People living in agricultural areas may be

exposed to pesticides through drift from agricultural fields in proximity to their homes. In

orchard-producing areas of Washington State,

pesticide levels in carpet dust and pesticide

metabolites in urine of residents increased with

self-reported proximity of homes to crop fields

(Lu et al. 2000) and during the pesticide application season (Curl et al. 2002). Children in

agricultural areas had five times the concentration of pesticides in their urine compared with

children in an urban area (Lu et al. 2000). The

presence of an agricultural worker in the home

also increases pesticide levels through “takehome” exposures (Curl et al. 2002; Curwin

et al. 2005; Lu et al. 2000).

Carpet dust can be a reservoir for pesticides and other chemicals because they are

protected from degradation. Levels of pesticide in carpet dust were 10- to 200-fold

higher than levels in soil around the home in

residential (Lewis et al. 1994) and agricultural

areas (Simcox et al. 1995). Previous studies

have found residues in carpet dusts from both

recently used pesticides (Colt et al. 1998;

Curwin et al. 2005; Lu et al. 2000) and pesticides used many decades ago (Colt et al.

1998, 2004; Rudel et al. 2003).

We previously developed a geographic

information system (GIS)-based method that

used satellite imagery to create historical crop

maps in the midwestern United States.

Residences were mapped, and the extent of

agricultural fields proximate to the homes was

determined as a way of identifying homes

with potential exposure to agricultural pesticides (Ward et al. 2000). Corn and soybeans

are the major crops in Iowa; > 95% of corn

and soybean acreage was treated with herbicides from the late 1970s through the 1990s

(Iowa State University 1997). The aim of the

present study was to determine whether crop

maps are useful for predicting residential levels of crop herbicides as determined by their

measurement in carpet dust samples from residences in a population-based case–control

study of non-Hodgkin lymphoma (NHL). 

Materials and Methods

Study population and data collection. The

study population in these analyses included

Iowa case and control participants for whom

we collected a dust sample in a populationbased case–control study of NHL. Cases were

included in these analyses because proximity to

crops and herbicide concentrations in dust

were not associated with risk of NHL. The

original study was conducted in four areas covered by the Surveillance, Epidemiology, and

End Results program of the National Cancer

Institute (Iowa; the Detroit, Michigan, metropolitan area; Los Angeles County, California;

and the Seattle, Washington, metropolitan

area), and the study design was described in

detail previously (Chatterjee et al. 2004; Colt

et al. 2004). Briefly, we identified cases newly

diagnosed with NHL between July 1998 and

March 2000 and 20–74 years of age. We

selected controls 20–64 years of age from the

general population of Iowa using random-digit

dialing and controls 65–74 years of age from

Medicare eligibility files. Controls were

matched by age group, sex, and race to cases.

We interviewed 361 (67%) of eligible cases in

Iowa and 276 (58%) of eligible controls.

We collected vacuum cleaner dust if participants had used their vacuum in the past year

and had owned half of their carpets for ≥ 5

years. A further criterion for inclusion in these

analyses was that a 750-m radius buffer around

the home was within the area of the two satellite

images (128 of 237 participants had dust samples and resided in the image area) (Figure 1).

Using a GIS and spring and summer Landsat

multispectral satellite images (U.S. Geological

Survey Earth Resources Observation and

Science 2006) from two path/rows in south

central Iowa (hereafter called study area), we

created land cover maps to identify corn and

soybean fields (> 90% of crops) and other land

uses for each year of the study (1998–2000),

and we used Farm Service Agency records for

validation (Ward et al. 2000). For the three

maps, the accuracy (percentage of sampled areas

on the crop maps for which the land cover was

correctly identified) ranged from 81 to 96% for

corn and from 87 to 93% for soybeans.

Environmental Health Perspectives • VOLUME 114 | NUMBER 6 | June 2006 893

Research

Address correspondence to M.H. Ward, Occupational

and Environmental Epidemiology Branch, Division of

Cancer Epidemiology and Genetics, National Cancer

Institute, NIH, DHHS, 6120 Executive Blvd., EPS

8104, Bethesda, MD 20892 USA. Telephone: (301)

435-4713. Fax: (301) 402-1819. E-mail: wardm@

mail.nih.gov

We thank P. Riggs of Colorado State University

for his contribution to the review of the analyses. 

