<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.2 20190208//EN" "http://jats.nlm.nih.gov/publishing/1.2/JATS-journalpublishing1.dtd"><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" article-type="research-article" dtd-version="1.2" xml:lang="en">
    <front>
        <journal-meta>
            <journal-id journal-id-type="pmc">F1000Research</journal-id>
            <journal-title-group>
                <journal-title>F1000Research</journal-title>
            </journal-title-group>
            <issn pub-type="epub">2046-1402</issn>
            <publisher>
                <publisher-name>F1000 Research Limited</publisher-name>
                <publisher-loc>London, UK</publisher-loc>
            </publisher>
        </journal-meta>
        <article-meta>
            <article-id pub-id-type="doi">10.12688/f1000research.175538.1</article-id>
            <article-categories>
                <subj-group subj-group-type="heading">
                    <subject>Research Article</subject>
                </subj-group>
                <subj-group>
                    <subject>Articles</subject>
                </subj-group>
            </article-categories>
            <title-group>
                <article-title>Assessment of Heavy Metals in Organic and Non-Organic Vegetables Post Severe Tropical Cyclone Gabrielle: A cross-sectional comparative analysis.</article-title>
                <fn-group content-type="pub-status">
                    <fn>
                        <p>[version 1; peer review: 4 approved with reservations]</p>
                    </fn>
                </fn-group>
            </title-group>
            <contrib-group>
                <contrib contrib-type="author" corresp="yes">
                    <name>
                        <surname>Dearing</surname>
                        <given-names>Chey</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Conceptualization</role>
                    <role content-type="http://credit.niso.org/">Data Curation</role>
                    <role content-type="http://credit.niso.org/">Formal Analysis</role>
                    <role content-type="http://credit.niso.org/">Funding Acquisition</role>
                    <role content-type="http://credit.niso.org/">Investigation</role>
                    <role content-type="http://credit.niso.org/">Methodology</role>
                    <role content-type="http://credit.niso.org/">Project Administration</role>
                    <role content-type="http://credit.niso.org/">Supervision</role>
                    <role content-type="http://credit.niso.org/">Validation</role>
                    <role content-type="http://credit.niso.org/">Visualization</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Original Draft Preparation</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Review &amp; Editing</role>
                    <uri content-type="orcid">https://orcid.org/0000-0002-6546-1647</uri>
                    <xref ref-type="corresp" rid="c1">a</xref>
                    <xref ref-type="aff" rid="a1">1</xref>
                    <xref ref-type="aff" rid="a2">2</xref>
                </contrib>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Ye</surname>
                        <given-names>Zhijing</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Conceptualization</role>
                    <role content-type="http://credit.niso.org/">Formal Analysis</role>
                    <role content-type="http://credit.niso.org/">Funding Acquisition</role>
                    <role content-type="http://credit.niso.org/">Investigation</role>
                    <role content-type="http://credit.niso.org/">Methodology</role>
                    <role content-type="http://credit.niso.org/">Visualization</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Original Draft Preparation</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Review &amp; Editing</role>
                    <xref ref-type="aff" rid="a1">1</xref>
                    <xref ref-type="aff" rid="a3">3</xref>
                </contrib>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Robertshaw</surname>
                        <given-names>Glen</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Conceptualization</role>
                    <role content-type="http://credit.niso.org/">Funding Acquisition</role>
                    <role content-type="http://credit.niso.org/">Investigation</role>
                    <role content-type="http://credit.niso.org/">Methodology</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Original Draft Preparation</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Review &amp; Editing</role>
                    <xref ref-type="aff" rid="a1">1</xref>
                </contrib>
                <aff id="a1">
                    <label>1</label>Eastern Institute of Technology, Taradale, Hawke's Bay, New Zealand</aff>
                <aff id="a2">
                    <label>2</label>Massey University College of Health, Palmerston North, Manawatu-Wanganui, New Zealand</aff>
                <aff id="a3">
                    <label>3</label>Department of Wine, Food and Molecular Biosciences, Lincoln University Faculty of Agriculture Horticulture Viticulture, Lincoln, Canterbury, New Zealand</aff>
            </contrib-group>
            <author-notes>
                <corresp id="c1">
                    <label>a</label>
                    <email xlink:href="mailto:cdearing@eit.ac.nz">cdearing@eit.ac.nz</email>
                </corresp>
                <fn fn-type="conflict">
                    <p>No competing interests were disclosed.</p>
                </fn>
            </author-notes>
            <pub-date pub-type="epub">
                <day>26</day>
                <month>12</month>
                <year>2025</year>
            </pub-date>
            <pub-date pub-type="collection">
                <year>2025</year>
            </pub-date>
            <volume>14</volume>
            <elocation-id>1461</elocation-id>
            <history>
                <date date-type="accepted">
                    <day>18</day>
                    <month>12</month>
                    <year>2025</year>
                </date>
            </history>
            <permissions>
                <copyright-statement>Copyright: &#x00a9; 2025 Dearing C et al.</copyright-statement>
                <copyright-year>2025</copyright-year>
                <license xlink:href="https://creativecommons.org/licenses/by/4.0/">
                    <license-p>This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
                </license>
            </permissions>
            <self-uri content-type="pdf" xlink:href="https://f1000research.com/articles/14-1461/pdf"/>
            <abstract>
                <title>Abstract*</title>
                <sec>
                    <title>Background</title>
                    <p>Heavy metals such as cadmium, lead, and mercury are ubiquitous in the environment, accumulating in plants, animals, water and human food. Human exposure through the consumption of vegetable crops is a global concern because such metals may be toxic even in trace amounts. There are many factors influencing heavy metal concentrations in vegetables including, soil properties, growing practices and flooding events. This study aimed to investigate heavy metal concentrations in vegetable samples in Hawke&#x2019;s Bay, New Zealand, one year post Severe Tropical Cyclone Gabrielle, which caused widespread flooding on the 14th of February 2023.</p>
                </sec>
                <sec>
                    <title>Methods</title>
                    <p>This cross-sectional study included organic and non-organic vegetables and sites impacted and not-impacted by cyclone flooding. In total, 736 vegetable samples were combined to form 153 representative samples collected from 14 markets grown at 10 growing sites. Samples were analysed by ICP-MS in an ISO-17025 accredited laboratory.</p>
                </sec>
                <sec>
                    <title>Results</title>
                    <p>Cadmium (p = 0.003) and nickel (p &lt; 0.001) contamination were higher in non-organic vegetables. Growing vegetables on flood-affected land was independently associated with reduced cadmium (p = 0.030) and nickel (p = 0.024) contamination. Three samples exceeded Codex Alimentarius lead permissible levels (0.1 mg kg&#x2212;1 fresh weight), and one sample exceeded cadmium permissible levels (0.05 mg kg&#x2212;1 fresh weight in Brassica).</p>
                </sec>
                <sec>
                    <title>Conclusions</title>
                    <p>This study suggests that Hawke&#x2019;s Bay vegetables by global standards, are generally low risk, for heavy metal toxicity and organic vegetables, carry the lowest risk. However, some vegetables do exceed maximum limits for lead and cadmium. We speculate that recent Severe Tropical Cyclone Gabrielle did not increase risk and may have reduced the risk of heavy metal toxicity from vegetable consumption.</p>
                </sec>
            </abstract>
            <kwd-group kwd-group-type="author">
                <kwd>Heavy Metals</kwd>
                <kwd>Vegetables</kwd>
                <kwd>Flooding</kwd>
                <kwd>Cadmium</kwd>
                <kwd>Nickel</kwd>
                <kwd>Thallium</kwd>
                <kwd>Arsenic</kwd>
                <kwd>Lead</kwd>
            </kwd-group>
            <funding-group>
                <award-group id="fund-1">
                    <funding-source>Eastern Institute of Technology</funding-source>
                    <award-id>noreferencenumber</award-id>
                    <award-id>granted13December2023</award-id>
                </award-group>
                <funding-statement>This work was supported by internal funding (no reference number, granted 13 December 2023) from the Eastern Institute of Technology.</funding-statement>
                <funding-statement>
                    <italic>The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</italic>
                </funding-statement>
            </funding-group>
        </article-meta>
    </front>
    <body>
        <sec id="sec5" sec-type="intro">
            <title>Introduction</title>
            <p>Heavy metals are naturally occurring elements with a wide range of useful applications. However, widespread use has led to some of these becoming ubiquitous in the environment, accumulating in plants, animals, water and human food.
                <sup>
                    <xref ref-type="bibr" rid="ref1">1</xref>
                </sup> There are growing concerns over the detrimental effects of heavy metals in food as these elements have serious dose-dependent toxicity risks (for reviews, see Refs. 
                <xref ref-type="bibr" rid="ref2">2</xref>&#x2013;
                <xref ref-type="bibr" rid="ref4">4</xref>). Heavy metal accumulation in humans can destroy key metabolic processes and produce oxidative stress increasing the risk of chronic disease.
                <sup>
                    <xref ref-type="bibr" rid="ref5">5</xref>
                </sup> Vegetables used for food are a particular area of concern because heavy metal concentrations are shown to exceed the joint World Health Organisation (WHO)/UN Food and Agriculture Organisation (FAO) permissible limits
                <sup>
                    <xref ref-type="bibr" rid="ref6">6</xref>
                </sup> in several locations worldwide.
                <sup>
                    <xref ref-type="bibr" rid="ref7">7</xref>,
                    <xref ref-type="bibr" rid="ref8">8</xref>
                </sup> Globally, monitoring of heavy metals in vegetable crops is becoming recognised as essential
                <sup>
                    <xref ref-type="bibr" rid="ref9">9</xref>
                </sup> because of the potential risk to public health.</p>
            <p>In New Zealand, potatoes, onions, radish and leafy green vegetables such as lettuce, silverbeet and spinach (reviewed in,
                <sup>
                    <xref ref-type="bibr" rid="ref10">10</xref>
                </sup> watercress
                <sup>
                    <xref ref-type="bibr" rid="ref11">11</xref>
                </sup> and edible seaweeds
                <sup>
                    <xref ref-type="bibr" rid="ref12">12</xref>
                </sup> have been analysed for heavy metal concentrations. Non-peer reviewed research has also investigated traditional M&#x0101;ori vegetables.
                <sup>
                    <xref ref-type="bibr" rid="ref13">13</xref>
                </sup> Cadmium in particular has accumulated in New Zealand agricultural systems, predominantly from the application of phosphate fertiliser to soils.
                <sup>
                    <xref ref-type="bibr" rid="ref10">10</xref>,
                    <xref ref-type="bibr" rid="ref11">11</xref>
                </sup> Consequently, cadmium concentrations exceeding the maximum allowable levels have been observed in spinach samples collected from nine commercial growing areas across New Zealand.
                <sup>
                    <xref ref-type="bibr" rid="ref14">14</xref>
                </sup> There is a paucity of research on Thallium in New Zealand vegetables, with a single study finding reportable limits in root or tuber vegetables.
                <sup>
                    <xref ref-type="bibr" rid="ref15">15</xref>
                </sup> No New Zealand study to our knowledge has examined a broad range of vegetables, nor has surveyed all available locally grown vegetables in a single location.</p>
            <p>Many factors including atmospheric, soil and water conditions as well as fertiliser choice are shown to influence heavy metal concentrations in vegetables.
                <sup>
                    <xref ref-type="bibr" rid="ref8">8</xref>,
                    <xref ref-type="bibr" rid="ref16">16</xref>,
                    <xref ref-type="bibr" rid="ref17">17</xref>
                </sup> One factor that remains controversial is the effect of organic growing practices. While consumers may commonly believe organically grown vegetables are lower in such elements, the evidence is controversial.
                <sup>
                    <xref ref-type="bibr" rid="ref18">18</xref>
                </sup> A study comparing conventional and organic greenhouse vegetable production found greater mercury and lead content in non-organic vegetables but greater cadmium content in organic vegetables.
                <sup>
                    <xref ref-type="bibr" rid="ref19">19</xref>
                </sup> A study focusing on tomatoes found lower lead and nickel in organic tomatoes but no differences for cadmium.
                <sup>
                    <xref ref-type="bibr" rid="ref20">20</xref>
                </sup> However, an earlier study also focusing on tomatoes found higher cadmium and lead in organic compared with non-organic samples.
                <sup>
                    <xref ref-type="bibr" rid="ref21">21</xref>
                </sup> A 2017 study
                <sup>
                    <xref ref-type="bibr" rid="ref18">18</xref>
                </sup> concluded that conventionally grown vegetables generally tend to contain higher concentrations of some metals, but results are not conclusive after they noted higher cadmium concentrations in organic parsley when compared with non-organic. A more recent study found a range of significantly higher concentrations of heavy metals including cadmium in non-organic leafy vegetables when compared with organic.
                <sup>
                    <xref ref-type="bibr" rid="ref17">17</xref>
                </sup> No study to our knowledge has compared heavy metal concentrations in organic compared with non-organic vegetables in New Zealand.</p>
            <p>Severe Tropical Cyclone Gabrielle impacted parts of Vanuatu, Australia and New Zealand in February 2023. Hawke&#x2019;s Bay is a region on the east coast of North Island of New Zealand known for producing high quality primary products including organic and non-organic fruit and vegetables. The Heretaunga Plains, at the southern end of Hawke&#x2019;s bay is a strongly alluviated area, on the lower reaches of three significant rivers (Ngaruroro, Tukituki, Tutaekuri). The region experienced widespread cyclone damage and flooding with some areas completely inundated as river stop banks breached. Water infrastructure failures occurred widely across the region.
                <sup>
                    <xref ref-type="bibr" rid="ref22">22</xref>
                </sup> Extreme flooding events are known to increase risk of toxic heavy metal exposures, as flooding can transport heavy metals from industrial, mining and waste disposal sites to floodplains in sediments.
                <sup>
                    <xref ref-type="bibr" rid="ref23">23</xref>
                </sup> While there are no upstream mining activities in Hawke&#x2019;s Bay, industrial and waste disposal sites flooded. The Awatoto industrial area in particular caused widespread concerns about the possibility of contamination, compounded by local authorities not following expert advice.
                <sup>
                    <xref ref-type="bibr" rid="ref24">24</xref>
                </sup> As many vegetable crops are grown on Hawke&#x2019;s Bay Heretaunga floodplain, there is a clear need to access the risk to health from heavy metal exposure in local vegetables. This current research aims to determine the levels of heavy metals in the Hawke&#x2019;s Bay commercially grown vegetable post Cyclone Gabrielle. A secondary aim is to investigate the impacts of flooding and organic growing practices as contributing factors. Our hypothesis is that non-organic vegetables and those grown on flooded land will be associated with higher heavy metal concentrations.</p>
        </sec>
        <sec id="sec6" sec-type="methods">
            <title>Methods</title>
            <sec id="sec7">
                <title>Sample collection and preparation</title>
                <p>Sample collection for this cross-sectional study was carried out during February 2024. This month was chosen because its one-year post Severe Tropical Cyclone Gabrielle that devastated parts of the local region in February 2023. During the design phase, key stakeholders were approached for permission to collect samples directly from fields. While some stakeholders were supportive, others were not because of the potential for commercial harm. Thus, 736 vegetables were randomly acquired from 14 market gardens grown at ten local growing locations which were then anonymised. Market gardens are small-scale productions of crops which are sold directly to consumers. Only commercially grown, sold in market, locally vegetables were included. We included all market gardens we could identify within a 50 km radius of the cities Napier and Hastings. Four organic market gardens grown at six local growing locations were included. To sell products labelled as organic in New Zealand, sellers must comply with the Fair Trading Act 1986 and the Organic Products and Production Act 2023 which are administered by the government agency, Ministry for Primary Industries.
                    <sup>
                        <xref ref-type="bibr" rid="ref25">25</xref>
                    </sup> In addition, the organic markets were certified by BioGro (three markets) and Asurequality (one market).</p>
                <p>All three authors reached consensus on the identification of vegetable genus and species prior to processing. No disagreements occurred; however, we did not engage an independent botanist for verification, nor did we deposit voucher specimens in a public herbarium. Vegetables were washed in fresh running tap water and excess water removed. Only edible parts of vegetables were used for samples. Onions had root and outer skin removed, root vegetables had skin removed. This was done to replicate normal food preparation practices.</p>
                <p>736 vegetables were combined to form 153 representative samples (approximately 200 g each). Representative samples are composite samples of one vegetable type. Each representative sample consisted of multiple sub-samples (one single vegetable, such as one carrot) from one market. Thus, each representative sample represents an average of one vegetable species from that market (see 
                    <xref ref-type="table" rid="T1">
Table 1</xref> for representative and sub sample numbers for each vegetable). Similar composite sampling for vegetable heavy metal analysis has been performed previously.
                    <sup>
                        <xref ref-type="bibr" rid="ref26">26</xref>
                    </sup> While this approach masks within-group variability and detection of outliers, it allows a substantially greater sample number to be included in the study. This approach is more likely to reflect the characteristics of the overall population and reduces bias from outliers. 736 vegetables were combined to form 153 representative samples was chosen to maximise the number of vegetables tested within the practical constraints of our funding.</p>
                <table-wrap id="T1" orientation="portrait" position="float">
                    <label>
Table 1. </label>
                    <caption>
                        <title>Vegetable samples grouped by genus.</title>
                    </caption>
                    <table content-type="article-table" frame="hsides">
                        <thead>
                            <tr>
                                <th align="left" colspan="1" rowspan="2" valign="top">Genus</th>
                                <th align="left" colspan="1" rowspan="2" valign="top">Vegetables</th>
                                <th align="left" colspan="3" rowspan="1" valign="top">Samples</th>
                            </tr>
                            <tr>
                                <th align="left" colspan="1" rowspan="1" valign="top">REP (n)</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">SUB Mean</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">
SUB SD</th>
                            </tr>
                        </thead>
                        <tbody>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">
                                    <bold>

