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  <front>
    <journal-meta><journal-id journal-id-type="nlm-ta">PLoS ONE</journal-id><journal-id journal-id-type="publisher-id">plos</journal-id><journal-id journal-id-type="pmc">plosone</journal-id><!--===== Grouping journal title elements =====--><journal-title-group><journal-title>PLoS ONE</journal-title></journal-title-group><issn pub-type="epub">1932-6203</issn><publisher>
        <publisher-name>Public Library of Science</publisher-name>
        <publisher-loc>San Francisco, USA</publisher-loc>
      </publisher></journal-meta>
    <article-meta><article-id pub-id-type="publisher-id">PONE-D-10-06246</article-id><article-id pub-id-type="doi">10.1371/journal.pone.0017015</article-id><article-categories>
        <subj-group subj-group-type="heading">
          <subject>Research Article</subject>
        </subj-group>
        <subj-group subj-group-type="Discipline-v2">
          <subject>Chemistry</subject>
          <subj-group>
            <subject>Analytical chemistry</subject>
            <subj-group>
              <subject>Chemical analysis</subject>
              <subj-group>
                <subject>Water analysis</subject>
              </subj-group>
            </subj-group>
          </subj-group>
          <subj-group>
            <subject>Applied chemistry</subject>
            <subj-group>
              <subject>Chemical radicals</subject>
              <subj-group>
                <subject>Perchloryl radicals</subject>
              </subj-group>
            </subj-group>
          </subj-group>
          <subj-group>
            <subject>Environmental chemistry</subject>
            <subj-group>
              <subject>Water chemistry</subject>
              <subj-group>
                <subject>Water analysis</subject>
              </subj-group>
            </subj-group>
            <subj-group>
              <subject>Pollutants</subject>
            </subj-group>
          </subj-group>
        </subj-group>
        <subj-group subj-group-type="Discipline-v2">
          <subject>Earth sciences</subject>
          <subj-group>
            <subject>Environmental sciences</subject>
            <subj-group>
              <subject>Environmental engineering</subject>
              <subj-group>
                <subject>Pollution</subject>
              </subj-group>
            </subj-group>
          </subj-group>
        </subj-group>
        <subj-group subj-group-type="Discipline-v2">
          <subject>Medicine</subject>
          <subj-group>
            <subject>Endocrinology</subject>
            <subj-group>
              <subject>Thyroid</subject>
            </subj-group>
          </subj-group>
          <subj-group>
            <subject>Epidemiology</subject>
            <subj-group>
              <subject>Biomarker epidemiology</subject>
              <subject>Environmental epidemiology</subject>
            </subj-group>
          </subj-group>
          <subj-group>
            <subject>Nutrition</subject>
          </subj-group>
          <subj-group>
            <subject>Public health</subject>
            <subj-group>
              <subject>Environmental health</subject>
            </subj-group>
          </subj-group>
        </subj-group>
        <subj-group subj-group-type="Discipline">
          <subject>Chemistry</subject>
          <subject>Diabetes and Endocrinology</subject>
          <subject>Public Health and Epidemiology</subject>
        </subj-group>
      </article-categories><title-group><article-title>Direct Measurement of Perchlorate Exposure Biomarkers in a Highly
                    Exposed Population: A Pilot Study</article-title><alt-title alt-title-type="running-head">Measurement of Perchlorate Exposure
                    Biomarkers</alt-title></title-group><contrib-group>
        <contrib contrib-type="author" equal-contrib="yes" xlink:type="simple">
          <name name-style="western">
            <surname>English</surname>
            <given-names>Paul</given-names>
          </name>
          <xref ref-type="aff" rid="aff1">
            <sup>1</sup>
          </xref>
          <xref ref-type="corresp" rid="cor1">
            <sup>*</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author" equal-contrib="yes" xlink:type="simple">
          <name name-style="western">
            <surname>Blount</surname>
            <given-names>Ben</given-names>
          </name>
          <xref ref-type="aff" rid="aff2">
            <sup>2</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author" xlink:type="simple">
          <name name-style="western">
            <surname>Wong</surname>
            <given-names>Michelle</given-names>
          </name>
          <xref ref-type="aff" rid="aff1">
            <sup>1</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author" xlink:type="simple">
          <name name-style="western">
            <surname>Copan</surname>
            <given-names>Lori</given-names>
          </name>
          <xref ref-type="aff" rid="aff3">
            <sup>3</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author" xlink:type="simple">
          <name name-style="western">
            <surname>Olmedo</surname>
            <given-names>Luis</given-names>
          </name>
          <xref ref-type="aff" rid="aff4">
            <sup>4</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author" xlink:type="simple">
          <name name-style="western">
            <surname>Patton</surname>
            <given-names>Sharyle</given-names>
          </name>
          <xref ref-type="aff" rid="aff5">
            <sup>5</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author" xlink:type="simple">
          <name name-style="western">
            <surname>Haas</surname>
            <given-names>Robert</given-names>
          </name>
          <xref ref-type="aff" rid="aff6">
            <sup>6</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author" xlink:type="simple">
          <name name-style="western">
            <surname>Atencio</surname>
            <given-names>Ryan</given-names>
          </name>
          <xref ref-type="aff" rid="aff7">
            <sup>7</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author" xlink:type="simple">
          <name name-style="western">
            <surname>Xu</surname>
            <given-names>Juhua</given-names>
          </name>
          <xref ref-type="aff" rid="aff6">
            <sup>6</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author" xlink:type="simple">
          <name name-style="western">
            <surname>Valentin-Blasini</surname>
            <given-names>Liza</given-names>
          </name>
          <xref ref-type="aff" rid="aff2">
            <sup>2</sup>
          </xref>
        </contrib>
      </contrib-group><aff id="aff1"><label>1</label><addr-line>California Environmental Health Tracking
                    Program, California Department of Public Health, Richmond, California, United
                    States of America</addr-line>
            </aff><aff id="aff2"><label>2</label><addr-line>Division of Laboratory Sciences, Centers for
                    Disease Control and Prevention, Atlanta, Georgia, United States of
                    America</addr-line>
            </aff><aff id="aff3"><label>3</label><addr-line>Environmental Health Investigations Branch,
                    California Department of Public Health, Richmond, California, United States of
                    America</addr-line>
            </aff><aff id="aff4"><label>4</label><addr-line>Comité Cívico del Valle,
                    Brawley, California, United States of America</addr-line>
            </aff><aff id="aff5"><label>5</label><addr-line>Commonweal, Bolinas, California, United States
                    of America</addr-line>
            </aff><aff id="aff6"><label>6</label><addr-line>Food and Drug Laboratory Branch, California
                    Department of Public Health, Richmond, California, United States of
                    America</addr-line>
            </aff><aff id="aff7"><label>7</label><addr-line>California Department of Toxic Substances and
                    Control, El Centro, California, United States of America</addr-line>
            </aff><contrib-group>
        <contrib contrib-type="editor" xlink:type="simple">
          <name name-style="western">
            <surname>Song</surname>
            <given-names>Yiqing</given-names>
          </name>
          <role>Editor</role>
          <xref ref-type="aff" rid="edit1"/>
        </contrib>
      </contrib-group><aff id="edit1">Brigham &amp; Women's Hospital, and Harvard Medical School, United
                States of America</aff><author-notes>
        <corresp id="cor1">* E-mail: <email xlink:type="simple">penglish@dhs.ca.gov</email></corresp>
        <fn fn-type="con">
          <p>Conceived and designed the experiments: PE BB LC MW SP. Performed the
                        experiments: RA RH JX LV-B BB. Analyzed the data: PE BB LV-B RH JX.
