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<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Environ. Sci.</journal-id>
<journal-title>Frontiers in Environmental Science</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Environ. Sci.</abbrev-journal-title>
<issn pub-type="epub">2296-665X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
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<article-meta>
<article-id pub-id-type="publisher-id">1509277</article-id>
<article-id pub-id-type="doi">10.3389/fenvs.2024.1509277</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Environmental Science</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Ecological risk and chemical speciation of heavy metals in surface sediments of the Pearl River Estuary for comprehensive assessment</article-title>
<alt-title alt-title-type="left-running-head">Liu et al.</alt-title>
<alt-title alt-title-type="right-running-head">
<ext-link ext-link-type="uri" xlink:href="https://doi.org/10.3389/fenvs.2024.1509277">10.3389/fenvs.2024.1509277</ext-link>
</alt-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Liu</surname>
<given-names>Zhiyan</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/2865290/overview"/>
<role content-type="https://credit.niso.org/contributor-roles/conceptualization/"/>
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</contrib>
<contrib contrib-type="author">
<name>
<surname>Wu</surname>
<given-names>Qian</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
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</contrib>
<contrib contrib-type="author">
<name>
<surname>He</surname>
<given-names>Chao</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/data-curation/"/>
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<contrib contrib-type="author" corresp="yes">
<name>
<surname>Zhang</surname>
<given-names>Peng</given-names>
</name>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
<xref ref-type="corresp" rid="c001">&#x2a;</xref>
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<contrib contrib-type="author">
<name>
<surname>Wang</surname>
<given-names>Xiaoqin</given-names>
</name>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
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<contrib contrib-type="author">
<name>
<surname>Luo</surname>
<given-names>Zifen</given-names>
</name>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
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<contrib contrib-type="author">
<name>
<surname>Yang</surname>
<given-names>Liaoyuan</given-names>
</name>
<xref ref-type="aff" rid="aff5">
<sup>5</sup>
</xref>
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<contrib contrib-type="author" corresp="yes">
<name>
<surname>Huang</surname>
<given-names>Chaoqing</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="corresp" rid="c001">&#x2a;</xref>
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<aff id="aff1">
<sup>1</sup>
<institution>School of Tourism and Culture</institution>, <institution>Guangdong Eco-engineering Polytechnic</institution>, <addr-line>Guangzhou</addr-line>, <country>China</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>School of Resource and Environmental Sciences</institution>, <institution>Wuhan University</institution>, <addr-line>Wuhan</addr-line>, <country>China</country>
</aff>
<aff id="aff3">
<sup>3</sup>
<institution>Collaborative Innovation Center for Emissions Trading System Co-constructed by the Province and Ministry</institution>, <addr-line>Wuhan</addr-line>, <country>China</country>
</aff>
<aff id="aff4">
<sup>4</sup>
<institution>Guangdong Provincial Academy of Environmental Science</institution>, <addr-line>Guangzhou</addr-line>, <country>China</country>
</aff>
<aff id="aff5">
<sup>5</sup>
<institution>Guangzhou Hongjing Ecological Technology Co., Ltd.</institution>, <addr-line>Guangzhou</addr-line>, <country>China</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>
<bold>Edited by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1766289/overview">Yal&#xe7;&#x131;n Tepe</ext-link>, Giresun University, T&#xfc;rkiye</p>
</fn>
<fn fn-type="edited-by">
<p>
<bold>Reviewed by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1875506/overview">Bayram Yuksel</ext-link>, Giresun University, T&#xfc;rkiye</p>
<p>
<ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/2876628/overview">Muhammad El-Alfy</ext-link>, National Institute of Oceanography and Fisheries (NIOF), Egypt</p>
</fn>
<corresp id="c001">&#x2a;Correspondence: Peng Zhang, <email>Peng_ZhangP@126.com</email>; Chaoqing Huang, <email>huangchaoqing@whu.edu.cn</email>
</corresp>
</author-notes>
<pub-date pub-type="epub">
<day>06</day>
<month>12</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="collection">
<year>2024</year>
</pub-date>
<volume>12</volume>
<elocation-id>1509277</elocation-id>
<history>
<date date-type="received">
<day>10</day>
<month>10</month>
<year>2024</year>
</date>
<date date-type="accepted">
<day>14</day>
<month>11</month>
<year>2024</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2024 Liu, Wu, He, Zhang, Wang, Luo, Yang and Huang.</copyright-statement>
<copyright-year>2024</copyright-year>
<copyright-holder>Liu, Wu, He, Zhang, Wang, Luo, Yang and Huang</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<p>The Pearl River Estuary, a vital ecological and economic zone in Southern China, has been heavily impacted by industrial discharges, leading to significant heavy metal contamination. To address the ecological implications of different chemical forms of heavy metals, this study systematically evaluated the total concentrations and chemical speciation of Cu, Zn, Cd, and Pb in surface sediments (0&#x2013;2&#xa0;cm) collected from 17 sites. Chemical speciation was determined using a modified BCR sequential extraction procedure, and pollution and ecological risks were assessed via the geo-accumulation index (<inline-formula id="inf1">
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</inline-formula>), potential ecological risk index (RI), and risk assessment code (RAC). The results showed that all four metals exceeded background values, with Cd presenting the highest enrichment (39 times) and contributing 97% of the ecological risk. Speciation analysis revealed that Cd predominantly exists in bioavailable forms, posing severe ecological threats. This study highlights the urgent need for targeted remediation strategies to mitigate Cd contamination and its ecological impact on the estuary.</p>
</abstract>
<kwd-group>
<kwd>heavy metal contamination</kwd>
<kwd>chemical speciation</kwd>
<kwd>ecological risk assessment</kwd>
<kwd>surface sediments</kwd>
<kwd>Pearl River Estuary</kwd>
</kwd-group>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Toxicology, Pollution and the Environment</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec id="s1">
<title>1 Introduction</title>
<p>The estuarine region is a critical transitional zone between terrestrial and marine ecosystems, where intricate interactions occur among physical, chemical, biological, and geological processes. Due to their inherent ecological sensitivity and fragility, estuarine environments are highly vulnerable to anthropogenic disturbances (<xref ref-type="bibr" rid="B9">Elliott and Quintino, 2007</xref>; <xref ref-type="bibr" rid="B30">Min et al., 2021</xref>). The Pearl River Estuary, one of China&#x2019;s three largest estuaries, serves as a critical breeding and conservation ground for juvenile fish and shrimp and provides habitat for numerous rare aquatic species (<xref ref-type="bibr" rid="B2">Chan and Wang, 2019</xref>). Recent rapid industrial, agricultural, and marine fishery development along the estuary&#x2019;s coastline has led to excessive pollutant discharges, resulting in severe environmental degradation-most notably, the pervasive contamination by heavy metals (<xref ref-type="bibr" rid="B59">Zhao et al., 2020</xref>; <xref ref-type="bibr" rid="B34">Niu et al., 2021a</xref>). Similar studies in other coastal and lagoon systems have shown comparable pollution patterns, highlighting the importance of understanding regional environmental conditions and pollution trends (<xref ref-type="bibr" rid="B44">Ustaoglu et al., 2024</xref>).</p>
<p>Heavy metals are characterized by their intrinsic biological toxicity, environmental persistence, and bioaccumulation potential. Once introduced into aquatic systems, such as rivers and estuaries, these metals accumulate in sediments, posing severe threats to benthic organisms and aquatic life, and may ultimately impact human health through biomagnification along the food chain. Consequently, heavy metals are considered a priority pollutant in aquatic ecosystems (<xref ref-type="bibr" rid="B33">Ndimele, 2012</xref>; <xref ref-type="bibr" rid="B11">Fu et al., 2014</xref>; <xref ref-type="bibr" rid="B23">Li et al., 2014</xref>). Research on the bioaccumulation of metals across different species offers further insight into the potential health risks and ecological implications (<xref ref-type="bibr" rid="B55">Yuksel et al., 2024</xref>). In response to these environmental challenges, the <italic>Guangdong Province Heavy Metal Pollution Prevention Plan</italic> and several ecological restoration projects have been implemented to reduce heavy metal emissions and improve water quality in the Pearl River Estuary (<xref ref-type="bibr" rid="B60">Zhen et al., 2016</xref>; <xref ref-type="bibr" rid="B59">Zhao et al., 2020</xref>). The Pearl River Estuary, acting as a &#x201c;source-sink&#x201d; transition zone, is heavily influenced by hydrodynamic and tidal forces, which intensify the dispersal and accumulation of heavy metals, making it a significant pollution hotspot for surrounding cities such as Guangzhou, Dongguan, and Shenzhen in the Greater Bay Area (<xref ref-type="bibr" rid="B34">Niu et al., 2021a</xref>). Therefore, a systematic evaluation of heavy metal contamination in the estuarine sediments is urgently needed (<xref ref-type="bibr" rid="B47">Wang et al., 2011</xref>; <xref ref-type="bibr" rid="B16">Huang et al., 2018</xref>; <xref ref-type="bibr" rid="B45">Wang et al., 2023</xref>).</p>