Support for data collection was provided by

National Cancer Institute (NCI) grant N01-PC67008. This research was also supported by the

Intramural Research Program of the National

Institutes of Health, NCI. J.R.N.’s time was supported

in part by research grant R01 CA92683 from the NCI

and by an intergovernmental personnel agreement

between the NCI Occupational and Environmental

Epidemiology Branch and Colorado State University. 

The authors declare they have no competing

financial interests.

Received 25 October 2005; accepted 2 February

2006.

Proximity to Crops and Residential Exposure to Agricultural Herbicides in Iowa

Mary H. Ward,1 Jay Lubin,1 James Giglierano,2 Joanne S. Colt,1 Calvin Wolter,2 Nural Bekiroglu,1,3

David Camann,4 Patricia Hartge,1 and John R. Nuckols5

1Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and

Human Services, Bethesda, Maryland, USA; 2Iowa Geological Survey, Iowa City, Iowa, USA; 3Department of Biostatistics, Marmara

University Medical School, Istanbul, Turkey; 4Southwest Research Institute, San Antonio, Texas, USA; 5Department of Environmental

and Radiological Health Sciences, Colorado State University, Fort Collins, Colorado, USA

Rural residents can be exposed to agricultural pesticides through the proximity of their homes to

crop fields. Previously, we developed a method to create historical crop maps using a geographic

information system. The aim of the present study was to determine whether crop maps are useful for

predicting levels of crop herbicides in carpet dust samples from residences. From homes of participants in a case–control study of non-Hodgkin lymphoma in Iowa (1998–2000), we collected vacuum cleaner dust and measured 14 herbicides with high use on corn and soybeans in Iowa. Of 112

homes, 58% of residences had crops within 500 m of their home, an intermediate distance for primary drift from aerial and ground applications. Detection rates for herbicides ranged from 0% for

metribuzin and cyanazine to 95% for 2,4-dichlorophenoxyacetic acid. Six herbicides used almost

exclusively in agriculture were detected in 28% of homes. Detections and concentrations were highest in homes with an active farmer. Increasing acreage of corn and soybean fields within 750 m of

homes was associated with significantly elevated odds of detecting agricultural herbicides compared

with homes with no crops within 750 m (adjusted odds ratio per 10 acres = 1.06; 95% confidence

interval, 1.02–1.11). Herbicide concentrations also increased significantly with increasing acreage

within 750 m. We evaluated the distance of crop fields from the home at < 100, 101–250, 251–500,

and 501–750 m. Including the crop buffer distance parameters in the model did not significantly

improve the fit compared with a model with total acres within 750 m. Our results indicate that crop

maps may be a useful method for estimating levels of herbicides in homes from nearby crop fields.

Key words: agriculture, exposure assessment, geographic information systems, herbicides, pesticides.

Environ Health Perspect 114:893–897 (2006). doi:10.1289/ehp.8770 available via http://dx.doi.org/

[Online 2 February 2006]
We determined the location of homes by

global positioning system (GPS) measurements using the Garmin GPS12 Personal

Navigator, a 12-channel small handheld

receiver (Garmin International Inc., Olathe,

KS, USA). Interviewers took the measurements

6.1 m (20 feet) away from the home. Quality

control checking of the GPS locations was done

by comparing addresses with GPS locations and

using digital aerial photography, rural directories, and other data as previously described

(Ward et al. 2005). One participant was

excluded because of an inaccurate address. GPS

locations that were not verified were remeasured by taking another GPS measurement at

the street in front of the home. The estimated

accuracy of the GPS measurements is 20 m

(Ward et al. 2005). As previously described

(Ward et al. 2005), residences were classified as

“in town” if they were located inside of incorporated areas, and as rural otherwise. 