                        Contributed reagents/materials/analysis tools: RH BB. Wrote the manuscript:
                        PE BB LC MW. Conducted participant recruitment and data collection: LO.</p>
        </fn>
      <fn fn-type="conflict">
        <p>The authors have declared that no competing interests exist.</p>
      </fn></author-notes><pub-date pub-type="collection">
        <year>2011</year>
      </pub-date><pub-date pub-type="epub">
        <day>4</day>
        <month>3</month>
        <year>2011</year>
      </pub-date><volume>6</volume><issue>3</issue><elocation-id>e17015</elocation-id><history>
        <date date-type="received">
          <day>7</day>
          <month>12</month>
          <year>2010</year>
        </date>
        <date date-type="accepted">
          <day>18</day>
          <month>1</month>
          <year>2011</year>
        </date>
      </history><!--===== Grouping copyright info into permissions =====--><permissions><copyright-year>2011</copyright-year><license><license-p>This is an open-access article distributed under the terms of the
                Creative Commons Public Domain declaration which stipulates that, once placed in the
                public domain, this work may be freely reproduced, distributed, transmitted,
                modified, built upon, or otherwise used by anyone for any lawful
                purpose.</license-p></license></permissions><abstract>
        <p>Exposure to perchlorate is ubiquitous in the United States and has been found to
                    be widespread in food and drinking water. People living in the lower Colorado
                    River region may have perchlorate exposure because of perchlorate in ground
                    water and locally-grown produce. Relatively high doses of perchlorate can
                    inhibit iodine uptake and impair thyroid function, and thus could impair
                    neurological development in utero. We examined human exposures to perchlorate in
                    the Imperial Valley among individuals consuming locally grown produce and
                    compared perchlorate exposure doses to state and federal reference doses. We
                    collected 24-hour urine specimen from a convenience sample of 31 individuals and
                    measured urinary excretion rates of perchlorate, thiocyanate, nitrate, and
                    iodide. In addition, drinking water and local produce were also sampled for
                    perchlorate. All but two of the water samples tested negative for perchlorate.
                    Perchlorate levels in 79 produce samples ranged from non-detect to 1816 ppb.
                    Estimated perchlorate doses ranged from 0.02 to 0.51 µg/kg of body
                    weight/day. Perchlorate dose increased with the number of servings of dairy
                    products consumed and with estimated perchlorate levels in produce consumed. The
                    geometric mean perchlorate dose was 70% higher than for the NHANES
                    reference population. Our sample of 31 Imperial Valley residents had higher
                    perchlorate dose levels compared with national reference ranges. Although none
                    of our exposure estimates exceeded the U. S. EPA reference dose, three
                    participants exceeded the acceptable daily dose as defined by bench mark dose
                    methods used by the California Office of Environmental Health Hazard
                    Assessment.</p>
      </abstract><funding-group><funding-statement>This project was funded in part by Grant/Cooperative Agreement Number
                    1U38EH000186-01 from the Centers for Disease Control and Prevention (CDC). The
                    funders had no role in study design, data collection and analysis, decision to
                    publish, or preparation of the manuscript. No additional external funding
                    received for this study.</funding-statement></funding-group><counts>
        <page-count count="8"/>
      </counts></article-meta>
  </front>
  <body>
    <sec id="s1">
      <title>Introduction</title>
      <p>Perchlorate occurs in the environment from both natural and man-made sources. It is
                primarily synthesized for use as an oxidant in solid rocket propellant. Perchlorate
                has been detected in food and drinking water from various regions of the U.S. <xref ref-type="bibr" rid="pone.0017015-Murray1">[1]</xref>–<xref ref-type="bibr" rid="pone.0017015-EPA1">[3]</xref>, and human exposure
                to perchlorate is widespread in the U.S. population <xref ref-type="bibr" rid="pone.0017015-Blount2">[4]</xref>. At high doses (mg/kg of body
                weight/day), perchlorate can affect the ability of the thyroid to absorb iodine and
                can limit the production of thyroid hormones, which are important for proper
                development in children <xref ref-type="bibr" rid="pone.0017015-NAS1">[5]</xref>. Continued inhibition of iodine uptake can lead to
                hypothyroidism, which can result in metabolic problems in adults and abnormal
                development during gestation and infancy. Low doses (µg/kg/day) of perchlorate
                have been associated with decreased thyroxine and increased thyroid-stimulating
                hormone levels in women with low urinary iodine levels <xref ref-type="bibr" rid="pone.0017015-Blount3">[6]</xref>. Even small changes in thyroid
                hormone levels are cause for concern, as mild hypothyroidism during pregnancy has
                been associated with subtle cognitive defects in children <xref ref-type="bibr" rid="pone.0017015-Berbel1">[7]</xref>–<xref ref-type="bibr" rid="pone.0017015-Klein1">[9]</xref>. Other compounds that inhibit
                iodine uptake are thiocyanate (SCN) and nitrate (NO<sub>3</sub>) <xref ref-type="bibr" rid="pone.0017015-Tonacchera1">[10]</xref>. These
                compounds are also present in dietary and water sources, and SCN is a major
                metabolite of cyanide found in cigarette smoke, so these compounds are important to
                consider when examining perchlorate's antithyroid effects <xref ref-type="bibr" rid="pone.0017015-DeGroef1">[11]</xref>.</p>
      <p>Although perchlorate exposure is widespread in the U.S. population, some locations
                may have higher exposure than others. One such location is the Lower Colorado River
                region. In Nevada, ammonium perchlorate manufacturing activities contaminated ground
                and surface waters, and eventually, Lake Mead and the Colorado River (<xref ref-type="fig" rid="pone-0017015-g001">Figure 1</xref>). The U.S. Environmental
                Protection Agency (EPA) and the State of Nevada are currently overseeing cleanup
                operations for the area. The Colorado River is a primary source of drinking water
                for 15 million–20 million people in Arizona, Nevada, and California and also
                serves as the sole source of irrigation water for California's Imperial Valley.