<p>The bioavailability and mobility of heavy metals in sediments are influenced not only by their total concentrations but also by their chemical forms and binding states. Sequential chemical extraction methods have been widely adopted to investigate these chemical forms in solid media such as sediments and soils (<xref ref-type="bibr" rid="B5">Choleva et al., 2020</xref>). Tessier first introduced the sequential extraction approach in 1979, and the European Community Bureau of Reference (BCR) later developed a widely used three-step sequential extraction protocol in 1987. The BCR protocol has since undergone numerous modifications to enhance its precision and applicability (<xref ref-type="bibr" rid="B43">Usero et al., 1998</xref>; <xref ref-type="bibr" rid="B57">Zemberyov&#xe1; et al., 2006</xref>). The fundamental principle of these multi-step extraction methods is to simulate varying environmental conditions using chemical reagents of increasing strength, gradually isolating different chemical forms of heavy metals. Tessier&#x2019;s method categorizes heavy metals into five chemical forms: exchangeable, carbonate-bound, iron-manganese oxide-bound, organic matter-bound, and residual. Subsequently, Rauret improved the BCR method in 1999 by grouping metals into four forms: acid-extractable, reducible, oxidizable, and residual. The acid-extractable form encompasses the exchangeable and carbonate-bound metals from Tessier&#x2019;s classification (<xref ref-type="bibr" rid="B36">Rauret et al., 1999</xref>; <xref ref-type="bibr" rid="B1">Anju and Banerjee, 2010</xref>). The exchangeable form is primarily adsorbed onto clay or humic substances. It is the most susceptible to release and migration, while the carbonate-bound form is quickly released under acidic conditions (<xref ref-type="bibr" rid="B32">Morera et al., 2001</xref>). The acid-extractable form is generally the most responsive to environmental changes, presenting high ecological risk and toxicity. Conversely, the residual form is bound within mineral and silicate lattices, rendering it relatively stable and posing minimal ecological risk (<xref ref-type="bibr" rid="B39">Sundaray et al., 2011</xref>). The distribution of heavy metals in various chemical forms indicates their mobility and transformation potential in sediments and soils, reflecting their bioavailability and ecological risks (<xref ref-type="bibr" rid="B22">Kim et al., 2015</xref>).</p>
<p>Numerous studies have assessed the ecological risks of heavy metals in surface sediments by analyzing their total concentrations, chemical forms, and distribution patterns using various technical methods (<xref ref-type="bibr" rid="B54">Yu et al., 2011</xref>). Among these, the Index of Geo-Accumulation (<inline-formula id="inf3">
<mml:math id="m3">
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<mml:msub>
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<mml:mrow>
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<mml:mi>o</mml:mi>
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</inline-formula>) and the Potential Ecological Risk Index (RI) are commonly employed to evaluate the overall pollution status and potential ecological risks of heavy metals in sediments (<xref ref-type="bibr" rid="B6">Cui et al., 2014</xref>; <xref ref-type="bibr" rid="B28">Maanan et al., 2015</xref>). The <inline-formula id="inf4">
<mml:math id="m4">
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<mml:mrow>
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</inline-formula> provides a straightforward reflection of natural and anthropogenic influences on sediment quality. At the same time, the RI considers the concentrations, toxicities, and environmental sensitivities to heavy metals, thus offering a comprehensive risk assessment framework (<xref ref-type="bibr" rid="B58">Zhang et al., 2016</xref>). However, both indices fail to consider the varying chemical forms of heavy metals, which play a decisive role in determining their toxicity and mobility in aquatic environments. The Risk Assessment Code (RAC), which emphasizes the chemical forms of heavy metals, offers a more accurate evaluation by correlating the bioavailable fractions with environmental risks (<xref ref-type="bibr" rid="B51">Yang et al., 2014</xref>).</p>
<p>Although extensive research has been conducted on heavy metal accumulation in the Pearl River Estuary, most studies have focused on total concentrations and pollution risks (<xref ref-type="bibr" rid="B17">Ip et al., 2004</xref>; <xref ref-type="bibr" rid="B52">Yang et al., 2012</xref>; <xref ref-type="bibr" rid="B59">Zhao et al., 2020</xref>), with limited attention paid to the chemical forms and their associated ecological risks. Comparative studies across various regions can provide a broader context, highlight unique pollution characteristics, or align them with global patterns (<xref ref-type="bibr" rid="B56">Yuksel et al., 2021</xref>; <xref ref-type="bibr" rid="B42">Topaldemir et al., 2023</xref>). Therefore, this study aims to bridge this gap by employing the <inline-formula id="inf5">
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</inline-formula>, RI, and RAC methodologies to comprehensively analyze the accumulation characteristics, chemical speciation, and ecological risks of four common heavy metals (Cu, Zn, Cd, and Pb) in the surface sediments of the Pearl River Estuary. The results of this study will provide a valuable theoretical foundation for the long-term management and mitigation of heavy metal pollution in the Pearl River Estuary.</p>
</sec>
<sec sec-type="materials|methods" id="s2">
<title>2 Materials and methods</title>
<sec id="s2-1">
<title>2.1 Study area overview</title>
<p>The Pearl River, formed by the convergence of the Xi River, Bei River, Dong River, and numerous tributaries within the Pearl River Delta, is the most extensive river system in southern China. The third longest in the country, spanning 2,320&#xa0;km with a drainage area of 450,000 square kilometers, of which 440,000 square kilometers are within Chinese territory (<xref ref-type="bibr" rid="B3">Chen et al., 2008</xref>). The Pearl River&#x2019;s annual runoff exceeds 330 billion cubic meters, second only to the Yangtze River, and it produces seven times the annual runoff of the Yellow River (<xref ref-type="bibr" rid="B50">Xu et al., 2010</xref>). This study focuses on the surface sediments of the Pearl River Estuary, extending from Duntouji in Guangzhou (113.51&#xb0;E, 23.05&#xb0;N) in the north to near the Jitimen Bridge in Zhuhai (113.26&#xb0;E, 22.09&#xb0;N), including the Lingding Yang region, which is characterized as a tide-dominated estuarine bay. Sampling was conducted in January 2022, with 17 sampling sites established across the study area, as depicted in <xref ref-type="fig" rid="F1">Figure 1</xref>.</p>
<fig id="F1" position="float">
<label>FIGURE 1</label>
<caption>
<p>Distribution of sediment sampling points in the Pearl River Estuary.</p>
</caption>
<graphic xlink:href="fenvs-12-1509277-g001.tif"/>
</fig>
</sec>
<sec id="s2-2">
<title>2.2 Sample collection and processing</title>
<p>Due to the mild and consistent climate conditions in the Pearl River Estuary region throughout the year, coupled with the continuous and stable industrial activities along its banks, the levels of heavy metal pollution in this area are generally consistent year-round (<xref ref-type="bibr" rid="B53">Ye et al., 2020</xref>). Sediment samples were collected using a grab sampler to obtain undisturbed surface sediments (0&#x2013;2&#xa0;cm) and stored in polyethylene bags at 0&#xb0;C&#x2013;4&#xb0;C. The samples were air-dried, and visible gravel, plants, and animal residues were removed. Subsequently, the sediments were oven-dried at 105&#xb0;C to a constant weight, ground using an agate mortar, and sieved through a 200-mesh screen. The processed samples were stored in a desiccator for further analysis. According to the GB 15618&#x2013;2008 Environmental Quality Standards for Soils, Cu, Zn, Cd, and Pb were selected as target heavy metals of concern (<xref ref-type="bibr" rid="B31">Ministry of Environmental Protection of China, 2008</xref>). The total concentrations of these metals in the sediment samples were determined after digestion using a mixture of HCl, HNO&#x2083;, HF, and HClO&#x2084;, following the four-acid digestion method (GB/T17140) (<xref ref-type="bibr" rid="B38">Standardization Administration of China, 1997</xref>). Cd and Pb concentrations were measured using a graphite furnace atomic absorption spectrophotometer, while Cu and Zn were quantified using a flame atomic absorption spectrophotometer. These different analytical techniques are chosen based on the concentration sensitivity and detection requirements specific to each metal. The graphite furnace atomic absorption spectrophotometer (GFAAS) offers a significantly higher sensitivity compared to FAAS, making it ideal for detecting trace levels of metals like Cd and Pb, which are often present in lower concentrations. In contrast, flame atomic absorption spectrophotometry (FAAS) is suitable for metals such as Cu and Zn, which are generally found in higher concentrations in environmental samples. FAAS is also efficient for rapid analysis when ultra-trace sensitivity is not required. Utilizing these methods in tandem ensures accurate measurement across different concentration ranges, enhancing the precision and reliability of the heavy metal quantification (<xref ref-type="bibr" rid="B13">Garnier et al., 2006</xref>; <xref ref-type="bibr" rid="B29">Medeiros et al., 2020</xref>).</p>