                                        <italic toggle="yes">Allium</italic>
</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Garlic, leek, onion, red onion, spring onion</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">29</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">7.0</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">5.6</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">
                                    <bold>

                                        <italic toggle="yes">Apium</italic>
</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Celery</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">5</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.4</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.5</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">
                                    <bold>

                                        <italic toggle="yes">Beta</italic>
</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Beetroot, chard, silver beat</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">15</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">2.6</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.6</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">
                                    <bold>

                                        <italic toggle="yes">Brassica</italic>
</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Bok choi, broccoli, cabbage, cauliflower, kale, red cabbage</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">39</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">2.2</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.7</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">
                                    <bold>

                                        <italic toggle="yes">Cucumis</italic>
</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Chinese winter melon, courgette</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">8</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">3.1</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.5</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">
                                    <bold>

                                        <italic toggle="yes">Cucurbita</italic>
</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Butternut pumpkin, grey pumpkin</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">9</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.0</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">NA</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">
                                    <bold>

                                        <italic toggle="yes">Foeniculum</italic>
</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Fennel</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">2</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.0</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">NA</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">
                                    <bold>

                                        <italic toggle="yes">Ipomoea</italic>
</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Chinese water spinach</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">1</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">20.0</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">NA</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">
                                    <bold>

                                        <italic toggle="yes">Lactuca</italic>
</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Lettuce</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">11</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">2.1</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.70</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">
                                    <bold>

                                        <italic toggle="yes">Lagenaria</italic>
</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Bottle gourd</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">2</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.0</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">NA</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">
                                    <bold>

                                        <italic toggle="yes">Luffa</italic>
</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Luffa gourd</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">2</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.0</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">NA</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">
                                    <bold>

                                        <italic toggle="yes">Momordica</italic>
</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Bitter melon</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">1</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">5.0</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">NA</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">
                                    <bold>

                                        <italic toggle="yes">Petroselinum</italic>
</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Parsley</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">4</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">12.8</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">14.2</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">
                                    <bold>

                                        <italic toggle="yes">Phaseolus</italic>
</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Green beans</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">4</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">17.3</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">39.7</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">
                                    <bold>

                                        <italic toggle="yes">Raphanus</italic>
</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Diakon</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">3</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.0</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">NA</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">
                                    <bold>

                                        <italic toggle="yes">Solanum</italic>
</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Cherry tomatoes, potatoes, tomatoes</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">13</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">12.9</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">10.9</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">
                                    <bold>