                Imperial County has approximately 160,000 residents, and about 20% of
                families are below the poverty level. Potentially elevated chemical exposures in
                this low-income population may raise issues of environmental equity.</p>
      <fig id="pone-0017015-g001" position="float">
        <object-id pub-id-type="doi">10.1371/journal.pone.0017015.g001</object-id>
        <label>Figure 1</label>
        <caption>
          <title>Map showing the Lower Colorado River from the source of perchlorate
                        contamination in the Las Vegas Wash to the All-American Canal in the
                        Imperial Valley.</title>
          <p>Source: U.S. Bureau of Reclamation.</p>
        </caption>
        <graphic mimetype="image" position="float" xlink:href="info:doi/10.1371/journal.pone.0017015.g001" xlink:type="simple"/>
      </fig>
      <p>The California Environmental Health Tracking Program (CEHTP) conducts surveillance on
                environmental exposures and environmentally-related chronic diseases. Due to
                concerns expressed about levels of perchlorate in produce grown in the lower
                Colorado River region <xref ref-type="bibr" rid="pone.0017015-Sanchez1">[12]</xref> and the lack of human exposure studies <xref ref-type="bibr" rid="pone.0017015-Kirk1">[13]</xref>, CEHTP partnered
                with the Centers for Disease Control and Prevention (CDC), the California Department
                of Public Health (CDPH) Food and Drug Laboratory Branch (FDLB), the California
                Department of Toxic Substances Control (DTSC), and 2 nongovernmental organizations
                (Commonweal and Comité Cívico del Valle) to measure perchlorate
                exposure in the Imperial Valley among persons consuming locally grown produce.</p>
    </sec>
    <sec id="s2" sec-type="materials|methods">
      <title>Materials and Methods</title>
      <p>We recruited a convenience sample of 31 residents living in Imperial County,
                California, from attendees of a community meeting related to local environmental
                concerns. Written informed consent was obtained from each participant. Adult
                residents who consumed local produce were eligible to participate. We designed a
                short questionnaire to elicit information on participants' demographic and
                personal characteristics (e.g., gender, age, weight, and height), smoking history,
                primary source of drinking water, water filter use in the home, water consumption,
                and source of produce. We also asked participants to complete a 24-hr dietary recall
                and a food diary to be completed for the 24-hr study period. All materials were
                available in Spanish and English. Written informed consent was obtained from all
                participants. All human subject protocols were approved by the Centers for Disease
                Control and Prevention's institutional review board. Participants received
                their individual results from the laboratory tests of urine, produce, and water and
                were also provided materials to help interpret the results. Group findings were
                presented at a participant meeting, and a local clinician was available for
                consultation with participants for any health concerns related to the study.</p>
      <p>In addition, participants were asked to collect samples of all drinking water and all
                local produce consumed during a 24-hr period. Study staff provided standardized
                containers to participants for collection of all samples. Participants were
                instructed to collect approximately 2 ounces of each produce sample in a plastic
                bag. Produce samples were stored in participants' freezers until collected by
                study staff. Participants were also instructed to collect a 24-hr urine specimen,
                beginning after the last void of the day. Urine was chilled in coolers or
                participants' refrigerators during sample collection and transported in coolers
                by study staff. Total urine volume was measured volumetrically and recorded for each
                study participant. Urine sample aliquots (5 mL) were removed from the vessels
                storing the 24-hr urine samples, transferred to Cryovials®, and stored at
                −20°C at the Imperial County Public Health Laboratory until shipped to CDC
                on dry ice. Produce samples were shipped to the CDPH FDLB, and water samples were
                analyzed by the DTSC.</p>
      <p>Water samples were analyzed to detect perchlorate by using EPA method 6850 (liquid
                chromatography-mass spectrometry) with a detection limit of 1 µg/L.
                Perchlorate was resolved by high-performance liquid chromatography from the sample
                matrix, ionized by using negative electrospray ionization, and partially fragmented
                by mass spectrometry (MS). Perchlorate was further fragmented into daughter ions
                upon collision with an inert gas and detected by tandem MS using mass-to-charge
                (m/z) ratios 83 (-ClO<sub>3</sub>), 85(-<sup>37</sup>ClO<sub>3</sub>) and
                    89(-Cl<sup>18</sup>O<sub>3</sub>) <xref ref-type="bibr" rid="pone.0017015-EPA2">[14]</xref>. Quantitation and identification of perchlorate were
                performed by comparing the ratios of the primary perchlorate mass transitions to
                internal standard mass transitions and by using retention times. Upon positive
                detection, 2 samples were further analyzed to confirm accuracy and precision.
                Samples were transferred to a clean centrifuge tube and evaporated to dryness under
                a stream of nitrogen at 70°C. The samples were then resuspended in 1.0 mL of
                deionized water.</p>
      <p>Produce was analyzed for perchlorate by using ion chromatography-tandem MS <xref ref-type="bibr" rid="pone.0017015-Krynitsky1">[15]</xref>. The produce
                was chopped with a food processor (Robot Coupe USA, Inc., Jackson, MS, USA) until
                the matrix was homogeneous. A portion of chopped produce (10 g) was transferred to a
                50 mL polypropylene tube, and 100 µL of 3.0 µg/mL (300 ng) labeled
                internal standard was added. This material was acidified by addition of 20 mL of
                1% (vol/vol) sodium hydroxide acetic acid, hand-shaken for 2 minutes and
                centrifuged at 2000 rpm for 15 minutes. Supernatant (5 mL) was passed through a 500
                mg–6 mL Supelclean™ ENVI-Carb™ cartridge (Sigma-Aldrich,
                Milwaukee, WI, USA) preconditioned with 6 mL of water by applying vacuum such that
                the flow rate was approximately 100 µL/second. This extract was filtered
                through a 0.20 µm PTFE filter and analyzed by ion chromatography-tandem MS
                using a Waters Quattro micro™ API triple-quad mass spectrometer operating in
                negative ion multiple reaction monitoring mode. The m/z transition from 99 to 83 was
                primary for quantitating perchlorate, and the m/z transition of 101 to 85 was
                confirmatory. <sup>18</sup>O<sub>4</sub>-labeled sodium perchlorate (ICON Services,
                Inc., Summit, NJ, USA) served as internal standard for monitoring the m/z
                transitions from 107 to 89 and 109 to 91 for quantitation and confirmation,
                respectively. The limit of quantitation was 1 ng/g. All produce results were
                reported using the wet weight of the edible portion of produce.</p>
      <p>Perchlorate and related anions (thiocyanate, nitrate, and iodide) were measured in
                urine by ion chromatography-tandem MS <xref ref-type="bibr" rid="pone.0017015-ValentnBlasini1">[16]</xref>. Analytes were
                quantified on the basis of the peak area ratio of mass transitions for analyte to
                stable isotope-labeled internal standard. Method detection limits were 0.05
                µg/L, 0.50 µg/L, 10 µg/L, and 500 µg/L, for perchlorate,
                iodide, thiocyanate, and nitrate, respectively. Analytic accuracy and precision were
                tested by concurrent analysis of quality control materials in the same analysis
                batch as the unknown specimen. Reported results met the accuracy and precision
                specifications of the quality control and quality assurance program of the Division
                of Laboratory Sciences, National Center for Environmental Health, CDC <xref ref-type="bibr" rid="pone.0017015-ValentnBlasini1">[16]</xref>. All
                analytes were detected in all samples with the exception of 4 urine samples that did
                not contain measurable levels of nitrate; nitrate levels in these two samples were
                assigned an imputed value of half the detection limit for calculating the geometric
                mean of the distribution.</p>
      <p>Urinary creatinine concentrations were determined by using an automated colorimetric
                method on a Roche/Hitachi Modular Analytics SWA system (Roche Diagnostics Corp., IN,
                USA). Creatinine excretion rate (g/24-hr) was calculated by multiplying measured
                urinary creatinine concentration by the total volume of 24-hr urine collected. The
                calculated 24-hr creatinine excretion rate was then compared with expected 24-hr
                creatinine excretion rate based on the study participant age, sex, height, and
                weight <xref ref-type="bibr" rid="pone.0017015-Cockcroft1">[17]</xref>,
                    <xref ref-type="bibr" rid="pone.0017015-Mage1">[18]</xref>, according
                to a formula in which k = 1.93 for males and 1.64 for
                        females:<disp-formula><graphic mimetype="image" position="float" xlink:href="info:doi/10.1371/journal.pone.0017015.e001" xlink:type="simple"/></disp-formula>We computed perchlorate dose in urine by multiplying the
                perchlorate concentration by the 24-hr urine volume and dividing the product by body
                weight (µg/kg/day). Total iodine intake was estimated by multiplying urine
                iodide concentration by 24-hr urine volume.</p>
      <p>Final perchlorate doses were compared to the U.S. EPA Reference Dose of 0.70
                µg/kg/day <xref ref-type="bibr" rid="pone.0017015-EPA3">[19]</xref>.