<p>The chemical forms of the heavy metals were extracted using the modified BCR sequential extraction procedure (<xref ref-type="bibr" rid="B36">Rauret et al., 1999</xref>; <xref ref-type="bibr" rid="B40">Sungur et al., 2014</xref>). The specific extraction protocol consisted of (1) Acid-extractable fraction (F1), including exchangeable and carbonate-bound metals, extracted using 0.11&#xa0;mol&#xb7;L&#x207b;<sup>1</sup> acetic acid; (2) Reducible fraction (F2), representing metals bound to iron-manganese oxides, extracted using 0.5&#xa0;mol&#xb7;L&#x207b;<sup>1</sup> hydroxylamine hydrochloride; (3) Oxidizable fraction (F3), representing metals bound to organic matter and sulfides, extracted by digesting the F2 residue with 30% (8.8&#xa0;mol&#xb7;L&#x207b;<sup>1</sup>) hydrogen peroxide followed by 1&#xa0;mol&#xb7;L&#x207b;<sup>1</sup> ammonium acetate; and (4) Residual fraction (F4), representing metals bound to silicates or within the mineral lattice, extracted by digesting the F3 residue using a mixture of three acids.</p>
<p>Three parallel samples and an environmental standard reference material (ESS-4) were used for quality control to ensure analytical precision and accuracy. The relative error of heavy metal concentrations in parallel samples was maintained below 10%, and the recovery rates of standard reference materials ranged from 95% to 120%. The detection limits for Cu, Zn, Cd, and Pb were 0.5&#xa0;mg&#xb7;kg&#x207b;<sup>1</sup>, 7.0&#xa0;mg&#xb7;kg&#x207b;<sup>1</sup>, 0.07&#xa0;mg&#xb7;kg&#x207b;<sup>1</sup>, and 2.0&#xa0;mg&#xb7;kg&#x207b;<sup>1</sup>, respectively.</p>
<p>While the BCR sequential extraction procedure is widely used for assessing heavy metal speciation, potential limitations exist. Interferences from other sediment components, such as organic matter or carbonate content, may affect the extraction accuracy, particularly for acid-extractable and reducible fractions. Additionally, the four-acid digestion method used for total concentration measurement could lead to variations in recovery rates for certain metals due to complex matrix effects (<xref ref-type="bibr" rid="B37">Ryan et al., 2008</xref>).</p>
</sec>
<sec id="s2-3">
<title>2.3 Data analysis</title>
<sec id="s2-3-1">
<title>2.3.1 Pollution characteristics and risk assessment based on total heavy metal concentrations</title>
<sec id="s2-3-1-1">
<title>2.3.1.1 Index of geo-accumulation (<inline-formula id="inf6">
<mml:math id="m6">
<mml:mrow>
<mml:msub>
<mml:mi>I</mml:mi>
<mml:mrow>
<mml:mi>g</mml:mi>
<mml:mi>e</mml:mi>
<mml:mi>o</mml:mi>
</mml:mrow>
</mml:msub>
</mml:mrow>
</mml:math>
</inline-formula>)</title>
<p>The Index of Geo-Accumulation (<inline-formula id="inf7">
<mml:math id="m7">
<mml:mrow>
<mml:msub>
<mml:mi>I</mml:mi>
<mml:mrow>
<mml:mi>g</mml:mi>
<mml:mi>e</mml:mi>
<mml:mi>o</mml:mi>
</mml:mrow>
</mml:msub>
</mml:mrow>
</mml:math>
</inline-formula>), is widely used to evaluate the extent of heavy metal pollution in sediments (<xref ref-type="bibr" rid="B20">Karbassi et al., 2008</xref>; <xref ref-type="bibr" rid="B21">Ke et al., 2017</xref>). It is calculated using <xref ref-type="disp-formula" rid="e1">Equation 1</xref> as follows:<disp-formula id="e1">
<mml:math id="m8">
<mml:mrow>
<mml:msub>
<mml:mi>I</mml:mi>
<mml:mrow>
<mml:mi>g</mml:mi>
<mml:mi>e</mml:mi>
<mml:mi>o</mml:mi>
</mml:mrow>
</mml:msub>
<mml:mo>&#x3d;</mml:mo>
<mml:msub>
<mml:mi mathvariant="italic">log</mml:mi>
<mml:mn>2</mml:mn>
</mml:msub>
<mml:mrow>
<mml:mfenced open="(" close=")" separators="|">
<mml:mrow>
<mml:mfrac>
<mml:msub>
<mml:mi>C</mml:mi>
<mml:mi>i</mml:mi>
</mml:msub>
<mml:mrow>
<mml:mn>1.5</mml:mn>
<mml:msub>
<mml:mi>B</mml:mi>
<mml:mi>i</mml:mi>
</mml:msub>
</mml:mrow>
</mml:mfrac>
</mml:mrow>
</mml:mfenced>
</mml:mrow>
</mml:mrow>
</mml:math>
<label>(1)</label>
</disp-formula>
<inline-formula id="inf8">
<mml:math id="m9">
<mml:mrow>
<mml:msub>
<mml:mi>I</mml:mi>
<mml:mrow>
<mml:mi>g</mml:mi>
<mml:mi>e</mml:mi>
<mml:mi>o</mml:mi>
</mml:mrow>
</mml:msub>
</mml:mrow>
</mml:math>
</inline-formula> is the geo-accumulation index, <inline-formula id="inf9">
<mml:math id="m10">
<mml:mrow>
<mml:msub>
<mml:mi>C</mml:mi>
<mml:mi>i</mml:mi>
</mml:msub>
</mml:mrow>
</mml:math>
</inline-formula> is the measured concentration of element I in the sediment, and <inline-formula id="inf10">
<mml:math id="m11">
<mml:mrow>
<mml:msub>
<mml:mi>B</mml:mi>
<mml:mi>i</mml:mi>
</mml:msub>
</mml:mrow>
</mml:math>
</inline-formula> is the geochemical background value of element I in the region. In this study, the background values for Cu, Zn, Cd, and Pb are derived from the geometric mean values for soils in Guangdong Province (<xref ref-type="bibr" rid="B4">China National Environmental Monitoring Center, 1990</xref>). The background values used are Cu (12.1&#xa0;mg&#xb7;kg&#x207b;<sup>1</sup>), Zn (42.7&#xa0;mg&#xb7;kg&#x207b;<sup>1</sup>), Cd (0.026&#xa0;mg&#xb7;kg&#x207b;<sup>1</sup>), and Pb (32.0&#xa0;mg&#xb7;kg&#x207b;<sup>1</sup>). Based on the calculated <inline-formula id="inf11">
<mml:math id="m12">
<mml:mrow>
<mml:msub>
<mml:mi>I</mml:mi>
<mml:mrow>
<mml:mi>g</mml:mi>
<mml:mi>e</mml:mi>
<mml:mi>o</mml:mi>
</mml:mrow>
</mml:msub>
</mml:mrow>
</mml:math>
</inline-formula> values, the degree of heavy metal pollution is categorized into seven classes, as shown in <xref ref-type="table" rid="T1">Table 1</xref> (<xref ref-type="bibr" rid="B10">Forstner et al., 1993</xref>).</p>
<table-wrap id="T1" position="float">
<label>TABLE 1</label>
<caption>
<p>Assessment of geo-accumulation index (<inline-formula id="inf12">
<mml:math id="m13">
<mml:mrow>
<mml:msub>
<mml:mi>I</mml:mi>
<mml:mrow>
<mml:mi>g</mml:mi>
<mml:mi>e</mml:mi>
<mml:mi>o</mml:mi>
</mml:mrow>
</mml:msub>
</mml:mrow>
</mml:math>
</inline-formula>) and sediment pollution degree.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="center">
<inline-formula id="inf13">
<mml:math id="m14">
<mml:mrow>
<mml:msub>
<mml:mi>I</mml:mi>
<mml:mrow>
<mml:mi>g</mml:mi>
<mml:mi>e</mml:mi>
<mml:mi>o</mml:mi>
</mml:mrow>
</mml:msub>
</mml:mrow>
</mml:math>
</inline-formula>
</th>
<th align="center">Level</th>
<th align="center">Pollution degree</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">&#x3c;0</td>
<td align="center">0</td>
<td align="center">Practically unpolluted</td>
</tr>
<tr>
<td align="center">0&#x2013;1</td>
<td align="center">1</td>
<td align="center">Unpolluted to moderately polluted</td>
</tr>
<tr>
<td align="center">1&#x2013;2</td>
<td align="center">2</td>
<td align="center">Moderately polluted</td>
</tr>
<tr>
<td align="center">2&#x2013;3</td>
<td align="center">3</td>
<td align="center">Moderately to heavily polluted</td>
</tr>
<tr>
<td align="center">3&#x2013;4</td>
<td align="center">4</td>
<td align="center">Heavily polluted</td>
</tr>
<tr>
<td align="center">4&#x2013;5</td>
<td align="center">5</td>
<td align="center">Heavily to extremely polluted</td>
</tr>
<tr>
<td align="center">&#x3e;5</td>
<td align="center">6</td>
<td align="center">Extremely polluted</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="s2-3-1-2">
<title>2.3.1.2 Potential ecological risk assessment method (RI)</title>
<p>The potential ecological risk assessment method, proposed by <xref ref-type="bibr" rid="B15">Hakanson (1980)</xref>, evaluates the ecological risks posed by heavy metals in sediments (<xref ref-type="bibr" rid="B28">Maanan et al., 2015</xref>; <xref ref-type="bibr" rid="B58">Zhang et al., 2016</xref>). The values are calculated using <xref ref-type="disp-formula" rid="e2">Equation 2</xref> as follows:<disp-formula id="e2">
<mml:math id="m15">
<mml:mrow>
<mml:msubsup>
<mml:mi>E</mml:mi>
<mml:mi>i</mml:mi>
<mml:mi>T</mml:mi>
</mml:msubsup>
<mml:mo>&#x3d;</mml:mo>
<mml:msub>
<mml:mi>T</mml:mi>
<mml:mi>i</mml:mi>
</mml:msub>
<mml:mo>&#xd7;</mml:mo>
<mml:mfrac>
<mml:mrow>
<mml:msubsup>
<mml:mi>C</mml:mi>
<mml:mi>s</mml:mi>
<mml:mi>i</mml:mi>
</mml:msubsup>
</mml:mrow>
<mml:mrow>
<mml:msubsup>
<mml:mi>C</mml:mi>
<mml:mi>n</mml:mi>
<mml:mi>i</mml:mi>
</mml:msubsup>
</mml:mrow>
</mml:mfrac>
</mml:mrow>
</mml:math>
<label>(2)</label>
</disp-formula>where <inline-formula id="inf14">
<mml:math id="m16">
<mml:mrow>
<mml:msubsup>
<mml:mi>E</mml:mi>
<mml:mi>i</mml:mi>
<mml:mi>T</mml:mi>
</mml:msubsup>
</mml:mrow>
</mml:math>
</inline-formula> is the potential ecological risk factor for element i, <inline-formula id="inf15">
<mml:math id="m17">
<mml:mrow>
<mml:msub>
<mml:mi>T</mml:mi>
<mml:mi>i</mml:mi>
</mml:msub>
</mml:mrow>
</mml:math>
</inline-formula> is the toxic response factor for element i, which depends on its toxicity and the sensitivity of the ecosystem to the element. In this study, the toxic response factors (<inline-formula id="inf16">
<mml:math id="m18">
<mml:mrow>
<mml:msub>
<mml:mi>T</mml:mi>
<mml:mi>i</mml:mi>
</mml:msub>
</mml:mrow>
</mml:math>
</inline-formula>) for heavy metals (Cu &#x3d; 5, Zn &#x3d; 1, Cd &#x3d; 30, and Pb &#x3d; 5 (<xref ref-type="bibr" rid="B41">Suresh et al., 2011</xref>; <xref ref-type="bibr" rid="B21">Ke et al., 2017</xref>). <inline-formula id="inf17">
<mml:math id="m19">
<mml:mrow>
<mml:msubsup>
<mml:mi>C</mml:mi>
<mml:mi>s</mml:mi>
<mml:mi>i</mml:mi>
</mml:msubsup>
</mml:mrow>
</mml:math>
</inline-formula> is the measured concentration of element I in the sediment (mg&#xb7;kg&#x207b;<sup>1</sup>), and <inline-formula id="inf18">
<mml:math id="m20">
<mml:mrow>
<mml:msubsup>
<mml:mi>C</mml:mi>
<mml:mi>n</mml:mi>
<mml:mi>i</mml:mi>
</mml:msubsup>
</mml:mrow>
</mml:math>
</inline-formula> is the background concentration of element I, derived from Guangdong Province soil background values.</p>
<p>The comprehensive ecological risk of multiple heavy metals is calculated using <xref ref-type="disp-formula" rid="e3">Equation 3</xref> as follows:<disp-formula id="e3">
<mml:math id="m21">
<mml:mrow>
<mml:mtext>RI</mml:mtext>
<mml:mo>&#x3d;</mml:mo>
<mml:mo>&#x2211;</mml:mo>
<mml:msubsup>
<mml:mi>E</mml:mi>
<mml:mi>i</mml:mi>
<mml:mi>r</mml:mi>
</mml:msubsup>
</mml:mrow>
</mml:math>
<label>(3)</label>
</disp-formula>RI represents the comprehensive potential ecological risk index for multiple heavy metals, and <inline-formula id="inf19">
<mml:math id="m22">
<mml:mrow>
<mml:mo>&#x2211;</mml:mo>
<mml:msubsup>
<mml:mi>E</mml:mi>
<mml:mi>i</mml:mi>
<mml:mi>r</mml:mi>