                                        <italic toggle="yes">Zea</italic>
</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Sweet corn</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">5</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">4.4</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.9</td>
                            </tr>
                        </tbody>
                    </table>
                    <table-wrap-foot>
                        <p>Presents the mean and standard deviation (SD) for the number of sub-samples (SUB). Each sub-sample consisted of a single vegetable (e.g., one carrot). Multiple such single vegetables were combined to form each representative sample (REP), which were subsequently analysed for heavy metals. Therefore, each REP functioned as a composite sample, comprising several individual vegetables sourced from a single market.</p>
                    </table-wrap-foot>
                </table-wrap>
                <p>Growing site locations and flooding impact on locations were verified using digital maps. Digital maps of flooding are available from Toit&#x016b; Te Whenua Land Information New Zealand, the New Zealand government&#x2019;s lead agency for geographical information and surveying.
                    <sup>
                        <xref ref-type="bibr" rid="ref27">27</xref>
                    </sup> Samples were kept in a freezer at -20&#x00b0;C prior to analysis by an ISO 17025 laboratory.</p>
                <p>Heavy metal quantification was made using inductively coupled plasma mass spectrometry (ICP-MS). Representative samples were first weighed to establish their fresh weight (FW). Samples were then dried at 60 &#x00b0;C until a constant weight was obtained. Samples were then ground and then underwent an aqua regia digestion procedure. These solutions were then diluted to 2% HNO3, 1% HCl before ICP-MS analysis. All reported heavy metal concentrations are standardised and expressed on a fresh weight (FW) basis.</p>
            </sec>
            <sec id="sec8">
                <title>Bias</title>
                <p>To minimise selection bias, vegetables were sampled from 14 market gardens across 10 growing sites, representing all the market gardens available. Flooding status for each site was verified using official digital maps. However, vegetables were not collected directly from the fields. To minimise pre-analytical bias, all samples underwent a standardised washing and preparation protocol. Minimising analytical bias was achieved with all samples being analysed in an ISO 17025 accredited laboratory. Composite sampling reduced the influence of extreme outliers, though our approach may also mask within-group variability. Bias from including values below the limit of detection (LOD), was minimised by using half LOD and confirming results through sensitivity analysis. Our sensitivity analysis restricted to vegetable types present across all four exposure categories (organic vs. non-organic and flooded vs. non-flooded). This truncation reduced the dataset to 37 representative samples comprising three vegetable types: broccoli, cabbage, and lettuce. ANOVA was then repeated for cadmium and nickel concentrations using the same statistical approach as the primary analysis. This allowed evaluation of whether the observed effects persisted when controlling for vegetable type.</p>
            </sec>
            <sec id="sec9">
                <title>Statistical analysis</title>
                <p>For each heavy metal, descriptive statistics were calculated. Mean, standard deviation and median were calculated using two approaches. Firstly, values were calculated by excluding all results below the limit of detection (LOD). Secondly, values were calculated including results below the LOD and recording all such values as half the LOD value. This technique is the most commonly used approach for handling LOD values in environmental datasets.
                    <sup>
                        <xref ref-type="bibr" rid="ref28">28</xref>
                    </sup> Representative sample values were then compared with permissible limits in the Codex Alimentarius CXS 193-1995 standard.
                    <sup>
                        <xref ref-type="bibr" rid="ref6">6</xref>
                    </sup> Representative samples were then compared by genus.</p>
                <p>Representative sample values were examined for gaussian distributions using the D&#x2019;Agostino &amp; Pearson omnibus normality test. Non-gaussian data was then log 10 transformed. Analysis of Variance (ANOVA) was performed with Post hoc Tukey and Partial eta squared effect size, which were classified as trivial (&#x03b7;
                    <sup>2</sup> &lt; 0.01), small (&#x03b7;
                    <sup>2</sup> = 0.01 to 0.06), moderate (&#x03b7;
                    <sup>2</sup> &gt; 0.06 to 0.14), and large (&#x03b7;
                    <sup>2</sup> &#x2265; 0.14).
                    <sup>
                        <xref ref-type="bibr" rid="ref29">29</xref>
                    </sup>
                </p>
            </sec>
        </sec>
        <sec id="sec10" sec-type="results">
            <title>Results</title>
            <p>The mercury content in all vegetable samples were found below the limit of detection (0.01 mg/kg FW) and were excluded from statistical analysis. For each metal, two sets of descriptive statistics were calculated by including and excluding samples below the LOD (
                <xref ref-type="table" rid="T2">
Table 2</xref>).</p>
            <table-wrap id="T2" orientation="portrait" position="float">
                <label>
Table 2. </label>
                <caption>
                    <title>Heavy metal concentrations (mg/kg FW) in Hawkes Bay vegetables.</title>
                </caption>
                <table content-type="article-table" frame="hsides">
                    <thead>
                        <tr>
                            <th align="left" colspan="1" rowspan="1" valign="top"/>
                            <th align="left" colspan="4" rowspan="1" valign="top">Including samples below LOD</th>
                            <th align="left" colspan="4" rowspan="1" valign="top">Excluding samples below LOD</th>
                            <th align="left" colspan="1" rowspan="1" valign="top"/>
                        </tr>
                        <tr>
                            <th align="left" colspan="1" rowspan="1" valign="top">Metal</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">n</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Mean</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">SD</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Median</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">n</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Mean</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">SD</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Median</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
Max</th>
                        </tr>
                    </thead>
                    <tbody>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Cadmium</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">153</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.011</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.014</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.005</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">131*</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.013</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.014</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.007</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.093</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Lead</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">153</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.014</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.058</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.005</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">12*</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.123</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.181</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.047</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.61</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Arsenic</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">153</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.014</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.02</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.01</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">2*</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.18</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.085</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.18</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.24</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Nickel</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">153</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.067</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.139</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.035</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">103*</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.097</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.163</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.055</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.5</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Chromium</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">153</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.02</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.072</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.01</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">4*</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.33</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.365</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.208</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.84</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Thallium</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">153</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.005</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.009</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.003</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">10*</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.034</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.017</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.037</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.058</td>
                        </tr>
                    </tbody>
                </table>
                <table-wrap-foot>
                    <p>Presents summary descriptive statistics of heavy metal concentrations in vegetables in the study. LOD = Limit of detection, Max=Maximum value detected, SD=Standard deviation. * For each metal descriptive statistics are presented with samples below the LOD excluded and included (recorded as half the LOD).</p>
                </table-wrap-foot>
            </table-wrap>
            <p>Four representative samples, three for lead (two 
                <italic toggle="yes">Lactuca sativa</italic> and one 
                <italic toggle="yes">Petroselinum crispum</italic>) and one for cadmium (
                <italic toggle="yes">Brassica rapa</italic>) exceeded permissible limits.
                <sup>
                    <xref ref-type="bibr" rid="ref6">6</xref>
                </sup> The majority of representative samples were below the LOD for lead, arsenic, chromium and thallium. Thus, these metals were excluded from further statistical analysis. Cadmium and nickel results are presented by vegetable genus (
                <xref ref-type="table" rid="T3">
Table 3</xref>).</p>
            <table-wrap id="T3" orientation="portrait" position="float">
                <label>
Table 3. </label>
                <caption>
                    <title>Cadmium and Nickel values in HB vegetables grouped by genus.</title>
                </caption>
                <table content-type="article-table" frame="hsides">
                    <thead>
                        <tr>
                            <th align="left" colspan="1" rowspan="2" valign="top">Genus</th>
                            <th align="left" colspan="1" rowspan="2" valign="top">Vegetables</th>
                            <th align="left" colspan="2" rowspan="1" valign="top">Cadmium mg/kg FW</th>
                            <th align="left" colspan="2" rowspan="1" valign="top">Nickel mg/kg FW</th>
                        </tr>
                        <tr>
                            <th align="left" colspan="1" rowspan="1" valign="top">Med</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Max</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Med</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
Max</th>
                        </tr>
                    </thead>
                    <tbody>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>

                                    <italic toggle="yes">Allium</italic>
</bold>
</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Garlic, leek, onion, red onion, spring onion</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.005</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.029</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.010</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.170</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>

                                    <italic toggle="yes">Apium</italic>
</bold>
</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Celery</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.016</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.019</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.027</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.030</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>

                                    <italic toggle="yes">Beta</italic>
</bold>
</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Beetroot, chard, silver beat</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.015</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.060</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.021</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.340</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>

                                    <italic toggle="yes">Brassica</italic>
</bold>
</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Bok choi, broccoli, cabbage, cauliflower, kale, red cabbage</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.006</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.093</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.035</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.270</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>

                                    <italic toggle="yes">Cucumis</italic>
</bold>
</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Chinese Winter Melon, courgette</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.004</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.067</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.094</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>

                                    <italic toggle="yes">Cucurbita</italic>
</bold>
</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Butternut pumpkin, grey pumpkin</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.003</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.005</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.057</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.500</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>

                                    <italic toggle="yes">Foeniculum</italic>
</bold>
</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Fennel</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.003</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.004</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.041</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.072</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>

                                    <italic toggle="yes">Ipomoea</italic>
</bold>
</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Chinese Water Spinach</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.019</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.019</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.040</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.040</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>

                                    <italic toggle="yes">Lactuca</italic>
</bold>
</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Lettuce</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.025</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.074</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.032</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.610</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>

                                    <italic toggle="yes">Lagenaria</italic>
</bold>
</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Bottle gourd</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.003</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.003</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.068</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.082</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>

                                    <italic toggle="yes">Luffa</italic>
</bold>
</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Luffa gourd</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.009</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.015</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.093</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.150</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>

                                    <italic toggle="yes">Momordica</italic>
</bold>
</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Bitter melon</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">5.0&#x00d7;10
                                <sup>&#x2212;4</sup>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">5.0&#x00d7;10
                                <sup>&#x2212;4</sup>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.057</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.057</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>

                                    <italic toggle="yes">Petroselinum</italic>
</bold>
</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Parsley</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.013</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.028</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.155</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.370</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>

                                    <italic toggle="yes">Phaseolus</italic>
</bold>
</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Green beans</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">7.5&#x00d7;10
                                <sup>&#x2212;4</sup>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.003</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.12</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.160</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>

                                    <italic toggle="yes">Raphanus</italic>
</bold>
</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Diakon</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.011</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.027</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.005</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.005</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>

                                    <italic toggle="yes">Solanum</italic>
</bold>
</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Cherry tomatoes, potatoes, tomatoes</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.003</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.013</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.005</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.070</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>