                We also compared measured perchlorate doses with an “acceptable daily
                dose” computed based on the current methodology of the California Office of
                Environmental Health Hazard Assessment (OEHHA) (C. Steinmaus, personal
                communication). In its Public Health Goal for Perchlorate, the OEHHA computed the
                health-protective water concentration for perchlorate <xref ref-type="bibr" rid="pone.0017015-OEHHA1">[20]</xref> as
                        follows:<disp-formula><graphic mimetype="image" position="float" xlink:href="info:doi/10.1371/journal.pone.0017015.e002" xlink:type="simple"/></disp-formula>In this formula, BMDL = the lower limit of
                a one-sided 95% CI of a perchlorate dose that reduces mean thyroidal iodine
                uptake by 5%; RSC = relative source contribution;
                BW/WC = the ratio of body weight to tap water consumption rate;
                and UF = an uncertainty factor of 10 to account for sensitive
                populations such as pregnant women and infants. Using current OEHHA methodology, we
                computed an acceptable daily dose (ADD) as follows:<disp-formula><graphic mimetype="image" position="float" xlink:href="info:doi/10.1371/journal.pone.0017015.e003" xlink:type="simple"/></disp-formula>Survey questionnaire data
                were double-entered into Excel speadsheets (Excel version 2003, SP3, Microsoft,
                Redmond, WA, USA) and then imported into SAS for analysis (SAS version 9.1.3., SAS
                Institute, Inc., Cary, NC, USA). We calculated geometric means and CIs for
                perchlorate, nitrate, thiocyanate, and total iodine intake. For each person,
                perchlorate concentration was summed for all produce sampled. Total number of
                servings of dairy products (i.e., milk, yogurt, and ice cream) was also summed for
                each person. We used the nonparametric Kruskal-Wallis test to compare levels of
                perchlorate dose by total dairy servings and perchlorate concentration in produce
                    <xref ref-type="bibr" rid="pone.0017015-Corder1">[21]</xref>. The
                perchlorate dose was log-normally distributed, so total servings of dairy and total
                concentration of perchlorate in produce were regressed on the log-normal perchlorate
                dose in linear regression models.</p>
    </sec>
    <sec id="s3">
      <title>Results</title>
      <sec id="s3a">
        <title>Participant Characteristics</title>
        <p>The age of participants ranged from 18 to 88, with an average of 41.1 years
                        (<xref ref-type="table" rid="pone-0017015-t001">Table 1</xref>). Among
                    participants, 22.6% had no high school education, 12.9% were high
                    school graduates, and 12.9% had at least a college degree. Almost all
                    (97%) participants were Hispanic, except for 1 non-Hispanic white.
                    Eighty-one percent of participants elected to have the interview conducted in
                    Spanish. Sixty-five percent of the participants were female, none of whom
                    reported being pregnant at the time of the interview. Seventy-five percent of
                    the women were of reproductive age (ages 15–49). Only 1 participant
                    reported being a tobacco smoker. Two respondents reported working in
                    agriculture. When asked to identify the primary source of drinking water in the
                    home, 36% of participants reported bottled water, 23% reported tap
                    water, and the remaining respondents reported various sources, such as well
                    water, delivery truck, and grocery store dispensers. Forty-five percent of
                    participants consumed &lt;25% of their water away from home. Total dairy
                    consumption averaged 1.05 servings per person per day, with a range of 0–5
                    servings.</p>
        <table-wrap id="pone-0017015-t001" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0017015.t001</object-id><label>Table 1</label><caption>
            <title>Participant Characteristics, Imperial County, 2009.</title>
          </caption><!--===== Grouping alternate versions of objects =====--><alternatives><graphic id="pone-0017015-t001-1" mimetype="image" position="float" xlink:href="info:doi/10.1371/journal.pone.0017015.t001" xlink:type="simple"/><table>
            <colgroup span="1">
              <col align="left" span="1"/>
              <col align="center" span="1"/>
              <col align="center" span="1"/>
            </colgroup>
            <thead>
              <tr>
                <td align="left" colspan="1" rowspan="1"/>
                <td align="left" colspan="1" rowspan="1">N</td>
                <td align="left" colspan="1" rowspan="1">%</td>
              </tr>
            </thead>
            <tbody>
              <tr>
                <td align="left" colspan="1" rowspan="1">Total Participants</td>
                <td align="left" colspan="1" rowspan="1">31</td>
                <td align="left" colspan="1" rowspan="1">100</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Age</td>
                <td align="left" colspan="1" rowspan="1"/>
                <td align="left" colspan="1" rowspan="1"/>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">18–25</td>
                <td align="left" colspan="1" rowspan="1">6</td>
                <td align="left" colspan="1" rowspan="1">19.4</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">26–35</td>
                <td align="left" colspan="1" rowspan="1">7</td>
                <td align="left" colspan="1" rowspan="1">22.6</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">36–45</td>
                <td align="left" colspan="1" rowspan="1">7</td>
                <td align="left" colspan="1" rowspan="1">22.6</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">46–55</td>
                <td align="left" colspan="1" rowspan="1">5</td>
                <td align="left" colspan="1" rowspan="1">16.1</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">&gt;55</td>
                <td align="left" colspan="1" rowspan="1">6</td>
                <td align="left" colspan="1" rowspan="1">19.4</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Gender</td>
                <td align="left" colspan="1" rowspan="1"/>
                <td align="left" colspan="1" rowspan="1"/>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Female</td>
                <td align="left" colspan="1" rowspan="1">20</td>
                <td align="left" colspan="1" rowspan="1">64.5</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Male</td>
                <td align="left" colspan="1" rowspan="1">11</td>
                <td align="left" colspan="1" rowspan="1">35.5</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Education</td>
                <td align="left" colspan="1" rowspan="1"/>
                <td align="left" colspan="1" rowspan="1"/>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">No high school</td>
                <td align="left" colspan="1" rowspan="1">7</td>
                <td align="left" colspan="1" rowspan="1">22.6</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Some high school</td>
                <td align="left" colspan="1" rowspan="1">2</td>
                <td align="left" colspan="1" rowspan="1">6.5</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">High school graduate</td>
                <td align="left" colspan="1" rowspan="1">4</td>
                <td align="left" colspan="1" rowspan="1">12.9</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Some college</td>
                <td align="left" colspan="1" rowspan="1">14</td>
                <td align="left" colspan="1" rowspan="1">45.2</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">College Graduate</td>
                <td align="left" colspan="1" rowspan="1">1</td>
                <td align="left" colspan="1" rowspan="1">3.2</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Graduate</td>
                <td align="left" colspan="1" rowspan="1"/>
                <td align="left" colspan="1" rowspan="1"/>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">or Professional Degree</td>
                <td align="left" colspan="1" rowspan="1">3</td>