Laboratory analyses. Details of the procedures for shipping samples, sieving, batching,

and quality control of the laboratory analyses

have been described (Colt et al. 2004). We measured the concentrations of 14 agricultural herbicides, each of which was used on at least 15%

of corn and/or soybean acres in Iowa in 1985,

1990, or 1995 (Iowa State University 1997).

These analytes included 10 neutral-extractable

herbicides (acetochlor, alachlor, atrazine,

cyanazine, fenoxyprop ethyl, fluazifop-p-butyl,

metolachlor, metribuzin, pendimethalin, trifluralin) and four acid-extractable herbicides

[2,4-dichlorophenoxyacetic acid (2,4-D), bromoxynil, bentazon, dicamba]. Some corn and soybean herbicides that were used this extensively

were not included because they could not be

measured using gas chromatography/mass spectrometry (GC/MS) (thifensulfuron, nicosulfuron, chlorimuron) or because they could not

be extracted using the neutral- or acid-extraction

methods (imazethapyr, glyphosate).

We analyzed neutral and acid extracts using

GC/MS in selective ion mode. Analyte

amounts were quantified using the internal

standard method. For the neutral extractions, 1

or 2 g of sieved fine dust was spiked with

atrazine-d5 and isopropalin as surrogates. The

spiked dust samples were sonicated and

extracted with ether:hexanes (1:1), and the

extracts were cleaned through a Florisil column.

The acid extractions were performed as previously described (Colt et al. 2004), except that

ioxynil and 3,4-dichlorophenoxyacetic acid

(3,4-D) were the extraction surrogates and the

cleaned extracts were derivatized by methylation. Laboratory spikes (concentrations from

1,000 ng/g for most herbicides to 4,000 ng/g

for bromoxynil and bentazon) of nine samples

indicated that all target analytes were efficiently

extracted with recovery means ranging from 71

to 124% and recovery standard deviations from

8 to 29%, except for fenoxyprop ethyl (149 ±

32%) and fluazifop-p-butyl (152 ± 26%).

Reported levels in dust were not adjusted for

spike recoveries. We conservatively estimated

the detection limits as a concentration 0.1 ng/g

below the lowest detected value, because many

of these analytes had not been previously measured in dust. Detection limits were in the

range of 24–62 ng/g dust except for dicamba

(75 ng/g), 2,4-D (85 ng/g), bentazone (88 ng/g),

pendimethalin (141 ng/g), and bromoxynil

(231 ng/g) (Table 1). Improvements can be

made by using labeled analytes as internal standards to compensate for matrix effects and for

extraction inefficiency. However, because most

of these standards are not commercially available

and must be synthesized, the cost of such standards was beyond the budget for this project.

Eighty-eight percent of the dust samples (n

= 112) were successfully analyzed for the target

analytes. Reasons we did not analyze the others

were that the identification label fell off the

dust bag before lab receipt (n = 3), there was an

insufficient quantity of sieved fine dust (n = 3),

or laboratory error (n = 10). The laboratory

error was the result of samples inadvertently

being left at room temperature with some fluorescent light exposure for an extended period.

The frequency of detection of individual

herbicides was often low, and interfering compounds that eluted together with the target analytes resulted in various types of “missing data.”

Pesticides with > 20% of samples with interferences were pendimethalin (38% of samples),

atrazine (29%), and fluazifop-p-butyl (21%).

Ward et al.

894 VOLUME 114 | NUMBER 6 | June 2006 • Environmental Health Perspectives

Table 1. Frequency of detection, median detected concentrations (ng/g), geometric mean, and geometric

SD for 14 herbicides measured in house dust samples from Iowa residences.