                                    <italic toggle="yes">Zea</italic>
</bold>
</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Sweet corn</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.003</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.054</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.110</td>
                        </tr>
                    </tbody>
                </table>
                <table-wrap-foot>
                    <p>Presents cadmium and nickel concentrations by vegetable genus. FW = Fresh weight.</p>
                </table-wrap-foot>
            </table-wrap>
            <p>Cadmium values were not gaussian (D&#x2019;Agostino &amp; Pearson omnibus normality test, K2 = 113.6, p &lt; 0.0001), however log 10 transformed values were gaussian (K2 = 3.448, p = 0.1783). ANOVA revealed significantly lower cadmium levels in vegetables grown organically or vegetables grown on cyclone Gabrielle flood affected land (
                <xref ref-type="table" rid="T4">
Table 4</xref>).</p>
            <table-wrap id="T4" orientation="portrait" position="float">
                <label>
Table 4. </label>
                <caption>
                    <title>ANOVA of cadmium in Hawkes Bay vegetables, using organics and flooding as factors.</title>
                </caption>
                <table content-type="article-table" frame="hsides">
                    <thead>
                        <tr>
                            <th align="left" colspan="1" rowspan="1" valign="top">Factors</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Sum of Squares</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Mean Square</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">F</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">p</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">&#x03b7;
                                <sup>2</sup>
                                <sub>p</sub>
                            </th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
(95 % CI)</th>
                        </tr>
                    </thead>
                    <tbody>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Organic</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">2.504</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">2.504</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">9.198</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.003</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.058</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">(0.007-0.145)</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Flooded</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.311</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.311</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">4.817</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.030</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.031</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">(0.000-0.104)</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Organic * Flooded</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.333</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.333</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.224</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.270</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.008</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">(0.000-0.059)</td>
                        </tr>
                    </tbody>
                </table>
                <table-wrap-foot>
                    <p>Presents the ANOVA output examining cadmium in Hawkes Bay vegetables, using organics and flooding as factors. Test for Equality of Variances (Levene's), F= 0.90, p=0.441, QQ plot of residuals were homoscedastic.</p>
                </table-wrap-foot>
            </table-wrap>
            <p>Post hoc Tukey (
                <xref ref-type="fig" rid="f1">
Figure 1</xref>) demonstrated non-organic non-flood affected vegetables were significantly higher for cadmium compared with organic and flooded categories of vegetables. This was borderline significant for organic not flooded. A separate sensitivity analysis was undertaken comparing the effect of flooding on non-organic vegetables. This revealed lower cadmium (Mann-Whitney U = 332.5, p = 0.030) for vegetables grown on flooded land.</p>
            <fig fig-type="figure" id="f1" orientation="portrait" position="float">
                <label>
Figure 1. </label>
                <caption>
                    <title>Organic and flooding factors for cadmium concentrations in Hawkes Bay vegetables.</title>
                    <p>Shows boxplots of cadmium concentrations by organic and flooding status. FW = Fresh weight.</p>
                </caption>
                <graphic id="gr1" orientation="portrait" position="float" xlink:href="https://f1000research-files.f1000.com/manuscripts/193530/52328396-b041-4a8d-9a3a-147e0b928910_figure1.gif"/>
            </fig>
            <p>Nickel values were not gaussian (D&#x2019;Agostino &amp; Pearson omnibus normality test, K2 = 258.1, p &lt; 0.0001), however log 10 transformed values were gaussian (K2 = 2.803, p = 0.2462). ANOVA revealed significantly lower nickel levels in vegetables grown organically or vegetables grown on cyclone Gabrielle flood affected land (
                <xref ref-type="table" rid="T5">
Table 5</xref>).</p>
            <table-wrap id="T5" orientation="portrait" position="float">
                <label>
Table 5. </label>
                <caption>
                    <title>ANOVA of nickel in Hawkes Bay vegetables, using organics and flooding as factors.</title>
                </caption>
                <table content-type="article-table" frame="hsides">
                    <thead>
                        <tr>
                            <th align="left" colspan="1" rowspan="1" valign="top">Factors</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Sum of Squares</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Mean Square</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">F</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">p</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">&#x03b7;
                                <sup>2</sup>
                                <sub>p</sub>
                            </th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
(95 % CI)</th>
                        </tr>
                    </thead>
                    <tbody>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="middle">Organic</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">5.323</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">5.323</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">21.038</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">&lt; .001</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">0.124</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">(0.042-0.227)</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="middle">Flooded</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">1.306</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">1.306</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">5.164</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">0.024</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">0.033</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">(0.000-0.108)</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="middle">Organic * Flooded</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">1.7</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">1.7</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">6.719</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">0.010</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">0.043</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">(0.002-0.123)</td>
                        </tr>
                    </tbody>
                </table>
                <table-wrap-foot>
                    <p>Presents the ANOVA output examining nickel in Hawkes Bay vegetables, using organics and flooding as factors. Test for Equality of Variances (Levene's), F= 0.51, p=0.676, QQ plot of residuals were homoscedastic.</p>
                </table-wrap-foot>
            </table-wrap>
            <p>Post hoc Tukey (
                <xref ref-type="fig" rid="f2">
Figure 2</xref>) demonstrated non-organic non-flood affected vegetables were significantly higher for nickel compared with organic and flooded categories of vegetables.</p>
            <fig fig-type="figure" id="f2" orientation="portrait" position="float">
                <label>
Figure 2. </label>
                <caption>
                    <title>Organic and flooding factors for nickel concentrations in Hawkes Bay vegetables.</title>
                    <p>Shows boxplots of nickel concentrations by organic and flooding status. FW = Fresh weight.</p>
                </caption>
                <graphic id="gr2" orientation="portrait" position="float" xlink:href="https://f1000research-files.f1000.com/manuscripts/193530/52328396-b041-4a8d-9a3a-147e0b928910_figure2.gif"/>
            </fig>
            <p>It was not possible to include genus or vegetable nor market or growing site or as factors for either the cadmium or nickel ANOVA due to a lack of data. However, a sensitivity analysis, truncating the data (n=37) to include only vegetables across all four categories of flooding and organic growing resulted in three vegetable types only: Broccoli, cabbage and lettuce. ANOVA for cadmium and nickel resulted in similar findings, though only nickel reached significance (
                <xref ref-type="table" rid="T6">
Table 6</xref>).</p>
            <table-wrap id="T6" orientation="portrait" position="float">
                <label>
Table 6. </label>
                <caption>
                    <title>Impact of flood and organic practices on cadmium and nickel content in collected vegetable samples.</title>
                </caption>
                <table content-type="article-table" frame="hsides">
                    <thead>
                        <tr>
                            <th align="left" colspan="1" rowspan="1" valign="top"/>
                            <th align="left" colspan="4" rowspan="1" valign="top">Descriptive Statistics</th>
                            <th align="left" colspan="3" rowspan="1" valign="top">ANOVA Statistics</th>
                        </tr>
                        <tr>
                            <th align="left" colspan="1" rowspan="1" valign="top"/>
                            <th align="left" colspan="1" rowspan="1" valign="top">Organic Category</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Flooded Category</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Mean</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">SD</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Factors</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">F</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">p</th>
                        </tr>
                    </thead>
                    <tbody>
                        <tr>
                            <td align="left" colspan="1" rowspan="4" valign="top">Cadmium</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Not Organic</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Flooded</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.011</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.012</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Organic</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">3.170</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.086</td>
                        </tr>
                        <tr>
                            <td colspan="1" rowspan="1"/>
                            <td align="left" colspan="1" rowspan="1" valign="top">Not Flooded</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.026</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.023</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Flooded</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">3.649</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.066</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Organic</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Flooded</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.005</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.008</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Organic*Flooded</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.038</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.846</td>
                        </tr>
                        <tr>
                            <td colspan="1" rowspan="1"/>
                            <td align="left" colspan="1" rowspan="1" valign="top">Not Flooded</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.009</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.007</td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="4" valign="top">Nickel</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Not Organic</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Flooded</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.050</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.049</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Organic</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">4.813</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.036</td>
                        </tr>
                        <tr>
                            <td colspan="1" rowspan="1"/>
                            <td align="left" colspan="1" rowspan="1" valign="top">Not Flooded</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.202</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.196</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Flooded</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">5.868</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.022</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Organic</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Flooded</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.019</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.014</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Organic*Flooded</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.244</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.625</td>
                        </tr>
                        <tr>
                            <td colspan="1" rowspan="1"/>
                            <td align="left" colspan="1" rowspan="1" valign="top">Not Flooded</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.075</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.1</td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                        </tr>
                    </tbody>
                </table>
                <table-wrap-foot>
                    <p>Presents the sensitivity test using only broccoli, cabbage and lettuce. Test for Equality of Variances p&gt;0.05, QQ plot of residuals were homoscedastic.</p>
                </table-wrap-foot>
            </table-wrap>
        </sec>
        <sec id="sec11" sec-type="discussion">
            <title>Discussion</title>
            <p>This study aimed to survey heavy metals in Hawke&#x2019;s Bay commercially grown vegetables after a severe tropical cyclone. As secondary aims, we also sought to examine flooding and organic growing as factors for contamination. We found that local commercially grown vegetables were relatively low in heavy metal concentrations by global standards, though lead and cadmium levels may warrant further investigation in this region. Our data suggest that organically grown vegetables are lower in cadmium and nickel concentrations. This supports the first part of our hypothesis, that organic vegetables would be associated with lower heavy metal concentrations. However, we cannot confirm our hypothesis that flooding would be associated with higher heavy metal concentrations. Our results lead us to propose that vegetables grown on cyclone flooded land have decreased cadmium and nickel concentrations compared with vegetables grown on non-flooded land. However, this remains speculative and further research will be required to confirm this last finding.</p>