                <td align="left" colspan="1" rowspan="1">9.7</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Ethnicity</td>
                <td align="left" colspan="1" rowspan="1"/>
                <td align="left" colspan="1" rowspan="1"/>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Hispanic</td>
                <td align="left" colspan="1" rowspan="1">30</td>
                <td align="left" colspan="1" rowspan="1">97</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Non-Hispanic</td>
                <td align="left" colspan="1" rowspan="1">1</td>
                <td align="left" colspan="1" rowspan="1">3</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Primary source of drinking water at home</td>
                <td align="left" colspan="1" rowspan="1"/>
                <td align="left" colspan="1" rowspan="1"/>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Bottled water</td>
                <td align="left" colspan="1" rowspan="1">11</td>
                <td align="left" colspan="1" rowspan="1">35.5</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Tap water (filtered)</td>
                <td align="left" colspan="1" rowspan="1">5</td>
                <td align="left" colspan="1" rowspan="1">16.1</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Tap water (unfiltered)</td>
                <td align="left" colspan="1" rowspan="1">2</td>
                <td align="left" colspan="1" rowspan="1">6.5</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Well water (filtered)</td>
                <td align="left" colspan="1" rowspan="1">1</td>
                <td align="left" colspan="1" rowspan="1">3.2</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Other</td>
                <td align="left" colspan="1" rowspan="1">12</td>
                <td align="left" colspan="1" rowspan="1">38.7</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Amount of water consumed away from home</td>
                <td align="left" colspan="1" rowspan="1"/>
                <td align="left" colspan="1" rowspan="1"/>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">&lt;25%</td>
                <td align="left" colspan="1" rowspan="1">14</td>
                <td align="left" colspan="1" rowspan="1">45.2</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">25–50%</td>
                <td align="left" colspan="1" rowspan="1">12</td>
                <td align="left" colspan="1" rowspan="1">38.7</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">51–75%</td>
                <td align="left" colspan="1" rowspan="1">4</td>
                <td align="left" colspan="1" rowspan="1">12.9</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">75–99%</td>
                <td align="left" colspan="1" rowspan="1">1</td>
                <td align="left" colspan="1" rowspan="1">3.2</td>
              </tr>
            </tbody>
          </table></alternatives></table-wrap>
      </sec>
      <sec id="s3b">
        <title>Water and Produce Perchlorate Results</title>
        <p>Results of water confirmation analyses showed consistency with original, primary
                    sample results. Additionally, method blanks, method standard recoveries, and
                    sample duplicate analyses' results were all within accepted control limits
                    and percentages. Only 2 of 68 water samples had detectable levels of perchlorate
                    (2.4 and 2.5 µg/L perchlorate), both of which were less than half the
                    California regulatory standard of 6 µg/L. Perchlorate levels in the 79
                    produce samples ranged from nondetectable to 1816 ppb (ng of perchlorate per g
                    of net weight of edible portion) (<xref ref-type="table" rid="pone-0017015-t002">Table 2</xref>). The highest perchlorate levels were detected in nopales
                    (cactus) (maximum of 1398 ppb) and quelites (Mexican greens) (2 samples tested,
                    with levels of 1720 and 1816 ppb, respectively). Many produce categories, such
                    as lettuce, broccoli, grapefruit, tomatoes, watermelon, and corn, had
                    perchlorate levels ≤4 ppb. For 14 samples with perchlorate levels falling
                    below the detection limit, we used an imputed value of half the detection limit.
                    The mean perchlorate level of all produce was 80.8 ppb. Most produce types had
                    too few samples per type to compute a representative average. Among those
                    persons who provided produce (n = 24), the mean perchlorate
                    level in their produce was 266 ppb per person. Produce perchlorate levels were
                    in the range of similar kinds of produce tested by the U.S. Food and Drug
                    Administration (FDA) in nationally representative samples, except for celery and
                    cucumbers, which were higher in our samples, and tomatoes and watermelon, which
                    were lower (<xref ref-type="table" rid="pone-0017015-t002">Table 2</xref>).</p>
        <table-wrap id="pone-0017015-t002" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0017015.t002</object-id><label>Table 2</label><caption>
            <title>Perchlorate levels found in locally grown produce compared to FDA
                            survey levels,<sup>a</sup> Imperial Valley, CA, 2009.</title>
          </caption><!--===== Grouping alternate versions of objects =====--><alternatives><graphic id="pone-0017015-t002-2" mimetype="image" position="float" xlink:href="info:doi/10.1371/journal.pone.0017015.t002" xlink:type="simple"/><table>
            <colgroup span="1">
              <col align="left" span="1"/>
              <col align="center" span="1"/>
              <col align="center" span="1"/>
              <col align="center" span="1"/>
            </colgroup>
            <thead>
              <tr>
                <td align="left" colspan="1" rowspan="1">Produce Type</td>
                <td align="left" colspan="1" rowspan="1">Number of samples</td>
                <td align="left" colspan="1" rowspan="1">Perchlorate range (ppb)</td>
                <td align="left" colspan="1" rowspan="1">FDA data*(ppb)</td>
              </tr>
            </thead>
            <tbody>
              <tr>
                <td align="left" colspan="1" rowspan="1">Broccoli</td>
                <td align="left" colspan="1" rowspan="1">2</td>
                <td align="left" colspan="1" rowspan="1">2.1–3.5</td>
                <td align="left" colspan="1" rowspan="1">1.3–8.3</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Canary melon</td>
                <td align="left" colspan="1" rowspan="1">1</td>
                <td align="left" colspan="1" rowspan="1">3.8</td>
                <td align="left" colspan="1" rowspan="1">NA<sup>b</sup></td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Cantaloupe</td>
                <td align="left" colspan="1" rowspan="1">10</td>
                <td align="left" colspan="1" rowspan="1">1.9–7.9</td>
                <td align="left" colspan="1" rowspan="1">1.4–70.3</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Carrot</td>
                <td align="left" colspan="1" rowspan="1">3</td>
                <td align="left" colspan="1" rowspan="1">3.9–8.4</td>
                <td align="left" colspan="1" rowspan="1">Non detect – 7.7</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Celery</td>
                <td align="left" colspan="1" rowspan="1">2</td>
                <td align="left" colspan="1" rowspan="1">22.9–28.0</td>
                <td align="left" colspan="1" rowspan="1">Non detect – 2.2</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Corn</td>
                <td align="left" colspan="1" rowspan="1">3</td>
                <td align="left" colspan="1" rowspan="1">ND-3.6</td>
                <td align="left" colspan="1" rowspan="1">Non detect</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Corn &amp; squash</td>
                <td align="left" colspan="1" rowspan="1">2</td>
                <td align="left" colspan="1" rowspan="1">2.2–4.1</td>
                <td align="left" colspan="1" rowspan="1">NA</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Cucumber</td>
                <td align="left" colspan="1" rowspan="1">2</td>
                <td align="left" colspan="1" rowspan="1">171.4–439.8</td>
                <td align="left" colspan="1" rowspan="1">Non detect – 64</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Grapefruit</td>
