabolites

than did child samples when the results were

Children’s Health • Take-home exposure among families of farm workers

Environmental Health Perspectives • VOLUME 110 | NUMBER 12 | December 2002 A 789

Table 3. Percentage of child and adult urine samples containing five DAP metabolites at levels greater

than the LOQ, at levels < LOQ, and at ND levels.

DMP DMTP DMDTP DEP DETP

Child urine (n = 211)

Above the LOQ (%)a 41 (19) 186 (88) 95 (45) 2 (0.9) 78 (37)

< LOQ (%)b 16 (7.6) 10 (4.7) 5 (2.4) 82 (39) 57 (27)

ND (%) 154 (73) 15 (7.1) 111 (53) 127 (60) 76 (36)

Adult urine (n = 213)

Above the LOQ (%) 43 (20) 197 (92) 114 (54) 0 (0.0) 103 (48)

< LOQ (%)b 17 (8.0) 2 (0.9) 3 (1.4) 69 (32) 58 (27)

ND (%) 153 (72) 14 (6.6) 96 (45) 144 (68) 52 (24)

ND, nondetectable. 

aPercentage of total samples. b< LOQ; LOQ (μg/L) are as follows: DMP = 7.4, DMTP = 1.1, DMDTP = 0.6, DEP = 2.9, DETP = 1.3. 

Table 4. GM, GSD, maximum values, and selected percentiles of dimethyl and diethyl DAP metabolite

levelsa in child and adult urine samples.

Percentiles

GM GSD 25th 50th 75th 95th Maximum

Concentration (μmol/L)

Child urine (n = 211)

Dimethylb 0.09 2.9 0.04 0.08 0.18 0.52 15.4

Diethylc 0.06 1.5 0.04 0.06 0.08 0.11 0.23

Adult urine (n = 213)

Dimethyl 0.13 6.9 0.03 0.09 0.44 3.02 98.0

Diethyl 0.06 1.5 0.05 0.06 0.08 0.12 0.30

Creatinine-adjusted (μmol/g)

Child urine (n = 211)

Dimethyl 0.14 3.2 0.06 0.14 0.31 1.02 8.42

Diethyl 0.09 2.0 0.06 0.09 0.14 0.29 0.72

Adult urine (n = 213)

Dimethyl 0.09 7.2 0.02 0.06 0.25 2.12 75.8

Diethyl 0.04 2.0 0.02 0.03 0.06 0.12 1.03

aIncludes generated values for NDs and < LOQs. bDimethyl metabolites are the molar sums of DMP, DMTP, and DMDTP. cDiethyl metabolites are the molar sums of DEP and DETP.
not creatinine adjusted (p = 0.01). However,

when the values were adjusted by urinary creatinine concentration, child urine samples

contained significantly higher concentrations

of dimethyl DAP metabolites than did adult

samples (p = 0.001).

Take-home exposure pathway. Linear

regression analysis indicated a significant association between azinphosmethyl concentrations in vehicle and house dust (p < 0.0001,

r2 = 0.41), as shown in Figure 1. Dimethyl

DAP levels in the urine of children and adults

living in the same household were also significantly associated (p < 0.0001, r2 = 0.18), as

shown in Figure 2. When the metabolite concentrations were creatinine adjusted, the significant associations between child and adult

levels remained (p < 0.0001, r2 = 0.15).

Questionnaire responses (n = 216) regarding household distance to nearest treated field

were evaluated to determine whether proximity was associated with exposure, using an

ANOVA procedure. First, household proximity to treated fields was defined by the six

questionnaire response categories: < 1 block,

1–2 blocks, 2–4 blocks, 4–8 blocks, 8

blocks–1 mile, and > 1 mile. Neither azinphosmethyl concentration in house dust nor

child dimethyl DAP level was significantly

associated with household proximity to

treated fields (house dust: p = 0.58; nonadjusted child urine: p = 0.34; creatinineadjusted child urine: p = 0.30). Previous

studies have used categories of ≤ 200 ft (60 m)

and > 200 ft (60 m) to investigate the relationship between proximity to treated fields

and azinphosmethyl concentrations in house

dust and dimethyl DAP concentrations in

urine (Loewenherz et al. 1997; Lu et al.

2000). To approximate this analysis, proximity to treated fields was categorized as < 1

block (n = 79) and > 1 block (n = 137). Again,

concentrations of azinphosmethyl in house

dust and of dimethyl DAPs in child urine did

not differ by distance category (house dust:

p = 0.58; nonadjusted child urine: p = 0.30;

creatinine-adjusted child urine: p = 0.40). 