Percent detecteda Median detected Geometric Geometric

Herbicide (n = 112) concentration (range) meanb SD

2,4-Dc 94.6 1244.9 (85.2–126,000) 1035.0 4.2

Dicambac 28.8 179.5 (75.4–908.0) 30.1 5.0

Metolachlor 21.4 129.5 (27.8–3180.0) 3.0 16.8

Trifluralin 15.2 51.9 (24.6–3650.0) 1.5 17.0

Pendimethalin 11.6 215.0 (141–1650.0) 17.5 6.1

Atrazine 8.0 510.0 (59.4–4720.0) 0.44 41.4

Bentazonc 8.1 225.0 (88.5–855.6) 5.6 6.4

Acetochlor 8.0 127.0 (52.7–4120.0) 16.5 3.1

Fluazifop-p-butyl 6.2 71.3 (29.1–304.0) 2.3 5.9

Alachlor 6.2 49.6 (28.4–394.0) 3.2 4.3

All herbicidesc 94.6 1444.2 (173.3–126,190) 1398.6 3.5

Agricultural herbicidesc,d 28.0 238.3 (34.7–10244.0) 72.5 3.5

Herbicides with long-term usee 43.2 193.5 (31.6–6469.0) 74.7 4.5

aDetection limits (ng/g): acetochlor, 53; alachlor, 28; atrazine, 59; bentazon, 88; dicamba, 75; fluazifop-p-butyl, 29; metolachlor, 28; pendimethalin, 141; trifluralin, 24; 2,4-D, 85. bGeometric means and SDs were derived from fitted log-normal

models. cBased on 111 samples. dHerbicides with primarily agricultural use: acetochlor, alachor, atrazine, bentazon, fluazifop-p-butyl, metolachlor. eHerbicides used on ≥ 15% of corn and/or soybean acreage in Iowa in 1985, 1990, and 1995:

atrazine, dicamba, metolachlor, trifluralin.

Residence locations

237 in the study area

400 not in the study area

Outline of Landsat scene

Figure 1. Satellite image boundaries and approximate residence locations from a case–control study of

NHL in Iowa, USA.
The distributions of the individual herbicide

concentrations were consistent with log-normal

distributions. We used a multiple imputation

method, which assigns a value for each missing

measurement by selecting a value from the

assumed log-normal distribution based on a linear regression model (Lubin et al. 2004).

Including factors significantly associated with

herbicide concentration in dust (farming status,

location of home inside or outside a town,

acreage of crops within specific distances of

home) in the imputation did not appreciably

change our results, so we used imputed values

from a regression model without covariates in

our final analyses. Maximum likelihood parameter estimates were used to “fill in” five

imputed concentrations for measurements that

were below the detection limit or imprecisely

reported because of interferences (Helsel 1990;

Lubin et al. 2004; Moschandreas et al. 2001).

Data analysis. Metribuzin and cyanazine

were not detected in any samples. Bromoxynil

and fenoxyprop ethyl were detected in < 5% of

dust samples and were excluded from analyses.

We grouped the remaining herbicides two ways

for our analyses. First, we summed the concentrations of the six herbicides used almost exclusively in agriculture (acetochlor, alachlor,

atrazine, bentazone, fluazifop-p-butyl, metolachlor; called here agricultural herbicides).

Second, we evaluated the four detected herbicides that were used on ≥ 15% acres of corn

and/or soybeans in Iowa in all three pesticideuse reporting years, 1985, 1990, and 1995

(atrazine, dicamba, metolachlor, trifluralin;

called long-term–use herbicides). The herbicides accounting for the highest treated acreage

of corn and soybeans, respectively, were atrazine

(67% of corn acres treated in 1995) and trifluralin (30% of soybean acres treated in 1995).

We also evaluated the concentration of individual herbicides that were detected in at least 5%

of the samples (Table 1).

The descriptive statistics of the herbicide

concentrations were based on the observed

concentrations and the concentrations from

one imputation, whereas the percent detections

did not include any imputed values over the

detection limit. We calculated percent detections and concentrations of the agricultural

herbicide group separately for homes with and

without an agricultural worker and by the location of residences within and outside of towns.

For each home, we determined the acreage

of corn and soybeans within 750 m of the

residence for each crop map year (1998–2000).

We chose 750 m because primary pesticide

drift from ground and aerial spraying—the

most common methods of application of herbicides to corn and soybeans in Iowa—occurs

within this distance (AgDRIFT Task Force

2002; Woods et al. 2001). We also determined

the acres of corn and soybeans within “donutshaped” buffer zones of < 100, 101–250,

251–500, and 501–750 m from homes. The

acres of each crop type in the buffer zones

changed little over the 3 years; therefore, we

averaged the acreage across the 3 years.