            <p>Heavy metal concentrations in local vegetables were found to be generally low in this current study. Results from vegetable samples globally often report substantially greater contamination and many frequently exceeded WHO/FOA permissible limits.
                <sup>
                    <xref ref-type="bibr" rid="ref7">7</xref>,
                    <xref ref-type="bibr" rid="ref8">8</xref>
                </sup> However, our results do highlight that lead concentrations and cadmium can still exceed WHO/FOA permissible limits in this region. Three of our 153 representative samples (2%) exceeded FAO/WHO lead limits and one sample was 6-fold higher than the limit. The evidence for lead toxicity even in low doses is compelling.
                <sup>
                    <xref ref-type="bibr" rid="ref4">4</xref>
                </sup> The two highest concentrations of lead (0.61 and 0.37 mg/kg FW) were both in lettuce representative samples. Thus, further research on individual lettuce samples is suggested in the Hawke&#x2019;s Bay region. Cadmium values were similar to a 2019 New Zealand study for onions, lettuce and spinach.
                <sup>
                    <xref ref-type="bibr" rid="ref14">14</xref>
                </sup> It&#x2019;s noteworthy that the 2019 study included samples from this region alongside other New Zealand locations. That the current results are similar to the previous study is an indication that recent flooding may not have increased risk for cadmium exposure.</p>
            <p>While lead and to a lesser extent cadmium may warrant further investigation, our study suggests that Hawke&#x2019;s Bay vegetables are low in mercury, arsenic and chromium. This is particularly important for mercury and arsenic as these are two chemicals are notable global public health concerns (for review see Mercury
                <sup>
                    <xref ref-type="bibr" rid="ref30">30</xref>
                </sup> and arsenic
                <sup>
                    <xref ref-type="bibr" rid="ref31">31</xref>
                </sup>). These toxic metals are ubiquitous in the environment: In New Zealand, blood mercury has been detected in 93% of children and 99% of adults.
                <sup>
                    <xref ref-type="bibr" rid="ref32">32</xref>
                </sup> While New Zealand data on human exposure to arsenic is lacking, millions of people globally are impacted.
                <sup>
                    <xref ref-type="bibr" rid="ref31">31</xref>
                </sup> It is well established that arsenic concentrations in vegetables can pose a risk for chronic arsenic poisoning.
                <sup>
                    <xref ref-type="bibr" rid="ref9">9</xref>
                </sup> Thus, low levels of these metals in Hawke&#x2019;s Bay vegetables, particularly mercury and arsenic are reassuring.</p>
            <p>Thallium is significantly more toxic than most other heavy metals, a growing global environmental problem, and vegetables are the primary pathway for human exposure (reviewed in Ref. 
                <xref ref-type="bibr" rid="ref33">33</xref>). Previously, a New Zealand study found approximately a sixth of sampled foods returned detectable thallium concentrations.
                <sup>
                    <xref ref-type="bibr" rid="ref15">15</xref>
                </sup> That study noted New Zealand potatoes samples had greater thallium concentrations than the United Kingdom. However, we did not detect thallium in Hawke&#x2019;s Bay potatoes, but only in 
                <italic toggle="yes">Brassica</italic> and 
                <italic toggle="yes">Lactuca.</italic> While the concentrations are low, because thallium is more toxic compared to other elements, we suggest further research examining thallium in vegetables is warranted. This is compounded by a lack of certainty regarding health-based guidance values (briefly reviewed in Ref. 
                <xref ref-type="bibr" rid="ref15">15</xref>), though our highest values are lower than thresholds recently suggested.
                <sup>
                    <xref ref-type="bibr" rid="ref33">33</xref>
                </sup>
            </p>
            <p>Organic growing practices have a small effect (&#x03b7;
                <sup>2</sup>
                <sub>p</sub> = 0.058, 95% CI 0.007-0.145) for lower (p = 0.003) cadmium levels and a moderate effect (&#x03b7;
                <sup>2</sup>
                <sub>p</sub> = 0.124, 95% CI 0.042-0.227) for lower (p &lt; 0.001) nickel levels. Previous research has been controversial in regard to cadmium. Some studies have reported higher cadmium
                <sup>
                    <xref ref-type="bibr" rid="ref18">18</xref>,
                    <xref ref-type="bibr" rid="ref19">19</xref>,
                    <xref ref-type="bibr" rid="ref21">21</xref>
                </sup> in some organic vegetable species when compared with non-organic. Other studies report no difference for cadmium
                <sup>
                    <xref ref-type="bibr" rid="ref20">20</xref>
                </sup> or lower levels
                <sup>
                    <xref ref-type="bibr" rid="ref17">17</xref>
                </sup> for organically grow vegetables. Previous research is also inconclusive on nickel for organic and non-organic practices.
                <sup>
                    <xref ref-type="bibr" rid="ref18">18</xref>,
                    <xref ref-type="bibr" rid="ref20">20</xref>
                </sup> The current study suggests lower concentrations for both cadmium and nickel with organic growing. Consumers wishing to minimise heavy metal exposure in Hawke&#x2019;s Bay can be offered some assurance that buying organic vegetables does carry lower risk.</p>
            <p>Severe Tropical Cyclone Gabrielle impacted Hawke&#x2019;s Bay with widespread water infrastructure failures in February 2023.
                <sup>
                    <xref ref-type="bibr" rid="ref22">22</xref>
                </sup> This included widespread inundation of the region&#x2019;s floodplains on which many vegetable crops are grown. While flooding events are known to increase the transport of heavy metals to floodplains,
                <sup>
                    <xref ref-type="bibr" rid="ref34">34</xref>,
                    <xref ref-type="bibr" rid="ref35">35</xref>
                </sup> the current study suggests that one year after the flooding there is no increased risk from Hawke&#x2019;s Bay vegetables which were grown on flooded land. Indeed, the flooding had a small effect (&#x03b7;
                <sup>2</sup>p = 0.031, 95% CI 0.000-0.104) for lower (p = 0.030) cadmium levels and a small effect (&#x03b7;
                <sup>2</sup>p = 0.033, 95% CI 0.000-0.108) for lower (p = 0.024) nickel levels. This leads us to speculate that the recent flooding may have decreased both cadmium and nickel levels. Cadmium has accumulated in New Zealand predominantly from the application of phosphate fertilizer to soils.
                <sup>
                    <xref ref-type="bibr" rid="ref10">10</xref>,
                    <xref ref-type="bibr" rid="ref11">11</xref>
                </sup> Thus, we propose that recent flooding may have transported new sediments less exposed to repeated application of fertiliser than the existing soils, which has reduced heavy metal bioaccumulation in vegetables. This remains a hypothesis and further empirical research would be required to verify this. Nevertheless, consumers can be offered some assurance that the recent cyclone flooding does not appear to have increased the risk of heavy metals in Hawke&#x2019;s Bay vegetables.</p>
            <p>Our study has several limitations. While growing locations and flooding were verified by our use of digital maps we did not collect the vegetable samples from the fields ourselves. Therefore, we cannot provide absolute certainly all our samples were grown where indicated. However, as all vegetable samples were collected from 14 Hawke&#x2019;s Bay market gardens, a strength of this study is that our data provides robust data on heavy metal concentrations from the region&#x2019;s markets. One limitation is that our use of representative samples means our heavy metal concentration ranges do not reflect the extremes that a single vegetable might exhibit. This approach masks within-group variability and prevents detection of outliers, potentially underestimating contamination risk. However, this also allowed us to sample a great number of vegetables which is a strength as this approach is more likely to reflect the characteristics of the overall population. Indeed, that representative samples still exceeded limits gives great assurance that Hawke&#x2019;s Bay vegetables can exceed such limits. A limitation is that we were unable to include genus or individual species as a factor. Cadmium concentration has previously been demonstrated to vary by vegetable species. However, a subsequent sensitivity sub-analysis including species as a factor did show similar results which somewhat alleviates this limitation. Our findings are likely generalisable to commercially grown vegetables in Hawke&#x2019;s Bay, as sampling included all available market gardens and both organic and non-organic practices. However, caution is needed when extrapolating to other regions or seasons, as soil composition, farming practices, and environmental conditions differ. Composite sampling also limits inference about individual vegetable variability.</p>
            <p>In conclusion, Hawke&#x2019;s Bay commercially grown vegetables are low in mercury, arsenic, cadmium, chromium, nickel and thallium when compared to other global locations. However, cadmium and lead levels in Hawke&#x2019;s Bay vegetables may occasionally exceed permissible limits. Lead in particular may warrant further investigation as two composite samples exceeded these limits. Organically grown vegetables are lower in cadmium and nickel concentrations when compared to non-organic grown vegetables in this region. The recent extreme flooding is unlikely to have increased risk of heavy mental exposures for eating local vegetables grown on flooded land. We propose that vegetables grown on cyclone flooded land have decreased risk for cadmium and nickel exposures compared with vegetables grown on non-flooded land, though further research is required to confirm this.</p>
        </sec>
        <sec id="sec12">
            <title>Ethical considerations</title>
            <p>This study was reviewed and deemed exempt from ethics approval by the Eastern Institute of Technology ethics committee with the reference number: [ENQ141223], dated [15 December 2023].</p>
        </sec>
    </body>
    <back>
        <sec id="sec15" sec-type="data-availability">
            <title>Data availability</title>
            <p>All underlying data are available on Zenodo under a 
                <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International</ext-link> (CC BY 4.0) licence. The main database is deposited at 
                <ext-link ext-link-type="uri" xlink:href="https://doi.org/10.5281/zenodo.15540807">https://doi.org/10.5281/zenodo.15540807</ext-link>.
                <sup>
                    <xref ref-type="bibr" rid="ref36">36</xref>
                </sup> The excel file contains grown location, organic growing status, cyclone flooded status, Genus and vegetable type, heavy metal concentrations (Cd, Pb, Hg, As, Ni, Cr and Tl) and log transformed concentrations for CD and N.</p>
            <sec id="sec16">
                <title>Reporting guidelines</title>
                <p>We conducted a cross-sectional analysis using the STROBE cross sectional reporting guidelines. This is available on Zenodo under a 
                    <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International</ext-link> (CC BY 4.0) licence. The STROBE checklist is deposited at 
                    <ext-link ext-link-type="uri" xlink:href="https://doi.org/10.5281/zenodo.17970356">https://doi.org/10.5281/zenodo.17970356</ext-link>.
                    <sup>
                        <xref ref-type="bibr" rid="ref37">37</xref>
                    </sup>
                </p>
            </sec>
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    <sub-article article-type="reviewer-report" id="report461969">
        <front-stub>
            <article-id pub-id-type="doi">10.5256/f1000research.193530.r461969</article-id>
            <title-group>
                <article-title>Reviewer response for version 1</article-title>
            </title-group>
            <contrib-group>
                <contrib contrib-type="author">
                    <name>
                        <surname>Al-Shammari</surname>
                        <given-names>Ali Hammood Rhaif</given-names>
                    </name>
                    <xref ref-type="aff" rid="r461969a1">1</xref>
                    <role>Referee</role>
                    <uri content-type="orcid">https://orcid.org/0009-0006-9357-2535</uri>
                </contrib>
                <aff id="r461969a1">
                    <label>1</label>Al-Muthanna University, Al-Rumaytha, Iraq</aff>
            </contrib-group>
            <author-notes>
                <fn fn-type="conflict">
                    <p>
                        <bold>Competing interests: </bold>No competing interests were disclosed.</p>
                </fn>
            </author-notes>
            <pub-date pub-type="epub">
                <day>13</day>
                <month>4</month>
                <year>2026</year>
            </pub-date>
            <permissions>
                <copyright-statement>Copyright: &#x00a9; 2026 Al-Shammari AHR</copyright-statement>
                <copyright-year>2026</copyright-year>
                <license xlink:href="https://creativecommons.org/licenses/by/4.0/">
                    <license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
                </license>
            </permissions>
            <related-article ext-link-type="doi" id="relatedArticleReport461969" related-article-type="peer-reviewed-article" xlink:href="10.12688/f1000research.175538.1"/>
            <custom-meta-group>
                <custom-meta>
                    <meta-name>recommendation</meta-name>
                    <meta-value>approve-with-reservations</meta-value>
                </custom-meta>
            </custom-meta-group>
        </front-stub>
        <body>
            <p>
                <bold>1. Article Summary</bold>
            </p>
            <p> Thank you for the opportunity to review this manuscript. I sincerely appreciate the considerable effort made by the authors in conducting this study.</p>
            <p> This study investigates the contamination of commercially available vegetables with heavy metals in the Hawke&#x2019;s Bay region of New Zealand, one year after the flooding caused by Cyclone Gabrielle. The methodology involved the analysis of 153 composite samples (representing a total of 736 individual vegetables) using inductively coupled plasma mass spectrometry (ICP-MS). The findings indicate that overall contamination levels are relatively low when compared to international standards. Additionally, organically grown vegetables were found to have lower concentrations of cadmium and nickel compared to non-organic counterparts. The study also presents an unexpected observation that vegetables grown on flood-affected land exhibited lower levels of certain heavy metals than those grown on non-flooded land.</p>