Linear regression analyses were conducted

to evaluate the relationship between pesticide

residue levels in household dust and the 

measured biologic levels in children.

Concentrations of azinphosmethyl in household dust were found to be significantly associated with dimethyl DAP concentrations in

child urine (nonadjusted: r

2 = 0.14, p < 0.0001;

creatinine adjusted: r

2 = 0.15, p < 0.0001).

Most workers reported working on more

than one crop during the 3 months before

their interview. Workers most frequently

reported working with apples (72%), pears

(60%), and cherries (37%).

Community status. Two methods

were employed to determine whether the

intervention and control communities were

significantly different in terms of OP pesticide exposure or contamination. Five families

were excluded from this analysis because the

communities in which they resided were not

randomized into control or intervention status. First, an ANOVA by randomized group

was conducted in which the unit of analysis

was the family (n = 213). Table 5 presents the

geometric mean DAP metabolite and OP

residue concentrations by community status.

No significant differences were found for urinary DAP levels. Table 5 presents the nonadjusted results; however, the results were

unchanged when urinary metabolite levels

were adjusted for creatinine concentration.

Because six pesticides were targeted in this

analysis, the Bonferroni adjustment for multiple comparisons was used, and the p-value

necessary to indicate significance was determined to be 0.008. No significant differences

were found for any of the pesticide concentrations in house dust or vehicle dust by community status. A second analysis employed an

ANOVA using community as the unit of

analysis (n = 24), and the geometric means

and SEs of the individual samples within the

communities were compared. Again, no significant differences were found in exposure

levels by community status (data not shown). 

Discussion

Take-home exposure. The results of this study

are consistent with the theory of a para-occupational or take-home exposure pathway; i.e.,

agricultural chemicals move from the workplace to residential environments through the

activities of farm workers. Workers in this

study were most commonly employed on

apple, pear, and cherry crops. More pounds

of azinphosmethyl are applied annually on

these crops in Washington State than any of

the other target pesticides (USDA 2000).

Azinphosmethyl is a Toxicity I insecticide

registered exclusively for agricultural use, and

according to the U.S. Department of

Agriculture, an estimated 360,000 lbs were

applied to apple, pear, and cherry crops in

Washington State in 1999 (USDA 2000).

Annual use of chlorpyrifos approached this

amount (300,000 lbs), but unlike azinphosmethyl, chlorpyrifos is generally sprayed

before worker contact with treated fields.

Therefore, the relatively high concentrations

of azinphosmethyl found in house dust and

vehicle dust samples in this study correspond

well with pesticide use and worker activity

patterns in Washington State in 1999.

Concentrations of azinphosmethyl in

house dust and vehicle dust from the same

household were strongly associated. This suggests that the vehicle used for travel to and

from work is a vector of chemical transmission, and that the residues found in the vehicle are markers of contamination on worker

clothing or skin. Although it is possible that

the observed association was due to a common source other than the workplace, there is

little evidence to support such an argument.

Analysis of azinphosmethyl levels in house

dust and residential proximity to farmland

did not reveal a significant pattern, so pesticide drift from agricultural spraying seems an

unlikely explanation for the association. It is

also possible that workers may have brought

agricultural chemicals such as azinphosmethyl

home for residential use, and that both home

and vehicle were thereby contaminated. This

scenario is plausible for a few individuals, but

is unlikely to be widespread among workers

in this region. A previous study in an adjacent

agricultural region found that only 6% of

pesticide applicators reported using azinphosmethyl at their residences (Loewenherz et al.

1997). This practice by a small fraction of the

Children’s Health • Curl et al.

A 790 VOLUME 110 | NUMBER 12 | December 2002 • Environmental Health Perspectives

Figure 1. Plot of azinphosmethyl concentrations

(n = 145) in vehicle dust (μg/g) versus azinphosmethyl concentrations in house dust (μg/g) by

household (r 2 = 0.41; p < 0.0001). This plot

includes samples with residue levels below

the LOQ.

Figure 2. Plot of urinary dimethyl DAP concentrations (n = 206) in adult urine (μmol/L) versus child

urine (μmol/L) by household (r 2 = 0.18; p < 0.0001).

Creatinine-adjusted results are similar (r 2 = 0.15; p <

0.0001). This plot includes samples with metabolite

levels below the LOQ.

100

10

1

0.1

0.01

0.01 0.1 1 10 100

Aziphosmethyl in vehicle dust (μg/g)

Azinphosmethyl in house dust (μg/g)

100

10

1

0.1

0.01

0.001

0.01 0.1 1 10 100

Adult dimethyl DAPs (μmol/L)

0.001

Child dimethyl DAPs (μmol/L)
current study population would not be sufficient to explain the observed association.