</mml:msubsup>
</mml:mrow>
</mml:math>
</inline-formula> is the sum of the individual potential ecological risk factors for each heavy metal. The classification standards for RI are shown in <xref ref-type="table" rid="T2">Table 2</xref> (<xref ref-type="bibr" rid="B21">Ke et al., 2017</xref>; <xref ref-type="bibr" rid="B46">Wang et al., 2018</xref>).</p>
<table-wrap id="T2" position="float">
<label>TABLE 2</label>
<caption>
<p>Classifications of potential ecological risk index (ERI).</p>
</caption>
<table>
<thead valign="top">
<tr>
<th rowspan="2" align="center">Assessment criterion</th>
<th colspan="5" align="center">Ecological risk index</th>
</tr>
<tr>
<th align="center">Low</th>
<th align="center">Moderate</th>
<th align="center">Considerable</th>
<th align="center">High</th>
<th align="center">Very high</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">
<inline-formula id="inf20">
<mml:math id="m23">
<mml:mrow>
<mml:msubsup>
<mml:mi mathvariant="normal">E</mml:mi>
<mml:mi mathvariant="normal">r</mml:mi>
<mml:mi mathvariant="normal">i</mml:mi>
</mml:msubsup>
</mml:mrow>
</mml:math>
</inline-formula>
</td>
<td align="center">
<inline-formula id="inf21">
<mml:math id="m24">
<mml:mrow>
<mml:msubsup>
<mml:mi mathvariant="normal">E</mml:mi>
<mml:mi mathvariant="normal">r</mml:mi>
<mml:mi mathvariant="normal">i</mml:mi>
</mml:msubsup>
</mml:mrow>
</mml:math>
</inline-formula> &#x3c; 40</td>
<td align="center">40 &#x2264; <inline-formula id="inf22">
<mml:math id="m25">
<mml:mrow>
<mml:msubsup>
<mml:mi mathvariant="normal">E</mml:mi>
<mml:mi mathvariant="normal">r</mml:mi>
<mml:mi mathvariant="normal">i</mml:mi>
</mml:msubsup>
</mml:mrow>
</mml:math>
</inline-formula> &#x3c; 80</td>
<td align="center">80 &#x2264; <inline-formula id="inf23">
<mml:math id="m26">
<mml:mrow>
<mml:msubsup>
<mml:mi mathvariant="normal">E</mml:mi>
<mml:mi mathvariant="normal">r</mml:mi>
<mml:mi mathvariant="normal">i</mml:mi>
</mml:msubsup>
</mml:mrow>
</mml:math>
</inline-formula> &#x3c;160</td>
<td align="center">160 &#x2264; <inline-formula id="inf24">
<mml:math id="m27">
<mml:mrow>
<mml:msubsup>
<mml:mi mathvariant="normal">E</mml:mi>
<mml:mi mathvariant="normal">r</mml:mi>
<mml:mi mathvariant="normal">i</mml:mi>
</mml:msubsup>
</mml:mrow>
</mml:math>
</inline-formula> &#x3c; 320</td>
<td align="center">
<inline-formula id="inf25">
<mml:math id="m28">
<mml:mrow>
<mml:msubsup>
<mml:mi mathvariant="normal">E</mml:mi>
<mml:mi mathvariant="normal">r</mml:mi>
<mml:mi mathvariant="normal">i</mml:mi>
</mml:msubsup>
</mml:mrow>
</mml:math>
</inline-formula> &#x2265; 320</td>
</tr>
<tr>
<td align="center">RI</td>
<td align="center">RI &#x3c; 150</td>
<td align="center">150 &#x2264; RI &#x3c; 300</td>
<td align="center">300 &#x2264; RI &#x3c; 600</td>
<td align="center">RI &#x2265; 600</td>
<td align="left"/>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
</sec>
<sec id="s2-3-2">
<title>2.3.2 Risk assessment based on different chemical forms of heavy metals</title>
<p>The Risk Assessment Code (RAC) method provides a deeper understanding of the relationship between bioavailability, mineral mobility, and the environmental risks of heavy metals (<xref ref-type="bibr" rid="B51">Yang et al., 2014</xref>). RAC is calculated as the proportion of the acid-extractable fraction relative to the total content of an element (<xref ref-type="bibr" rid="B27">Lv et al., 2013</xref>), as shown in <xref ref-type="disp-formula" rid="e4">Equation 4</xref> below:<disp-formula id="e4">
<mml:math id="m29">
<mml:mrow>
<mml:mtext>RAC</mml:mtext>
<mml:mo>&#x3d;</mml:mo>
<mml:mfrac>
<mml:msub>
<mml:mi>C</mml:mi>
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mn>1</mml:mn>
</mml:mrow>
</mml:msub>
<mml:mrow>
<mml:msub>
<mml:mi>C</mml:mi>
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mn>1</mml:mn>
</mml:mrow>
</mml:msub>
<mml:mo>&#x2b;</mml:mo>
<mml:msub>
<mml:mi>C</mml:mi>
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msub>
<mml:mo>&#x2b;</mml:mo>
<mml:msub>
<mml:mi>C</mml:mi>
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mn>3</mml:mn>
</mml:mrow>
</mml:msub>
<mml:mo>&#x2b;</mml:mo>
<mml:msub>
<mml:mi>C</mml:mi>
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mn>4</mml:mn>
</mml:mrow>
</mml:msub>
</mml:mrow>
</mml:mfrac>
<mml:mo>&#xd7;</mml:mo>
<mml:mn>100</mml:mn>
<mml:mo>%</mml:mo>
</mml:mrow>
</mml:math>
<label>(4)</label>
</disp-formula>where <inline-formula id="inf26">
<mml:math id="m30">
<mml:mrow>
<mml:msub>
<mml:mi>C</mml:mi>
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mn>1</mml:mn>
</mml:mrow>
</mml:msub>
</mml:mrow>
</mml:math>
</inline-formula> is the acid-extractable fraction content (mg&#xb7;kg&#x207b;<sup>1</sup>), <inline-formula id="inf27">
<mml:math id="m31">
<mml:mrow>
<mml:msub>
<mml:mi>C</mml:mi>
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msub>
</mml:mrow>
</mml:math>
</inline-formula> is the reducible fraction content (mg&#xb7;kg&#x207b;<sup>1</sup>), <inline-formula id="inf28">
<mml:math id="m32">
<mml:mrow>
<mml:msub>
<mml:mi>C</mml:mi>
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mn>3</mml:mn>
</mml:mrow>
</mml:msub>
</mml:mrow>
</mml:math>
</inline-formula> is the oxidizable fraction content (mg&#xb7;kg&#x207b;<sup>1</sup>), and <inline-formula id="inf29">
<mml:math id="m33">
<mml:mrow>
<mml:msub>
<mml:mi>C</mml:mi>
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mn>4</mml:mn>
</mml:mrow>
</mml:msub>
</mml:mrow>
</mml:math>
</inline-formula> is the residual fraction content (mg&#xb7;kg&#x207b;<sup>1</sup>). The risk levels based on RAC values are classified as shown in <xref ref-type="table" rid="T3">Table 3</xref>.</p>
<table-wrap id="T3" position="float">
<label>TABLE 3</label>
<caption>
<p>Classification standards for risk assessment method (RAC).</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="center">Acid-extractable content (%)</th>
<th align="center">Risk level</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">RAC &#x3c; 1</td>
<td align="center">No risk</td>
</tr>
<tr>
<td align="center">1 &#x2264; RAC &#x3c; 10</td>
<td align="center">Low risk</td>
</tr>
<tr>
<td align="center">10 &#x2264; RAC &#x3c; 30</td>
<td align="center">Medium risk</td>
</tr>
<tr>
<td align="center">30 &#x2264; RAC &#x3c; 50</td>
<td align="center">High risk</td>
</tr>
<tr>
<td align="center">RAC &#x3e; 50</td>
<td align="center">Very high risk</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
</sec>
</sec>
<sec sec-type="results" id="s3">
<title>3 Results</title>
<sec id="s3-1">
<title>3.1 Descriptive statistics of total Cu, Zn, Cd, and Pb in sediments</title>
<sec id="s3-1-1">
<title>3.1.1 Concentrations of Cu, Zn, Cd, and Pb in surface sediments of the Pearl River estuary</title>
<p>The concentrations of Cu, Zn, Cd, and Pb in surface sediments of the Pearl River Estuary are illustrated in <xref ref-type="fig" rid="F2">Figure 2</xref>. The measured concentrations varied as follows: Cu ranged from 30.14 to 150.74&#xa0;mg&#xb7;kg&#x207b;<sup>1</sup>, Zn from 78.63 to 367.16&#xa0;mg&#xb7;kg&#x207b;<sup>1</sup>, Cd from 0.18 to 2.63&#xa0;mg&#xb7;kg&#x207b;<sup>1</sup>, and Pb from 40.57 to 115.11&#xa0;mg&#xb7;kg&#x207b;<sup>1</sup>. All sampling sites exhibited heavy metal concentrations exceeding the background values for soils in Guangdong Province. Among these, Cd showed the most significant elevation, with a mean concentration of 1.05&#xa0;mg&#xb7;kg&#x207b;<sup>1</sup>, surpassing the background value by 39 times. The mean concentrations of Cu (58.71&#xa0;mg&#xb7;kg&#x207b;<sup>1</sup>), Zn (88.23&#xa0;mg&#xb7;kg&#x207b;<sup>1</sup>), and Pb (67.17&#xa0;mg&#xb7;kg&#x207b;<sup>1</sup>) exceeded the background values by 3.85, 2.54, and 1.10 times, respectively.</p>
<fig id="F2" position="float">
<label>FIGURE 2</label>
<caption>
<p>Descriptive statistics and comparative parameters of Cu, Zn, Cd, and Pb in surface sediments of the Pearl River Estuary (unit: mg&#xb7;kg&#x207b;<sup>1</sup>).</p>
</caption>
<graphic xlink:href="fenvs-12-1509277-g002.tif"/>
</fig>
</sec>
<sec id="s3-1-2">
<title>3.1.2 Geo-accumulation index (<inline-formula id="inf30">
<mml:math id="m34">
<mml:mrow>
<mml:msub>
<mml:mi mathvariant="bold-italic">I</mml:mi>
<mml:mrow>
<mml:mi mathvariant="bold-italic">g</mml:mi>
<mml:mi mathvariant="bold-italic">e</mml:mi>
<mml:mi mathvariant="bold-italic">o</mml:mi>
</mml:mrow>
</mml:msub>
</mml:mrow>
</mml:math>
</inline-formula>) and pollution degree assessment of heavy metals</title>
<p>The geo-accumulation index (<inline-formula id="inf31">
<mml:math id="m35">
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</inline-formula>) and corresponding pollution assessment for the four heavy metals across 17 sampling sites are summarized in <xref ref-type="table" rid="T4">Table 4</xref>. Results indicate varying pollution levels for different metals based on <inline-formula id="inf32">
<mml:math id="m36">
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</inline-formula> values. Cd exhibited the highest pollution level, with an average <inline-formula id="inf33">
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</inline-formula> of 4.47 (ranging from 2.09 to 6.08), classified as severely polluted (Class 5). Conversely, Pb showed a relatively low pollution level with an average <inline-formula id="inf34">
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</inline-formula> of 0.42 (ranging from &#x2212;0.12 to 1.26), indicating a clean status (Class 1). The mean <inline-formula id="inf35">
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</inline-formula> values for Cu and Zn were 1.50 and 1.07, respectively, both classified as moderately polluted (Class 2).</p>
<table-wrap id="T4" position="float">
<label>TABLE 4</label>
<caption>
<p>Geo-accumulation index (<inline-formula id="inf36">
<mml:math id="m40">
<mml:mrow>
<mml:msub>
<mml:mi>I</mml:mi>
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</inline-formula>) and pollution degree assessment for Cu, Zn, Cd, and Pb in surface sediments of the Pearl River Estuary.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th rowspan="2" align="center">Sampling site</th>