Because the average acreage of corn and soybean fields within each buffer zone was highly

correlated [r ranged from 0.78 (100 m) to

0.90 (501–750 m)], we evaluated the summed

acres of corn and soybeans in all analyses.

We conducted two main types of analyses.

First, we used logistic regression to compute

odds ratios (ORs) and 95% confidence intervals (CIs) of detecting one or more herbicides

in each herbicide group in relation to the crop

acreage anywhere within 750 m of the home

and in relation to acres of crops within each

buffer zone. Second, we used linear regression

to model the logarithm of the concentration in

relation to crop acreage anywhere within 750 m

of the home and in relation to acres within each

buffer zone. Linear regression models were run

for each of the five data sets that contained the

measured and imputed concentrations. Final

parameter estimates and CIs were determined

using the SAS procedure MIANALYZE (version 8.02; SAS Institute Inc., Cary, NC, USA).

We computed likelihood ratio tests comparing

the significance of nested models using models

based on one imputed value. All analyses were

evaluated for confounding by the presence of a

current or past farmer in the home and whether

the home was inside or outside a town. The

ORs for herbicide detections were adjusted for

agricultural employment because adjustment

resulted in a change of 10% or more.

Results

Detection rates for individual herbicides ranged

from 0% for metribuzin and cyanazine to 95%

for 2,4-D (Table 1). One or more of the six

agricultural herbicides were detected in 28% of

homes, whereas the four long-term–use herbicides were detected in 43% of homes. A few

herbicides with high use (e.g., atrazine) were not

detected frequently. Metolachlor was the most

frequently detected herbicide in the agricultural

herbicide group, whereas dicamba was the most

frequently detected among the long-term–use

herbicides. All homes with detections of one or

more of the agricultural and long-term–use

herbicides also had detections of 2,4-D. The

concentration of 2,4-D was > 2-fold higher than

any other herbicide. Herbicides with the highest

geometric mean concentrations were 2,4-D,

dicamba, pendimethalin, and acetochlor.

Homes where the respondent was currently

employed in agricultural work had the highest

frequency of detections of one or more of the

six agricultural herbicides (85%), compared

with homes of past farmers (37%) and homes

of respondents who had never farmed or

worked in jobs with pesticide exposure (16%)

(Table 2). The geometric mean concentrations

of agricultural herbicides in dust were about

3-fold higher among current agricultural workers’ homes compared with past workers and

those never employed in these jobs.

Most residences (72%) were located in

towns. Agricultural herbicide detections in residences in towns were less frequent (15%) than

in rural residences (61%), and the herbicide

concentration was about 2.5-fold higher in

rural homes (Table 2). At increasing buffer

zone distances, the percentage of homes with

crops in the zone increased and was always

greater for rural homes (data not shown). For

example, the percentage of homes with crops

within 500 m was 58% (town, 44%; rural,

94%). Likewise, the median acreage in each

zone was substantially higher for rural residences; for example, at 501–750 m, the median

acreage was 171 for rural homes compared with

20 acres for town residences. The maximum

acres in each zone are constrained by the differing zone areas (range, < 100 m = 7.8 acres to

501–750 m = 243 acres). The frequency of

detections and concentrations of agricultural

herbicide increased with increasing acreage

within 750 m (Table 2). Seventy-five percent of

homes with > 300 acres of crops within 750 m

had detections of agricultural herbicides. The

concentrations of the agricultural herbicides

were more than 4-fold higher compared with

homes with no crops within 750 m.

Increasing acreage of corn and soybeans

within 750 m of the home was associated with

Proximity to crops and agricultural herbicides

Environmental Health Perspectives • VOLUME 114 | NUMBER 6 | June 2006 895

Table 2. Frequency of detections, geometric mean, and geometric SD for agricultural herbicides measured

in house dust, by agricultural work status, residence location, and acres of crops near the homes.