            <p> 
                <bold>
                    <bold>2. Overall Assessment</bold>
                </bold>
            </p>
            <p> The study addresses an important topic with clear relevance to public health and food safety, particularly in the context of natural disasters. The use of advanced analytical techniques and a relatively large sample size represent notable strengths. However, there are several important methodological and analytical limitations that affect the robustness of the conclusions. In particular, the study has limited capacity for causal inference, especially regarding the impact of flooding. Additionally, there are shortcomings in the control of key confounding variables, as well as insufficient detail to fully support reproducibility of the study.</p>
            <p> 
                <bold>
                    <bold>3. Major Limitations</bold>
                </bold>
            </p>
            <p> 
                <italic>
                    <bold>1) Limited capacity for causal inference</bold>
                </italic> 
                <list list-type="bullet">
                    <list-item>
                        <p>The cross-sectional design does not allow for direct assessment of the impact of flooding.</p>
                    </list-item>
                    <list-item>
                        <p>There are no pre-cyclone baseline data for comparison.</p>
                    </list-item>
                    <list-item>
                        <p>The conclusion suggesting that flooding may reduce heavy metal concentrations is not sufficiently supported by the available evidence.</p>
                    </list-item>
                </list> 
                <italic>
                    <bold>2) Lack of control for key confounders</bold>
                </italic> 
                <list list-type="bullet">
                    <list-item>
                        <p>The analysis did not account for important variables such as: 
                            <list list-type="bullet">
                                <list-item>
                                    <p>Vegetable type</p>
                                </list-item>
                                <list-item>
                                    <p>Plant genus/species</p>
                                </list-item>
                                <list-item>
                                    <p>Growing location</p>
                                </list-item>
                                <list-item>
                                    <p>Market source</p>
                                </list-item>
                            </list> </p>
                    </list-item>
                    <list-item>
                        <p>These factors are known to significantly influence heavy metal accumulation and should be considered in the analysis.</p>
                    </list-item>
                </list> 
                <italic>
                    <bold>3) Use of composite samples</bold>
                </italic> 
                <list list-type="bullet">
                    <list-item>
                        <p>This approach may: 
                            <list list-type="bullet">
                                <list-item>
                                    <p>Mask true variability within samples</p>
                                </list-item>
                                <list-item>
                                    <p>Reduce the ability to detect extreme or high-risk values</p>
                                </list-item>
                                <list-item>
                                    <p>Limit the accuracy of risk assessment</p>
                                </list-item>
                            </list> </p>
                    </list-item>
                </list> 
                <italic>
                    <bold>4) Market-based sampling</bold>
                </italic> 
                <list list-type="bullet">
                    <list-item>
                        <p>Collecting samples from markets rather than directly from farms reduces the accuracy of: 
                            <list list-type="bullet">
                                <list-item>
                                    <p>Identifying the true origin of each sample</p>
                                </list-item>
                                <list-item>
                                    <p>Correctly classifying exposure (particularly flood status)</p>
                                </list-item>
                            </list> </p>
                    </list-item>
                </list> 
                <italic>
                    <bold>5) Lack of supporting environmental data</bold>
                </italic> 
                <list list-type="bullet">
                    <list-item>
                        <p>The study does not include measurements of: 
                            <list list-type="bullet">
                                <list-item>
                                    <p>Soil</p>
                                </list-item>
                                <list-item>
                                    <p>Irrigation water</p>
                                </list-item>
                                <list-item>
                                    <p>Sediments</p>
                                </list-item>
                            </list> </p>
                    </list-item>
                    <list-item>
                        <p>This limits the ability to interpret the findings and weakens the proposed mechanistic explanations.</p>
                    </list-item>
                </list> 
                <bold>
                    <bold>4. Recommendations to the Authors (Constructive Feedback)</bold>
                </bold>
            </p>
            <p> To improve the scientific quality of the study, the following recommendations are suggested:</p>
            <p> 
                <italic>
                    <bold>1) Revise the conclusions</bold>
                </italic> 
                <list list-type="bullet">
                    <list-item>
                        <p>Reduce causal language throughout the manuscript.</p>
                    </list-item>
                    <list-item>
                        <p>Emphasize that the findings are exploratory rather than causal.</p>
                    </list-item>
                    <list-item>
                        <p>Clearly state that the observed effect of flooding remains hypothetical.</p>
                    </list-item>
                </list> 
                <italic>
                    <bold>2) Strengthen the statistical analysis</bold>
                </italic> 
                <list list-type="bullet">
                    <list-item>
                        <p>Attempt to include vegetable type or plant genus/species as a factor in the analysis (if feasible).</p>
                    </list-item>
                    <list-item>
                        <p>At minimum, provide a more in-depth discussion of their potential impact.</p>
                    </list-item>
                    <list-item>
                        <p>Consider the use of more advanced statistical approaches (e.g., mixed-effects models).</p>
                    </list-item>
                </list> 
                <italic>
                    <bold>3) Clarify the limitations of composite sampling</bold>
                </italic> 
                <list list-type="bullet">
                    <list-item>
                        <p>More explicitly highlight how composite sampling may affect the results.</p>
                    </list-item>
                    <list-item>
                        <p>Acknowledge the potential underestimation of risk due to masking of extreme values.</p>
                    </list-item>
                </list> 
                <italic>
                    <bold>4) Improve the description of the sampling methodology</bold>
                </italic> 
                <list list-type="bullet">
                    <list-item>
                        <p>Provide a clearer and more detailed explanation of how samples were selected.</p>
                    </list-item>
                    <list-item>
                        <p>Clarify the process used to link market samples to their corresponding growing locations.</p>
                    </list-item>
                </list> 
                <italic>
                    <bold>5) Strengthen the discussion section</bold>
                </italic> 
                <list list-type="bullet">
                    <list-item>
                        <p>Link findings to environmental mechanisms more cautiously.</p>
                    </list-item>
                    <list-item>
                        <p>Either support interpretations with additional references or moderate the strength of speculative explanations.</p>
                    </list-item>
                </list> 
                <bold>
                    <bold>5. Points Marked as &#x201c;Partly&#x201d; (Critique &amp; Revision Points)</bold>
                </bold>
            </p>
            <p> 
                <italic>
                    <bold>1): Study Design &amp; Technical Soundness</bold>
                </italic> 
                <list list-type="bullet">
                    <list-item>
                        <p>The study lacks baseline data for the sites prior to the cyclone, which limits the ability to attribute observed reductions in heavy metals to flooding. As such, the relationship remains associative rather than causally established. Furthermore, sampling from markets rather than directly from farms reduces the accuracy of environmental source attribution.</p>
                    </list-item>
                    <list-item>
                        <p>The authors should explicitly acknowledge this as a 
                            <bold>major limitation</bold> within the manuscript. 
                            <list list-type="bullet">
                                <list-item>
                                    <p>If possible, incorporating or comparing with historical data from the same region would strengthen the argument for any observed post-flood changes.</p>
                                </list-item>
                            </list> </p>
                    </list-item>
                </list> 
                <italic>
                    <bold>2): Statistical Analysis &amp; Interpretation</bold>
                </italic> 
                <list list-type="bullet">
                    <list-item>
                        <p>The use of composite sampling masks within-group variability and may dilute extreme values. In addition, the conclusion that flooding &#x201c;reduced&#x201d; contamination is speculative and not supported by direct soil or environmental measurements. 
                            <list list-type="bullet">
                                <list-item>
                                    <p>The discussion should be strengthened by incorporating more detailed geological or chemical hypotheses explaining how newly deposited sediments might influence soil chemistry and metal bioavailability.</p>
                                </list-item>
                                <list-item>
                                    <p>The authors should also include a clearer statistical discussion of how composite sampling may affect 
                                        <bold>statistical power</bold> and the potential risks of overlooking critical outliers.</p>
                                </list-item>
                            </list> </p>
                    </list-item>
                </list> 
                <italic>
                    <bold>3): Conclusions</bold>
                </italic> 
                <list list-type="bullet">
                    <list-item>
                        <p>The conclusion regarding the role of newly deposited sediments following flooding is not supported by direct physical or chemical measurements of soil within this study. 
                            <list list-type="bullet">
                                <list-item>
                                    <p>This point should be reframed as a 
                                        <bold>hypothesis for future research</bold>, rather than presented as a definitive conclusion of the current study.</p>
                                </list-item>
                            </list> </p>
                    </list-item>
                </list> 
                <bold>
                    <bold>1. Abstract</bold>
                </bold> 
                <list list-type="bullet">
                    <list-item>
                        <p>The abstract states that flooding reduced the risk of heavy metal contamination. This is a strong claim that is not adequately supported and is only presented as speculation later in the manuscript. The abstract should adopt a more cautious tone when presenting this finding.</p>
                    </list-item>
                    <list-item>
                        <p>The term 
                            <italic>low risk </italic>is too general and not scientifically precise. A more appropriate phrasing would be:</p>
                        <p> 
                            <italic>relatively low concentrations compared to international limits.</italic>
                        </p>
                    </list-item>
                    <list-item>
                        <p>The abstract does not report the 
                            <bold>effect size (&#x03b7;&#x00b2;p)</bold>, which represents an important strength of the statistical analysis and should be highlighted.</p>
                    </list-item>
                </list> 
                <bold>
                    <bold>2. Introduction</bold>
                </bold> 
                <list list-type="bullet">
                    <list-item>
                        <p>There is a logical gap in linking water infrastructure failure to potential heavy metal contamination, without clearly specifying the types of industries present in the Awatoto area. Providing more context about local industrial activities would strengthen this argument.</p>
                    </list-item>
                    <list-item>
                        <p>The introduction places considerable emphasis on cadmium, but does not provide sufficient background on nickel, despite it being one of the key findings of the study. A more balanced discussion of relevant heavy metals is recommended.</p>
                    </list-item>
                    <list-item>
                        <p>The manuscript does not adequately explain the underlying 
                            <bold>mechanisms of metal transport</bold> or 
                            <bold>sediment dynamics</bold>, which are essential for supporting later interpretations related to flooding effects.</p>
                    </list-item>
                </list> 
                <bold>
                    <bold>3. Methods</bold>
                </bold> 
                <list list-type="bullet">
                    <list-item>
                        <p>
                            <bold>Scientific and methodological issues:</bold> 
                            <list list-type="bullet">
                                <list-item>
                                    <p>