                <td align="left" colspan="1" rowspan="1">2</td>
                <td align="left" colspan="1" rowspan="1">2.3–2.7</td>
                <td align="left" colspan="1" rowspan="1">Non detect</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Green pepper</td>
                <td align="left" colspan="1" rowspan="1">1</td>
                <td align="left" colspan="1" rowspan="1">33.9</td>
                <td align="left" colspan="1" rowspan="1">5.4–26.7</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Lettuce</td>
                <td align="left" colspan="1" rowspan="1">2</td>
                <td align="left" colspan="1" rowspan="1">&lt;1</td>
                <td align="left" colspan="1" rowspan="1">Non detect – 6.7</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Lemon</td>
                <td align="left" colspan="1" rowspan="1">1</td>
                <td align="left" colspan="1" rowspan="1">1.4</td>
                <td align="left" colspan="1" rowspan="1">NA</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Melon (non specified)</td>
                <td align="left" colspan="1" rowspan="1">4</td>
                <td align="left" colspan="1" rowspan="1">1.6–6.4</td>
                <td align="left" colspan="1" rowspan="1">NA</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Nopal (cactus)</td>
                <td align="left" colspan="1" rowspan="1">5</td>
                <td align="left" colspan="1" rowspan="1">37.9–1398.4</td>
                <td align="left" colspan="1" rowspan="1">NA</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Onion (red)</td>
                <td align="left" colspan="1" rowspan="1">2</td>
                <td align="left" colspan="1" rowspan="1">&lt;1–293.7</td>
                <td align="left" colspan="1" rowspan="1">NA</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Onion (non specified)</td>
                <td align="left" colspan="1" rowspan="1">5</td>
                <td align="left" colspan="1" rowspan="1">&lt;1–3.8</td>
                <td align="left" colspan="1" rowspan="1">Non detect</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Onion (white)</td>
                <td align="left" colspan="1" rowspan="1">1</td>
                <td align="left" colspan="1" rowspan="1">1</td>
                <td align="left" colspan="1" rowspan="1">NA</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Orange</td>
                <td align="left" colspan="1" rowspan="1">1</td>
                <td align="left" colspan="1" rowspan="1">5</td>
                <td align="left" colspan="1" rowspan="1">Non detect – 5.4</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Potato</td>
                <td align="left" colspan="1" rowspan="1">4</td>
                <td align="left" colspan="1" rowspan="1">&lt;1–2.0</td>
                <td align="left" colspan="1" rowspan="1">Non detect – 1.0</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Quelites (Mexican greens)</td>
                <td align="left" colspan="1" rowspan="1">2</td>
                <td align="left" colspan="1" rowspan="1">1719.9–1816</td>
                <td align="left" colspan="1" rowspan="1">NA</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Tomato</td>
                <td align="left" colspan="1" rowspan="1">7</td>
                <td align="left" colspan="1" rowspan="1">&lt;1–3.3</td>
                <td align="left" colspan="1" rowspan="1">54.1–102</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Watermelon</td>
                <td align="left" colspan="1" rowspan="1">17</td>
                <td align="left" colspan="1" rowspan="1">&lt;1–3.7</td>
                <td align="left" colspan="1" rowspan="1">Non detect – 42.6</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Total Samples</td>
                <td align="left" colspan="1" rowspan="1">79</td>
                <td align="left" colspan="1" rowspan="1"/>
                <td align="left" colspan="1" rowspan="1"/>
              </tr>
            </tbody>
          </table></alternatives></table-wrap>
      </sec>
      <sec id="s3c">
        <title>Urine Results</title>
        <p>The 24-hr creatinine output was normally distributed with a mean of 1.33 g/day
                    (SD = 0.494 g/day). The 24-hr creatinine output was
                    compared with the predicted 24-hr creatinine output <xref ref-type="bibr" rid="pone.0017015-Cockcroft1">[17]</xref> to evaluate completeness
                    of urine collection. The average ratio of measured-to-predicted 24-hr creatinine
                    was 1.03, indicating good compliance of the study participants for collecting
                    all urine within the defined 24-hr study period.</p>
        <p>Perchlorate concentrations in urine ranged from1.08 µg/L to 32.2
                    µg/L, with a geometric mean of 6.44 µg/L (<xref ref-type="table" rid="pone-0017015-t003">Table 3</xref>). The 24-hr perchlorate dose in urine
                    ranged from 0.02 to 0.51 µg/kg of body weight/day. The geometric mean
                    perchlorate dose for study participants was 0.11 µg/kg/day (95% CI,
                    0.08–0.15). Perchlorate concentrations and doses were higher in the
                    Imperial Valley study participants compared to reference values from the
                    National Health and Nutrition Examination Survey (NHANES).</p>
        <table-wrap id="pone-0017015-t003" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0017015.t003</object-id><label>Table 3</label><caption>
            <title>Range and Geometric Means of Major Analytes, Imperial County, 2009,
                            compared to NHANES<xref ref-type="table-fn" rid="nt101">a</xref>.</title>
          </caption><!--===== Grouping alternate versions of objects =====--><alternatives><graphic id="pone-0017015-t003-3" mimetype="image" position="float" xlink:href="info:doi/10.1371/journal.pone.0017015.t003" xlink:type="simple"/><table>
            <colgroup span="1">
              <col align="left" span="1"/>
              <col align="center" span="1"/>
              <col align="center" span="1"/>
              <col align="center" span="1"/>
              <col align="center" span="1"/>
              <col align="center" span="1"/>
            </colgroup>
            <thead>
              <tr>
                <td align="left" colspan="1" rowspan="1">Analyte</td>
                <td align="left" colspan="1" rowspan="1">Min</td>
                <td align="left" colspan="1" rowspan="1">Max</td>
                <td align="left" colspan="1" rowspan="1">GM</td>
                <td align="left" colspan="1" rowspan="1">95% C.I.</td>
                <td align="left" colspan="1" rowspan="1">NHANES GM(95% CI)</td>
              </tr>
            </thead>
            <tbody>
              <tr>
                <td align="left" colspan="1" rowspan="1">Perchlorate (µg/L)</td>
                <td align="left" colspan="1" rowspan="1">1.08</td>
                <td align="left" colspan="1" rowspan="1">32.2</td>
                <td align="left" colspan="1" rowspan="1">6.44</td>
                <td align="left" colspan="1" rowspan="1">4.83–8.58</td>
                <td align="left" colspan="1" rowspan="1">3.35 (3.08–3.65)</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Perchlorate</td>
                <td align="left" colspan="1" rowspan="1"/>
                <td align="left" colspan="1" rowspan="1"/>
                <td align="left" colspan="1" rowspan="1"/>
                <td align="left" colspan="1" rowspan="1"/>
                <td align="left" colspan="1" rowspan="1"/>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">(µg/g creatinine)</td>
                <td align="left" colspan="1" rowspan="1">1.24</td>
                <td align="left" colspan="1" rowspan="1">37.4</td>
                <td align="left" colspan="1" rowspan="1">6.98</td>
                <td align="left" colspan="1" rowspan="1">5.10–9.57</td>
                <td align="left" colspan="1" rowspan="1">3.46 (3.20–3.73)</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Perchlorate dose</td>
                <td align="left" colspan="1" rowspan="1"/>
                <td align="left" colspan="1" rowspan="1"/>
                <td align="left" colspan="1" rowspan="1"/>
                <td align="left" colspan="1" rowspan="1"/>
                <td align="left" colspan="1" rowspan="1"/>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">(µg/kg/day)</td>
                <td align="left" colspan="1" rowspan="1">0.02</td>