Percent of homes Geometric mean (ng/g)

No. (%) with detections (geometric SD)

Agricultural work

Never 79 (71) 16 111.5 (2.5)

Current 13 (12) 85 366.0 (4.6)

Former 19 (17) 37 121.9 (2.5)

Residence in town

Yes 80 (72) 15 101.7 (2.2)

No 31 (28) 61 245.7 (4.2)

Acres of corn and soybeans within 750 m of home

0 29 (26) 14 45.2 (2.0)

1–200 58 (52) 17 63.1 (3.2)

201–300 8 (7) 62 143.3 (2.4)

≥ 301 16 (14) 75 200.3 (6.3)
an increased probability of detecting one or

more of the agricultural herbicides (Table 3).

Compared with homes with no crops within

750 m, homes with ≥ 201 acres within 750 m

were associated with significantly elevated ORs

for detecting one or more of the agricultural

herbicides. ORs were somewhat attenuated

after adjustment for agricultural employment

but were still significantly elevated. Each 10-

acre increase in crops within 750 m was associated with a 6% increase in herbicide detections

(adjusted OR = 1.06; 95% CI, 1.02–1.11).

Increasing acreage within 750 m was also positively associated with the probability of detecting the long-term–use herbicides, although the

estimate for the highest acreage category was

unstable because only one home had no detection of one of the herbicides with long-term use

(Table 3). For both the agricultural herbicides

and long-term–use herbicides, decreasing distance of the crop acreage from the home (evaluated as acres within the four buffer regions) did

not explain significantly more of the variance in

herbicide detections compared with the models

with crop acreage anywhere within 750 m (data

not shown). The distance to the closest crop

field was significantly associated with detections

of the agricultural herbicide group; however,

distance alone explained less of the variance

than acreage within buffer zones of 100, 250,

500, and 750 m (data not shown).

The concentration of agricultural herbicides was significantly associated with the total

crop acreage within 750 m of the homes

(Table 4). Adjusting for presence of an agricultural worker in the home did not improve

the model fit and did not change the parameter estimates substantially (data not shown).

For each 10-acre increase in crops within

750 m, there was a significant increase (1.05-

fold) in agricultural herbicide concentration.

For example, among those with 200 acres of

crops near their home, there was a 2.7-fold

(1.0520) increase in herbicide concentration

compared with those with no crops within

750 m. Including separate parameters for crop

acres in each buffer zone did not significantly

improve the model fit (p > 0.05), and there

was no clear pattern in the relationship

between herbicide concentrations and distance

of acres from the home (Table 4). However,

each acre within the 100–250 m buffer region

was associated with a marginally significant

increase in herbicide concentrations (e.g., 2.4-

fold increase for 10 acres). For the group of

four herbicides with high use in all time periods, we observed a similar and somewhat

stronger relationship between crop acres

within 750 m and herbicide concentrations

(Table 4). For each 10-acre increase in crops

within 750 m, there was a significant 1.06-

fold increase in herbicide concentrations.

Including terms for crop acreage within the

four buffer zones did not improve the model

fit significantly (p > 0.05).

We evaluated the association between the

concentration of individual herbicides

detected in > 5% of homes and both crop

acreage within 750 m and crop acreage within

each buffer region. Total acreage within 750

m was significantly (p < 0.05) associated with

the concentration of 2,4-D, dicamba, and

metolachlor but not with atrazine, acetochlor,

alachlor, pendimethalin, trifluralin, fluazifopp-butyl, or bentazone (data not shown).

Including terms for the distance of the acreage

from homes did not significantly improve the

model fit for any of these herbicides. 

Discussion

In the U.S. Midwest, > 90% of corn and soybean fields are treated with herbicides. We

found that increasing acreage of corn and soybean fields within 750 m of homes was associated with a greater probability of detecting one

or more of the major corn and soybean

herbicides in homes. The total concentrations

of agricultural and long-term–use herbicides

were significantly associated with increasing

acreage within 750 m of homes; likewise, concentrations of the herbicides 2,4-D, dicamba,

and metolachlor were also associated with

increasing acreage. Employment in agriculture

was positively associated with the presence and

concentrations of the herbicides in homes;

however, the relationship between agricultural

herbicides detections and levels in dust and

crop acreage remained after adjusting for

agricultural employment.