<th colspan="2" align="center">Cu</th>
<th colspan="2" align="center">Zn</th>
<th colspan="2" align="center">Cd</th>
<th colspan="2" align="center">Pb</th>
<th colspan="2" align="center">Average</th>
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<tr>
<th align="center">
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</inline-formula>
</th>
<th align="center">Pollution degree</th>
<th align="center">
<inline-formula id="inf38">
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</th>
<th align="center">Pollution degree</th>
<th align="center">
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</th>
<th align="center">Pollution degree</th>
<th align="center">
<inline-formula id="inf40">
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<th align="center">Pollution degree</th>
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</inline-formula>
</th>
<th align="center">Pollution degree</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">S1</td>
<td align="center">3.05</td>
<td align="center">MHP</td>
<td align="center">2.37</td>
<td align="center">MP</td>
<td align="center">5.32</td>
<td align="center">SP</td>
<td align="center">1.04</td>
<td align="center">MMP</td>
<td align="center">2.95</td>
<td align="center">MP</td>
</tr>
<tr>
<td align="center">S2</td>
<td align="center">0.90</td>
<td align="center">LP</td>
<td align="center">0.30</td>
<td align="center">LP</td>
<td align="center">4.11</td>
<td align="center">HP</td>
<td align="center">0.54</td>
<td align="center">C</td>
<td align="center">1.46</td>
<td align="center">MMP</td>
</tr>
<tr>
<td align="center">S3</td>
<td align="center">1.31</td>
<td align="center">MMP</td>
<td align="center">0.96</td>
<td align="center">LP</td>
<td align="center">4.70</td>
<td align="center">HP</td>
<td align="center">0.16</td>
<td align="center">C</td>
<td align="center">1.78</td>
<td align="center">MMP</td>
</tr>
<tr>
<td align="center">S4</td>
<td align="center">2.52</td>
<td align="center">MP</td>
<td align="center">2.14</td>
<td align="center">MP</td>
<td align="center">5.66</td>
<td align="center">SP</td>
<td align="center">1.07</td>
<td align="center">MMP</td>
<td align="center">2.85</td>
<td align="center">MP</td>
</tr>
<tr>
<td align="center">S5</td>
<td align="center">2.91</td>
<td align="center">MP</td>
<td align="center">2.52</td>
<td align="center">MP</td>
<td align="center">6.08</td>
<td align="center">SP</td>
<td align="center">1.26</td>
<td align="center">MMP</td>
<td align="center">3.19</td>
<td align="center">MHP</td>
</tr>
<tr>
<td align="center">S6</td>
<td align="center">1.06</td>
<td align="center">MMP</td>
<td align="center">0.86</td>
<td align="center">LP</td>
<td align="center">4.16</td>
<td align="center">HP</td>
<td align="center">0.56</td>
<td align="center">C</td>
<td align="center">1.66</td>
<td align="center">MMP</td>
</tr>
<tr>
<td align="center">S7</td>
<td align="center">1.35</td>
<td align="center">MMP</td>
<td align="center">0.63</td>
<td align="center">LP</td>
<td align="center">4.47</td>
<td align="center">HP</td>
<td align="center">0.30</td>
<td align="center">C</td>
<td align="center">1.69</td>
<td align="center">MMP</td>
</tr>
<tr>
<td align="center">S8</td>
<td align="center">1.41</td>
<td align="center">MMP</td>
<td align="center">1.20</td>
<td align="center">MMP</td>
<td align="center">5.47</td>
<td align="center">SP</td>
<td align="center">0.73</td>
<td align="center">C</td>
<td align="center">2.20</td>
<td align="center">MP</td>
</tr>
<tr>
<td align="center">S9</td>
<td align="center">0.92</td>
<td align="center">LP</td>
<td align="center">0.80</td>
<td align="center">LP</td>
<td align="center">5.22</td>
<td align="center">SP</td>
<td align="center">0.33</td>
<td align="center">C</td>
<td align="center">1.82</td>
<td align="center">MMP</td>
</tr>
<tr>
<td align="center">S10</td>
<td align="center">0.94</td>
<td align="center">LP</td>
<td align="center">1.01</td>
<td align="center">MMP</td>
<td align="center">5.39</td>
<td align="center">SP</td>
<td align="center">0.30</td>
<td align="center">C</td>
<td align="center">1.91</td>
<td align="center">MMP</td>
</tr>
<tr>
<td align="center">S11</td>
<td align="center">1.03</td>
<td align="center">MMP</td>
<td align="center">0.57</td>
<td align="center">LP</td>
<td align="center">4.29</td>
<td align="center">HP</td>
<td align="center">0.24</td>
<td align="center">C</td>
<td align="center">1.41</td>
<td align="center">MMP</td>
</tr>
<tr>
<td align="center">S12</td>
<td align="center">0.73</td>
<td align="center">LP</td>
<td align="center">0.30</td>
<td align="center">LP</td>
<td align="center">3.71</td>
<td align="center">MHP</td>
<td align="center">0.12</td>
<td align="center">C</td>
<td align="center">1.15</td>
<td align="center">MMP</td>
</tr>
<tr>
<td align="center">S13</td>
<td align="center">1.22</td>
<td align="center">MMP</td>
<td align="center">0.70</td>
<td align="center">LP</td>
<td align="center">3.43</td>
<td align="center">MHP</td>
<td align="center">0.04</td>
<td align="center">C</td>
<td align="center">1.35</td>
<td align="center">MMP</td>
</tr>
<tr>
<td align="center">S14</td>
<td align="center">1.53</td>
<td align="center">MMP</td>
<td align="center">1.10</td>
<td align="center">MMP</td>
<td align="center">4.07</td>
<td align="center">HP</td>
<td align="center">0.38</td>
<td align="center">C</td>
<td align="center">1.77</td>
<td align="center">MMP</td>
</tr>
<tr>
<td align="center">S15</td>
<td align="center">2.12</td>
<td align="center">MP</td>
<td align="center">1.41</td>
<td align="center">MMP</td>
<td align="center">3.97</td>
<td align="center">MHP</td>
<td align="center">0.65</td>
<td align="center">C</td>
<td align="center">2.04</td>
<td align="center">MP</td>
</tr>
<tr>
<td align="center">S16</td>
<td align="center">1.36</td>
<td align="center">MMP</td>
<td align="center">0.58</td>
<td align="center">LP</td>
<td align="center">2.19</td>
<td align="center">MHP</td>
<td align="center">0.08</td>
<td align="center">C</td>
<td align="center">1.05</td>
<td align="center">MMP</td>
</tr>
<tr>
<td align="center">S17</td>
<td align="center">1.11</td>
<td align="center">MMP</td>
<td align="center">0.70</td>
<td align="center">LP</td>
<td align="center">3.78</td>
<td align="center">MHP</td>
<td align="center">0.16</td>
<td align="center">LP</td>
<td align="center">1.44</td>
<td align="center">MMP</td>
</tr>
<tr>
<td align="center">Average</td>
<td align="center">1.50</td>
<td align="center">MMP</td>
<td align="center">1.07</td>
<td align="center">MMP</td>
<td align="center">4.47</td>
<td align="center">HP</td>
<td align="center">0.42</td>
<td align="center">C</td>
<td align="center">1.87</td>
<td align="center">MMP</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Note: C &#x3d; clean, LP, lightly polluted; MP, moderately polluted; MMP, mildly to moderately polluted; MHP, moderately to heavily polluted; HP, heavily polluted; SP, severely polluted.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>Overall, the pollution ranking for the four metals from highest to lowest is: Cd (Class 5) &#x3e; Cu (Class 2) &#x3e; Zn (Class 2) &#x3e; Pb (Class 1). The average <inline-formula id="inf42">
<mml:math id="m46">
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<mml:msub>
<mml:mi>I</mml:mi>
<mml:mrow>
<mml:mi>g</mml:mi>
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</inline-formula> values for the 17 sampling sites ranged from 1.05 to 3.19, corresponding to pollution levels between Class 2 and Class 4. The most severe pollution was observed at Site S5, which was classified as moderately to heavily polluted (Class 4), while Sites S4, S8, and S15 were categorized as moderately polluted (Class 3). The remaining 13 sampling sites were moderately polluted (Class 2).</p>
</sec>
<sec id="s3-1-3">
<title>3.1.3 Potential ecological risk assessment of heavy metals in sediments</title>
<p>The potential ecological risk indices (ERI) for Cu, Zn, Cd, and Pb in the surface sediments of the Pearl River Estuary are presented in <xref ref-type="table" rid="T5">Table 5</xref>. The ERI values for Cu ranged from 12.45 to 62.29, indicating low to moderate risk, whereas Zn and Pb showed low risk at all sampling points, with ERI values ranging from 1.84 to 8.60 and 6.34 to 17.99, respectively. In contrast, Cd exhibited extremely high ERI values, ranging from 205.93 to 3,036.77, suggesting severe ecological risk across all sites.</p>
<table-wrap id="T5" position="float">
<label>TABLE 5</label>
<caption>
<p>Potential ecological risk assessment of Cu, Zn, Cd, and Pb in surface sediments of the Pearl River Estuary.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="center">Sampling site</th>
<th align="center">ERI (Cu)</th>
<th align="center">Risk level</th>
<th align="center">ERI (Zn)</th>
<th align="center">Risk level</th>
<th align="center">ERI (Cd)</th>
<th align="center">Risk level</th>
<th align="center">ERI (Pb)</th>
<th align="center">Risk level</th>
<th align="center">RI</th>
<th align="center">Risk level</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">S1</td>
<td align="center">62.29</td>
<td align="center">M</td>
<td align="center">7.74</td>
<td align="center">L</td>
<td align="center">1797.23</td>
<td align="center">VH</td>
<td align="center">15.41</td>
<td align="center">L</td>
<td align="center">1882.67</td>
<td align="center">H</td>
</tr>
<tr>
<td align="center">S2</td>
<td align="center">14.00</td>
<td align="center">L</td>
<td align="center">1.84</td>
<td align="center">L</td>
<td align="center">775.10</td>
<td align="center">VH</td>
<td align="center">10.92</td>
<td align="center">L</td>
<td align="center">801.86</td>
<td align="center">H</td>
</tr>
<tr>
<td align="center">S3</td>
<td align="center">18.61</td>
<td align="center">L</td>
<td align="center">2.91</td>
<td align="center">L</td>
<td align="center">1,166.53</td>
<td align="center">VH</td>
<td align="center">8.38</td>