                <td align="left" colspan="1" rowspan="1">0.51</td>
                <td align="left" colspan="1" rowspan="1">0.112</td>
                <td align="left" colspan="1" rowspan="1">0.082–0.152</td>
                <td align="left" colspan="1" rowspan="1">0.066 (0.060–0.071)</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Thiocyanate</td>
                <td align="left" colspan="1" rowspan="1"/>
                <td align="left" colspan="1" rowspan="1"/>
                <td align="left" colspan="1" rowspan="1"/>
                <td align="left" colspan="1" rowspan="1"/>
                <td align="left" colspan="1" rowspan="1"/>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">(µg/g creatinine)</td>
                <td align="left" colspan="1" rowspan="1">178</td>
                <td align="left" colspan="1" rowspan="1">2715</td>
                <td align="left" colspan="1" rowspan="1">816</td>
                <td align="left" colspan="1" rowspan="1">634–1051</td>
                <td align="left" colspan="1" rowspan="1">1500 (1400–1620)</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Nitrate</td>
                <td align="left" colspan="1" rowspan="1"/>
                <td align="left" colspan="1" rowspan="1"/>
                <td align="left" colspan="1" rowspan="1"/>
                <td align="left" colspan="1" rowspan="1"/>
                <td align="left" colspan="1" rowspan="1"/>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">(µg/g creatinine)</td>
                <td align="left" colspan="1" rowspan="1">&lt;700</td>
                <td align="left" colspan="1" rowspan="1">152,110</td>
                <td align="left" colspan="1" rowspan="1">21,770</td>
                <td align="left" colspan="1" rowspan="1">11,595–40,876</td>
                <td align="left" colspan="1" rowspan="1">44,500 (42,300–46,800)</td>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">Total iodine intake</td>
                <td align="left" colspan="1" rowspan="1"/>
                <td align="left" colspan="1" rowspan="1"/>
                <td align="left" colspan="1" rowspan="1"/>
                <td align="left" colspan="1" rowspan="1"/>
                <td align="left" colspan="1" rowspan="1"/>
              </tr>
              <tr>
                <td align="left" colspan="1" rowspan="1">(µg/day)</td>
                <td align="left" colspan="1" rowspan="1">40.2</td>
                <td align="left" colspan="1" rowspan="1">679</td>
                <td align="left" colspan="1" rowspan="1">158</td>
                <td align="left" colspan="1" rowspan="1">121–206</td>
                <td align="left" colspan="1" rowspan="1">N/A</td>
              </tr>
            </tbody>
          </table></alternatives><table-wrap-foot>
            <fn id="nt101">
              <label>a</label>
              <p>Representative data for adult U. S. residents from the National
                                Health and Nutrition Examination Survey, 2001–2002 (Blount et
                                al. 2006; Blount et al. 2007).</p>
            </fn>
          </table-wrap-foot></table-wrap>
        <p>The geometric means for urine levels of thiocyanate and nitrate were 816 and
                    21,800 µg/g creatinine, respectively, compared to geometric means for the
                    general U. S. population of 1500 and 44,500 µg/g creatinine based on data
                    from NHANES 2001–2002 (Blount, unpublished data). The geometric mean for
                    daily iodine intake was 158 µg/day, which is slightly higher than the
                    Recommended Daily Intake (RDI, 150 µg/day) established by the U.S. FDA .
                    Eighteen participants, including 10 of the 15 women of reproductive age, had
                    daily iodine intakes below 150 µg/day.</p>
        <p>When stratifying perchlorate dose by total dairy servings, geometric means of
                    perchlorate dose increased with increasing dairy consumption (<xref ref-type="fig" rid="pone-0017015-g002">Figure 2</xref>) (nonparametric
                    Kruskal-Wallis test, p = 0.24). In a linear regression
                    model on log perchlorate dose, each serving of dairy products was associated
                    with a 24% increase in urine perchlorate levels
                    (p = 0.04; r<sup>2</sup> = 0.14).
                    Perchlorate dose in urine showed a similar increase with categories of
                    perchlorate concentration (using natural breaks in perchlorate distribution) in
                    consumed produce (<xref ref-type="fig" rid="pone-0017015-g003">Figure 3</xref>)
                    (nonparametric Kruskal-Wallis test, p = 0.03). Each 10 ppb
                    of perchlorate in produce was associated with a 1% increase in log
                    perchlorate dose (p = 0.09;
                    r<sup>2</sup> = 0.13).</p>
        <fig id="pone-0017015-g002" position="float">
          <object-id pub-id-type="doi">10.1371/journal.pone.0017015.g002</object-id>
          <label>Figure 2</label>
          <caption>
            <title>Geometric mean perchlorate dose in urine (µg/kg of body
                            weight/day) and 95% confidence intervals in study participants by
                            total dairy servings, Imperial County, California, 2009.</title>
          </caption>
          <graphic mimetype="image" position="float" xlink:href="info:doi/10.1371/journal.pone.0017015.g002" xlink:type="simple"/>
        </fig>
        <fig id="pone-0017015-g003" position="float">
          <object-id pub-id-type="doi">10.1371/journal.pone.0017015.g003</object-id>
          <label>Figure 3</label>
          <caption>
            <title>Geometric mean perchlorate dose in urine (µg/kg of body
                            weight/day) and 95% confidence intervals in study participants by
                            perchlorate concentration in produce for each participant, Imperial
                            County, California, 2009.</title>
          </caption>
          <graphic mimetype="image" position="float" xlink:href="info:doi/10.1371/journal.pone.0017015.g003" xlink:type="simple"/>
        </fig>
      </sec>
    </sec>
    <sec id="s4">
      <title>Discussion</title>
      <p>In this study of perchlorate exposure among Imperial County residents, we found
                perchlorate exposure doses that were, on average, 70% higher than the NHANES
                U.S. reference population (<xref ref-type="table" rid="pone-0017015-t003">Table
                    3</xref>). Although all study participants had measurable perchlorate levels in
                their urine, none of the exposure doses exceeded the U.S. EPA reference dose of 0.70
                µg/kg/day. We also compared perchlorate exposure estimates with the acceptable
                daily dose (0.37 µg/kg/day) derived by using current methodology of the
                California Office of Environmental Health Hazard Assessment. Three study
                participants had perchlorate exposure dose estimates in excess of this acceptable
                daily dose. The health implications of this perchlorate exposure are unknown.</p>
      <p>The developing fetus is thought to be a particularly sensitive life stage for
                perchlorate exposure <xref ref-type="bibr" rid="pone.0017015-NAS1">[5]</xref>. Measurement of perchlorate in amniotic fluid confirms
                that perchlorate can cross the placenta <xref ref-type="bibr" rid="pone.0017015-Blount4">[22]</xref>. Therefore, we further examined
                perchlorate exposure in the 48% of our study population who were women of
                reproductive age. We found that 1 of these women exceeded the acceptable daily dose
                calculated by using OEHHA methodology. Among the 15 women of reproductive age in our
                study, 10 (75%) had low iodine intake (below the RDI). Continued perchlorate
                exposure above the OEHHA threshold among these women could increase their risk of
                subclinical hypothyroidism, which has been linked to subtle cognitive defects in
                children. To what degree perchlorate exposure contributes to the prevalence of
                subclinical hypothyroidism (2%–3%) in women of reproductive age
                is unknown <xref ref-type="bibr" rid="pone.0017015-Poppe1">[23]</xref>.</p>
      <p>Perchlorate exposure is of health concern because perchlorate inhibits uptake of