Previous studies in Washington State found

a positive relationship between the self-reported

proximity of homes to orchards and both the

concentrations of organophosphate insecticides

in house dust and levels of the metabolites in

children’s urine (Fenske et al. 2002; Simcox

et al. 1995). In those studies, levels increased

with increasing proximity of homes to crops

over distances ranging from approximately 15

to 400 m. Curwin et al. (2005) compared dust

concentrations of several of the same corn and

soybean herbicides that we measured (atrazine,

acetochlor, metolachlor, 2,4-D) in farm and

nonfarm homes in Iowa and found that

self-reported distance to treated fields was not

associated with herbicide levels in dust among

nonfarm homes. However, the closest distance

category was < 0.25 mile (402 m). Curwin

et al. (2005) did not evaluate distance to crop

fields among farm homes because all were

Ward et al.

896 VOLUME 114 | NUMBER 6 | June 2006 • Environmental Health Perspectives

Table 4. Exponentiated parameter estimates for agricultural herbicides and long-term–use herbicides in

carpet dust (ng/g) in relation to acres of crops anywhere within 750 m and within specific buffer regions

from home (n = 111 samples for each model).

Model Parameter Exp(β) 95% CI

Agricultural herbicides

Model A Intercept 64.02 47.31–86.63

Model B Intercept 38.44 24.88–59.37

Per 10 acres: < 750 m 1.05 1.03–1.07

Model C Intercept 40.90 24.85–67.32

Per acre: < 100 m 0.99 0.58–1.68

Per acre: 100–250 m 1.09 0.98–1.21

Per acre: 250–500 m 0.98 0.95–1.01

Per acre: 500–750 m 1.01 1.00–1.02

Long-term–use herbicides

Model A Intercept 76.90 55.85–105.87

Model B Intercept 36.86 24.70–55.01

Per 10 acres: < 750 m 1.06 1.04–1.08

Model C Intercept 39.66 25.94–60.62

Per acre: < 100 m 0.77 0.43–1.40

Per acre: 100–250 m 1.12 1.00–1.27

Per acre: 250–500 m 0.98 0.95–1.02

Per acre: 500–750 m 1.01 1.00–1.02

Table 3. ORs and 95% CIs of detecting one or more herbicides in relation to the total acreage of corn and soybeans within 750 m of residences.

Acres of corn and soybeans within 750 ma

No crops within 750 mb 1–200 acres 201–300 acres > 300 acres OR (95% CI) per

Herbicide Detect/NDc OR Detect/NDc OR (95% CI) Detect/NDc OR (95% CI) Detect/NDc OR (95% CI) 10 acres

Agricultural herbicides

Crude OR 5/24 1.0 10/48 1.3 (0.4–4.5) 5/3 10.4 (1.8–61.7) 12/4 18.7 (3.9–88.1) 1.09 (1.05–1.13)

Adjusted for agricultural jobs 1.0 1.2 (0.3–4.4) 7.2 (1.1–49.3) 7.4 (1.3–41.3) 1.06 (1.02–1.11)

Long-term–use herbicides 

Crude OR 7/22 1.0 21/37 1.8 (0.7–4.9) 5/3 5.2 (1.0–27.7) 15/1 47.1 (5.2–423) 1.08 (1.04–1.12)

Adjusted for agricultural jobs 1.0 1.7 (0.6–5.1) 3.6 (0.6–22.9) 19.9 (2.0–201) 1.05 (1.01–1.10)

aAverage acreage in 1998–2000. bReference group. cNumber of homes where herbicides were detected/number of homes where herbicides were not detected (ND).
Proximity to crops and agricultural herbicides

Environmental Health Perspectives • VOLUME 114 | NUMBER 6 | June 2006 897

reported to be < 402 m from a treated field.

Both studies relied on self-reported estimates of

distances to crop fields, which may introduce

misclassification. Neither study mapped the

crops near homes, which would have allowed

for an objective evaluation of both proximity

and acreage of crops near homes.

We evaluated the relationship of acreage

within specific distance intervals (donut-shaped

circular buffers) with herbicide concentrations.