<td align="center">L</td>
<td align="center">1,196.44</td>
<td align="center">H</td>
</tr>
<tr>
<td align="center">S4</td>
<td align="center">42.90</td>
<td align="center">M</td>
<td align="center">6.61</td>
<td align="center">L</td>
<td align="center">2,281.40</td>
<td align="center">VH</td>
<td align="center">15.70</td>
<td align="center">L</td>
<td align="center">2,346.60</td>
<td align="center">H</td>
</tr>
<tr>
<td align="center">S5</td>
<td align="center">56.25</td>
<td align="center">M</td>
<td align="center">8.60</td>
<td align="center">L</td>
<td align="center">3,036.77</td>
<td align="center">VH</td>
<td align="center">17.99</td>
<td align="center">L</td>
<td align="center">3,119.60</td>
<td align="center">H</td>
</tr>
<tr>
<td align="center">S6</td>
<td align="center">15.67</td>
<td align="center">L</td>
<td align="center">2.71</td>
<td align="center">L</td>
<td align="center">802.25</td>
<td align="center">VH</td>
<td align="center">11.02</td>
<td align="center">L</td>
<td align="center">831.65</td>
<td align="center">H</td>
</tr>
<tr>
<td align="center">S7</td>
<td align="center">19.18</td>
<td align="center">L</td>
<td align="center">2.32</td>
<td align="center">L</td>
<td align="center">999.19</td>
<td align="center">VH</td>
<td align="center">9.23</td>
<td align="center">L</td>
<td align="center">1,029.93</td>
<td align="center">H</td>
</tr>
<tr>
<td align="center">S8</td>
<td align="center">19.87</td>
<td align="center">L</td>
<td align="center">3.44</td>
<td align="center">L</td>
<td align="center">1993.50</td>
<td align="center">VH</td>
<td align="center">12.46</td>
<td align="center">L</td>
<td align="center">2029.28</td>
<td align="center">H</td>
</tr>
<tr>
<td align="center">S9</td>
<td align="center">14.16</td>
<td align="center">L</td>
<td align="center">2.61</td>
<td align="center">L</td>
<td align="center">1,676.15</td>
<td align="center">VH</td>
<td align="center">9.41</td>
<td align="center">L</td>
<td align="center">1702.33</td>
<td align="center">H</td>
</tr>
<tr>
<td align="center">S10</td>
<td align="center">14.35</td>
<td align="center">L</td>
<td align="center">3.02</td>
<td align="center">L</td>
<td align="center">1886.46</td>
<td align="center">VH</td>
<td align="center">9.20</td>
<td align="center">L</td>
<td align="center">1913.03</td>
<td align="center">H</td>
</tr>
<tr>
<td align="center">S11</td>
<td align="center">15.33</td>
<td align="center">L</td>
<td align="center">2.22</td>
<td align="center">L</td>
<td align="center">877.46</td>
<td align="center">VH</td>
<td align="center">6.34</td>
<td align="center">L</td>
<td align="center">901.35</td>
<td align="center">H</td>
</tr>
<tr>
<td align="center">S12</td>
<td align="center">12.45</td>
<td align="center">L</td>
<td align="center">1.84</td>
<td align="center">L</td>
<td align="center">588.05</td>
<td align="center">VH</td>
<td align="center">6.91</td>
<td align="center">L</td>
<td align="center">609.25</td>
<td align="center">H</td>
</tr>
<tr>
<td align="center">S13</td>
<td align="center">17.48</td>
<td align="center">L</td>
<td align="center">2.43</td>
<td align="center">L</td>
<td align="center">484.15</td>
<td align="center">VH</td>
<td align="center">7.69</td>
<td align="center">L</td>
<td align="center">511.77</td>
<td align="center">C</td>
</tr>
<tr>
<td align="center">S14</td>
<td align="center">21.66</td>
<td align="center">L</td>
<td align="center">3.21</td>
<td align="center">L</td>
<td align="center">754.05</td>
<td align="center">VH</td>
<td align="center">9.75</td>
<td align="center">L</td>
<td align="center">788.67</td>
<td align="center">H</td>
</tr>
<tr>
<td align="center">S15</td>
<td align="center">32.66</td>
<td align="center">L</td>
<td align="center">4.00</td>
<td align="center">L</td>
<td align="center">702.97</td>
<td align="center">VH</td>
<td align="center">11.73</td>
<td align="center">L</td>
<td align="center">751.36</td>
<td align="center">H</td>
</tr>
<tr>
<td align="center">S16</td>
<td align="center">19.30</td>
<td align="center">L</td>
<td align="center">2.24</td>
<td align="center">L</td>
<td align="center">205.93</td>
<td align="center">VH</td>
<td align="center">7.91</td>
<td align="center">L</td>
<td align="center">235.39</td>
<td align="center">M</td>
</tr>
<tr>
<td align="center">S17</td>
<td align="center">16.24</td>
<td align="center">L</td>
<td align="center">2.44</td>
<td align="center">L</td>
<td align="center">618.56</td>
<td align="center">VH</td>
<td align="center">8.37</td>
<td align="center">L</td>
<td align="center">645.60</td>
<td align="center">H</td>
</tr>
<tr>
<td align="center">Average</td>
<td align="center">24.26</td>
<td align="center">L</td>
<td align="center">3.54</td>
<td align="center">L</td>
<td align="center">1,214.46</td>
<td align="center">VH</td>
<td align="center">10.49</td>
<td align="center">L</td>
<td align="center">1,252.75</td>
<td align="center">H</td>
</tr>
<tr>
<td align="center">RERL</td>
<td align="center">L</td>
<td align="left"/>
<td align="center">L</td>
<td align="left"/>
<td align="center">VH</td>
<td align="left"/>
<td align="center">L</td>
<td align="left"/>
<td align="center">H</td>
<td align="left"/>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Note: L &#x3d; low, M &#x3d; moderate, C &#x3d; considerable, H &#x3d; high, VH, very high; RERL, regional ecological risk levels.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>The average ERI values in descending order were as follows: Cd (1,214.46) &#x3e; Cu (24.26) &#x3e; Pb (10.49) &#x3e; Zn (3.54). The comprehensive potential ecological risk index (RI) for the four heavy metals ranged from 235.39 at Site S16 to 3,119.60 at Site S5, with an average RI value of 1,252.75. Among the 17 sampling sites, Site S5 was categorized as a moderate ecological risk, Site S15 as a considerable ecological risk, and the remaining 15 as a high ecological risk.</p>
</sec>
</sec>
<sec id="s3-2">
<title>3.2 Descriptive statistics of different chemical forms of Cu, Zn, Cd, and Pb in sediments</title>
<sec id="s3-2-1">
<title>3.2.1 Percentage content of different chemical forms of Cu, Zn, Cd, and Pb in surface sediments</title>
<p>
<xref ref-type="fig" rid="F3">Figure 3</xref> presents the percentage content of different Cu, Zn, Cd, and Pb chemical forms in the surface sediments. The distribution of chemical forms for each metal is as follows:</p>
<fig id="F3" position="float">
<label>FIGURE 3</label>
<caption>
<p>Percentage content of different chemical forms of Cu, Zn, Cd, and Pb in surface sediments.</p>
</caption>
<graphic xlink:href="fenvs-12-1509277-g003.tif"/>
</fig>
<p>Cu: The acid-extractable form accounted for 2.52%&#x2013;12.38%, the reducible form 3.56%&#x2013;23.98%, the oxidizable form 43.00%&#x2013;63.66%, and the residual form 7.93%&#x2013;37.50%. On average, the proportions of the chemical forms were ranked as follows: oxidizable (52.13%) &#x3e; residual (22.75%) &#x3e; reducible (17.57%) &#x3e; acid-extractable (7.55%).</p>
<p>Zn: The acid-extractable form ranged from 7.21% to 29.73%, the reducible form from 12.15% to 27.22%, the oxidizable form from 17.13% to 40.48%, and the residual form from 14.89% to 50.93%. The average proportions were residual (33.07%) &#x3e; oxidizable (28.08%) &#x3e; reducible (19.58%) &#x3e; acid-extractable (19.28%).</p>
<p>Cd: The acid-extractable form ranged from 17.37% to 50.63%, the reducible form from 13.63% to 34.33%, the oxidizable form from 8.87% to 51.49%, and the residual form from 4.52% to 19.91%. On average, the chemical form distribution was acid-extractable (35.45%) &#x3e; reducible (32.82%) &#x3e; oxidizable (21.86%) &#x3e; residual (9.87%).</p>
<p>Pb: The acid-extractable form accounted for 1.07%&#x2013;4.27%, the reducible form 21.23%&#x2013;46.33%, the oxidizable form 15.38%&#x2013;53.26%, and the residual form 21.04%&#x2013;35.16%. The average distribution was: reducible (34.76%) &#x3e; oxidizable (34.66%) &#x3e; residual (28.45%) &#x3e; acid-extractable (2.12%).</p>
</sec>
<sec id="s3-2-2">
<title>3.2.2 Risk assessment code (RAC) for different chemical forms of heavy metals</title>
<p>The Risk Assessment Code (RAC) values and corresponding risk levels for Cu, Zn, Cd, and Pb are shown in <xref ref-type="fig" rid="F4">Figure 4</xref>. The results are summarized as follows:</p>
<fig id="F4" position="float">
<label>FIGURE 4</label>
<caption>
<p>Risk levels of heavy metals based on risk assessment code (RAC).</p>
</caption>
<graphic xlink:href="fenvs-12-1509277-g004.tif"/>
</fig>
<p>Cu: RAC values ranged from 2.52% to 12.38%. Only two sampling sites (S15 and S16) were classified as medium risk (11.76%), while the remaining 15 sites showed low risk (88.24%).</p>
<p>Zn: RAC values ranged from 7.21% to 29.73%. Only Site S9 was classified as low risk, whereas the remaining 16 sampling sites were categorized as medium risk (94.12%).</p>
<p>Cd: RAC values ranged from 17.37% to 50.36%, indicating medium to very high risk. Seven sampling sites (S1, S4, S5, S2, S8, S7, and S9) were classified as medium risk (41.18%), nine sites (S15, S14, S11, S12, S16, S17, S10, S3, and S6) as high risk (52.94%), and Site S1 as very high risk.</p>
<p>Pb: RAC values ranged from 1.07% to 4.27%, indicating low risk across all sites.</p>
<p>The average RAC values across all sites rank as follows: Cd (35.45%) &#x3e; Zn (19.28%) &#x3e; Cu (7.55%) &#x3e; Pb (2.12%). Based on these values, Cd is classified as high risk, Zn as medium risk, and Cu and Pb as low risk.</p>
</sec>
</sec>
<sec id="s3-3">
<title>3.3 Comprehensive assessment of total and speciated heavy metal pollution risk</title>
<p>A comprehensive assessment of the total and speciated pollution risks for Cu, Zn, Cd, and Pb is presented in <xref ref-type="table" rid="T6">Table 6</xref>, using the <inline-formula id="inf43">
<mml:math id="m47">
<mml:mrow>
<mml:msub>
<mml:mi>I</mml:mi>
<mml:mrow>
<mml:mi>g</mml:mi>
<mml:mi>e</mml:mi>
<mml:mi>o</mml:mi>
</mml:mrow>
</mml:msub>
</mml:mrow>
</mml:math>