                iodine, which is essential for thyroid function. Thiocyanate (elevated after
                exposure to cyanide in tobacco smoke, for example) and nitrate may interact with
                perchlorate in an additive fashion that also inhibits iodine uptake <xref ref-type="bibr" rid="pone.0017015-Tonacchera1">[10]</xref>. The levels
                of thiocyanate and nitrate found in this study were comparable to or lower than
                levels found previously in other populations <xref ref-type="bibr" rid="pone.0017015-Blount2">[4]</xref>. We also evaluated urinary
                levels of iodine in this population. The geometric mean for iodine was found to be
                158 µg/day, indicating a generally adequate intake of iodine. If a
                person's estimated iodine intake was less than the recommended daily intake of
                150 µg/day, then he or she was advised to increase iodine intake through diet
                or by taking iodine-containing supplements. Additionally, for study participants who
                chose to add salt to their food, we mentioned iodized salt in small amounts as an
                effective iodine source.</p>
      <p>We did not find that the drinking water samples collected in the 24-hr study period
                had significant levels of perchlorate. Only 2 samples had detectable levels (&gt;1
                µg/L detection limit), and both were below the Maximum Contaminant Limit (6
                µg/L) for California. Conversely, we found several samples of produce consumed
                by participants to have high levels of perchlorate, notably nopales (opuntia cactus)
                and quelites, with levels exceeding 1700 ppb. These results are consistent with
                other published measurements of relatively high levels of perchlorate in opuntia
                cactus <xref ref-type="bibr" rid="pone.0017015-Harvey1">[24]</xref>.
                Perchlorate levels in some produce tested, such as cantaloupe, grapefruit, pepper,
                broccoli, and lemon, are in the same range as levels that have been reported
                previously <xref ref-type="bibr" rid="pone.0017015-Murray1">[1]</xref>,
                    <xref ref-type="bibr" rid="pone.0017015-Sanchez2">[27]</xref>, and
                others, including watermelon and tomato, had lower levels than previously detected.
                We found moderate perchlorate contamination in cucumbers (439.8 ppb), similar to
                previously published results <xref ref-type="bibr" rid="pone.0017015-Yu1">[25]</xref>.</p>
      <p>Consistent with the scientific literature, we found that perchlorate dose was related
                to intake of dairy products and produce. Dairy products, fruits and vegetables have
                been characterized by other scientists as contributing significantly to perchlorate
                intakes <xref ref-type="bibr" rid="pone.0017015-Murray2">[26]</xref>,
                    <xref ref-type="bibr" rid="pone.0017015-Sanchez2">[27]</xref>. In our
                study we found that each 10 ppb of perchlorate measured in produce was related to a
                marginally-significant 1% increase in estimated perchlorate dose
                (p = 0.09). Similarly we found that each serving of milk/dairy
                products, based on questionnaire data, was associated with a 24% increase in
                estimated perchlorate dose (p = 0.04). While our regression
                modeling indicates that dairy consumption and produce perchlorate levels are
                directly related to estimated perchlorate dose, these models only explained a
                relatively small amount of the total variance in estimated perchlorate dose in the
                study population (13–14%). The most plausible explanation of the low
                relatively R<sup>2</sup> of our regression models is that we could not include a
                variable for the total intake amount of perchlorate from produce (no serving size
                data) and milk/dairy products (no measurement of perchlorate levels of serving
                size). Furthermore, the physiological half life of perchlorate in the human body
                (∼8 hrs) would lead to imperfect overlap between questionnaire data and
                perchlorate excreted into the 24-hr urine, and consumption of other food items
                besides local produce during the study period would introduce further
                variability.</p>
      <p>This study had several strengths and limitations. To our knowledge, this is the first
                effort to directly measure perchlorate exposure biomarkers in this population with
                potentially elevated exposure. In addition to perchlorate, we also measured
                toxicologically-related anions thiocyanate, nitrate, and iodide in each study
                participant's urine. Our exposure assessment was further strengthened by
                measuring these anions in a 24-hr urine sample so that the data was less variable
                than spot urine measurements. Furthermore, this biomonitoring data was paired with
                simultaneously collected produce and drinking water samples to assist in identifying
                potential exposure sources. However, we were able to only assess dairy consumption
                through interview and were unable to directly measure perchlorate levels in dairy
                samples. Instead, we reference previously published perchlorate levels in dairy milk
                in this region <xref ref-type="bibr" rid="pone.0017015-Sanchez1">[12]</xref>.</p>
      <p>Our perchlorate dose estimate method assumed that exactly 24 hrs worth of urine was
                collected. Comparison of measured 24-hr creatinine with predicted 24-hr creatinine
                indicates that, on average, the predicted and measured creatinine excretion agree
                remarkably well (average ratio = 1.03). However, 1 woman
                excreted only 54% of the creatinine expected for her age and body weight,
                perhaps due to differences in lean body mass or to incomplete collection of the
                urine samples within the 24-hr period. Thus, our exposure estimates for this 1 study
                participant may be biased towards being lower than actual exposure levels.</p>
      <p>Additional limitations include the small convenience sample of study participants
                which prevent us from generalizing these results to Imperial County residents and
                the single sampling day, which did not enable us to study variability of exposure
                across days and seasons. Additionally, we did not examine any health or thyroid
                endpoints.</p>
      <p>In conclusion, we found that our sample of 31 Imperial Valley residents had higher
                perchlorate dose levels compared with national reference ranges. Additionally, 3
                participants exceeded the acceptable daily dose calculated by using methods of the
                California Office of Environmental Health Hazard Assessment. Several produce samples
                collected had high perchlorate levels, exceeding 1700 ppb. Continued biomonitoring
                of perchlorate exposure in this population could help evaluate whether reducing
                perchlorate contamination of the Colorado River over time leads to reduced human
                exposure.</p>
    </sec>
  </body>
  <back>
    <ack>
      <p>We would like to thank Holly Maag at the Imperial County Public Health Laboratory for
                storing the urine samples and the assistance of Esther Bejarano, Veronica Hinojosa,
                Bianka Velez at Comité Cívico del Valle for help with interviewing
                participants and collecting specimen samples. Rustum Chin and Eddie Lui of the
                Environmental Contaminant Laboratory of the California Department of Toxic
                Substances and Control conducted the water analysis. Craig Steinmaus made helpful
                comments on methodology and interpretation. We are grateful to Alexa Wilkie for
                project support. The findings and conclusions of this report are those of the
                authors and do not necessarily represent the official position of CDC.</p>
    </ack>
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