The lack of association between proximity

and exposure is not consistent with previous

work in Washington State (Loewenherz et al.

1997; Lu et al. 2000; Simcox et al. 1995).

There are at least two possible explanations

for this inconsistency. First, this study

occurred in the Yakima Valley region of

Washington State, which differs geographically from the Wenatchee area, where previous work took place. Wenatchee is located in

a region marked by canyons, which may be

more conducive to wind patterns responsible

for spray drift. Alternatively, the current

study was not focused on the spray drift pathway and simply may not have been effective

in examining the association between proximity and exposure. The interview question

regarding proximity to treated fields may have

been insufficient because the type of pesticide

applied on nearby fields was not specified,

and distances were self-reported in terms of

blocks. Therefore, though there is no evidence that spray drift confounded the

reported associations, it is not possible to rule

out this pathway as a potential source of

exposure for this population.

The association found here between

dimethyl DAP levels in child and adult urine

can also be viewed as supportive of a takehome exposure pathway, but this evidence is

less persuasive. The dimethyl DAP metabolites may have been the result of exposure to a

variety of OP pesticides and not only those

used exclusively in agriculture. In addition,

this association could be due to coexposures

from another source such as diet. Without a

reference or control population for comparison, it is difficult to draw a firm conclusion

from these data. In sum, the existence of a

take-home pathway in this population is best

supported by the association of azinphosmethyl residue levels in vehicle dust and

house dust.

These results concur with previous work in

Washington State, which found that concentrations of azinphosmethyl and phosmet in the

house dust of agricultural workers were elevated

over concentrations of these pesticides in the

house dust of nonagricultural workers, regardless of residential proximity to farmland (Lu et

al. 2000). The study by Lu et al. also reported

that residues of agricultural pesticides were

detected on the work boots, steering wheels,

and children’s hands of many of the agricultural families, but not of the reference families.

Exposure measurements. This study provides baseline exposure measurements for the

agricultural communities participating in the

larger intervention project. Azinphosmethyl

residues were detected in 85% of the house

dust samples, and thus azinphosmethyl concentration in dust will likely serve as the most

reliable indicator of take-home exposure. The

high prevalence of azinphosmethyl in homes

is consistent with findings from previous

exposure studies in nearby agricultural

regions of Washington State. A 1992 study of

OP pesticide concentrations in the house dust

of agricultural and reference families found

detectable levels of azinphosmethyl in the

house dust of every family sampled (Simcox

et al. 1995). A study in the same region in

1995 reported similar results (Lu et al. 2000). 

Concentrations of azinphosmethyl in

household dust were predictive of dimethyl

DAP concentrations in child urine, both with

and without adjustment for urinary creatinine. However, the r2 values of 0.14 and 0.15

(for nonadjusted and creatinine-adjusted urinary DAP levels, respectively) suggest that

azinphosmethyl concentration in household

dust did not explain more than 15% of the

variability in the children’s dimethyl DAP

metabolite levels. Therefore, though household dust appears to be a good indicator of

children’s azinphosmethyl exposure, this measurement alone is likely not sufficient to fully

characterize that exposure.

This study used a Nilfisk GS-80 vacuum

cleaner for dust sampling instead of the

HVS3 vacuum (Cascade Stamp Sampling

Systems, Bend, OR), which has been used in

previous work (Lu et al. 2000; Simcox et al.

1995). Because over 400 dust samples were

collected, the portability and maneuverability

of the Nilfisk unit made it a more convenient

choice. A study by the Agency for Toxic

Substances and Disease Registry compared

geometric mean lead concentrations collected

by the two vacuum cleaners and found that

the vacuums differed significantly in collection efficiency in living rooms and bedrooms,

though not in entryways (Sterling et al.

1999). The HVS3 had consistently higher

collection efficiency than the Nilfisk, and

therefore the results of the current study may

underpredict dust residue levels compared

with studies using the HVS3.

Dimethyl DAP metabolite levels were

much higher than diethyl DAP metabolite

levels in both child and adult urine, which

concurs with findings in previous studies

(Aprea et al. 2000; Koch et al. 2002; Lu et al.

2001). However, the median DMTP level in

the urine of the children in this study (5.8

μg/L) was lower than that of children of

applicators (21 μg/L), as reported in a 1995

study occurring in a similar geographic region

(Loewenherz et al. 1997). This difference

may be due to a combination of factors. First,

pesticide exposure levels in agricultural communities have a strong seasonal association,

concurrent with agricultural spray periods

(Koch et al. 2002). Most azinphosmethyl

applications occur between May and July.