We found that the distance of the crop acreage

from homes did not explain significantly more

of the variance in herbicide detections and concentrations compared with the total crop acreage

within a 750-m buffer, nor was there a clear pattern of increase in herbicide concentrations with

proximity. The crop acreage in adjacent buffer

zones was highly correlated (r > 0.88). Because

of the large size of crop fields in Iowa, all homes

with crops within 100 m and 101–250 m also

had crop acreage at farther distances.

Because we evaluated acreage of crops

within specific distance intervals rather than the

distance to the nearest crop field, our analyses

cannot be directly compared with those of

Simcox et al. (1995) and Curwin et al. (2005).

Those studies evaluated only the relationship

between distance of crop fields from homes and

pesticide levels in house dust and did not

account for the size of crop fields near homes. A

metric based on distance alone assumes that a

large crop field and a small crop field at the same

distance have the same influence on residential

herbicide levels. We determined that increasing

acreage within 750 m of homes was a significant

predictor of herbicide levels in dust. However,

acreage was highly correlated with increasing

distance in our study, and the resultant

collinearity could have resulted in the lack of a

clear relationship between the proximity of crop

acreage and herbicide concentrations. A high

degree of collinearity between predictive variables can result in poor parameter estimations

with high variances (Kleinbaum et al. 1998).

Our analysis of the relationship between

herbicide concentrations and crop acres near

the home was limited to acreage within 750 m.

Therefore, additional studies to evaluate acreage

beyond 750 m will be important. Development

of a metric that addresses weaknesses inherent

in the methods used to date (e.g., weighted

measurements of acreage) is needed to evaluate

the relationship between the acreage of crop

fields in proximity to homes and residential

pesticide levels. Further research is also needed

to determine if other factors such as meteorologic conditions, pesticide transport associated

with wind-blown aerosols and soil (secondary

drift), and physicochemical properties of herbicides are important predictors of herbicide concentrations in environmental and biologic

samples. For example, the vapor pressure of the

pesticide and meteorologic conditions are

important factors in the estimation of exposure

to drift from agricultural pesticide applications

(Lee et al. 2002; Ramaprasad et al. 2004).

These factors and local variation in herbicide

use are likely to explain why the detections of

herbicides in dust samples did not directly correspond with agricultural herbicide use rates. 

Previous studies have demonstrated the

importance of the “take-home” pathway of

exposure for families living with an agricultural

worker (Curl et al. 2002; Lu et al. 2000), which

we confirmed. We found that currently active

agricultural workers had substantially higher frequencies of detection of agricultural herbicides

in their homes than did former agricultural

workers and nonfarmers. Concentrations of

these pesticides were > 4-fold higher in homes of

current agricultural workers compared with

homes with no agricultural workers. A total of

72% of our study population resided within

town boundaries, compared with 91% in a similar analysis in Nebraska by Ward et al. (2000).

Among residences within towns, 74% had crops

within 750 m of the home, and 15% of homes

in town had detections of one or more agricultural herbicide; thus, residence in a town may

not preclude exposure to crop pesticides.

Proximity to pesticide applications as

reported in the California Pesticide Use

Reporting (CPUR) database has been used as a

surrogate for exposure in recent studies of

reproductive outcomes and cancer (Bell et al.

2001; Reynolds et al. 2002), but the relationship between proximity and human exposure

in those studies was not determined. A recent

study using the CPUR data demonstrates that

estimates of residential proximity to pesticide

applications can differ substantially depending

on the distance used to calculate the metric

(Rull and Ritz 2003), with potentially large

effects on risk estimates. Our study indicates

that residential exposure to commonly used

agricultural herbicides is higher among those

living within 750 m of agricultural fields and

among those with an agricultural worker in the

home. Further research is needed to determine

how well measurements of corn and soybean

herbicides in residential dust samples predict

human exposure. Results of this study suggest

that satellite-based crop maps may be a useful

method for estimating levels of herbicides in

homes from nearby crop fields and thereby

serve as a surrogate measure of potential exposure to agricultural pesticides.

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