</inline-formula>, ERI, and RAC indicators. Results indicate that Cd poses the highest risk levels across all three indicators. Cu is categorized as moderately to mildly polluted (MMP) according to the <inline-formula id="inf44">
<mml:math id="m48">
<mml:mrow>
<mml:msub>
<mml:mi>I</mml:mi>
<mml:mrow>
<mml:mi>g</mml:mi>
<mml:mi>e</mml:mi>
<mml:mi>o</mml:mi>
</mml:mrow>
</mml:msub>
</mml:mrow>
</mml:math>
</inline-formula>, with both its single-element ecological risk and bioavailable fraction risk being classified as low risk. Zn is also classified as MMP by <inline-formula id="inf45">
<mml:math id="m49">
<mml:mrow>
<mml:msub>
<mml:mi>I</mml:mi>
<mml:mrow>
<mml:mi>g</mml:mi>
<mml:mi>e</mml:mi>
<mml:mi>o</mml:mi>
</mml:mrow>
</mml:msub>
</mml:mrow>
</mml:math>
</inline-formula> and as low risk in terms of its single-element ecological risk; however, its bioavailable fraction presents a medium risk, indicating a higher potential ecological risk than Cu. Pb is considered clean according to <inline-formula id="inf46">
<mml:math id="m50">
<mml:mrow>
<mml:msub>
<mml:mi>I</mml:mi>
<mml:mrow>
<mml:mi>g</mml:mi>
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</mml:mrow>
</mml:msub>
</mml:mrow>
</mml:math>
</inline-formula>, with its single-element ecological risk and bioavailable fraction risk classified as low risk.</p>
<table-wrap id="T6" position="float">
<label>TABLE 6</label>
<caption>
<p>Comprehensive assessment of total and speciated pollution risk for Cu, Zn, Cd, and Pb.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="center">Heavy metal</th>
<th align="center">
<inline-formula id="inf47">
<mml:math id="m51">
<mml:mrow>
<mml:msub>
<mml:mi>I</mml:mi>
<mml:mrow>
<mml:mi>g</mml:mi>
<mml:mi>e</mml:mi>
<mml:mi>o</mml:mi>
</mml:mrow>
</mml:msub>
</mml:mrow>
</mml:math>
</inline-formula> level</th>
<th align="center">ERI level</th>
<th align="center">RAC level</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">Cu</td>
<td align="center">MMP</td>
<td align="center">LR</td>
<td align="center">LR</td>
</tr>
<tr>
<td align="center">Zn</td>
<td align="center">MMP</td>
<td align="center">LR</td>
<td align="center">MR</td>
</tr>
<tr>
<td align="center">Cd</td>
<td align="center">MP</td>
<td align="center">VER</td>
<td align="center">EHR</td>
</tr>
<tr>
<td align="center">Pb</td>
<td align="center">C</td>
<td align="center">LR</td>
<td align="center">LR</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Note: MMP, moderate to mildly polluted; MP, moderately polluted, C &#x3d; clean, LR, low risk; MR, medium risk; VHR, very high risk; HER, extremely high risk.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>This assessment highlights that Cd poses the most significant risk across all indicators, necessitating prioritized mitigation efforts, whereas Cu and Pb present minimal risks. Despite Zn&#x2019;s lower total concentration, its higher bioavailable fraction contributes to a medium risk, warranting attention to potential ecological impacts.</p>
<p>These ecological risks are closely linked to the chemical forms of the metals. For Cd, the dominance of its acid-extractable fraction indicates high bioavailability, which increases its mobility and potential for uptake by aquatic organisms, posing severe ecological threats. In contrast, Zn, although lower in total concentration, shows a substantial bioavailable fraction, highlighting its potential for mobility and ecological impact under variable environmental conditions. On the other hand, Cu and Pb have lower proportions of bioavailable forms and higher residual fractions, suggesting they are less mobile and present a reduced ecological risk (<xref ref-type="bibr" rid="B49">Xu et al., 2016</xref>; <xref ref-type="bibr" rid="B24">Liu et al., 2021</xref>).</p>
</sec>
</sec>
<sec sec-type="discussion" id="s4">
<title>4 Discussions</title>
<sec id="s4-1">
<title>4.1 Total heavy metal pollution and ecological risk in the Pearl River Estuary</title>
<p>Cu, Zn, Cd, and Pb concentrations in the Pearl River Estuary are substantially higher than other major river systems in China, such as the Yangtze River and Bortala River (Xinjiang), as shown in <xref ref-type="table" rid="T7">Table 7</xref>. While the sediment Cd concentrations in the Yangtze and Liao Rivers slightly surpass those in the Pearl River Estuary, Cu, Zn, and Pb concentrations are significantly lower. This finding underscores that the Pearl River Estuary remains one of the most heavily contaminated estuaries in China concerning heavy metal pollution in sediments, likely due to the highly industrialized nature of the lower Pearl River region. Furthermore, all heavy metal concentrations in the sediments exceed the background values for Guangdong Province soils (<xref ref-type="table" rid="T7">Table 7</xref>), indicating substantial anthropogenic contributions to heavy metal contamination, particularly for Cd, which is elevated by 39 times compared to the background level. Numerous studies have consistently identified Cd as the most severe contaminant in this estuary over the years (<xref ref-type="bibr" rid="B48">Xie et al., 2012</xref>; <xref ref-type="bibr" rid="B25">Liu et al., 2017</xref>; <xref ref-type="bibr" rid="B18">Jia et al., 2021</xref>). Despite the implementation of stringent control measures in recent years, the persistence and accumulation of these metals imply that heavy metal pollution remains a critical environmental challenge in the region, especially for Cd, which necessitates sustained remediation efforts.</p>
<table-wrap id="T7" position="float">
<label>TABLE 7</label>
<caption>
<p>Comparison of metal ion concentrations (mg&#xb7;kg&#x207b;<sup>1</sup>) in surface sediments of the Pearl River Estuary and other rivers in China.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="center">Heavy metal</th>
<th align="center">Cu</th>
<th align="center">Zn</th>
<th align="center">Cd</th>
<th align="center">Pb</th>
<th align="center">Reference</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">This Study (Pearl River Estuary)</td>
<td align="center">58.71</td>
<td align="center">151.23</td>
<td align="center">1.05</td>
<td align="center">67.17</td>
<td align="center">&#x2014;</td>
</tr>
<tr>
<td align="center">Guangdong Province Soil Background</td>
<td align="center">12.1</td>
<td align="center">42.7</td>
<td align="center">0.026</td>
<td align="center">32.0</td>
<td align="center">
<xref ref-type="bibr" rid="B4">China National Environmental Monitoring Center (1990)</xref>
</td>
</tr>
<tr>
<td align="center">Yangtze River</td>
<td align="center">44.75</td>
<td align="center">120.42</td>
<td align="center">0.4</td>
<td align="center">39.32</td>
<td align="center">&#x2014;</td>
</tr>
<tr>
<td align="center">Bortala River</td>
<td align="center">30.09</td>
<td align="center">99.19</td>
<td align="center">0.17</td>
<td align="center">31.98</td>
<td align="center">
<xref ref-type="bibr" rid="B58">Zhang et al. (2016)</xref>
</td>
</tr>
<tr>
<td align="center">Liao River</td>
<td align="center">17.83</td>
<td align="center">50.24</td>
<td align="center">1.2</td>
<td align="center">10.57</td>
<td align="center">
<xref ref-type="bibr" rid="B21">Ke et al. (2017)</xref>
</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>Regarding potential ecological risk, the single-element ecological risk index (ERI) for Cu, Zn, and Pb at all sampling points indicates low risk (<xref ref-type="table" rid="T5">Table 5</xref>). In contrast, Cd poses a high ecological risk across the 17 sites. The contribution of each metal&#x2019;s ERI to the comprehensive potential ecological risk index (RI) is ranked as follows: Cd (96.94%) &#x3e; Cu (1.94%) &#x3e; Pb (0.84%) &#x3e; Zn (0.28%), clearly indicating Cd&#x2019;s dominant role in the overall ecological risk. This result aligns with a study by <xref ref-type="bibr" rid="B58">Zhang et al. (2016)</xref>, which assessed the potential ecological risk of heavy metals in the Bortala River. Their findings similarly showed that Cd accounted for 97% of the total ecological risk, followed by Pb, Cu, and Zn. The congruence between these studies further confirms that Cd exhibits the highest single-element ecological risk and makes the most significant contribution to the comprehensive ecological risk in the Pearl River Estuary.</p>
</sec>
<sec id="s4-2">
<title>4.2 Correlation analysis of heavy metals in sediments of the Pearl River estuary</title>
<p>Heavy metals in sediments often exhibit complex interrelationships, influenced by factors such as the original composition of heavy metals in the parent rock, soil formation processes, and anthropogenic activities. High and significant correlations between heavy metals can imply a common source of contamination (<xref ref-type="bibr" rid="B58">Zhang et al., 2016</xref>; <xref ref-type="bibr" rid="B21">Ke et al., 2017</xref>; <xref ref-type="bibr" rid="B12">Fu et al., 2022</xref>). This study conducted Pearson correlation analysis to examine the interrelationships between Cu, Zn, Cd, and Pb in the sediments from 17 sampling sites. As shown in <xref ref-type="table" rid="T8">Table 8</xref>, all four metals exhibited significant correlations (P &#x3c; 0.01), suggesting that they share a common source, potentially linked to the intense industrial development in the lower reaches of the Pearl River. Many heavy metals are discharged into the river through industrial effluents, leading to severe sediment contamination in the estuary. These findings are consistent with research by <xref ref-type="bibr" rid="B58">Zhang et al. (2016)</xref>, who studied the distribution of heavy metals in sediments from the Lingding Yang area of the Pearl River Estuary over the past century. Their results indicated that the strong correlations between heavy metals suggest a common source or that the environmental conditions and biogeochemical processes have remained highly consistent and stable. Other studies have attributed the primary sources of heavy metals in the Pearl River Estuary to industrial activities, shipping, and agricultural production, with Cd contamination particularly associated with the lead-zinc mining and smelting industries in the upper reaches of the Beijiang and Dongjiang Rivers in Guangdong Province (<xref ref-type="bibr" rid="B14">Gu et al., 2014</xref>; <xref ref-type="bibr" rid="B8">Du et al., 2019</xref>; <xref ref-type="bibr" rid="B26">Lu et al., 2020</xref>; <xref ref-type="bibr" rid="B35">Niu et al., 2021b</xref>). Pollutants from these industries are transported downstream, exacerbating heavy metal contamination in the estuarine sediments.</p>