                                        <bold>Sampling strategy:</bold> The reliance on market-based sampling (market gardens) rather than direct field sampling represents a major methodological limitation, as it introduces uncontrolled variables (e.g., potential contamination during transportation, handling, or display).</p>
                                </list-item>
                                <list-item>
                                    <p>
                                        <bold>Composite sampling:</bold> Combining 736 individual vegetables into 153 composite samples reduces statistical variability and may mask important within-sample variation and extreme values.</p>
                                </list-item>
                            </list> </p>
                    </list-item>
                    <list-item>
                        <p>The authors did not specify how samples were washed prior to analysis (e.g., deionized water vs. tap water). This is particularly important, as it may influence the measured concentrations of metals such as lead and nickel.</p>
                    </list-item>
                    <list-item>
                        <p>The Methods section is overly long and somewhat fragmented. It would benefit from clearer organization into distinct subsections, such as: 
                            <list list-type="bullet">
                                <list-item>
                                    <p>Sampling</p>
                                </list-item>
                                <list-item>
                                    <p>Sample preparation</p>
                                </list-item>
                                <list-item>
                                    <p>Analytical methods</p>
                                </list-item>
                                <list-item>
                                    <p>Statistical analysis</p>
                                </list-item>
                            </list> </p>
                    </list-item>
                </list> 
                <bold>
                    <bold>4. Discussion</bold>
                </bold> 
                <list list-type="bullet">
                    <list-item>
                        <p>The claim that flooding introduced )clean( sediments is speculative and not supported by physical or environmental measurements. A robust scientific discussion cannot rely on hypotheses for which key variables (e.g., sediment composition) were not directly assessed.</p>
                    </list-item>
                    <list-item>
                        <p>The comparison with the 2019 study is appropriate; however, the manuscript should further explore and explain the observed differences, particularly regarding elevated lead levels in lettuce.</p>
                    </list-item>
                    <list-item>
                        <p>The repeated use of terms such as 
                            <italic>speculate</italic> and 
                            <italic>speculation</italic> weakens the confidence in the findings. It would be preferable to adopt more scientifically grounded language, for example: 
                            <italic>suggests a potential mechanism</italic>, while still maintaining appropriate caution.</p>
                    </list-item>
                </list> 
                <bold>
                    <bold>5. Conclusions</bold>
                </bold> 
                <list list-type="bullet">
                    <list-item>
                        <p>The conclusion stating that flooding is unlikely to have increased the risk is reasonable and appropriately cautious. However, the stronger claim that flooding may have reduced the risk exceeds the limits of the available data and is not sufficiently supported.</p>
                    </list-item>
                    <list-item>
                        <p>This section should include a clear recommendation for conducting 
                            <bold>longitudinal studies</bold> to monitor heavy metal accumulation over subsequent growing seasons, in order to verify whether the observed reductions are sustained over time.</p>
                    </list-item>
                </list> </p>
            <p> </p>
            <p> </p>
            <p>Is the work clearly and accurately presented and does it cite the current literature?</p>
            <p>Partly</p>
            <p>If applicable, is the statistical analysis and its interpretation appropriate?</p>
            <p>Partly</p>
            <p>Are all the source data underlying the results available to ensure full reproducibility?</p>
            <p>Yes</p>
            <p>Is the study design appropriate and is the work technically sound?</p>
            <p>Partly</p>
            <p>Are the conclusions drawn adequately supported by the results?</p>
            <p>Partly</p>
            <p>Are sufficient details of methods and analysis provided to allow replication by others?</p>
            <p>Yes</p>
            <p>Reviewer Expertise:</p>
            <p>Environmental BiotechnologyHeavy Metal Analysis in Fruits and VegetablesNanotoxicologyEnvironmental Toxicology</p>
            <p>I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.</p>
        </body>
    </sub-article>
    <sub-article article-type="reviewer-report" id="report469790">
        <front-stub>
            <article-id pub-id-type="doi">10.5256/f1000research.193530.r469790</article-id>
            <title-group>
                <article-title>Reviewer response for version 1</article-title>
            </title-group>
            <contrib-group>
                <contrib contrib-type="author">
                    <name>
                        <surname>ALOthman</surname>
                        <given-names>Zeid A.</given-names>
                    </name>
                    <xref ref-type="aff" rid="r469790a1">1</xref>
                    <role>Referee</role>
                    <uri content-type="orcid">https://orcid.org/0000-0001-9970-2480</uri>
                </contrib>
                <aff id="r469790a1">
                    <label>1</label>King Saud University, Riyadh, Riyadh Province, Saudi Arabia</aff>
            </contrib-group>
            <author-notes>
                <fn fn-type="conflict">
                    <p>
                        <bold>Competing interests: </bold>No competing interests were disclosed.</p>
                </fn>
            </author-notes>
            <pub-date pub-type="epub">
                <day>10</day>
                <month>4</month>
                <year>2026</year>
            </pub-date>
            <permissions>
                <copyright-statement>Copyright: &#x00a9; 2026 ALOthman ZA</copyright-statement>
                <copyright-year>2026</copyright-year>
                <license xlink:href="https://creativecommons.org/licenses/by/4.0/">
                    <license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
                </license>
            </permissions>
            <related-article ext-link-type="doi" id="relatedArticleReport469790" related-article-type="peer-reviewed-article" xlink:href="10.12688/f1000research.175538.1"/>
            <custom-meta-group>
                <custom-meta>
                    <meta-name>recommendation</meta-name>
                    <meta-value>approve-with-reservations</meta-value>
                </custom-meta>
            </custom-meta-group>
        </front-stub>
        <body>
            <p>
                <bold>Comments and suggestions:</bold>
            </p>
            <p> 
                <bold>1. </bold>&#x201c;However, some vegetables do exceed maximum limits for lead and cadmium.&#x201d; - Mention the results here.</p>
            <p> 2. &#x201c;Keywords: Heavy Metals, Vegetables, Flooding, Cadmium, Nickel, Thallium, Arsenic, Lead&#x201d; - Use more suitable keywords which are not mentioned in title of the manuscript.</p>
            <p> 3. &#x201c;Globally, monitoring of heavy metals in vegetable crops is becoming recognised as essential
                <ext-link ext-link-type="uri" xlink:href="http:?#ref9">
                    <sup>9</sup>
                </ext-link>&#x00a0;because of the potential risk to public health.&#x201d; --- The authors need to discuss a paragraph related application of various materials in environmental applications for metal analysis. They can check the below related references, which may improve the supporting information.</p>
            <p>
                <ext-link ext-link-type="uri" xlink:href="http:?#ref9"> Colloids and Surfaces A: Physicochemical and Engineering Aspects 647, 2022, 129077</ext-link>
            </p>
            <p>
                <ext-link ext-link-type="uri" xlink:href="http:?#ref9"> Journal of Colloid and Interface Science 646, 2023, 129-140</ext-link>
            </p>
            <p>
                <ext-link ext-link-type="uri" xlink:href="http:?#ref9"> Electrochimica acta 386, 2015, 138482</ext-link>
            </p>
            <p>
                <ext-link ext-link-type="uri" xlink:href="http:?#ref9"> Chemical Engineering Journal 270, 2015, 9-21</ext-link>
            </p>
            <p>
                <ext-link ext-link-type="uri" xlink:href="http:?#ref9"> Separation Science and Technology 55 (10), 2020, 1766-1775</ext-link>
            </p>
            <p>
                <ext-link ext-link-type="uri" xlink:href="http:?#ref9"> Desalination and Water Treatment 57 (46), 2016, 21863-21869</ext-link>
            </p>
            <p>
                <ext-link ext-link-type="uri" xlink:href="http:?#ref9"> 4. &#x201c;736 vegetables were combined to form 153 representative samples&#x201d; -What basic condition were selected for sample collection?</ext-link>
            </p>
            <p>
                <ext-link ext-link-type="uri" xlink:href="http:?#ref9"> 5. &#x201c;
                    <bold>Table 1. Vegetable samples grouped by genus.</bold>&#x201d; &#x2013; Prepare all tables in three-lines format.</ext-link>
            </p>
            <p>
                <ext-link ext-link-type="uri" xlink:href="http:?#ref9"> 6. Provide details for each chemicals used including city and country.</ext-link>
            </p>
            <p>
                <ext-link ext-link-type="uri" xlink:href="http:?#ref9"> 7. &#x201c;To minimise selection bias, vegetables were sampled from 14 market gardens across 10 growing sites, representing all the market gardens available.&#x00a0;
                    <italic>:</italic>&#x201d; &#x2013; Supportive information needed.</ext-link>
            </p>
            <p>
                <ext-link ext-link-type="uri" xlink:href="http:?#ref9"> 8. &#x201c;Bias from including values below the limit of detection (LOD), was minimised by using half LOD and confirming results through sensitivity analysis.&#x201d; &#x2013; More supporting information needed here.</ext-link>
            </p>
            <p>
                <ext-link ext-link-type="uri" xlink:href="http:?#ref9"> 9. &#x201c;We found that local commercially grown vegetables were relatively low in heavy metal concentrations by global standards, though lead and cadmium levels may warrant further investigation in this region.&#x201d; - How this was concluded, explain with supportive information.</ext-link>
            </p>
            <p>
                <ext-link ext-link-type="uri" xlink:href="http:?#ref9"> 10. &#x201c;However, as all vegetable samples were collected from 14 Hawke&#x2019;s Bay market gardens, a strength of this study is that our data provides robust data on heavy metal concentrations from the region&#x2019;s markets.&#x201d; &#x2013;&#x00a0; How accurate the method?</ext-link>
            </p>
            <p>
                <ext-link ext-link-type="uri" xlink:href="http:?#ref9"> 11. &#x201c;
                    <bold>Figure 1. Organic and flooding factors for cadmium concentrations in Hawkes Bay vegetables.</bold>&#x201d; &#x2013; Add optimal conditions in each figure.</ext-link>
            </p>
            <p>
                <ext-link ext-link-type="uri" xlink:href="http:?#ref9"> 12. All the typos and grammar need to check thoroughly in the manuscript.</ext-link>
            </p>
            <p>Is the work clearly and accurately presented and does it cite the current literature?</p>
            <p>Yes</p>
            <p>If applicable, is the statistical analysis and its interpretation appropriate?</p>
            <p>I cannot comment. A qualified statistician is required.</p>
            <p>Are all the source data underlying the results available to ensure full reproducibility?</p>
            <p>No source data required</p>
            <p>Is the study design appropriate and is the work technically sound?</p>
            <p>Yes</p>
            <p>Are the conclusions drawn adequately supported by the results?</p>
            <p>Yes</p>
            <p>Are sufficient details of methods and analysis provided to allow replication by others?</p>
            <p>Yes</p>
            <p>Reviewer Expertise:</p>
            <p>Analytical Chemistry and material chemistry</p>
            <p>I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.</p>
        </body>
    </sub-article>
    <sub-article article-type="reviewer-report" id="report469797">
        <front-stub>
            <article-id pub-id-type="doi">10.5256/f1000research.193530.r469797</article-id>
            <title-group>
                <article-title>Reviewer response for version 1</article-title>
            </title-group>
            <contrib-group>
                <contrib contrib-type="author">
                    <name>
                        <surname>Aarab</surname>
                        <given-names>Iliasse</given-names>
                    </name>
                    <xref ref-type="aff" rid="r469797a1">1</xref>
                    <role>Referee</role>
                    <uri content-type="orcid">https://orcid.org/0009-0008-8701-542X</uri>
                </contrib>
                <aff id="r469797a1">
                    <label>1</label>Ibn Tofail University, Kenitra, Morocco</aff>
            </contrib-group>
            <author-notes>
                <fn fn-type="conflict">
                    <p>
                        <bold>Competing interests: </bold>No competing interests were disclosed.</p>
                </fn>
            </author-notes>
            <pub-date pub-type="epub">
                <day>10</day>
                <month>4</month>
                <year>2026</year>
            </pub-date>
            <permissions>
                <copyright-statement>Copyright: &#x00a9; 2026 Aarab I</copyright-statement>
                <copyright-year>2026</copyright-year>
                <license xlink:href="https://creativecommons.org/licenses/by/4.0/">
                    <license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
                </license>
            </permissions>
            <related-article ext-link-type="doi" id="relatedArticleReport469797" related-article-type="peer-reviewed-article" xlink:href="10.12688/f1000research.175538.1"/>
            <custom-meta-group>
                <custom-meta>
                    <meta-name>recommendation</meta-name>
                    <meta-value>approve-with-reservations</meta-value>
                </custom-meta>
            </custom-meta-group>
        </front-stub>
        <body>
            <p>
                <bold>General Comments</bold>
            </p>
            <p> This manuscript presents a cross-sectional assessment of heavy metal concentrations in vegetables produced in Hawke&#x2019;s Bay, New Zealand, one year after Severe Tropical Cyclone Gabrielle. The topic is timely and relevant, particularly in the context of increasing climate-related extreme events and their potential impact on food safety. The inclusion of both organic and non-organic production systems, as well as flooded versus non-flooded sites, is a notable strength.</p>