Sampling in the 1995 study occurred in June,

whereas most of the sampling in the current

study occurred between July and October.

Thus, this study probably missed some of the

highest exposures. Second, Lu et al. (2000)

report that dimethyl OP metabolite concentrations tend to be higher in the urine of children of pesticide applicators than children of

fieldworkers. Unlike the participants in the

1995 study, many of the participants in the

current study were not pesticide applicators. 

Study limitations. This study had four

notable limitations. First, urine was collected as

several spot samples instead of 24-hr total

voids. This necessitated the assumption of

steady-state conditions for urinary output and

may have introduced random variability into

the metabolite measurements. Further, these

spot samples were pooled to yield a single sample representing the exposure of each study participant. Ideally, spot samples would have been

analyzed individually to prevent the dilution of

samples containing high contaminant concentrations and to allow investigation of intraindividual variability. The decision to pool the spot

samples was made out of necessity and was

based on limited analytic time and resources.

The second limitation of this study was

noted previously. Because sampling occurred

between July and October, peak exposures

occurring during the active spray period were

Children’s Health • Take-home exposure among families of farm workers

Environmental Health Perspectives • VOLUME 110 | NUMBER 12 | December 2002 A 791

Table 5. GM concentrations of urinary DAP

metabolites (μmol/L) and OP pesticides (μg/g) by

control or intervention status.a No significant differences between control or intervention households were found.

Intervention Control p-Valueb

Child urine

No. 101 105

Dimethylc 0.08 0.10 0.13

Diethyld 0.06 0.06 0.12

Adult urine

No. 102 106

Dimethyl 0.16 0.11 0.15

Diethyl 0.06 0.06 0.78

House dust

No. 74 78

Azinphosmethyl 0.55 0.51 0.77

Malathion 0.05 0.06 0.16

m-Parathion 0.02 0.04 0.02e

Phosmet 0.02 0.02 0.09

Chlorpyrifos 0.07 0.05 0.09

Diazinon 0.01 0.01 0.88

Vehicle dust

No. 92 93

Azinphosmethyl 0.70 0.78 0.68

Malathion 0.02 0.01 0.41

m-Parathion 0.01 0.01 0.84

Phosmet 0.01 0.01 0.45

Chlorpyrifos 0.03 0.03 0.90

Diazinon 0.001 0.003 0.07

aFive households were not included in this analysis

because they were located in communities not randomized into control or intervention status. bp-Values calculated using ANOVA techniques to determine significant

differences between groups. cDimethyl metabolites represent the molar sum of DMP, DMTP, and DMDTP. dDiethyl

metabolites represent the molar sum of DEP and DETP.

eNot significant when corrected for multiple comparisons.
probably not captured. Third, for many of the

compounds investigated, high proportions of

the samples contained residues at levels below

the limit of quantitation. Our treatment of

censored data aimed to produce more realistic

variation in these concentrations, but the true

values of these samples are still unknown.

However, we also analyzed this data set with

values below the LOQ set to zero, and again

with these values set to one-half the LOQ, and

found all statistical results to be unchanged.

Significant associations between paired data

also remained when the censored data were

excluded, demonstrating that the reported

associations were not dependent on the generated values. Nonetheless, improved analytic

methods would provide more information

about the exposure patterns of pesticides commonly present at levels below the LOQ.

Finally, this research is limited by ambiguity regarding the appropriateness of creatinine adjustment for children’s urinary

exposure measurements (Boeinger et al.

1993). Factors known to affect urinary creatinine levels include weight, age, muscularity,

and diet, and differences in these factors are

expected to produce differences in creatinine

concentrations between children and adults

(Wilder 2001). The differential influence of

creatinine adjustment on child and adult

exposure measurements is illustrated by the

comparison of dimethyl DAP metabolite levels within these two populations. With no

adjustment, adult dimethyl DAP levels are

significantly greater than corresponding child

levels. However, when the data were creatinine adjusted, this finding was reversed,

demonstrating that the adjustment elevated

exposure estimates for children relative to

adults. Without definitive evidence regarding

the validity of creatinine adjustment in exposure estimates for children, analyses where

adjusted and nonadjusted results do not concur cannot be fully interpreted.

Conclusion 

This work provides OP pesticide exposure

measurements for a large, chiefly Hispanic

population of agricultural workers and their

families. Patterns of OP pesticide exposure in

this population were supportive of the

hypothesis that the take-home exposure pathway contributes significantly to residential

pesticide contamination in farm worker

homes that include young children. The study

also provides baseline exposure measurements

for a community intervention project in the

Yakima Valley, and has demonstrated that

intervention and control communities within

the study had similar exposure levels at the

outset of the intervention activities.

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