<table-wrap id="T8" position="float">
<label>TABLE 8</label>
<caption>
<p>Correlation analysis of Cu, Zn, Cd, and Pb in the Pearl River Estuary.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="center"/>
<th align="center">Cu</th>
<th align="center">Zn</th>
<th align="center">Cd</th>
<th align="center">Pb</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">Cu</td>
<td align="center">1.000</td>
<td align="left"/>
<td align="left"/>
<td align="left"/>
</tr>
<tr>
<td align="center">Zn</td>
<td align="center">0.969&#x2a;&#x2a;</td>
<td align="center">1.000</td>
<td align="left"/>
<td align="left"/>
</tr>
<tr>
<td align="center">Cd</td>
<td align="center">0.629&#x2a;&#x2a;</td>
<td align="center">0.778&#x2a;&#x2a;</td>
<td align="center">1.000</td>
<td align="left"/>
</tr>
<tr>
<td align="center">Pb</td>
<td align="center">0.863&#x2a;&#x2a;</td>
<td align="center">0.913&#x2a;&#x2a;</td>
<td align="center">0.786&#x2a;&#x2a;</td>
<td align="center">1.000</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Note: &#x2a;&#x2a; represents a highly significant correlation at P &#x3c; 0.01 (two-tailed); &#x2a; represents a significant correlation at P &#x3c; 0.05 (two-tailed).</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s4-3">
<title>4.3 Chemical speciation and environmental risks of heavy metals</title>
<p>The RAC results indicate that Cd poses the highest pollution and ecological risk among the four metals. The acid-extractable fraction of Cd, representing its most bioavailable form, constitutes more than one-third of its total concentration, the highest proportion among the studied metals. Furthermore, the combined content of the acid-extractable, reducible, and oxidizable forms of Cd exceeds 90% of its total concentration, with the residual fraction contributing less than 10%. This distribution pattern suggests that anthropogenic activities heavily influence Cd in the surface sediments of the Pearl River Estuary, which are present predominantly in bioavailable forms. Consequently, Cd is highly susceptible to environmental changes, such as variations in water chemistry, and can be quickly released back into the water, where it may be assimilated by aquatic organisms, adversely impacting their growth and survival.</p>
<p>In contrast, Pb exhibits the lowest RAC value, with the acid-extractable fraction accounting for only 2.12% of its total concentration and a relatively high residual fraction (28.45%), indicating low ecological risk. Cu also has a low proportion of acid-extractable content but shows the highest percentage of oxidizable forms, exceeding 50% of its total concentration. This suggests Cu may migrate from sediments to the water column under oxidative conditions. On the other hand, Zn has a relatively high proportion of acid-extractable content and a substantial residual fraction (33.07%), the highest among the four metals. The other chemical forms of Zn (acid-extractable, reducible, and oxidizable) are present in relatively similar proportions, ranging from 19.28% to 28.08%, indicating that Zn can quickly shift between its bioavailable forms under both oxidative and reductive conditions.</p>
<p>Although research on heavy metals in river sediments across China began relatively late, most major river basins have been comprehensively studied. The distribution of heavy metal chemical forms shows significant variation among different river systems, likely due to the diverse anthropogenic activities and environmental conditions that influence heavy metal partitioning in sediments (<xref ref-type="bibr" rid="B19">Jiang et al., 2020</xref>; <xref ref-type="bibr" rid="B7">Deng et al., 2023</xref>).</p>
<p>The estuarine environmental conditions, such as pH, salinity, and organic matter content, are considered to interpret the observed speciation patterns further. For instance, the high bioavailability of cadmium (Cd) in the Pearl River Estuary may be attributed to the estuary&#x2019;s slightly acidic conditions and moderate salinity levels, which enhance the solubility and mobility of Cd. Similarly, the presence of oxidizable forms of Cu is likely influenced by the organic-rich sediments found in this region, which provide binding sites for metal-organic complexes. These environmental factors contribute to the differential bioavailability and mobility observed for each metal.</p>
</sec>
</sec>
<sec sec-type="conclusion" id="s5">
<title>5 Conclusion</title>
<p>The concentrations of Cu, Zn, Cd, and Pb in surface sediments across all sampling sites in the Pearl River Estuary significantly exceed the background values for soils in Guangdong Province, with Cd (1.05&#xa0;mg&#xb7;kg&#x207b;<sup>1</sup>) showing the most pronounced contamination, surpassing the background value by a factor of 39. The geo-accumulation index (<inline-formula id="inf48">
<mml:math id="m52">
<mml:mrow>
<mml:msub>
<mml:mi>I</mml:mi>
<mml:mrow>
<mml:mi>g</mml:mi>
<mml:mi>e</mml:mi>
<mml:mi>o</mml:mi>
</mml:mrow>
</mml:msub>
</mml:mrow>
</mml:math>
</inline-formula>) values for the sampling sites indicate varying pollution levels, ranging from Class 2 to Class 4, with the pollution severity ranked as follows: Cd (Class 5) &#x3e; Cu (Class 2) &#x3e; Zn (Class 2) &#x3e; Pb (Class 1). The average single-element ecological risk index (ERI) for heavy metals in descending order is Cd &#x3e; Cu &#x3e; Pb &#x3e; Zn, with Cd classified as an extremely high risk. At the same time, Cu, Zn, and Pb are categorized as low-risk. The comprehensive potential ecological risk index (RI) classifies the Pearl River Estuary sediments as high ecological risk, with Cd contributing 97% of the total risk. The Risk Assessment Code (RAC) analysis further ranks the metals in terms of risk as Cd &#x3e; Zn &#x3e; Cu &#x3e; Pb. Cd is classified as high risk, Zn as medium risk, and Cu and Pb as low risk.</p>
<p>This study reveals that Cd poses the highest ecological risk among the four metals, with its predominantly bioavailable forms making it highly susceptible to environmental changes and, thus, a significant threat to the estuarine ecosystem. These findings highlight the need for continuous, targeted management strategies to mitigate Cd contamination and reduce its ecological impact in the Pearl River Estuary. By focusing on heavy metal contamination and ecological risks within the estuary&#x2019;s specific context, we used background values relevant to the region. While international standards, such as those from the European Union or EPA, provide useful comparative benchmarks, they may not fully capture the unique environmental and regulatory conditions of this estuarine ecosystem in southern China. Future research should incorporate these benchmarks, examine the long-term impacts of Cd bioavailability on aquatic health, and assess the efficacy of remediation techniques over time to support adaptive management in this unique ecological context.</p>
</sec>
</body>
<back>
<sec sec-type="data-availability" id="s6">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/<xref ref-type="sec" rid="s11">Supplementary Material</xref>, further inquiries can be directed to the corresponding authors.</p>
</sec>
<sec sec-type="author-contributions" id="s7">
<title>Author contributions</title>
<p>ZhL: Conceptualization, Data curation, Methodology, Writing&#x2013;original draft, Writing&#x2013;review and editing. QW: Data curation, Investigation, Software, Visualization, Writing&#x2013;review and editing. CH: Data curation, Investigation, Software, Visualization, Writing&#x2013;review and editing. PZ: Conceptualization, Data curation, Methodology, Supervision, Writing&#x2013;original draft, Writing&#x2013;review and editing. XW: Data curation, Investigation, Software, Visualization, Writing&#x2013;review and editing. ZiL: Investigation, Validation, Writing&#x2013;review and editing. LY: Investigation, Validation, Writing&#x2013;review and editing. CQH: Conceptualization, Data curation, Methodology, Supervision, Writing&#x2013;original draft, Writing&#x2013;review and editing.</p>
</sec>
<sec sec-type="funding-information" id="s8">
<title>Funding</title>
<p>The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Key Research and Development Program Project in Guangdong Province (2020B1111350001), the Science and Technology Innovation Project in Guangdong Forestry (2024KJCX005), and the UNEP/GEF &#x201c;Implementation of the Strategic Action Plan for the South China Sea and Gulf of Thailand&#x201d; (Mangroves, seagrass beds, wetlands) (S1-32GFL-000631, SB-009056).</p>
</sec>
<sec sec-type="COI-statement" id="s9">
<title>Conflict of interest</title>
<p>Author LY was employed by Guangzhou Hongjing Ecological Technology Co., Ltd.</p>
<p>The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="ai-statement" id="s10">
<title>Generative AI statement</title>
<p>The author(s) declare that no Generative AI was used in the creation of this manuscript.</p>
</sec>
<sec sec-type="disclaimer" id="s11">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
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<sec id="s12">
<title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fenvs.2024.1509277/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fenvs.2024.1509277/full&#x23;supplementary-material</ext-link>
</p>
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<supplementary-material xlink:href="Table2.xls" id="SM2" mimetype="application/xls" xmlns:xlink="http://www.w3.org/1999/xlink"/>
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