            <p> The study benefits from a relatively large number of collected samples and the use of ICP-MS in an ISO-accredited laboratory. The availability of underlying data is also consistent with open science practices.</p>
            <p> However, there are substantial methodological and interpretative limitations that significantly weaken the strength of the conclusions. In particular, the use of composite sampling, limitations in sampling traceability, and overinterpretation of observational findings require careful reconsideration. The manuscript would benefit from major revisions before it can be considered robust.</p>
            <p> 
                <bold>Major Comments</bold>
            </p>
            <p> 
                <underline>
                    <italic>1. Sampling Design and Representativeness</italic>
                </underline>
            </p>
            <p> </p>
            <p> The most significant limitation of this study is the use of composite samples (736 individual vegetables combined into 153 representative samples) . While the authors justify this approach as a way to increase coverage, it has important consequences:</p>
            <p> -It masks within-group variability</p>
            <p> -It prevents identification of outliers</p>
            <p> -It reduces the ability to assess true exposure risk.</p>
            <p> This limitation is acknowledged but underestimated in its impact. Given that food safety assessments often depend on extreme values rather than means, this approach weakens the reliability of the conclusions.</p>
            <p> In addition, samples were collected from markets rather than directly from fields. This introduces uncertainty regarding:</p>
            <p> -True origin of samples</p>
            <p> -Farming practices (organic vs non-organic)</p>
            <p> -Flood exposure classification</p>
            <p> These issues should be more explicitly discussed, and conclusions should be tempered accordingly.</p>
            <p> </p>
            <p> 
                <underline>
                    <italic>2. Analytical Method Validation</italic>
                </underline>
            </p>
            <p> </p>
            <p> Although ICP-MS analysis in an ISO 17025 accredited laboratory is appropriate, the manuscript lacks critical validation details, including:</p>
            <p> -Use of certified reference materials (CRMs)</p>
            <p> -Recovery rates</p>
            <p> -Limits of quantification (LOQ)</p>
            <p> -Measurement uncertainty</p>
            <p> These elements are essential for assessing data quality and should be included.</p>
            <p> </p>
            <p> 
                <italic>
                    <underline>3. Statistical Analysis</underline>
                </italic>
            </p>
            <p> </p>
            <p> The use of ANOVA following log transformation is appropriate in principle. However, the analysis does not adequately reflect the structure of the data: 
                <list list-type="bullet">
                    <list-item>
                        <p>No adjustment for hierarchical structure (market, site, species)</p>
                    </list-item>
                    <list-item>
                        <p>Inability to include key variables due to data limitations</p>
                    </list-item>
                    <list-item>
                        <p>Reduced sensitivity analysis (n = 37) is underpowered</p>
                    </list-item>
                </list> A mixed-effects modeling approach would be more appropriate if the data structure allowed it. At minimum, the limitations of the current statistical approach should be more clearly acknowledged.</p>
            <p> </p>
            <p> 
                <underline>
                    <italic>4. Interpretation of Flooding Effects</italic>
                </underline>
            </p>
            <p> </p>
            <p> The conclusion that flooding may have reduced cadmium and nickel concentrations is not sufficiently supported by the data, as the study lacks pre-cyclone baseline measurements, does not include soil or sediment analyses to substantiate changes in contamination sources, and relies on a cross-sectional design that does not allow causal inference; consequently, the proposed explanation based on sediment dilution remains speculative and should be clearly presented as a hypothesis rather than a definitive conclusion</p>
            <p> </p>
            <p> 
                <underline>
                    <italic>5. Risk Assessment</italic>
                </underline>
            </p>
            <p> The manuscript concludes that vegetables are &#x201c;generally low risk,&#x201d; yet this statement is not sufficiently supported because no dietary exposure assessment has been performed; a more comprehensive evaluation should include estimated daily intake (EDI), Target Hazard Quotient (THQ), and Hazard Index (HI), and without these key indicators, conclusions regarding public health risk remain incomplete and potentially misleading.</p>
            <p> </p>
            <p> 
                <underline>
                    <italic>6.Conclusions and Overinterpretation</italic>
                </underline>
            </p>
            <p> Several conclusions are overstated relative to the data, as the claim of &#x201c;low risk&#x201d; is not fully supported given that some samples exceed permissible limits, the interpretation of flooding effects implies causality despite the observational design, and the differences between organic and non-organic vegetables remain modest in effect size; therefore, the conclusions should be revised to better reflect the observational nature of the study, acknowledge methodological constraints, and incorporate a more cautious interpretation of the findings.</p>
            <p> </p>
            <p> </p>
            <p>Is the work clearly and accurately presented and does it cite the current literature?</p>
            <p>Partly</p>
            <p>If applicable, is the statistical analysis and its interpretation appropriate?</p>
            <p>Partly</p>
            <p>Are all the source data underlying the results available to ensure full reproducibility?</p>
            <p>Yes</p>
            <p>Is the study design appropriate and is the work technically sound?</p>
            <p>Partly</p>
            <p>Are the conclusions drawn adequately supported by the results?</p>
            <p>Partly</p>
            <p>Are sufficient details of methods and analysis provided to allow replication by others?</p>
            <p>Partly</p>
            <p>Reviewer Expertise:</p>
            <p>Trace Element Analysis / Heavy Metals in Food and Environment</p>
            <p>I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.</p>
        </body>
    </sub-article>
    <sub-article article-type="reviewer-report" id="report461971">
        <front-stub>
            <article-id pub-id-type="doi">10.5256/f1000research.193530.r461971</article-id>
            <title-group>
                <article-title>Reviewer response for version 1</article-title>
            </title-group>
            <contrib-group>
                <contrib contrib-type="author">
                    <name>
                        <surname>Bwala</surname>
                        <given-names>Mathias Nzitiri</given-names>
                    </name>
                    <xref ref-type="aff" rid="r461971a1">1</xref>
                    <role>Referee</role>
                </contrib>
                <aff id="r461971a1">
                    <label>1</label>National Environmental Standards and Regulations Enforcement Agency, Maiduguri, Nigeria</aff>
            </contrib-group>
            <author-notes>
                <fn fn-type="conflict">
                    <p>
                        <bold>Competing interests: </bold>No competing interests were disclosed.</p>
                </fn>
            </author-notes>
            <pub-date pub-type="epub">
                <day>28</day>
                <month>2</month>
                <year>2026</year>
            </pub-date>
            <permissions>
                <copyright-statement>Copyright: &#x00a9; 2026 Bwala MN</copyright-statement>
                <copyright-year>2026</copyright-year>
                <license xlink:href="https://creativecommons.org/licenses/by/4.0/">
                    <license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
                </license>
            </permissions>
            <related-article ext-link-type="doi" id="relatedArticleReport461971" related-article-type="peer-reviewed-article" xlink:href="10.12688/f1000research.175538.1"/>
            <custom-meta-group>
                <custom-meta>
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                    <meta-value>approve-with-reservations</meta-value>
                </custom-meta>
            </custom-meta-group>
        </front-stub>
        <body>
            <p>
                <bold>Overall Evaluation</bold>
            </p>
            <p> This manuscript addresses an important environmental and public health issue: heavy metal contamination in vegetables following Severe Tropical Cyclone Gabrielle in Hawke&#x2019;s Bay, New Zealand. The study is timely and locally relevant, particularly given concerns regarding flooding-related contamination and agricultural safety.</p>
            <p> </p>
            <p> The analytical methodology (ICP-MS in an ISO 17025 laboratory) is appropriate and technically sound. The dataset is relatively large in scope (736 vegetables consolidated into 153 composite samples), and the attempt to assess both organic practices and flooding exposure is commendable.</p>
            <p> </p>
            <p> However, there were some methodological, statistical, interpretative, and structural concerns that need to be addressed before the manuscript can be considered robust and publication-ready.</p>
            <p> </p>
            <p> The authors are also advise to use consistent hyphenation and terminology e.g &#x201c;non-organic&#x201d;, &#x201c;nonflooded&#x201d; throughout the manuscript.</p>
            <p> </p>
            <p> 
                <bold>MAJOR CONCERNS/COMMENTS</bold>
            </p>
            <p> </p>
            <p> 
                <bold>1. Study Design&#x00a0;</bold>
            </p>
            <p> Although the authors correctly describe the study as cross-sectional, several statements imply causal inference, particularly regarding flooding:</p>
            <p> &#x201c;
                <italic>Flooding may have reduced cadmium and nickel levels</italic>.&#x201d;</p>
            <p> &#x201c;
                <italic>Recent cyclone flooding may have decreased risk</italic>.&#x201d;</p>
            <p> There is no &#x201c;pre-cyclone baseline data&#x201d; presented. Therefore, the study cannot determine whether the flooding even had increased, decreased, or had no effect on the levels of contamination; as the comparisons are between flooded vs. non-flooded planting zones in a one year post-event. Technically, without pre-event measurements, conclusions about impact of the cyclone are speculative.</p>
            <p> </p>
            <p> 
                <bold>Recommendation: </bold>Reframe conclusions strictly as associative, not causal. Remove speculative mechanistic explanations unless supported by soil/sediment data.</p>
            <p> </p>
            <p> 
                <bold>2. Composite Sampling Strategy</bold>
            </p>
            <p> The decision to combine 736 vegetables into 153 composite samples raises major concerns about the possible lost of variability because the composite sampling might underestimate true maximum exposure levels (within-market variability, individual outliers and worst-case exposure risk), particularly since 3 lead exceedances and 1 cadmium exceedance were detected.</p>
            <p> </p>
            <p> Additionally, there is an issue of unequal sub-sample numbers, for instance Table 1 shows extreme variation in sub-sample counts (e.g., Phaseolus SD = 39.7).</p>
            <p> This shows some inconsistencies in the weighting .</p>
            <p> </p>
            <p> 
                <bold>Recommendation: </bold>Provide statistical justification for composite approach and also discuss explicitly how this impacts risk assessment reliability.</p>
            <p> </p>
            <p> 
                <bold>3. Statistical Modeling Concerns</bold>
            </p>
            <p> Although the authors analyzed the data using two-Way ANOVA model without key covariates. This approach includes both organic and flooded statuses but excludes species, market and planting zones. Vegetable species are known to be strongly influenced by the levels of contamination of heavy metals in the soil and this might varies between species.&#x00a0;</p>
            <p> </p>
            <p> The sensitivity analysis (n = 37) is underpowered and does not sufficiently resolve this limitation.</p>
            <p> </p>
            <p> 
                <bold>Recommendation: </bold>I strongly recommend that the authors should use a mixed-effects model with either species (fixed or random effect) or/and planting zones (random effect)</p>
            <p> </p>
            <p> </p>
            <p> 
                <bold>4. Interpretation of Effect Sizes:</bold>
            </p>
            <p> </p>
            <p> The partial eta squared values reported for instance in Cd: &#x03b7;&#x00b2;p = 0.058 and 0.031 (small) for organic and flooding respectively whereas Ni (flooding): &#x03b7;&#x00b2;p = 0.033 (small), despite these being small effects, the discussion presents them as practically meaningful.</p>
            <p> </p>
            <p> 
                <bold>Recommendation: </bold>Please, distinguish clearly between statistical significance and practical significance. Emphasize the magnitude rather than p-values alone.</p>
            <p> </p>
            <p> </p>
            <p> 
                <bold>5. Risk Assessment Interpretation</bold>
            </p>
            <p> </p>
            <p> The conclusion states that Hawke&#x2019;s Bay vegetables are generally low risk, however there was no dietary intake assessment (EDI), no hazard quotient (HQ) and no cancer risk modeling conducted.</p>
            <p> </p>
            <p> Without these, the statement &#x201c;low risk&#x201d; is not quantitatively supported.</p>
            <p> </p>
            <p> 
                <bold>Recommendation: </bold>I therefore recommend that the author should either add dietary risk assessment calculations or simply rephrase to &#x201c;generally low concentrations relative to Codex limits.&#x201d;</p>
            <p>Is the work clearly and accurately presented and does it cite the current literature?</p>
            <p>Yes</p>
            <p>If applicable, is the statistical analysis and its interpretation appropriate?</p>
            <p>Partly</p>
            <p>Are all the source data underlying the results available to ensure full reproducibility?</p>
            <p>Yes</p>
            <p>Is the study design appropriate and is the work technically sound?</p>
            <p>Partly</p>
            <p>Are the conclusions drawn adequately supported by the results?</p>
            <p>Partly</p>
            <p>Are sufficient details of methods and analysis provided to allow replication by others?</p>
            <p>Yes</p>
            <p>Reviewer Expertise:</p>
            <p>Ecotoxicology</p>
            <p>I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.</p>
        </body>
    </sub-article>
</article>
